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BUILDING INFORMATION MODELING–BASED
PROCESS TRANSFORMATION TO IMPROVE
PRODUCTIVITY IN THE SINGAPORE
CONSTRUCTION INDUSTRY
LIAO LONGHUI
(B.Eng., Chongqing Univ., China; M.Mgt., Harbin Inst. of
Tech., China)
A THESIS SUBMITTED
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
DEPARTMENT OF BUILDING
NATIONAL UNIVERSITY OF SINGAPORE
2018
Supervisor:
Associate Professor Teo Ai Lin, Evelyn
Examiners:
Associate Professor Hwang Bon-Gang
Dr Wang Qian
Professor Ma Zhiliang, Tsinghua University
I
DECLARATION
I hereby declare that this thesis is my original work and it has been written by me in
its entirety. I have duly acknowledged all the sources of information which have been
used in the thesis.
This thesis has also not been submitted for any degree in any university previously.
Liao Longhui
26 January 2018
II
ACKNOWLEDGEMENTS
I would like to express my thanks and gratitude to the following people. Without their
time, guidance, help, support, and encouragement, this thesis would certainly not
exist.
First and foremost, I would like to thank my supervisor Associate Professor Teo Ai
Lin, Evelyn for her enlightenment, guidance, constructive feedbacks, consistent
encouragement, and incredible patience on all occasions throughout my Ph.D. study. I
would like to express my sincere gratitude to my thesis committee members Professor
Low Sui Pheng and Professor George Ofori. Their enthusiasm about research,
positive attitudes towards problem solving, and resourcefulness greatly inspired me
both in study and in career development. Without their diligent efforts, this thesis
would not be possible, and the journal articles originated from this research would not
have been published. Besides, the research scholarship from the National University
of Singapore (NUS) during my Ph.D. candidature is also gratefully acknowledged.
In addition, special thanks must go to Ms. Liu Yige and Mr. Wei Kewu from the
China Construction (South Pacific) Development Co. Pte. Ltd. for their help in the
data collection process.
I am grateful to all my colleagues in the NUS Centre of Excellence in BIM
Integration, including Mr. Vishal Kumar, Mr. Lim Yong Khoon, and Mr. Gwee Seng
Kwong for their continued support, as well as to my friends in the Department of
Building, especially Mr. Sun Yuting, Mr. Wang Yi, and Mr. Zhang Yajian for their
friendship and encouragement throughout my research.
III
Finally, and most importantly, I am greatly indebted to my family. I heartfully thank
my wife Ms. Li Linhui for all her sacrifices and unflinching affection. I would like to
thank my parents for their unconditional and endless love and support, which powers
me in my academic pursuits all these years.
IV
TABLE OF CONTENTS
DECLARATION.............................................................................................. I
ACKNOWLEDGEMENTS ........................................................................... II
SUMMARY ................................................................................................. VIII
LIST OF TABLES ....................................................................................... XII
LIST OF FIGURES ..................................................................................... XV
LIST OF ABBREVIATIONS .................................................................. XVII
Chapter 1: Introduction .................................................................................. 1
1.1 Background ................................................................................................ 1
1.2 Significance and Rationale of Research .................................................... 4
1.3 Research Problems .................................................................................. 13
1.4 Knowledge Gaps ..................................................................................... 15
1.5 Research Objectives ................................................................................ 18
1.6 Scope of Research ................................................................................... 20
1.7 Research Hypotheses ............................................................................... 23
1.8 Structure of the Thesis ............................................................................. 23
Chapter 2: Review of Productivity Performance and Relevant Policies in
the Singapore Construction Industry........................................................... 27
2.1 Introduction ............................................................................................. 27
2.2 Overview of Productivity Performance in Singapore ............................. 27
2.3 Productivity-Enhancing Policies in Singapore ........................................ 30
2.3.1 Higher quality workforce ........................................................................... 31
2.3.2 Higher capital investments ......................................................................... 32
2.3.3 Better integration of value chain ................................................................ 34
2.4 Summary .................................................................................................. 36
Chapter 3: Review of Traditional and BIM-Based Project Delivery ........ 37
3.1 Introduction ............................................................................................. 37
3.2 Traditional Project Delivery Process ....................................................... 37
3.3 Current Delivery Process ......................................................................... 40
3.4 Full BIM-Enabled Delivery Processes .................................................... 44
3.4.1 Integrated Project Delivery ........................................................................ 44
V
3.4.2 Virtual Design and Construction ............................................................... 57
3.4.3 Design for Manufacturing and Assembly .................................................. 71
3.5 Comparisons among Project Delivery Processes .................................... 82
3.5.1 Differences among project delivery processes .......................................... 82
3.5.2 Relationships between full BIM-enabled processes .................................. 90
3.6 Summary .................................................................................................. 93
Chapter 4: Review of Non-Value Adding (NVA) Activities and Proposal
of a BIM Implementation Readiness (BIMIR) Evaluation Model ............ 95
4.1 Introduction ............................................................................................. 95
4.2 NVA Activities and Their Causes and Resulting Wastes ....................... 95
4.2.1 Identifying NVA activities ......................................................................... 95
4.2.2 Resulting wastes ...................................................................................... 100
4.2.3 Causes of NVA activities ......................................................................... 104
4.3 BIMIR .................................................................................................... 112
4.4 A BIMIR Model for Building Projects .................................................. 118
4.4.1 Existing BIM readiness models ............................................................... 118
4.4.2 A fuzzy BIMIR model ............................................................................. 119
4.5 Summary ................................................................................................ 133
Chapter 5: Review of Factors Affecting BIM Implementation and
Proposal of an Organizational Change Framework ................................. 135
5.1 Introduction ........................................................................................... 135
5.2 Factors Affecting BIM Implementation ................................................ 135
5.2.1 Hindrances to full BIM implementation .................................................. 135
5.2.2 Drivers for full BIM implementation ....................................................... 140
5.3 A Proposed Organizational Change Framework for BIM Implementation
......................................................................................................... 144
5.3.1 Organizational change theories ................................................................ 144
5.3.2 A proposed organizational change framework for building projects ....... 154
5.3.3 Conceptual model .................................................................................... 162
5.4 Summary ................................................................................................ 162
Chapter 6: Research Methodology ............................................................. 164
6.1 Introduction ........................................................................................... 164
6.2 Research Design .................................................................................... 166
6.2.1 Survey ...................................................................................................... 166
6.2.2 Case study ................................................................................................ 169
6.3 Methods of Data Collection ................................................................... 170
6.3.1 Questionnaires and interviews ................................................................. 170
6.3.2 Observations ............................................................................................ 175
6.3.3 Analysis of past documents ..................................................................... 176
VI
6.4 Methods of Data Analysis ..................................................................... 176
6.5 Summary ................................................................................................ 178
Chapter 7: Data Analysis and Discussions ................................................ 180
7.1 Introduction ........................................................................................... 180
7.2 Analysis Results and Discussions of Survey I ...................................... 181
7.2.1 Profile of respondents and their organizations ......................................... 181
7.2.2 Level of agreement of NVA activities ..................................................... 184
7.2.3 BIMIR of building projects in Singapore ................................................ 192
7.2.4 Resulting wastes ...................................................................................... 198
7.2.5 Causes to NVA activities ......................................................................... 207
7.3 Analysis Results and Discussions of Survey II ..................................... 220
7.3.1 Profile of respondents and their organizations ......................................... 220
7.3.2 Hindrances to change towards full BIM implementation ........................ 223
7.3.3 Drivers for change towards full BIM implementation ............................. 235
7.3.4 Interpreting the critical hindrances to change (CHCs) and critical drivers
for change (CDCs) with the organizational change framework........................ 243
7.3.5 Proposed managerial strategies for reducing the CHCs and strengthening
the CDCs ........................................................................................................... 260
7.4 Summary ................................................................................................ 273
Chapter 8: Case Study ................................................................................. 275
8.1 Introduction ........................................................................................... 275
8.2 Background of Case Projects ................................................................. 275
8.3 Critical Changes .................................................................................... 278
8.4 Performance Assessment ....................................................................... 282
Chapter 9: Developing a BIM-Based Process Transformation (BBPT)
Model for Building Projects in Singapore ................................................. 287
9.1 Introduction ........................................................................................... 287
9.2 Comparing the CHCs and CDCs among BIMIR Statuses .................... 287
9.2.1 Profile of respondents and their organizations involved in both surveys 287
9.2.2 Linking Survey I and Survey II ............................................................... 290
9.2.3 Comparison among projects with different BIMIR ................................. 291
9.2.4 Areas needing improvement .................................................................... 295
9.3 A BBPT Model ...................................................................................... 309
9.4 Validation of the BBPT Model .............................................................. 314
9.5 Summary ................................................................................................ 318
Chapter 10: Conclusions and Recommendations ..................................... 320
10.1 Summary of Research Findings ........................................................... 320
VII
10.1.1 Critical NVA industry practices and resulting wastes in the Singapore
construction industry......................................................................................... 320
10.1.2 A fuzzy BIMIR evaluation model for building projects ........................ 322
10.1.3 BIMIR statuses and productivity performance of building projects in
Singapore .......................................................................................................... 323
10.1.4 A proposed organizational change framework ...................................... 324
10.1.5 Critical factors hindering and driving change towards full BIM
implementation ................................................................................................. 324
10.1.6 A BBPT model....................................................................................... 327
10.2 Contributions ....................................................................................... 328
10.2.1 Contribution to scholarship .................................................................... 328
10.2.2 Contribution to practice ......................................................................... 329
10.3 Limitations ........................................................................................... 332
10.4 Recommendations for Future Research ............................................... 334
Bibliography ................................................................................................. 336
Appendices .................................................................................................... 355
Appendix 1: Questionnaire of Survey I ....................................................... 355
Appendix 2: Questionnaire of Survey II ..................................................... 363
Appendix 3: Questionnaire for the Validation of the BBPT model ............ 368
Appendix 4: A Calculation Example of the Fuzzy BIMIR Model .............. 371
Appendix 5: List of Publications from This Thesis ..................................... 375
VIII
SUMMARY
Productivity is always a problem. In particular, productivity performance in the
Singapore construction industry did not reach the local government’s target from
2013 to 2016. To meet the productivity growth target set in 2010, the local
government has enacted a series of legislations. Among which, the most important
one is that building plans for all new building projects with a gross floor area (GFA)
of 5,000 m2 and above must be submitted in building information modeling (BIM)
format for regulatory approvals since July 2015. Even local firms started to
implement BIM, both physical and information fragmentation appeals to still exist
across the planning, design, and downstream phases. Thus, the Singapore
construction industry is using BIM partially.
This study aims to develop a BIM-based process transformation (BBPT) model to
help project teams move towards higher levels of BIM implementation, reduce
wastes, and thus enhance productivity performance in building projects in Singapore.
Firstly, the traditional project delivery process (without BIM use), current delivery
process (in the current context of Singapore), and full BIM-enabled delivery
processes were reviewed and adapted for use in the Singapore context. Full BIM-
enabled delivery approaches include Integrated Project Delivery, Virtual Design and
Construction, and Design for Manufacturing and Assembly which have been
increasingly recognized and used in the global construction industry. By comparing
the current process with the full BIM-enabled processes, non-value adding (NVA)
activities were identified. In this study, four statuses of BIM implementation
readiness (BIMIR) at the project level were defined, including status one (S1, no BIM
implementation), status two (S2, lonely BIM implementation), status three (S3,
collaborative BIM implementation), and status four (S4, full BIM implementation).
Based on the NVA activities, a fuzzy BIMIR model was developed, using the fuzzy
IX
synthetic evaluation approach, to evaluate the BIMIR statuses of building projects
that plan to implement BIM in Singapore.
Secondly, based on Leavitt’s diamond model, MIT90s framework, and their
derivatives, an organizational change framework was proposed for building projects
implementing BIM, which consists of 29 change attributes from the perspectives of
people, process, technology, and external environment.
Two surveys and a case study were conducted in the Singapore construction industry.
The analysis results of Survey I identified 38 critical NVA activities. Using the data
related to the frequency of occurrence of these NVA activities, the BIMIR statuses of
73 surveyed building projects were evaluated. Among which, 15, 47, and 11 projects
were assessed in BIMIR S1, S2, and S3, respectively, while none in BIMIR S4. The
results of five stability tests suggested that the fuzzy BIMIR evaluation model was
stable and could be used to predict the BIMIR status of any other building project in a
similar context. In addition, it was found that as BIMIR increased, the criticality of 13
wastes in project groups of different BIMIR statuses would decrease, lessoning
detrimental effects on productivity. All the 53 causes to the critical NVA activities
that were identified from the literature review were significantly important, especially
those related to contractors.
Moreover, the analysis results of Survey II suggested that 44 hindrances to change
and 31 drivers for change had significant influence on the overall lonely BIM
implementation status in Singapore. These significant factors were interpreted with
the proposed organizational change framework. The rankings and theoretical
rationale behind these factors helped tailor managerial strategies on people (eight),
process (10), technology (five), and external environment (two) aspects.
X
Because of the key role of the contractors, a case study of BIM implementation and
transformation (moving towards a higher BIMIR status or a more collaborative and
integrated BIM-based delivery process) was conducted in a large construction and
development firm based in Singapore. The critical changes made in Project A
(BIMIR S3) illustrated the dynamics of this firm to move from a lower BIMIR status
(S2 in Project B) towards a higher level of BIM implementation.
Lastly, the BBPT model was developed for building projects that plan to implement
BIM, which generalized the main findings of this study. This model consists of two
part-models: a BIMIR evaluation model and a BIMIR improvement model. The
former can evaluate the BIMIR status of a particular building project in the planning
stage, while the latter can analyze the critical factors that hinder this project to be in
the current BIMIR status and driver the project to change towards a higher BIMIR
status, and provide managerial strategies with four priorities for the project
organization to move towards full BIM implementation in terms of people, process,
technology, and external environment. The increased BIMIR status creates fewer
NVA activities, and thus will, by reducing detrimental effects of the resulting wastes,
improve productivity performance. The 33 projects that were involved in both
surveys illustrated the use of the BBPT model.
As little research has attempted to investigate the BIMIR of building projects and
study BIM implementation from the organizational change perspective, the
development of the fuzzy BIMIR evaluation model and the proposed organizational
change framework significantly contributes to the literature. In addition, the BBPT
model allows project leadership teams to have a good understanding of the status
quos in their projects, and how to implement prioritized transformation strategies to
move towards higher levels of BIM implementation, thus contributing to practices.
Overseas practitioners may also use this model, with minor adjustments, because
XI
BIM implementation in publicly funded construction and building projects in the
global construction industry is also commonly encouraged, specified, or mandated.
XII
LIST OF TABLES
Table 1.1 YoY changes in labor productivity in Singapore from 2010 to 2016 (%) 1
Table 1.2 Conclusion of existing and additional foreign worker curbs 6
Table 2.1 First CPR in Singapore 28
Table 2.2 Levy schedule for WPHs from 2014 to 2017 (S$) 31
Table 3.1 Key stakeholders and activities in the traditional delivery process 39
Table 3.2 Key stakeholders and activities in the current BIM adoption process 43
Table 3.3 Key IPD characteristics and descriptions 46
Table 3.4 BIM support for achieving IPD characteristics 49
Table 3.5 Summary of the key activities related to BIM in the current process and the
proposed IPD, VDC, and DfMA processes in the Singapore construction industry 83
Table 3.6 Major differences among the proposed project delivery processes in
Singapore 85
Table 3.7 Differences and supporting statements in literature between the traditional,
current, and full BIM-enabled processes in Singapore 87
Table 4.1 Major NVA practices characterized by project stakeholders and project
phasing in the current project delivery in Singapore 96
Table 4.2 Potential wastes affecting productivity more seriously 100
Table 4.3 Fuzzy numbers of linguistic terms 125
Table 4.4 Generic translation of NVAI score to BIMIR status 131
Table 4.5 Adjusted translation of NVAI score to BIMIR status 133
Table 5.1 Hindrances to BIM Implementation 136
Table 5.2 Drivers for full BIM implementation 141
Table 5.3 Proposed organizational change framework for building projects
implementing BIM 156
Table 6.1 Tendering limits of contractors registration system (S$ million) 168
Table 6.2 Summary of the interviews in the pilot study 172
Table 7.1 Profile of the respondents and their organizations in Survey I 182
Table 7.2 Level of agreement ranking and t-test results of the NVA activities 185
Table 7.3 Profile of the interviewees in Survey I 191
XIII
Table 7.4 Weighting for project phasing and its NVA activities 192
Table 7.5 NVAI scores of the surveyed building projects in Singapore 194
Table 7.6 BIMIR statuses of the surveyed building projects 195
Table 7.7 Generation of five groups of random numbers 196
Table 7.8 New NVAI scores and changes of the surveyed building projects in five
stability tests 197
Table 7.9 Mean and ranking of resulting wastes 200
Table 7.10 ANOVA results of the WC between BIMIR statuses 202
Table 7.11 Post hoc test results for the wastes different between BIMIR statuses 204
Table 7.12 Spearman’s rank correlation results of the WC between BIMIR statuses
207
Table 7.13 Importance ranking and t-test results of the causes to the NVA activities
208
Table 7.14 ANOVA results of the causes between BIMIR statuses 214
Table 7.15 Post hoc test results for the causes different between BIMIR statuses 216
Table 7.16 Spearman’s rank correlation of the causes between BIMIR statuses 220
Table 7.17 Profile of the respondents and their organizations in Survey II 220
Table 7.18 Profile of the interviewees in Survey II 223
Table 7.19 Significance ranking and t-test results of the hindrances to change 224
Table 7.20 Mean scores and ranking of the CHCs between upfront and downstream
stakeholders 232
Table 7.21 Significance ranking and t-test results of the drivers for change 235
Table 7.22 Mean scores and ranking of the CDCs between upfront and downstream
stakeholders 242
Table 8.1 Profile of the interviews in the case study 275
Table 9.1 Profile of the respondents and their organizations involved in both surveys
288
Table 9.2 Overall mean scores and rankings of the CHCs and CDCs in different
samples 290
Table 9.3 Mean scores and rankings of the CHCs: BIMIR S1 vs. BIMIR S2 vs.
BIMIR S3 292
Table 9.4 Spearman’s rank correlation of the CHCs: BIMIR S1 vs. BIMIR S2 vs.
BIMIR S3 293
XIV
Table 9.5 Mean scores and rankings of the CDCs: BIMIR S1 vs. BIMIR S2 vs.
BIMIR S3 294
Table 9.6 Spearman’s rank correlation of the CDCs: BIMIR S1 vs. BIMIR S2 vs.
BIMIR S3 295
Table 9.7 Overall paths of reducing the CHCs and strengthening the CDCs from the
organizational change perspective 297
Table 9.8 Key areas of improvement in the organizational change framework 300
Table 9.9 Priority rules of implementing strategies for changing from a lower BIMIR
status to higher BIMIR statuses 303
Table 9.10 Prioritized change areas and managerial strategies for a project
organization to move from BIMIR S1 304
Table 9.11 Prioritized change areas and managerial strategies for a project
organization to move from BIMIR S2 305
Table 9.12 Prioritized change areas and managerial strategies for a project
organization to move from BIMIR S3 306
Table 9.13 Profile of the validation experts 314
Table 9.14 Validation results of the BIMIR evaluation model 316
Table A.1 Calculation process of the NVAI of a surveyed building project 372
XV
LIST OF FIGURES
Figure 1.1 Percentage of contractors engaging with BIM on more than 30% of their
work (by country) 3
Figure 1.2 Foreign employment changes in Singapore from 2010 to 2016 5
Figure 1.3 Rationale of the study 7
Figure 1.4 Deconstruction of the BBPT model 20
Figure 1.5 Structure of the thesis 24
Figure 3.1 Current partial BIM implementation in the Singapore construction industry
41
Figure 3.2 Eleven essential principles of IPD 47
Figure 3.3 Use of BIM in an integrated environment enables IPD process 50
Figure 3.4 Stakeholder involvement in the commonly-used IPD process overseas 51
Figure 3.5 Stakeholder involvement in the proposed IPD process for the Singapore
construction industry 53
Figure 3.6 Stakeholder involvement in the proposed VDC process for the Singapore
construction industry 65
Figure 3.7 DfMA envelope 72
Figure 3.8 Stakeholder involvement in the proposed DfMA process for the Singapore
construction industry 76
Figure 3.9 Distinctive differences and similarities of IPD, VDC, and DfMA 91
Figure 3.10 Relationships between IPD, VDC, and DfMA when using BIM 91
Figure 3.11 How integrated information supports the creation of a high-performance
building 92
Figure 4.1 Productivity and cost affected by RFI, rework, idle time, activity delay,
and change order 103
Figure 4.2 How BIM coordination enhances productivity and cost performance by
reducing major wastes 104
Figure 4.3 BIM maturity stages in previous studies 115
Figure 4.4 Triangular membership function 123
Figure 4.5 Membership functions of linguistic values 125
Figure 4.6 Central method of defuzzification 130
XVI
Figure 4.7 Translation of NVAI score into linguistic terms (frequency of occurrence)
131
Figure 5.1 Leavitt’s diamond model 145
Figure 5.2 An enhanced diamond model 147
Figure 5.3 An organizational interaction diamond model 148
Figure 5.4 A modified Leavitt’s system model 150
Figure 5.5 A conceptual model of technology impact 151
Figure 5.6 MIT90s framework 152
Figure 5.7 Conceptual framework of collaboration adapted from MIT90s framework
154
Figure 6.1 Research methodology 165
Figure 7.1 Framework of people management from the organizational change
perspective 244
Figure 7.2 Framework of process management from the organizational change
perspective 250
Figure 7.3 Framework of technology management from the organizational change
perspective 254
Figure 7.4 Framework of external environment management from the organizational
change perspective 256
Figure 9.1 BIM-based process transformation model for building projects 310
XVII
LIST OF ABBREVIATIONS
2D Two-Dimensional
3D Three-Dimensional
4D Four-Dimensional
5D Five-Dimensional
6D Six-Dimensional
AEC Architectural, Engineering, and Construction
AGC Association of General Contractors
AHP Analytic Hierarchy Process
AIA American Institute of Architects
AIACC American Institute of Architects, California Council
ANN Artificial Neural Network
ANOVA Analysis of Variance
BBPT BIM-Based Process Transformation
BCA Building and Construction Authority
BIM Building Information Modeling
BIMIR BIM Implementation Readiness
BLM Building Lifecycle Management
BOM Bill of Materials
CAD Computer-Aided Design
CCS Cuneco Classification System
CDMC Collaborative Decision-Making and Control
CDC Critical Driver for Change
CHC Critical Hindrance to Change
CITM Construction Industry Transformation Map
CNC Computer Numerically Controlled
CPC Construction Productivity Centre
CPCF Construction Productivity and Capability Fund
CPR Construction Productivity Roadmap
DBB Design-Bid-Build
DFA Design-Fabricate-Assemble
DfMA Design for Manufacturing and Assembly
DSS Decision-Support System
EIKP Early Involvement of Key Participants
EMS External Environment Management Strategy
XVIII
E-submission Electronic Submission
ESC Economic Strategies Committee
FSE Fuzzy Synthetic Evaluation
FWLs Foreign Worker Levies
GDP Gross Domestic Product
GFA Gross Floor Area
GLS Government Land Sales
HDB Housing and Development Board
HR Human Resource
HVAC Heating, Ventilation, and Air Conditioning
ICT Information and Communications Technology
IDD Integrated Digital Delivery
IFC Industry Foundation Classes
IPD Integrated Project Delivery
IT Information Technology
JDVG Jointly Developed and Validated Project Goals
LOD Level of Detail
LSD Least Significant Difference
LWKP Liability Waivers among Key Participants
MAPE Mean Absolute Percentage Error
MEP Mechanical, Electrical, and Plumbing
MF Model Function
MND Ministry of National Development
MOF Ministry of Finance
MOM Ministry of Manpower
MPC Multi-Party Contract
MPE Mean Percentage Error
MYE Man-Year Entitlement
NBIMS National Building Information Modeling Standards
nD n-Dimensional
NVA Non-Value Adding
NVAI Non-Value Adding Index
OSM Off-Site Manufacture
PBUs Prefabricated Bathroom Units
PE Percentage Error
PeMS People Management Strategy
XIX
PMET Professional, Managerial, Executive, and Technical
PROMETHEE Preference Ranking Organization Method for Enrichment
Evaluations
PrMS Process Management Strategy
PPVC Prefabricated Prefinished Volumetric Construction
QS Quantity Surveying
QTO Quantity Take-Off
RFI Request for Information
S1 Status One
S2 Status Two
S3 Status Three
S4 Status Four
SCPW Singapore Construction Productivity Week
SDOS Singapore Department of Statistics
SMCCV Sutter Medical Center Castro Valley
SMEC Small and Medium Enterprises Committee
SME Small and Medium-Sized Enterprise
SPSS Statistical Package for the Social Sciences
SRR Shared Risk and Reward
TFN Triangular Membership Number
TMS Technology Management Strategy
URA Urban Redevelopment Authority
US United States
UK United Kingdom
VAP Value-Added Per Employee
VDC Virtual Design and Construction
VE Validation Experts
WC Waste Criticality
WPHs Work Permit Holders
WTU Workforce Training and Upgrading
YoY Year-on-Year
1
Chapter 1: Introduction
1.1 Background
The construction industry is a large contributor to Gross Domestic Product (GDP) in
the total economy of Singapore and is essential to determine economic growth. The
relationship between the construction industry and GDP in developed countries is
usually in the range of 7% to 10% and in developing countries around 3% to 6%
(Samari et al., 2014). In 2010, Singapore’s Economic Strategies Committee (ESC)
found that there is significant room to improve productivity in every sector of the
Singapore economy, and GDP growth should be driven by productivity to stay
competitive. Thus, the ESC set a target for Singapore to achieve productivity growth
of 2% to 3% per year over the next ten years to enable the local GDP to grow on
average by 3% to 5% per year (ESC, 2010).
Table 1.1 shows the year-on-year (YoY) changes in the labor productivity in
Singapore from 2010 to 2016. According to the Building and Construction Authority
(BCA, 2013a), the labor productivity figures published by the Singapore Department
of Statistics (SDOS) as well as the productivity indicator adopted by the ESC as
benchmark are measured in terms of value-added per employee (VAP). At the firm
level, VAP can be estimated by a firm’s value added over the total number of
employees on the firm’s payroll. From Table 1.1 it can be seen that the labor
productivity growth was suboptimal recently, especially in the construction sector.
Table 1.1 YoY changes in labor productivity in Singapore from 2010 to 2016 (%)
Year 2010 2011 2012 2013 2014 2015 2016
Total 11.6 2.3 -0.1 0.9 -0.2 -0.2 1.0
Construction 4.0 2.2 2.7 -5.5 1.1 1.9 -0.5
Note: The figures are based on GDP at 2010 Market Prices and Gross Value Added at
2010 Basic Prices.
Source: SDOS (2017).
2
To meet the productivity growth target in the construction industry, the Singapore
government has undertaken a series of fundamental legislations, such as formulating
the first Construction Productivity Roadmap (CPR) in 2010 (BCA, 2011a), which
focused on helping local firms to adopt technology. Besides, continuous efforts have
been made by the BCA, such as the Construction Productivity and Capability Fund
(CPCF) and Productivity Improvement Projects scheme (Zeng and Chew, 2013). In
the meantime, local builders are required to provide project-level and trade-level
productivity figures through the Electronic Productivity Submission System.
More importantly, in this CPR, the BCA has enforced a five-year BIM adoption
roadmap, in which building information modeling (BIM) electronic submissions (e-
submissions) for regulatory approvals have been mandated in three phases. New
building projects with a gross floor area (GFA) of 20,000 m2 and above were required
to submit their architectural plans in BIM format since July 2013 and to submit their
structural and Mechanical, Electrical, and Plumbing (MEP) plans in BIM format
since July 2014. Eventually, all new building projects with a GFA of 5,000 m2 and
above were required to make architectural, structural, and MEP BIM e-submissions
since July 2015 (BCA, 2011b). This targets the local construction industry to widely
use BIM (80%). In the BIM roadmap, five strategies were proposed, including public
sector taking the lead in specifying BIM requirements for all new public sector
building projects, removing impediments by introducing BIM submission templates,
building capability and capacity by intensive BIM training programs, promoting
success stories, and incentivizing early BIM adopters (BCA, 2011a, 2011b).
According to a survey on BIM implementation by contractors which was conducted
by McGraw Hill Construction (2014a), Singapore was not among the countries in
which the contractors were engaging with BIM on more than 30% of their work
before 2015 (see Figure 1.1). In other words, the Singapore construction industry is
3
experiencing a major shift from traditional methods towards the use of automation
that has been made possible through BIM implementation. Although the local
government has been driving the local industry to use BIM, the state of BIM adoption
is uneven in the local market (McGraw Hill Construction, 2014b). The largest
contractors tend to be very advanced to adopt BIM and thus reap the benefits more
fully, whereas the others are in the beginning phase. Overall, the Singapore
construction industry is not very BIM-ready for project-wide collaboration
throughout the construction value chain (Lam, 2014). This may cause various
inefficiencies in the project lifecycle, seriously affecting productivity (Nath et al.,
2015).
27%23% 23%
29% 28%
39%33%
37%
24%
55%
43%
50% 52% 54%
66%71% 71% 72% 73%
79%
Japan New
Zealand
South
Korea
Canada UK France Australia Germany Brazil US
2013 2015
Figure 1.1 Percentage of contractors engaging with BIM on more than 30% of their
work (by country)
Source: McGraw Hill Construction (2014a)
The identification and analysis of such inefficiencies in the building projects that
were recently completed in Singapore can improve the management of future projects.
By eliminating non-value adding (NVA) time such as idle time, waiting time, and
transportation time, productivity can be improved and projects can probably be
completed within time limit (Nath et al., 2015; Nikakhtar et al., 2015). Therefore,
changes are imperative among current industry practices in the Singapore
construction industry.
4
1.2 Significance and Rationale of Research
Productivity performance is not only a determinant of a firm’s long-term viability, but
also a benchmark of overall competitive advantage of an industry and an economy
(Jergeas et al., 2000). Since many countries have suffered from suboptimal
productivity performance in the construction industry, much research work needs to
be done to formulate strategies for enhancing productivity (Ranasinghe et al., 2011).
Therefore, productivity improvement should be a top management priority for
construction industry leaders.
The construction industry is a prime source of employment generation which offers
job opportunities to millions of unskilled, semi-skilled, and skilled workforce.
Singapore’s Ministry of Manpower (MOM) and Ministry of National Development
(MND) (1999) observed that the most critical reason of the low or even negative
productivity growth in the Singapore construction industry was mainly a heavy
reliance on lower-skilled foreign workforce. The ESC (2010) stated that the rapid
increase in foreign workforce had enabled Singapore to grow the economy in the past
decade, but it in turn made the economy become more dependent on foreign workers.
It is a must to shift to achieving the GDP growth by expanding productivity rather
than labor force. Accordingly, the Small and Medium Enterprises Committee (SMEC,
2014) recommended that underlying the restructuring efforts are productivity
improvements through reduced reliance on the foreign manpower, supplemented by
programs and incentives for the local companies to raise productivity. Hence, the
MOM (2014, 2015) maintained its approach of taking progressive steps to raise the
quality of the construction workforce and reduce the reliance on the foreign labor,
which was in line with the government’s efforts to achieve economic growth driven
by sustained productivity improvements rather than manpower growth. As a result,
the labor market remained tight by the end of 2016 (see Figure 1.2). Much of the
5
foreign employment growth was driven by the construction sector. These statistics
underlined that regardless of any changes in labor law or make-up of the future
workforce, there is significant scope to deliver efficiency improvements in the
industry’s overall performance levels. Thus, productivity in the local construction
industry must be improved.
Figure 1.2 Foreign employment changes in Singapore from 2010 to 2016
Source: MOM (2016)
Table 1.2 presents existing and additional foreign worker curbs, namely foreign
worker levies (FWLs) and Man-Year Entitlement (MYE), specified by the Ministry
of Finance (MOF) of Singapore. According to the ESC (2010) and Wan (2013), a
progressive increase in FWLs would incentivize the local companies to invest in
improving productivity. This is one of the most efficient and flexible ways of
ensuring that the Singapore economy’s dependence on the foreign workforce does not
grow excessively. It will allow for fluctuations in the foreign workforce to cater to
business cycles, for example, in the construction industry (ESC, 2010).
3.8
19.6
34.9 31.6
9.7 6.8
-10.1
54.4
79.8
67.1
48.4
26 22.6
-2.5
-20
-10
0
10
20
30
40
50
60
70
80
90
100
2010 2011 2012 2013 2014 2015 2016
(Thousand) Construction
Total (excluding foreign domestic workers)
6
Table 1.2 Conclusion of existing and additional foreign worker curbs
Implementation Descriptions of foreign worker curbs
July 2010 Progressive increases in FWLs to be introduced over July 2010 to
July 2012 for S Pass and Work Permit Holders (WPHs). Progressive
cuts in MYE for construction sector to be implemented from July
2010 to July 2013
January 2012 Further increases in FWLs to be phased in from January 2012 to July
2013
July 2012 Further reduction in MYE for construction sector, bringing
cumulative cuts to 45% by July 2013
July 2013 Additional FWLs will kick in starting July 2013
July 2014 Additional FWLs to kick in
July 2015 More FWLs to kick in: in particular, this sharpens the distinction
between skilled and unskilled workers
July 2015 The MYE-waiver levy rate for higher skilled or R1 workers will be
lowered from $750 to $600 from 1 July 2015 onwards (Budget 2015)
July 2016 The levy for basic skilled or R2 WPHs employed within MYE will
be increased from $600 to $700 in July 2016 (Budget 2014)
July 2016 Basic tier levy for R2 workers will be raised from $550 currently to
$650 on 1 July 2016 (Budget 2015)
July 2017 Basic tier levy for R2 workers will be raised from $550 currently to
$650 on 1 July 2016 and then $700 on 1 July 2017 (Budget 2015)
Source: Wan (2013); MOF (2014, 2015).
Besides, the National Building Information Modeling Standards (NBIMS, 2007)
committee stated that the construction industry in developed countries such as the
United States (US) and Finland was experiencing a major shift from the traditionally
labor-intensive ways of working towards the application of automation and
integration when BIM technology was available to their construction practitioners.
This trend would result in the identification and reduction of the inefficiencies such as
design errors, waiting for instructions, and reworks, leading to improved productivity
(Alshawi, 2007; Khosrowshahi and Arayici, 2012; Nath et al., 2015, 2016). The
practitioners in the Singapore construction industry should also have the ability to
respond to such inefficiencies for productivity gain.
Figure 1.3 presents a big picture of this study’s rationale. The widely-accepted
definition of BIM proposed by the NBIMS committee stated that “a basic premise of
BIM is collaboration by different stakeholders at different phases of the life cycle of a
facility to insert, extract, update or modify information in the BIM to support and
7
reflect the roles of that stakeholder” (NBIMS, 2007). This can transform the way that
owners, contractors, designers, planners, and the myriad of other roles come together
to perform their jobs (Autodesk, 2012). Because of the policies of promoting BIM in
Singapore, the local practitioners will be equipped with better BIM skills in a variety
of BIM workshops (Cheng, 2013). This may increase the percentage of skilled
manpower at the industry level. For instance, fewer draftspersons will be needed as
they can be replaced by more skilled BIM modelers. Kaner et al. (2008) observed in
four case studies in two middle-sized structural engineering firms that using BIM to
model and produce error-free fabrication shop drawings saved approximately 2.6%,
20.3%, 27.7%, and 47.3% of working hours, respectively, comparing with completing
the drawings traditionally with object-oriented computer-aided design (CAD) tools.
Full BIM-enabled delivery processes (Chapter 3):· Integrated project delivery· Virtual design and construction· Design for manufacturing and assembly
Current project delivery process (Chapter 3)
Traditional project delivery process (Chapter 3)
Drivers for change (Chapter 5)· People· Process· Technology· External environment
BIM-based transformation
Hindrances to change (Chapter 5)· People· Process· Technology· External environment
NVA activities in current delivery process (Chapter 4)
Wastes (Chapter 4)
Productivity
Lean philosophy
Move from CAD to BIM
Achieve
Cut down
ReduceEstablish
Affect Help
Improve
Figure 1.3 Rationale of the study
BIM implementation has been driven in the global construction industry. Smith
(2014) found that one common way for governments to commit on BIM is to
encourage, specify, or mandate BIM use in publicly funded construction and building
projects. This is consistent with some latest studies on the situation and development
8
of world-wide BIM implementation (Cheng and Lu, 2015; Silva et al., 2016;
McAuley et al., 2017). For example, different levels of the public sector in the US
have released BIM standards to effectively implement BIM that is mandated in
various states; the third version of the National BIM Standard-US would be released
in late 2015, offering a full consensus standard for BIM in the planning, design,
construction, and operations of building projects (Cheng and Lu, 2015). The United
Kingdom (UK) government has required a minimum of Level two collaborative BIM
on all centrally funded public projects since April 2016 and issued a suite of
guidelines to specify information management in the design, construction, and
operations phases (Silva et al., 2016; McAuley et al., 2017). The large number of
large public sector owners in Norway set out particular requirements for all project
participants to use open standards for BIM since July 2016, such as the latest
Statsbygg BIM manual. Denmark developed Cuneco Classification System (CCS)
which provides a common language and methods for creating unambiguous exchange
of information throughout the construction process from idea to operation; the
standards CCS Identification and CCS Levels of Information were released in 2015
and 2016, respectively (McAuley et al., 2017). The buildingSMART Finland
published the InfraBIM requirements in 2015, which would be used as general
technical references and modeling guidelines, accompanied by the Inframodel 3 data
exchange format, during procurement and construction. Supported by the German
government and industry, Planen-bauen 4.0 would guide and steer the digital design,
construction, operation, and facility management for all types of projects as well as
all procurement types and forms of contracts since December 2015, requiring all new
projects to implement BIM from 2020 onwards. Other countries such as South Korea,
France, and Spain would include BIM implementation in all public sector projects by
2016, 2017, and March 2018, respectively (Cheng and Lu, 2015; McAuley et al.,
2017).
9
Take the US, where BIM is largely industry-driven, for example. Individual industry
players in the US have initiated BIM adoption, prompting the government to
gradually implement corresponding policies. Because of governmental incentives and
strong promotion by software suppliers, many firms are willing to invest in BIM
software and training (Juan et al., 2017). In contrast with the US’s bottom-up
approach, a top-down approach is adopted in Singapore, where the government is the
dominant force in promoting BIM application. Cheng and Lu (2015) observed that
Singapore leads BIM implementation in Asia because many efforts have been made
by the local government to achieve industry-wide BIM implementation. Specifically,
out of the 35 BIM standards in Asia, 12 were issued by Singapore.
Despite the efforts made, changing towards a higher level of BIM implementation is
generally slow in most construction practitioners (Porwal and Hewage, 2013). One
possible reason for this is that these practitioners tend to be entrenched in the
traditional project delivery using the traditional CAD approach (Eastman et al., 2011)
or adopt a wait-and-see attitude (Juan et al., 2017). Besides, there are many
challenges for BIM implementation, such as owners’ request in limited phases and
subcontractors’ poor knowledge and skills to implement BIM (Kiani et al., 2015).
Thus, BIM implementation still needs to be enhanced in the global construction
industry.
For example, the gap analysis of the BIM adoption roadmap in Singapore revealed
that the local construction industry has been facing many issues: owners may lack
knowledge of BIM implementation and cannot see beyond initial cost; design
consultants tend to over-emphasize the BIM e-submissions and lack time to perform
design coordination; general contractors may not use the design models created by
the designers as such models were not created in the way contractors would have
built the buildings; subcontractors, especially those small- and medium-sized
10
enterprises (SMEs), lack investment for hardware, software, and training; and facility
managers are rarely involved in the design and planning phase, and lack BIM use in
facility management (Lam, 2014). Consequently, the contractors may need to re-build
the models, taking much time, which in turn hinders the collaboration with the
designers (Sattineni and Mead, 2013).
Thus, physical and information fragmentation appears to exist between the upfront
planning and design phases and downstream phases in the Singapore construction
industry. The fragmentation problem would inevitably result in inefficient work
practices and costly changes at the later stages of a project. The litmus test for
successful project management should not be whether the project was free of poor
productivity, but rather, if productivity was enhanced progressively to the benefit of
the industry and the economy. Generally, researchers measure productivity
improvement in terms of time saving (Chelson, 2010; Fan et al., 2014; Nath et al.,
2015). But from a broader point of view, time saving is derived from the
identification and reduction of inefficiencies such as defects, reworks, overproduction,
waiting time, unnecessary inventory, too many requests for information (RFIs),
unnecessary movement, delays, incomplete design, and so on. This can be achieved
through diagnosing the NVA activities in the project lifecycle. By cutting down the
NVA activities, such inefficiencies (or wastes) can be reduced, and thus, construction
productivity at the project level can be improved (Ofori, 2005), which is consistent
with lean thinking (Ohno, 1998). Also, this concept accords with the VAP method. In
a macro sense, enhanced BIM implementation reduces the NVA activities in the
project, and fewer NVA activities need fewer man-days (fewer employees and/or less
time) to complete this project.
It has been advocated that construction practitioners should begin to embrace the
notion of Virtual Design and Construction (VDC). It is a concept or approach to build,
11
visualize, analyze, and evaluate project performance virtually and early before a large
expenditure of time and resources is made on site. BIM forms the backbone of this
approach and its success requires a relook of the current project delivery process
(Chua and Yeoh, 2015). Besides, similar to the manufacturing industry, the
construction industry may apply the notion of Design for Manufacturing and
Assembly (DfMA), which allows discrete sections of the final construction to be
manufactured in a factory and then be transported to site for final assembly
(McFarlane and Stehle, 2014). The digital environment of product design in the
manufacturing industry has moved beyond design to enable analysis,
manufacturability, modularization, and production, and has also encompassed its
supply chain. To realize the full potential of the digital environment of a building
project in the same way, the construction industry must embrace the notion of VDC
(Kam and Fischer, 2004). Both the VDC and DfMA approaches has been promoted
by the BCA to drive the whole construction value chain to work collaboratively in the
project lifecycle. Meanwhile, Integrated Project Delivery (IPD) is another option.
This is a new project delivery method, which can be defined using its widely-
accepted principles: the relationships between the key participants in a project are
bound with multi-party collaboration contracts, and these participants participate in
the project very early, even before the design starts (El Asmar et al., 2013). Early
collaboration, under right conditions, can directly address the fragmentation problem.
To facilitate the change towards higher levels of BIM implementation, the issue
regarding whether the local industry is subjectively ready or objectively pushed by
the government to implement BIM should be studied. Key project stakeholders must
have a clear and comprehensive view of the factors that affect them to perform as
they currently use BIM in building projects and to change their current work practices.
In this study, these factors were studied from the perspectives of people, process,
technology, and external environment. In a building project context, people represent
12
the major stakeholders (such as the government, owner, architect, engineers,
contractors, manufacturer, supplier, and facility manager) and their employees;
process refers to the work practices in the project delivery process, and how the
project is delivered (such as when referring to project delivery process or process
transformation); technology describes the tools and methods that help the project
team deliver their scopes of work; and external environment refers to the environment
in which the project team operates, such as construction market situation and local
policies.
Technology itself such as BIM tools is ready and available to enable new ways of
working that result in more predictable, accurate, and responsible building outcomes
(Autodesk, 2008). The tools have been constantly improving, and more powerful
hardware and a wide range of software can be chosen from. However, technology
alone cannot influence the required changes, and well-defined business process for
information sharing between computable models is required (Bernstein and Pittman,
2004; Khosrowshahi and Arayici, 2012). Successful process transformation from the
current partial BIM use to full BIM implementation requires the alignment of the
project stakeholders’ goals and activities with the project outcomes. Collaboration is
the precondition to BIM implementation.
The present study is timely as the policies of promoting wide BIM adoption in the
construction industry is currently underway in Singapore, and the local industry is
still trying to balance between its own way of doing things and the government
dominance. This study provides a good opportunity to address the productivity issue
by studying and promoting BIM implementation.
13
1.3 Research Problems
Table 1.1 indicates that the productivity growth target in the Singapore construction
industry was not fulfilled since 2013. Besides, productivity is always a problem. Even
if productivity performance in next years is good enough to meet the government’s
requirement, productivity performance enhancing tools such as BIM are still needed
to make it better. Productivity is of high significance as it reflects the overall
competitiveness of an industry as well as an economy (Jergeas et al., 2000;
Ranasinghe et al., 2011). Therefore, this is an area in which much research work
needs to be done.
A series of legislations have already been proposed by the Singapore government and
implemented in recent years in the local construction industry, such as the widely
mandatory BIM e-submissions (BCA, 2011a). However, BIM implementation was
fragmented in different phases and firms. In most projects, building information
models developed by the design consultants are three-dimensional (3D) and object-
based. Although basic data are harvested from the model, such as two-dimensional
(2D) plans, elevations, sections, and quantity take-offs (QTOs) for materials and
labor (Khosrowshahi and Arayici, 2012), the deliverables are often less coordinated
and merely submitted to the government for approvals. These BIM models are
currently rarely shared with downstream quantity surveying (QS) consultants, general
contractors, trade contractors, fabricators, and facility managers. Due to these reasons,
the models need to be re-created in the construction stage and are usually unavailable
in the operations and maintenance stage. This is the issue of BIM implementation in
Singapore that causes many problems. Planning, designing, building, and managing a
building are a complex process that requires smooth communication and
collaboration among the project team, which requires open data sharing among the
project stakeholders (Khosrowshahi and Arayici, 2012). It can take years for the
14
Singapore construction industry to completely shift to full BIM-enabled project
delivery where BIM is available in every single project phase and affordable to all the
major stakeholders.
A project is a knowledge-intensive entity. The team’s knowledge bandwidth affects
its ability to solve problems and deal with emerging challenges during the project
delivery. Such dynamics require ongoing learning and augmentation of the
knowledge bandwidth. Upgrading the local practitioners’ skills can only be realized
progressively. In addition, people seek change, but do not want to be changed (Senge,
1990). This is why management strategies and priorities are investigated in this study
to prepare process transformation options to the current urgency for the local industry.
Based on the above analysis, the research problems are:
a) The suboptimal productivity performance needs to be enhanced in the
construction industry. In particular, productivity growth from 2013 to 2016 did
not reach the Singapore government’s target;
b) NVA BIM implementation practices and their causes and influence on
productivity in the Singapore construction industry were not identified;
c) A tool for diagnosing BIM implementation levels of building projects in
Singapore was not developed;
d) The factors that affect the local construction practitioners to change towards full
BIM implementation were not identified; and
e) Process transformation strategies for building projects of different BIM
implementation levels were not formulated.
15
1.4 Knowledge Gaps
Many studies have studied productivity in the construction industry. Some of them
have identified factors affecting productivity and their classification (Thomas et al.,
1990; Doloi, 2008; Dai et al., 2009; Chelson, 2010; Panas and Pantouvakis, 2010; Dai
and Goodrum, 2012; Dolage and Chan, 2013; Love et al., 2013; Fan et al., 2014;
Shan, 2014), and effects of BIM on productivity during construction (Chelson, 2010;
Azhar, 2011; Barlish and Sullivan, 2012; Fan et al., 2014; Shan, 2014). In the context
of Singapore, a notable study on the critical challenges and possible solutions of
productivity measurement was conducted by Hwang and Soh (2013), but this
previous study focused on productivity measurement at the trade level.
Process wastes have been identified and dealt with by researchers (Formoso et al.,
1999; Alwi et al., 2002; Forsberg and Saukkoriipi, 2007; Koskenvesa et al., 2010;
Sarhan and Fox, 2013b). Nikakhtar et al. (2015) categorized noticeable wastes (such
as waiting time, handling time, and delays) hidden in construction processes due to
the nature of operations and NVA work, and studied how they could be reduced by
adopting the lean thinking using computer simulation. Sacks et al. (2010)
recommended an interaction between BIM functionalities and the lean philosophy in
reducing construction inefficiencies. However, none of these studies have explored
the wastes produced by NVA BIM implementation activities in the project lifecycle
in the Singapore context. One of the generic principles of the lean thinking,
identifying and eliminating NVA activities, is highly recommended in this study to
specifically assist in identifying and reducing the wastes in the current practices
involving partial BIM implementation.
Besides, NVA activities and their resulting wastes may exist in the current industry
practices in the Singapore construction industry (Lam, 2014). This is because some
16
building projects in Singapore, unless being pushed by the government, may be
unwilling to change their customized way of working to better implement BIM. Juan
et al. (2017) developed a model to predict the wiliness of the architectural
consultancy firms in Taiwan to apply automated design checking. Nevertheless, a
mathematical model that can assess the BIM implementation readiness (BIMIR)
statuses of building projects in the Singapore construction industry was not
developed. This study will fill this gap.
As can be seen from the above, there is a need to transform the Singapore
construction industry, and one of the useful tools in this transformation would be
BIM (Nath et al., 2015). It is process transformation that validates BIM
implementation in the construction industry (Arayici et al., 2011; Autodesk, 2012;
Khosrowshahi and Arayici, 2012; Enegbuma et al., 2014). Researchers (Lee et al.,
2005; Lee and Sexton, 2007) studied the process transformation in terms of people,
process, and technology. Lee and Sexton (2007) explored the feasibility of industry
absorbing and diffusing n-dimensional (nD) modeling technology, and found that
there ought to be intrinsic links between technology, people, and process. They
reported that although people appreciate the potential significant benefits of nD
modeling technology, it could be too embryonic and too far removed from the
“comfort zones” of construction firms because the technology requires heavy
investments and contains too many risks. Khosrowshahi and Arayici (2012) stated
that BIM implementation is a major change management task, involving a diversity
of risk areas. Enegbuma et al. (2014) investigated the managerial relationships
between BIM perceptions (people, process, and technology), strategic information
technology (IT) planning, collaborative planning, and BIM adoption, and pointed out
a path for major managerial decision-making choices in improving BIM maturity.
This echoes sentiment in studies concentrating on deploying automatic QTO in the
Singapore construction industry (Teo and Heng, 2007; Teo, 2008). However, no
17
research has been carried out so far to study the process transformation strategies
from an organizational change perspective when BIM has been mandated by the local
government. In this study, external environment is added to supplement the three-
factor (people, process, and technology) structure. The strategies can provide
guidance for building projects to move towards higher BIMIR statuses.
Previous studies have reported that many factors would affect successful BIM
implementation in building projects. For example, studies of the SmartMarket Report
series (Young et al., 2008, 2009; Bernstein et al., 2010, 2012; Lee et al., 2012) may
be the most comprehensive survey studies in the US, European, and Korean markets.
Gu and London (2010) identified the factors for selecting appropriate BIM software
applications in a company and for exchanging data using the applications. Jung and
Joo (2011) considered factors such as property, relation, standards, and construction
business function when setting up a BIM framework. Khosrowshahi and Arayici
(2012) diagnosed the UK construction industry by identifying the challenges and
barriers when changing traditional construction methods towards mature BIM
adoption to develop a clear understanding of BIM implementation. Strategies such as
getting people to understand the potential and the value of BIM over 2D drafting
were recommended. Kiani et al. (2015) identified the strategic implementation of
BIM-based scheduling in Iran, and explored obstacles of using BIM in the planning
and design stages. Also, strategies for improving the efficiency of BIM
implementation in developing countries were identified. Nevertheless, these studies
failed to consider other factors such as contractual relationships among the
participating firms in the project context.
This study intends to identify the critical factors affecting building projects to change
towards higher BIMIR statuses in Singapore. Such factors are important in any type
of management or new technology adoption because these factors allow firms to
18
focus their resources and efforts on certain areas, and also help them identify problem
areas and take necessary corrective actions.
Many articles and books have discussed the obstacles and success factors underlying
successful BIM adoption. However, these studies mostly introduced sparse
recommendations based on successful case histories (Manning and Messner, 2008;
Kuprenas and Mock, 2009), although a few presented these as the results of surveys. Case
studies have the advantage of finding unique ways of solving problems, observing new
phenomena, or testing theoretical assumption, but they are limited in their capacity for
providing a compiled list of solutions or for determining the criticality of issues. This
study will study a holistic view of the critical factors affecting the local industry to move
from their current project delivery practices towards full BIM implementation. Such
factors require high level attention and management priority to be really implemented
(Won et al., 2013).
1.5 Research Objectives
This study will assist project teams in reducing inefficiencies when using BIM in
their project delivery processes in the Singapore construction industry. Without
realizing the factors that affect successful adoption of BIM technology and work
processes in the construction industry, organizations will not be able to know what
improvement efforts need to be made, where these efforts should be focused on, and
which efforts can obtain best results (Leong and Tilley, 2008; Sarhan and Fox, 2013a).
Considering the fact that collaborative atmospheres for BIM implementation among
project teams may not exist in the short term, the construction sector must take steps
to change some unproductive current industry practices to save man power and time.
To effectively move from the current BIM adoption practices towards full BIM
19
implementation in the local industry, considerations must be given to various NVA
activities in the project lifecycle, especially during the design stage and the design-
construction interface stage.
Based on these discussions, the aim of this study is to:
“Develop a BIM-based process transformation (BBPT) model to assist project teams
in moving towards higher levels of BIM implementation, reducing wastes, and thus
enhancing productivity performance in building projects in Singapore”.
The BBPT model will enable key project stakeholders to understand the readiness of
their projects to implement BIM, to identify critical inefficiencies and the factors that
cause the inefficiencies in their project delivery approaches, and to implement
managerial strategies for strengthening the drivers that are likely to motivate them
and overcoming the hindrances that they are likely to face. If BIM is used
collaboratively in these building projects, many inefficiencies, such as delays in
drawing production, tremendous RFIs, and waiting for conformations or instructions,
would be considerably eliminated (Eastman et al., 2011). Therefore, once the current
industry practices of partial BIM implementation are transformed through diagnosing
the key project stakeholders’ BIM implementation activities and changing them,
productivity performance will eventually be enhanced.
The specific research objectives of this study are to:
(1) Identify the critical NVA activities in the current BIM implementation practices
in Singapore, assess their influence on productivity, and examine the leading
causes to these activities;
(2) Develop a BIMIR evaluation model for building projects in Singapore;
(3) Investigate the BIMIR statuses and productivity performance of building projects
in Singapore;
20
(4) Propose an organizational change framework for building projects that implement
BIM;
(5) Examine the critical factors driving and hindering the local construction industry
to change towards full BIM implementation; and
(6) Develop a BBPT model that can evaluate the BIMIR statuses of building
projects, propose managerial strategies for moving towards higher levels of BIM
implementation, and determine the priorities of implementing the proposed
strategies.
Figure 1.4 outlines the BBPT model and links the research aim and objectives. The
model consists of two part-models and can be further deconstructed into six
components. These components are associated with the six research objects
mentioned above. The application of the fuzzy synthetic evaluation (FSE) approach
and the definition of the four BIMIR statuses will be elaborated in Chapter four. The
detailing of the model will be developed, described, and explained in Chapter nine.
Model Part I: BIMIR evaluation(Fuzzy synthetic evaluation)
Model Part II: BIMIR movement (Organizational change perspective)
BBPT model
BIMIR statuses: (1) no BIM; (2) lonely BIM; (3) collaborative BIM; (4) full BIM
Evaluation criteria: critical NVA activities
BIMIR status evaluation of projects in Singapore;Productivity and causes in projects of four statuses
Objective1
Objective2
Objective3
Objective4
Objective5
Objective6
Organizational change framework
Hindrances to change and drivers for change
Management strategies for BIMIR improvement;Implementation priorities of strategies
Figure 1.4 Deconstruction of the BBPT model
1.6 Scope of Research
This research is driven by the increasing concern of the Singapore government about
the suboptimal productivity in the construction industry, and the need of the industry
21
to be integrated and collaborative. BIM has been recognized by governments and
more and more professionals in the construction industry worldwide. For instance, the
US made it mandatory to use BIM in government projects since 2008 and Hong Kong
re-modelled its existing Mass Transit Railway projects for facility management
purpose (Cheng, 2013). Previous studies (Rogers, 2013; Mohd-Nor, 2014; Wong et
al., 2014) have added many managerial values to BIM implementation in the
construction industry, such as critical success factors of strategic BIM adoption in
architectural and engineering consulting services as well as BIM capabilities in QS
practices. In addition, although it is originated from automobile production
management, the lean thinking is also recognized in enhancing project performance
by eliminating wastes in the project delivery process. In this study, several specific
boundaries are identified below:
· Research focus. This study focuses on applying BIM to facilitate process
transformation in building projects in Singapore for two reasons. Firstly,
productivity performance in the local construction industry is of great concern
due to the image of low-tech of and over-reliance on the foreign workers. BIM
has been deemed as one of the useful tools in enhancing productivity
performance through optimizing project delivery process and reducing the
inefficiencies in this process (Nath et al., 2015). Secondly, it is process
transformation in the construction industry that makes BIM implementation
valuable (Arayici et al., 2011; Autodesk, 2012; Khosrowshahi and Arayici, 2012;
Enegbuma et al., 2014). Although the Singapore government has made BIM e-
submissions (all architectural and engineering plans) mandatory for all new
building projects greater than 5,000 m2 after July 2015, currently the mandate
mainly stresses on the design stage, where construction activities do not begin,
rather than full BIM implementation in the entire project lifecycle (Lam, 2014).
Design consultants work in their own areas for regulatory planning approvals,
without considering the collaboration with downstream parties and disciplines.
22
The contractors and operations and maintenance team are usually not involved
upfront to contribute their expertise. This is not the spirit of BIM as it results in
separate BIM adoption among the major stakeholders in different phases. Thus,
there is a need for the current project delivery process to be transformed towards
full BIM-enabled delivery, namely the IPD, VDC, and DfMA approaches. This
transformation reduces wastes such as defects, change orders, waiting for
information, and reworks, enhancing overall productivity performance in building
projects in Singapore.
· Project lifecycle. The Singapore government has mandated BIM e-submissions in
the design stage, and has been driving the collaboration and integration
throughout the construction value chain. Without sufficient interactions between
design consultants, contractors, and fabricators, the efforts made for the
submissions in the design stage cannot be reused in the work during the
construction stage, let alone the operations and maintenance stage. Thus, this
study intends to identify and transform the critical NVA activities in the current
BIM-based project delivery process. In this study, the project lifecycle refers to
the planning, design, design-construction, construction, handover/closeout, and
operations and maintenance stages in the building project context. The demolition
stage is beyond the scope of this study.
· Major stakeholders. Government agencies, owners, architectural consultants,
engineering consultants, general contractors, trade contractors, fabricators,
suppliers, operations and maintenance teams, and so on are the key project
stakeholders that carry out BIM implementation activities in different stages of
the pre-defined project lifecycle. The potential causes to the NVA activities will
be examined based on these roles.
23
1.7 Research Hypotheses
Based on the research problems identified (see Section 1.3) and the literature review
(see Chapter two to Chapter five), the following hypotheses are formulated in this
research:
Hypothesis 1: The construction industry agrees upon frequent NVA activities in the
current project delivery in the Singapore context;
Hypothesis 2: The BIMIR statuses of building projects in Singapore are low;
Hypothesis 3: The higher the BIMIR status, the lower the criticality of the wastes and
the higher the productivity performance;
Hypothesis 4: Moving towards higher levels of BIM implementation is hindered by a
set of critical hindrances which can be interpreted from the organizational
change perspective; and
Hypothesis 5: Moving towards higher levels of BIM implementation is driven by a set
of critical drivers which can be interpreted from the organizational change
perspective.
1.8 Structure of the Thesis
This thesis is organized into 10 chapters, as shown in Figure 1.5. Following this
introductory chapter, Chapter two provides an overview of productivity performance
in the Singapore construction industry. The target and present situation of
productivity performance are described. Policy initiatives that are meant to enhance
productivity are reviewed, especially those regulations related to promoting BIM
implementation in the local construction industry.
24
Chapter 1:Introduction
Chapter 2: Review ofproductivity-related policies in Singapore
Chapter 3: Review of Traditional and BIM-based project delivery processes
Chapter 4: Review of NVA activities and proposal of a fuzzy BIMIR evaluation model
Chapter 5: Review of factors affecting BIM implementation and proposal of an organizational change framework
Chapter 6:Research design and data collection
Chapter 7:Data processing and analysis of two surveys
Chapter 8:BIM adoption and project delivery process
transformation in a construction and development firm - a case study
Chapter 9:Development of a BBPT model
Chapter 10:Conclusions, limitations, and recommendations
Part II: Research methodology
Part III: Data analysis anddiscussions
Part V: Conclusions
Part I: Literature review and proposals
Part IV: Model development
Figure 1.5 Structure of the thesis
Chapter three reviews the literature related to BIM implementation process. The
characteristics of five project delivery approaches are reviewed, namely traditional
project delivery process, current project delivery process in the Singapore
construction industry, and full BIM-enabled delivery processes (including IPD, VDC,
and DfMA) which are adapted in the context of Singapore.
Chapter four reviews the literature on the NVA activities in the project lifecycle
which also result from the comparison between the current project delivery and the
full BIM-enabled delivery. Possible resulting unproductive wastes and potential
causes contributed by the key roles are identified. Besides, four BIMIR statuses are
25
defined with support from previous studies. Using the critical NVA activities as
evaluation criteria, a fuzzy BIMIR evaluation model for building projects is
developed.
In Chapter five, hindrances to and drivers for change towards full BIM
implementation are identified. More importantly, based on existing theories of
organizational change, an organizational change framework for building projects
using BIM is proposed, which consists of 29 change attributes in terms of people,
process, technology, and external environment. This framework also serves as the
process transformation framework.
Chapter six focuses on the research methodology. A flow chart is presented to show
the research process. Two rounds of surveys and a case study were conducted, and
data were collected through questionnaires, interviews, observations, and past
document analysis. Multiple methods were selected and explained for the data.
Chapter seven reports the in-depth data analysis results of the two surveys and
relevant discussions. The critical NVA activities in current BIM implementation
practices were identified, which enabled to calculate the BIMIR statuses of surveyed
building projects. Chapter eight describes the findings of the case study in a large
construction and development firm based in Singapore.
Chapter nine is dedicated to the development of the BBPT model for building
projects. The application of the model was explained step by step in the building
project context in Singapore. In addition, the building projects that were involved in
both surveys were used to demonstrate how this model works.
26
Chapter ten is the final chapter and concludes the thesis. This chapter presents main
findings, contributions to scholarship and industry. Validation of the research
objectives and hypotheses as well as limitations of the study are discussed.
Recommendations for future research work are also proposed.
27
Chapter 2: Review of Productivity Performance and Relevant
Policies in the Singapore Construction Industry
2.1 Introduction
This chapter provides an overview of existing body of background knowledge related
to productivity in the Singapore construction industry. To begin with, a general
review of productivity performance by the end of 2016 in the construction industry is
presented, which indicates the progress under the first CPR and the beginning of the
second CPR. Thereafter, principal policies related to incentives, funds, advanced
technologies, and so on under the second CPR, which would be implemented in the
next few years for enhancing productivity performance in Singapore, are reviewed.
Most of these productivity development programs are issued by the BCA, MOM,
MOF, and MND in Singapore. Finally, there is still a need for understanding the BIM
adoption status in the process of project delivery transformation for productivity gains
in Singapore.
2.2 Overview of Productivity Performance in Singapore
In 2010, the ESC (2010) recommended a challenging target for Singapore to achieve
productivity growth of 2% to 3% per year over the next ten years, more than double
the 1% rate which was achieved over the last decade. The main sources of weak
productivity growth in the last ten years were in service industries such as restaurants
and real estate services, and in the construction industry as well.
To support the ESC’s recommendation to raise productivity for sustained economic
growth, the MND and BCA incorporated the inputs of industry practitioners and the
recommendations by the International Panel of Experts, and formulated the first
28
holistic CPR to transform the local construction industry and uplift its productivity.
Endorsed by the National Productivity and Continual Education Council in
November 2010, this roadmap aimed to build a highly integrated and technologically
advanced construction sector which is led by progressive firms and supported by a
skilled and competent workforce by 2020 (BCA, 2011a, 2011c). A four-pronged
approach was proposed (see Table 2.1).
Table 2.1 First CPR in Singapore
Prongs Associated Initiatives
Regulating demand
and supply of low cost,
lower skilled foreign
workforce through
FWL and MYE system
(1) Cutting the MYE progressively to regulate the supply of
low cost foreign workers;
(2) Imposing higher levy to moderate the demand for low
cost foreign workers.
Enhancing quality of
the construction
workforce
(1) Enhancing construction registration of tradesmen
scheme. The BCA would expand key construction trades
recognized under this scheme by 2011;
(2) Introducing new tiered-levy framework. From July 2011,
unskilled workers would be phased out from the construction
sector. Higher skilled foreign workers would be
distinguished from basic skilled workers to enjoy lower levy.
This levy differential would be progressively raised to
encourage employers to upgrade and retain more
experienced and higher skilled workers;
(3) Upgrading workforce at all levels. The Workforce
Training and Upgrading (WTU) scheme under the CPCF co-
funds training courses and skills assessments.
Imposing regulatory
requirements and
setting minimum
standards to drive
widespread adoption of
labor-saving
technology
(1) Enhancing buildability framework, which requires
architects and engineers to adopt “easier-to-construct”
building designs. A new constructability component would
also be introduced to require contractors to adopt more
labor-saving construction methods and technology;
(2) Driving BIM implementation. The BCA would mandate
BIM e-submissions of architectural, structural, and MEP
plans for building works for regulatory approvals by 2015.
Offering financial
incentives to encourage
manpower
development,
technology adoption,
and capability building
Enhancing the CPCF, including a BIM fund, to help firms
cope with upcoming manpower policy changes. This could
extend funding support to cover more firms, expand funding
scope, and raise funding support levels to push firms to
make a swift switch to technology in place of labor.
Source: BCA (2011a, 2011c).
To steer the industry towards enhancing productivity, the BCA established the
Construction Productivity Centre (CPC) in April 2010 and the Centre of Construction
29
Information Technology in September 2010. The purposes are to educate and raise
the local industry’s awareness and ownership on productivity improvements and
manpower development. A customer-centric account management approach was
adopted to administer the incentives to encourage technology adoption, manpower
development, and capability building by the local firms (BCA, 2011a, 2011c). Some
key initiatives of the CPC include:
· Showcasing best practices and successful stories in productivity improvements
through a bi-monthly publication called “Build Smart” for industry players;
· Recognizing industry productivity leaders through awards, such as construction
productivity award and BIM award, to cultivate a culture of productivity
excellence; and
· Establishing benchmark indicators for productivity improvements to create
greater ownership in productivity improvement across the construction sector.
In the meantime, the BCA started to organize the Singapore Construction
Productivity Week (SCPW) annually in 2011. This event further heightens the
industry’s awareness of the latest initiatives and best practices for productivity
improvements. Skilled builder competition, BIM competition, BuildTech exhibition,
and site visits of innovative projects would be held. Thus, the SCPW could serve as a
learning platform for the construction industry to gain valuable insights into best
practices, new technologies, and skills that positively influence productivity
enhancement at the individual, firm, and industry levels.
The BCA and the MND would continue to review and evaluate the effectiveness of
the strategies under the first CPR to drive the industry to improve productivity and
build capabilities. Despite the abovementioned efforts made by the Singapore
government and the local industry since 2010, productivity performance was not
desirable under this CPR. It can be seen from Table 1.1 that the YoY changes in labor
30
productivity from 2011 to 2016 in the Singapore construction industry were 2.2%
(2011), 2.7% (2012), -5.5% (2013), 1.1% (2014), 1.9% (2015), and -0.5% (2016),
respectively, measured in terms of VAP (SDOS, 2017). The figures indicated that
labor productivity: (1) overall improved in the last seven years; (2) usually increased
in a few years, followed by a decrease in a certain year; and (3) experienced smaller
and smaller yearly changes (absolute values) to some extent. The large decrease (-
5.5%) in 2013 might be because the mandatory BIM e-submissions in the
architectural discipline took effect in July 2013 and the submissions in other
disciplines were then not mandated. In conclusion, the targeted productivity growth
was not achieved from 2013 to 2016 and higher productivity gains in next few years
need to be pursued.
Challenges to improve productivity were briefly discussed. In addition to the heavy
reliance on lower-skilled foreign workforce and entrenchment in the traditional CAD-
based project delivery approach which were analyzed in Section 1.2, other challenges
were identified from the literature review. For example, Ofori (2005) found that the
major challenges to improve productivity in Singapore were delays due to compliance
with regulations, errors in design, poor skills of workers, reworks of rectifying defects,
inadequate pre-project planning, and changes in design. Hwang and Soh (2013) also
identified that lack of planning and control would be a challenge.
2.3 Productivity-Enhancing Policies in Singapore
Because of the suboptimal productivity performance, productivity-enhancing
initiatives are imperative. The second CPR was rolled out in June 2015, which adds
on the first CPR and targets three key areas, namely a higher quality workforce,
higher capital investments, and better integrated construction value chain (BCA,
2015e).
31
2.3.1 Higher quality workforce
First, building up higher skilled or R1 workers. The MOM would require all
construction firms to have at least 10% of their WPHs to be qualified as R1 workers
from January 2017 onwards (MOF, 2014; BCA, 2015c). This is to improve the skills
profile of the construction workforce.
According to the BCA and MOM (2014), about 15% of WPHs had been qualified as
R1 workers in the Singapore construction industry, but they were unevenly
distributed across construction firms. To accelerate the formation of a higher-skilled
construction workforce across the entire industry, the BCA would implement a two-
year upgrading phase from the beginning of 2015, requiring the construction firms to
upgrade 5% of their own WPHs to R1 status by the end of 2015, and another 5% by
the end of 2016. This would enlarge the pool of R1 WPHs in Singapore. The
construction firms that failed to meet the upgrading requirement would be disallowed
from hiring new R2 WPHs, for a maximum period of 12 months, or shorter until they
met the 10% minimum R1 proportion requirement.
Next, incentivizing the upgrading of workers by widening levy differential. Table 2.2
presents the levy rates of WPHs from 2014 to 2017 in the construction sector. It can
be seen that the levy differentials between R1 workers and R2 workers would be
widened from S$250 in 2014 to S$400 till 2017.
Table 2.2 Levy schedule for WPHs from 2014 to 2017 (S$)
Time July 2014 July 2015 July 2016 July 2017
R1 R2 R1 R2 R1 R2 R1 R2
MYE Quota 300 550 300 550 300 650 300 700
MYE Waiver 700 950 600 950 600 950 600 950
Source: MOF (2015).
Also, the upgrading of workers was incentivized through co-funding the WTU
scheme under the second CPCF. To incentivize firms to adopt technology and build
32
capability, the government would co-fund more training courses at professional,
managerial, executive, and technical (PMET) levels. Specifically, up to 90% would
be funded for local construction workers, and up to 40% for experienced foreign
workers in 2015 and 2016.
In addition, building up a local core of workforce. A variety of scholarships or
sponsorships would be provided for university students, building specialists
(supervisors and foremen), Institute of Technical Education applicants, and so on to
attract locals.
Last but not least, launching a five-year rebranding roadmap to attract and retain a
strong pipeline of local talent into the built environment sector. This roadmap was
jointly formulated by the MND, the BCA, industry stakeholders, and the Institutes of
Higher Learning in May 2014. Key initiatives under the roadmap would help drive
transformation in the sector to offer conducive work environments, better human
resource (HR) practices, and meaningful careers for attracting local talents. Beyond
these, this roadmap would also focus on raising awareness on the sector through
structured internships for students and attachment programs for teachers. A
memorandum of understanding was signed between the BCA and the construction
industry joint committee to promote the adoption of good HR practices in firms
through a new pledge for a better built environment workplace (BCA, 2014c).
2.3.2 Higher capital investments
According to BCA (2015a), S$450 million was set aside in March 2015 for the
second tranche of the CPCF in the next three years to help the construction sector
make higher investments in impactful and productive technologies as well as improve
the quality of its workforce. This added on the existing S$335 million committed to
33
enhance productivity in the sector. It was expected to benefit about 7,000 firms to
drive higher productivity gains.
Public sectors would take the lead in government projects. Firstly, the BCA would
work with other key public agencies to operationalize their productivity roadmaps
through a structured framework, which would guide them to meeting the national
productivity growth target. Secondly, to incentivize consultants and contractors to be
more productive, tendering advantages would be given to progressive consultants and
contractors. This was gradually implemented since September 2014. Lastly, the MOF
(2014) stated that, for selected Government Land Sales (GLS) sites, the use of
productive technologies such as Prefabricated Bathroom Units (PBUs) and
Prefabricated Prefinished Volumetric Construction (PPVC) would be mandated in the
tender conditions. A minimum percentage level of prefabrication would be stipulated
for industrial GLS sites. For example, two selected GLS sites at Yishun Avenue 4 and
Jurong West Street 41 should meet these new requirements (BCA, 2014b). PPVC
involves the assembly of whole rooms or apartment units complete with internal
fixtures that are produced off-site and installed on site in a Lego-like manner.
On the other hand, the private sector would be incentivized to adopt a greater extent
of DfMA under the second tranche of the CPCF (BCA, 2015a). DfMA is the process
of designing products to optimize manufacturing functions, and to ensure minimized
cost, maximum quality, delivery time reliability, and customer satisfaction (Belay,
2009). With DfMA, maximum off-site production and assembly as well as minimum
assembly work on site can be targeted. Prefabrication enables more work to be done
in a controlled factory environment, and productivity to be raised through automation
and better quality control, much like in a manufacturing process. To support the shift
towards DfMA, a strong core of PMET personnel would be needed to lead to the
advancement of the sector. Starting with developers, architects and engineers design
34
for off-site manufacturing and on-site assembly and installation. Also, a bigger pool
of at least 30% of higher skilled workers would be required to anchor the workforce,
which was consistent with the strategies to build up higher quality workforce
mentioned in Section 2.3.1 (BCA, 2015a).
2.3.3 Better integration of value chain
The BCA has been encouraging the local firms to collaborate with their partners in
their project teams using common BIM models. BIM enables all parties in the
construction value chain to better visualize designs, detect design problems early,
enhance planning and coordination, and reduce reworks for projects (Chelson, 2010;
Eastman et al., 2011; Khosrowshahi and Arayici, 2012). Under the BIM roadmap of
the first CPR, the BCA has mandated BIM e-submissions of all architectural,
structural, and MEP plans for building projects for regulatory approvals from July
2015 onwards. Also, it has been providing a BIM fund under the first CPCF tranche
to help firms to increasingly adopt BIM technology (BCA, 2011a). The BIM fund
intended to help the local firms to incorporate BIM technology into their work
processes at two levels:
· Firm level scheme. It supported individual firms to build up capability in BIM
modeling, visualization, value-added simulation and analysis, and project
documentation; and
· Project collaboration scheme with a cap of S$210,000. It supported the firms to
build up capability in BIM project collaboration to reduce design conflicts and
costly reworks downstream.
BCA (2015d) stated that applications for firm-level BIM adoption would not be
accepted since 22 May 2015. Applications having been accepted earlier by the BCA
would continue to be processed for approval under the first CPCF Tranche.
35
To encourage wider adoption of BIM in the industry, the BIM funding support was
enhanced. The enhancement allowed a firm to double the number of applications
from three to six. But the firm was required to use a slightly bigger sized project and
achieve a 20% instead of a 10% productivity improvement for the fourth to sixth
application. The S$210,000 cap for project collaboration scheme was removed to
encourage more project partners to use BIM collaboratively. An one time support for
the expenditure of manpower involved in setting up the firm’s BIM deployment plan
was also added to the list of supportable items (BCA, 2015d).
Nevertheless, according to Lam (2014), only 20% applications were at project
collaboration scheme, indicating a lack of BIM collaboration under the first tranche
of the CPCF. Therefore, to help firms reap the full benefits of BIM and build up their
multidisciplinary collaboration capabilities beyond just modelling, the new BIM fund
(version two) was released in July 2015 (BCA, 2016). This fund would help BIM-
ready firms defray part (up to 70%) of their costs in training, consultancy, and
software and hardware. Applicants are required to submit a joint application together
with another firm of a different discipline.
Furthermore, the Singapore government has been driving the use of VDC, an
integrated approach that combines BIM technology and advanced management
methods to improve productivity. Specifically, it has been proven to increase
profitability, improve reliability and predictability before project execution, and
enhance project efficiency to higher levels (Li et al., 2009; Kunz and Fischer, 2012).
The BCA Academy has been partnering with the Stanford University’s Center for
Integrated Facility Engineering, a leading research center for VDC, to offer advanced
management programs at the chief executive officer as well as senior and middle
management levels. These programs aim to help the local construction industry
practitioners, from developers to consultants and contractors, to understand the value
36
of VDC and BIM, and take an integrated approach in the planning, design,
construction, and operation processes of construction projects (BCA, 2014a).
2.4 Summary
This chapter reviewed productivity performance and relevant productivity-enhancing
polices in the Singapore construction industry. Although these initiatives formed a
holistic plan to improve productivity, this study focused on the collaboration and
integration across the entire construction value chain. One important reason for this
was the global recognition of the BIM technology that may bring considerable
changes to the project lifecycle. As a key enabler to integrate various activities along
the planning, design, construction, and operations and maintenance phases, BIM
promotion has been one of the most critical policies implemented in Singapore.
In line with the Singapore government’s efforts to slow down the growth of the
foreign workforce to a more sustainable pace, the local construction industry has to
continue to be restructured and transformed along with the rest of the local economy.
Productive growth is an effective way to remain globally competitive and to attract
and retain more locals and skilled labor. Both the local government and the industry
practitioners should be motivated and prepared to implement BIM to achieve the
targeted productivity growth.
37
Chapter 3: Review of Traditional and BIM-Based Project
Delivery
3.1 Introduction
This chapter reviews the literature on revolutionary project delivery process of
adopting BIM in the construction industry, from the traditional project delivery
process, the current project delivery process in the Singapore context, to three
collaborative approaches that are increasingly used recently in the global construction
industry. With BIM as a facilitator, IPD, VDC, and DfMA deliver projects with
different stresses in the project lifecycle. The key activities related to BIM in these
processes are summarized. These processes are reviewed from the perspectives of
team building, technology adoption, project phasing and execution, and so on. In
addition, a comprehensive comparison among these processes is provided.
3.2 Traditional Project Delivery Process
Despite the rapid evolvement of project delivery process in the construction industry,
the traditional delivery approach was the basis for transformations. Arain (2005)
stated that history is important and knowledge from experience would assist in
making good decisions in project management now.
According to the Association of General Contractors (AGC, 2006), traditional, 2D-
based design evolved from pencils, to overlay drafting, to layers and levels seen in
CAD programs. These long market-accepted “flat” media, separate nature of the
layers, and multiple design and consulting disciplines had contributed to the 2D,
layered, and disconnected process prevalent today. In the 2D-based delivery process,
the tools and process available to a project team contributed to a distinct inability to
38
see, think, and document from an integrated 3D (and beyond) way. For example, the
implications of a moved beam on a duct simply could not be known or seen in a 2D
environment. They must be imagined. Thus, the 2D design process allowed the
possibility that the design was not complete, as not all areas were drawn.
The roles and activities of the major stakeholders in a building project were
summarized in the traditional delivery process. According to the American Institute
of Architects and the American Institute of Architects, California Council (AIA and
AIACC, 2007), the project team includes two categories of major stakeholders,
primary participants and key supporting participants. The former is those participants
that have substantial involvement and responsibilities throughout the project,
including the owner, architect, and general contractor; the latter includes primary
design consultants (specifically, the structural engineer and MEP engineers in this
study) and subcontractors (specifically, including the manufacturer and supplier in
this study). The government and operations and maintenance team are also included
in “people” which is defined in Section 1.2. Table 3.1 presents a brief conclusion of
the major stakeholders and key activities in each project phase in the traditional
delivery process. This approach is commonly known as a design-bid-build (DBB)
method, a viable and most widely used delivery method for most construction
projects, especially for public building projects (Miller et al., 2000; Ibbs et al., 2003;
Ling et al., 2004; Autodesk, 2008). Architectural and engineering contracts tend to be
solely awarded to provide design services before the construction phase. The design
was usually not totally fixed until the construction phase because trade contractor
input was not available until then. Due to this disconnect, this delivery process
usually results in frequent claims and disputes among the participants as well as cost
and time overruns (Azhar et al., 2014). Therefore, the construction industry needed
alternative delivery methods.
39
Table 3.1 Key stakeholders and activities in the traditional delivery process
Phases Major stakeholders Key activities
Predesign Owner, architect Owner: setting project requirements,
objectives, and so on
Schematic
design
Owner, architect;
Structural engineer, MEP
engineers
Architect: developing conceptual
design alternatives using CAD;
Owner: giving feedbacks and making
decisions
Design
development
Owner, architect;
Structural engineer, MEP
engineers
Architect and engineers: developing
detailed design using CAD;
Owner: giving feedbacks and making
final decisions
Construction
documentation
Owner, architect;
Structural engineer, MEP
engineers
Architect and engineers: producing
drawings and specifications for
downstream uses such as preparing
tender documents
Agency permit/
Bidding
Government;
Owner, architect;
Structural engineer, MEP
engineers;
Tenders (general contractor,
subcontractors)
Architect and engineers: submitting
building plans for regulatory approvals;
passing necessary documents to the
general contractor who wins the bid;
Construction Government;
Owner, architect;
Structural engineer, MEP
engineers;
General contractor,
subcontractors
General contractor: reproducing 2D
construction drawings using CAD,
doing site preparation, planning
schedule, outsourcing works to
subcontractors, communicating with
the owner and design consultants
especially for changes management and
RFIs; preparing final 2D as-built
drawings
Operations and
maintenance
Owner;
Facility manager
Owner: engaging operations and
maintenance team;
Facility manager: managing the
building based on the final 2D as-built
drawings
Source: AIA and AIACC (2007); Eastman et al. (2011); AIACC (2014).
The transformation needed in the construction industry may be achieved through the
use of technology. Nevertheless, despite large investments in equipment, CAD, and
web-based collaboration technologies over the past two decades, the industry has not
seen anticipated gains in productivity (Howard and Björk, 2008). One root cause is
the information and knowledge fragmentation both vertically over project phases and
horizontally over the value chain. Consequently, decisions made are often less than
optimal. Another reason is the inherent wastes occurred in the construction delivery
processes (Chua and Yeoh, 2015).
40
3.3 Current Delivery Process
Productivity is always a problem that needs to be continuously addressed. In
particular, the Singapore government has expressed its great concerns in recent years
about productivity in the construction industry. As mentioned in Section 2.2, the
undesired productivity performance from 2013 to 2016 did not meet the targeted
productivity growth set in 2010. To improve this situation, the BCA has identified
and mandated BIM e-submissions as one of the key technological tools to improve
productivity in the construction industry (Nath et al., 2015).
The future of the design and construction industry is going to be driven by
technology. The best example emerging today is the use of 3D, intelligent design
information, commonly referred to as BIM, which is expected to gradually move the
Singapore construction industry away from a “2D based” delivery process towards a
“model based” process. In a 3D based delivery process, BIM technology allows
project teams to see and collaborate in 3D models. More important than the
technology-enabled way, is the information that team members get, the interactivity
and linkages that the technology fosters, and the intelligence and analysis that this
linked data promotes. Use of the intelligence housed within a BIM model allows
teams to see and interact differently, which is far more intelligent than teams involved
in a 2D based process. Model reviews, virtual huddles, and electronic computer-aided
virtual environments change the environment, duration, nature, and results of
construction process. Shop drawings may be waived when shop models or computer
numerically controlled (CNC) fabrication models are used. RFIs may become
obsolete, or at least be significantly reduced in number, and be resolved much quicker
if the models were deployed as jobsite tools (AGC, 2006).
41
Therefore, as one of the four prongs in the first CPR mentioned in Section 2.2, BIM
e-submissions of all building plans for all new building works with a GFA of 5,000
m2 and above has been mandated by the Singapore government (BCA, 2011a, 2011c).
This initiative has been driving the local building project delivery process into a
status of partial BIM implementation (see Figure 3.1). Currently BIM implementation
in Singapore tends to stress on the design stage, where on-site activities do not begin,
and on regulatory approvals, rather than considering downstream uses.
Architectural design
Structural design
MEP design
Architectural model
Structural model
MEP model
General contractor,trade contractors,
manufacturers, suppliers
Re-producing all BIM models
Coordination, shop drawings, as-built
submissions to Architect
Not sharing, or sharing
incomplete BIM models
Submissions to government
2D drawingsfor operations &
maintenance
Figure 3.1 Current partial BIM implementation in the Singapore construction industry
It can be seen from Figure 3.1 that currently BIM is not used consistently in different
phases of a building project in Singapore. The BCA’s gap analysis of the first BIM
roadmap revealed many problems in the current state of BIM adoption, such as firm-
based rather than project-wide BIM collaboration (Lam, 2014). Specifically, the
owner may lack relevant knowledge and be detached from BIM processes. BIM
implementation requires a significant amount of resources upfront. Even though
return on investment has been widely justified (Singh et al., 2011), the owner might
not be able to see beyond initial costs. The architect and engineers tend to use their
design models only for their own benefits; they may over emphasize the mandatory
BIM e-submissions and lack time to perform design coordination for downstream
uses. More importantly, their design models are of poor quality, and rarely shared
with downstream parties because it is not their responsibilities to do that. Without
knowing the downstream BIM uses, the design team may not be able to identify the
42
reusable project information and important information exchanges (Anumba et al.,
2010). In addition, the contractors and facility manager are usually not involved in the
design phases to contribute their expertise. Thus, the general contractor must re-build
the design models based on the 2D drawings, specifications, and incomplete design
models from the design consultants to coordinate key trades, produce shop drawings,
and develop and submit as-built BIM models to the architect during construction
(Sattineni and Mead, 2013; Lam, 2014). Moreover, most trade contractors lack BIM
skills and the facility manager rarely uses BIM (Lam, 2014).
BIM implementation in many projects can be as basic as the availability of a 3D
model produced by one or more specialty contractors or suppliers, such as a steel
fabricator or mechanical contractor (AGC, 2006). It is not unusual, particularly while
the 2D conversions continue to be the norm, for multiple models to be made available
on the same project.
The general contractor may make use of intelligent models for portions of the project
scope to assist with many of its traditional activities (AGC, 2006). Many of these
BIM uses include:
· Assisting in scoping during bidding and purchasing;
· Reviewing portions of the scope for analyses such as value engineering;
· Coordinating construction sequencing (even if just for two trades); and
· Demonstrating project approaches during marketing presentations.
Only portions of the project scope and only specific trades may be modeled in each of
the above cases. Even though, these BIM uses are not widely adopted across the
contractors in Singapore. Therefore, the current way of delivering a building project
in the Singapore construction industry can be figured out based on the traditional
delivery process as the whole process has not been changed fundamentally. In fact,
43
those activities completed with CAD are substituted by those with 3D modeling
technology (see Table 3.2). From this table, it is notable that the key stakeholders and
the phases they become involved remain unchanged.
Table 3.2 Key stakeholders and activities in the current BIM adoption process
Phases Major stakeholders Key activities
Predesign Owner, architect Owner: setting project requirements,
objectives, and so on;
Architect: developing site model and massing
models using BIM
Schematic
design
Owner, architect;
Structural engineer,
MEP engineers;
Owner: giving feedbacks and making
decisions;
Architect: developing conceptual architectural
model using BIM;
Engineers: developing structural and MEP
models using BIM according to the conceptual
architectural model
Design
development
Owner, architect;
Structural engineer,
MEP engineers;
Owner: giving feedbacks and making final
decisions;
Architect: updating architectural BIM model;
Engineers: updating structural and MEP BIM
models according to the latest architectural
BIM model
Construction
documentation
Owner, architect;
Structural engineer,
MEP engineers;
Architect and engineers: producing drawings
and specifications for downstream uses such as
preparing tender documents
Agency permit/
Bidding
Government;
Owner, architect;
Structural engineer,
MEP engineers;
Tenders (general
contractor,
subcontractors)
Architect and engineers: submitting building
plans in BIM format for government’s
approvals; passing necessary documents (2D
drawings, and design models if willing to
share) to the general contractor who wins the
bid
Construction Government;
Owner, architect;
Structural engineer,
MEP engineers;
General contractor,
subcontractors
General contractor: re-producing 2D
construction drawings using CAD and re-
building design models using BIM for
construction uses; doing site preparation,
planning schedule, outsourcing works to
subcontractors, communicating with the owner
and design consultants especially for changes
management and RFIs; preparing final 2D as-
built drawings
Operations and
maintenance
Owner;
Facility manager
Owner: engaging operations and maintenance
team;
Facility manager: managing the building based
on the final 2D as-built drawings
Source: AIA and AIACC (2007); Eastman et al. (2011); BCA (2013b); AIACC
(2014).
44
Overall, the current project delivery process adopted in Singapore is a partial BIM
adoption process. If the upfront design consultant team does not collaborate with the
downstream parties, the true spirit of BIM will not be realized as it results in
fragmented BIM uses between the designers, contractors, and facility manager. As a
result, there will be many problems in the construction and operations and
maintenance phases, such as frequent RFIs, change orders, and reworks. Therefore, it
is necessary to develop proper strategies to drive for better BIM collaboration and
integration throughout the construction value chain.
3.4 Full BIM-Enabled Delivery Processes
3.4.1 IPD
3.4.1.1 Overview of IPD
IPD is defined as “a project delivery approach that integrates people, systems, and
business structures and practices into a process that collaboratively harnesses the
talents and insights of all participants to optimize project results, increase value to the
owner, reduce waste, and maximize efficiency through all phases of design,
fabrication, and construction” (AIA and AIACC, 2007).
Traditional project delivery methods have been found as inefficient and litigious
(Eastman et al., 2011; Azhar et al., 2014). As a result, the construction industry is in
an urgent need of alternative delivery methods. Recently IPD has emerged as a
method with a potential to revolutionize the project delivery. It is expected to meet
the need of reducing inefficiencies and wastes that are embedded in the current
planning, design, and construction practices (Kent and Becerik-Gerber, 2010).
45
Despite its potential, IPD implementation is still in its infancy (AIACC, 2014), and
not many projects have been reported to be delivered using this new delivery method
(Azhar et al., 2014). For instance, Khemlani (2009) studied how the IPD process was
used in an IPD project, the Sutter Medical Center Castro Valley (SMCCV) in
California. The results showed that the time for structural design was reduced from an
expected 15 months to eight months, and was informed of more information from
other disciplines than usually available, leading to better design quality. Another
metric was that despite all the time spent planning the design process and meetings to
do 3D coordination (all of which were billable hours), the cost for design was at or
below the anticipated level. Thus, the design was proceeding with higher quality, at a
faster pace, and with no quantifiable increase in the cost. The SMCCV project could
be a landmark project in the US construction industry, because it was the first one to
show that IPD was not just an utopian vision but a practical reality that could actually
be implemented in both large and small projects. In addition, credit had to go to all
the firms involved in this project, including the core IPD team and the larger project
team. Their experience and willingness to adjust their routine work practices to take
advantage of the opportunities of IPD had been the key to the success of this project.
The primary goal of IPD is to maximize collaboration and coordination for the
entirety of a project. Typically one contract for building an IPD team is agreed upon
by the owner, architect, engineers, general contractor, and any other party who may
have a primary role in the project. At a minimum, though, an IPD project includes
tight collaboration between the owner, architect and/or engineers, and general
contractor which is ultimately responsible for construction of the project, from early
design through project handover (AIACC, 2007, 2014). Any subcontractors or
consultants may have a similar agreement with one of these parties.
46
To distinguish IPD from other delivery models, researchers (Cohen, 2010; AIACC,
2014; Azhar et al,. 2014) identified, at a minimum, early involvement of key
participants (EIKP), shared risk and reward (SRR), multi-party contract (MPC),
collaborative decision-making and control (CDMC), liability waivers among key
participants (LWKP), and jointly developed and validated project goals (JDVG) as
key characteristics of IPD projects (see Table 3.3). It is expected that by employing
these key characteristics in an IPD project, most shortfalls of the commonly-used
project delivery methods can be addressed.
Table 3.3 Key IPD characteristics and descriptions
Characteristics Descriptions
JDVG The owner, with the help of the project team, clearly defines
achievable project goals and benchmarks for measuring them. Risks
and rewards are associated with achieving the goals
EIKP Continuous involvement of the owner, architect and/or key engineers,
as well as general contractor and/or key subcontractors from early
design through project completion
SRR The IPD team members mutually share the reward of achieving
project targets and simultaneously bear the risk of missing the
targeted cost, schedule, and quality
MPC The IPD team members sign a single, multi-party agreement (or equal
interlocking agreements) that clearly defines the roles and
responsibilities of all the team members
CDMC The IPD team members agree upon a clear and specific set of
predetermined criteria for joint decision-making and collective control
of the project
LWKP The IPD team members waive any claim amongst themselves except
for in the instance of a wilful default to reinforce the sense of unity
and a collaborative environment
Source: Cohen (2010); AIACC (2014); Azhar et al. (2014).
In order for the construction industry to change with confidence from the traditional
delivery method to IPD, key IPD principles should be implemented. Although there
were many versions of the key IPD principles concluded in the last decade (AIA and
AIACC, 2007, 2009; AIACC, 2007), a latest conclusion of them (see Figure 3.2) was
given in Integrated Project Delivery: An Updated Working Definition, Version 3 by
AIACC (2014).
47
Which are fostered
by these
Optimize thewhole
Trust Respect IntegrationJoint
ownership
The whole point
TransparencySafe
environmentGood
technologyShare risk& award
Early & clear value definition
CollaborationWhile arise
out of these
To get this
you need these
Figure 3.2 Eleven essential principles of IPD (AIACC, 2014)
A brief description of these essential principles is given below (AIACC, 2014):
(1) Optimize the whole, not the parts. The point of integrating a project team is to
deliver the whole project in a way that gives what the owner values. Whether that
is optimized design solutions, increased efficiency over the building’s lifetime, or
a fast track schedule requires that all the key parties in the project make decisions
that are best for the project, rather than their own slices of the pie.
(2) Early and clear goal definition. To optimize the whole, the team must agree on
what the “whole” is. Project goals are developed early and agreed upon by all the
key participants.
(3) Collaboration. The project team members must collaborate closely, deeply, and
continuously to optimize the whole.
(4) Integration. The team members can’t work collaboratively if they cannot easily
share information, find appropriate time and spaces to communicate, understand
how their different design processes interact with each other, and get many other
systems integrated together across disciplines.
(5) Joint ownership. Meaningful collaboration requires the key participants to have a
sense of ownership over the project and its end goals.
(6) Respect. Meaningful collaboration also requires respect to each other. The team
members mutually commit to treating each other with respect and valuing each
professional’s input. Any individuals can contribute innovative solutions, so roles
48
are not defined as strictly as those in traditional projects, but rather assigned to
the best qualified person.
(7) Trust. Meaningful collaboration cannot occur without trust.
(8) Transparency. Trust requires transparency. All the team members have access to
accurate and latest information. Often an investment in technology compatibility
will be necessary to ensure their access to the information when they need.
(9) Safe environment. Mutual trust also requires the project environment to be safe
for the team members to provide suggestions without fear of taking responsibility
for being wrong.
(10) Shared risk and reward. An integrated project depends on best-for-project
thinking and behaviors when the team makes decisions. In reality, it is difficult
for a firm to sacrifice its own profitability for the good of the project. Risk/reward
sharing is predefined to cost or benefit the participants according to the project
outcomes rather than contributions from individual firms. Best-for-project decisions
will benefit all the participants, and one that attempts to benefit a particular firm
at the expense of the project will reduce all the participants’ profitability.
(11) Good technology. Advanced technologies are useful for the integration of
building systems together across firms and disciplines. Technologies such as BIM,
cloud servers, and teleconference tools are crucial.
In summary, IPD encourages early contribution of knowledge and experience. The
key stakeholders from each project phase must be involved in the design stage, which
shapes the project and its performance (Fischer et al., 2014).
3.4.1.2 IPD and BIM
AIACC (2007) stated that although IPD can possibly be achieved without BIM, BIM
implementation is recommended because it is essential to efficiently achieve the
49
collaboration required for IPD. BIM technology has provided a foundation for better
and proficient collaboration among the project participants and has been proved to be
an effective tool for managing construction projects (Eastman et al., 2011). BIM tools
and processes enable the team to integrate both design and construction expertise to
effectively support design decisions (AIACC, 2014).
Azhar et al. (2014) studied how BIM was useful to achieve the above characteristics
of IPD, and found that BIM can act as a catalyst for IPD implementation because it
supports several key IPD characteristics. Table 3.4 shows the proposed relations
between attributes of BIM and the key characteristics of IPD. In some cases, the
relations between BIM attribute and IPD characteristic are direct and simple to
understand, whereas the other relationships may not be so straightforward. For
example, consistency and accuracy of data in BIM has a direct impact on decision-
making, and better-calculated goals can be set for the project with more accurate data
and collaborative team efforts. Besides, BIM has made it possible to visualize the
design prior to actual fabrication; this attribute empowers the IPD team with better
design and project control. With BIM, QTOs are easier and more accurate, leading to
better cost estimations which can in turn strengthen grounds for better risk and reward
arrangements. BIM interface allows multi-user collaboration, which can be helpful in
promoting multi-party contract.
Table 3.4 BIM support for achieving IPD characteristics (Azhar et al., 2014)
Attributes of BIM Key characteristics of IPD supported
Consistency and accuracy of data CDMC, JDVG
Design visualization CDMC, JDVG
Ease of quantity takeoff SRR, JDVG
Multi-user collaboration EIKP, JDVG, MPC
Energy efficiency and sustainability CDMC, SRR, JDVG
Reporting CDMC, JDVG, SRR
50
Autodesk (2008) examined the impact of IPD on the building industry and found that
BIM could be central to the process changes that IPD would bring. The growing BIM
implementation is a core enabling process for the enhanced collaboration that IPD
demands. Figure 3.3 presents how the use of BIM in an integrated environment
enables the IPD working process and results in more predictable, accurate, and
responsible project outcomes. Therefore, BIM solutions enable IPD and can deliver
dramatic advances in building technology, but the full potential of BIM will not be
achieved without adopting structural changes to the existing project delivery methods.
Previous studies (Porwal and Hewage, 2013; AIACC, 2014) found that IPD has
materialized as a project delivery method that could most effectively and fully
facilitate BIM implementation in building and construction projects. Thus, the
situation when IPD does not adopt BIM was not discussed in this research.
Figure 3.3 Use of BIM in an integrated environment enables IPD process (Autodesk,
2008)
51
3.4.1.3 IPD process
The Integrated Project Delivery: An Updated Working Definition (AIACC, 2014)
gave a common definition of the IPD process, which is mainly used in the US
construction industry and other similar countries overseas (see Figure 3.4).
Criteria
design
Detailed
design
Implementation
documents
Agency coord
/Final buyout
Agency
Owner
Architect
Engineers
General contractor
Trade contractors
Facility manager
Lifecycle phase Concept-
ualization
Sta
keho
lder
(P
eopl
e)
Constr-
uction
Closeout
Figure 3.4 Stakeholder involvement in the commonly-used IPD process overseas
(AIACC, 2014)
The project phases named in Figure 3.4 differ from those in the traditional and current
project delivery processes (predesign, schematic design, design development,
construction documentation, agency permit/bidding, construction, and operations and
maintenance) to take advantage of two critical factors (AIACC, 2014):
· In addition to the design expertise of a traditional design team, expertise in
construction aspects (scheduling, material performance and availability, means
and methods, and so on) is available throughout the design process; and
· BIM tools and processes enable the team to integrate this broader range of
knowledge in order to provide effective support for design decisions.
Therefore, inputs from a broader team which is coupled with BIM tools to model and
simulate the project will enable the design to a higher level of completion. The
conceptualization, criteria design, and detailed design phases involve more proactive
efforts than their counterparts in the traditional flow (AIA and AIACC, 2007; AIACC,
2007). Thus, the phases in the IPD process (criteria design, detailed design,
52
implementation documents, and agency review) and those in the traditional and
current delivery processes (schematic design, design development, construction
documentation, and agency permit) are named slightly different. More or fewer
activities are included in the phases of an IPD project because efforts are moved
forward, but these phases are logically defined as the same as their traditional
counterparts. However, no study in the literature by far has attempted to investigate
the IPD process in Singapore.
The overseas IPD process needs to be adapted for use in the Singapore context, as
shown in Figure 3.5. The reasons are: (1) the Singapore construction industry is
usually policy-driven. The local government sees the value of BIM implementation
and puts it into practice, specifying or encouraging the industry to follow. For
example, the Singaporean government has lead the promotion of computer-assisted
building permit reviews, first in 2001 by implementing an electronic drawing review
system called e-Plan Check, again in 2006 by incorporating 3D automated electronic
building reviews into standard procedures, and currently by mandating building plans
e-submissions in BIM format (Eastman et al., 2011; Khosrowshahi and Arayici, 2012;
Cheng and Lu, 2015; Solihin and Eastman, 2015; Juan et al., 2017); (2) Unlike in the
US, where BIM implementation is largely industry-driven, a top-down approach is
adopted in Singapore, where the government is the dominant force promoting BIM
implementation (Juan et al., 2017). The government believes that BIM
implementation can indeed improve productivity. By far Singapore is the only
country that has mandated almost all new building projects to use BIM; and (3) As
policy makers, key government agencies in Singapore such as the Urban
Redevelopment Authority (URA) and the BCA participate early and actively in local
building projects.
53
AR2 CrD DtD ID FB
3 HC O&M
Agency
Owner
Architect
Engineers
General contractor
Trade contractors
Facility manager
Lifecycle phase1 CC CS
Notes: 1. CC=conceptualization, AR=agency review, CrD=criteria design, DtD=detailed design,
ID=implementation documents, FB=final buyout, CS=construction, HC=handover & closeout,
O&M=operations and maintenance; 2. “AR” phase runs concurrently with “CrD”, “DtD”, and
“ID” phases; 3. “FB” phase completes the buyout of remaining contracts such as trade
contractors not involved during design and materials without long lead time.
Sta
keh
old
er (
Peo
ple
)
Figure 3.5 Stakeholder involvement in the proposed IPD process for the Singapore
construction industry (adapted from Kent and Becerik-Gerber (2010) and AIACC
(2014))
The proposed IPD approach is described in a typical IPD project. Prior to actual
kickoff of the design process, or concurrent with the very earliest steps, significant
preparatory work need to be done, including (AIACC, 2014):
· Key project participants are selected to form an IPD team through a multi-party
contract and its subcontracts (if any), including the owner, architect, key
engineers, general contractor, and key trade contractors. Key regulatory agencies
are also essential and involved;
· Team communication and coordination processes are established, and
collaboration training is done;
· The risk and reward sharing structure that will best incentivize the
accomplishment of the project’s goals is developed;
· Key technologies such as BIM and data exchange protocols are established; and
· Co-location facility and frequency are determined.
1. Conceptualization. This phase mainly includes the following activities (AIACC,
2014):
· As many key stakeholders as possible are involved to contribute their insights.
54
· Key project parameters are captured, such as schedule and performance metrics
(economic, energy, and so on);
· Cost benchmarks are identified and initial cost targets are determined; and
· Preliminary schedule is developed.
2. Agency review. This phase actually runs concurrently with the criteria design,
detailed design, and implementation documents phases, and mainly includes the
following activities:
· The regulatory agencies provide high-level compliance information such as
relevant policies in Singapore (AIACC, 2014);
· The regulatory agencies work with the IPD team to develop a mutually agreeable
permit submittals schedule. Because of their involvement in the design process,
the general contractor and trade contractors will need to participate in submittals
preparation and respond to the agencies’ comments (AIACC, 2014);
· The IPD team applies for and obtains planning approval of one selected BIM
massing model at the end of the criteria design phase (BCA, 2013b);
· The IPD team applies for and obtains regulatory approvals of all building plans at
the end of the detailed design phase (BCA, 2013b); and
· The IPD team prepares submittals to meet legal requirements during the
implementation documents phase (AIACC, 2014).
3. Criteria design. Project targets and metrics whereby the success of the project will
be measured are developed and widely agreed upon in this phase. The following
activities are usually included (AIACC, 2014):
· Refining and fixing the key project parameters such as project scope, basic design
(massing, elevations, floor plans, and so on), system selection (structural, skin,
heating, ventilation, and air conditioning (HVAC), and so on), targeted cost,
overall schedule, and building components to be prefabricated;
55
· Engaging all key trade contractors; and
· Developing procurement schedule.
4. Detailed design. This phase is longer and more intense than the design
development phase in the traditional and current project delivery processes because
more work is accomplished. The team finalizes all the design decisions that are
necessary to ensure that changes will not be necessary during construction. The
design is fully defined without ambiguity and uncertainty. This phase mainly includes
the following activities (AIACC, 2014):
· All building elements are defined;
· All building systems are fully engineered and coordinated. This includes the final
system coordination that in the traditional delivery process was usually deferred
until the construction phase when trade contractor input was available;
· The team will decide the level of detail (LOD) required; and
· Specifications are developed based on the fixed systems.
5. Implementation documents. Because the entire building and all the systems are
fully defined and coordinated at the beginning of this phase (the end of the detailed
design phase), this phase is much shorter than the construction documentation phase
in the traditional and current project delivery processes. This phase mainly includes
the following activities (AIACC, 2014):
· Merging the traditional shop drawing process into the design as the general
contractor, trade contractors, and suppliers have documented the construction
intent of the building systems and components;
· Commencing prefabrication of some systems and procurement of long lead items
as early as necessary because the design is fixed;
· Developing specifications to provide narrative documentation of the design intent
wherever necessary;
56
· Generating documents where needed for processes such as financing,
procurement (bill of materials, BOM), regulatory permitting, and so on; and
· Documenting information for assembly, site layout, detailed schedule, and other
legal requirements.
6. Final buyout. IPD assumes the early involvement of all key trade contractors and
vendors, so buyout of work packages they provide occurs through developing prices
throughout the design phases, culminating at the end of the implementation
documents phase. The accelerated project definition during the criteria and detailed
design phases allows early procurement of long lead, custom, or prefabricated items.
The final buyout phase is much shorter than the bidding phase in the traditional and
current delivery methods, because downstream parties has already been engaged
earlier (AIA and AIACC, 2007). Therefore, this phase intends to complete the buyout
of remaining contracts such as trade contractors not involved during the design
process and materials without long lead time (AIACC, 2014).
7. Construction. In traditional projects, construction is often treated as the final stage
of design where design issues that were not addressed upfront are worked out.
However, in an IPD project, because construction expertise is integrated upfront
using BIM, the design is finalized during the detailed design phase and means and
methods are worked out during the implementation documents phase (AIACC, 2014).
However, some construction administration processes in the IPD project remain
similar to traditional practices. For example:
· Quality control, inspection, and testing will be relatively unchanged;
· Change orders, particularly for owner directed changes, must be formally
negotiated and documented; and
· Scheduling and progress will be periodically reviewed.
57
8. Handover/Closeout. The pain share and gain share arrangements will be resolved
in this phase based on the achievement of the project targets. Besides, many other
aspects of the closeout of the IPD project are similar to those of traditional projects.
Some examples (AIACC, 2014) include:
· Finalization of an as-built model or other documentation. However, the model is
developed and updated using BIM;
· Punch list correction;
· Warranty obligations; and
· Occupancy and completion notification.
9. Operations and maintenance. AIA and AIACC (2007) stated that the IPD phases
concluded at the closeout phase. However, it also stated that facilities managers, end
users, contractors, and suppliers are all involved at the start of the design process in
the world of utopian IPD in the future. Fischer et al. (2014) argued that the IPD
approach brings the contractors’ and operators’ knowledge to the design stage with
user needs, and the output of the design phases must be the design of a facility that is
valuable for its users, can be built, and can be operated. Therefore, an ideal delivery
model of IPD may incorporate the operations and maintenance phase, especially as a
solution for the issue of BIM in the Singapore construction industry.
3.4.2 VDC
3.4.2.1 Overview of VDC
The concept of VDC is still continually evolving. Kunz and Fischer (2012) defines
VDC as “the use of integrated multidisciplinary performance models of design-
construction projects to support explicit and public business objectives”. Performance
models of a building project represent the goals and outcomes of different
stakeholders, including the owner and its architectural, engineering, and construction
58
(AEC) services providers. Chua and Yeoh (2015) stated that VDC is an approach for
the designers and contractors working together as a collaborative team to build,
visualize, analyze, and evaluate the project performance on multidisciplinary models
in the design stage before tremendous time and resources are consumed during
construction.
Three stages of implementing VDC have been suggested by Kunz and Fischer (2012).
Firstly, visualization. The project team creates design models in a 3D environment to
virtually perform design, construction, and operations, based on performance metrics
(such as buildability score, constructability score, and schedule compliance) that are
predicted from the models and tracked in the process. This stage is commonly used
within the global construction industry (Li et al., 2009; Kunz and Fischer, 2012).
Secondly, integration. It tries to integrate various processes and different disciplines
involved in this project. The team develops computer-based automated methods to
reliably exchange data between disparate modeling and analysis applications.
Industry Foundation Classes (IFC) has been designed to enable this process. For
example, an integrated set of design models of different disciplines can be created
based on a shared IFC-based architectural model (Kunz and Fischer, 2012).
Fischer et al. (2014) found that visualization and simulation are the engine of VDC.
Visualization is, by far, the most effective way for the stakeholders to describe and
explain themselves accurately and to analyze their own work and that of others (Kunz
and Fischer, 2012). By using detailed and accurate 3D models, the AEC service
providers are able to communicate more clearly and effectively with each other, and
with the owner. In fact, many owners have little or no experience building anything
and cannot understand complex 2D shop drawings. 3D models are much easier for
most owners to comprehend. Meanwhile, simulation also allows a project team to
59
make better informed predictions by showing how close different design alternatives
come to desired project outcomes and allowing the team members to see the
consequences of their decisions (Fischer et al., 2014). Also, multiple design options
can be carried forward for comparison (Parrish et al., 2008). For example, span steel
as well as precast and timber structural systems can be kept in play along with
appropriate MEP systems (Fischer et al., 2014).
Thirdly, automation. This is to automate some tasks in the design and construction
processes, which will be realized based on good visualization and integration (Kunz
and Fischer, 2012). Automated methods will be used at this implementation stage to
perform routine design tasks or help build subassemblies in a factory for on-site
installation. Moreover, Chua and Yeoh (2015) advocated the use of intelligence for
automation. In the first place, a repository of design and construction knowledge
should be built, and a series of automated tasks are then performed on the design
models by reference to this knowledge. For example, design detailing can be
automated using the knowledge from design codes, which can then drive automated
prefabrication processes. In addition, the design can be checked for manufacturability
before it is sent to the CNC cutter for automated fabrication. Gao and Fischer (2006)
found that in the design development phase, it is possible for the contractors to
integrate standard building products so that more off-site prefabrication and
assembling would be available. These building products would appear in the schedule
with precise styles and specifications for manufacturing.
In order for automation to improve design, project organizations need to dramatically
change their processes to perform more high-value design and analysis, and spend
much less time and billable efforts for routine design. To support fabrication, the
project has to change the traditional, DBB approach to a design-fabricate-assemble
(DFA) approach. Kunz and Fischer (2012) reported that automation fundamentally
60
enables breakthrough performance in scheduling. The traditional DBB process
probably could not be compressed to build major projects within six months. In
contrast, this previous study suggested that many major projects could be built within
about six months if the DFA approach is adopted, and major subsystems are well
designed and integrated, with the fabrication being carefully crafted and controlled.
Automation capability maturity could be measured by schedule conformance or
design and construction phase productivity and cost (Kunz and Fischer, 2012). For
example, the Heathrow Airport project detailed and pre-assembled rebar cages on a
cycle time of one week or less, including detailing, fabrication, assembly, delivery,
installation, and concrete pour. On-site installation of pre-assembled systems was far
more rapidly than it ever did during field construction, leading to performance gains
in schedule performance, final product quality, and cost reliability and control (Kunz
and Fischer, 2012).
In conclusion, VDC is designed to support a multidisciplinary project team.
Appropriate stakeholders including the architect, engineers, general and multiple
specialty contractors, owner representatives, suppliers, and so on are involved early
from the early design stage to provide well-informed inputs to the design. An area
that requires significant development is user interface for multidisciplinary
stakeholders and for field workers. The user interfaces that currently exist may not
facilitate these inputs, resulting in a long design process. At the later stages of the
project, design decisions should be communicated to the field, particularly to the
crews who install various building components.
Li et al. (2009) identified eight advantages of VDC, including inspiration of novel
design, design error detection, construction plan rehearsal and optimization, detection
of unsafe areas, construction site management, construction communication, project
61
information and knowledge management, and reduction of creeping managerialism.
The realization of VDC relies on constantly evolving solutions, and its wider
implementation depends largely on how its benefits are recognized by local
practitioners.
3.4.2.2 VDC and BIM
Autodesk (2008) found that integration of design and construction practices can
leverage new tools and technologies, including:
· BIM design tools: providing platforms for integrated processes which are built on
coordinated information and result in enhanced coordination, fewer RFIs and
change orders, and fewer reworks;
· 3D and four-dimensional (4D) visualization: enhancing scope definition,
stakeholder engagement, and decision-making;
· Model-based analyses: using digital analytical tools to understand structural
performance, cost estimates, and other inferential reasoning from the design
while it is underway;
· 4D modeling: coordinating construction activities and increasing the reliability of
scheduling;
· Fabrication from 3D models: resulting in elimination of shop drawings, better
tolerance and lead time, and faster field assembly;
· Model-based BOM: providing faster, more accurate QTOs for cost estimating,
energy analysis, and so on; and
· Laser scanning: capturing existing (as-built) conditions that can be combined
with design and construction models to provide reliable as-built models.
Most of these tools and technologies are either BIM uses or can be supported by BIM
implementation. Therefore, although being perceived mainly as a design tool or at
62
best a visualization tool in 3D or 4D, BIM can form the backbone of VDC (Chua and
Yeoh, 2015).
Typically, a composite building information model refers to one single model that
represents overall virtual design of a building. Within this model may reside other
models. Each of them is specific to a certain scope of work such as electrical systems,
plumbing piping, mechanical equipment, structural frame, or architectural elements.
These individual models are “linked” together so that the individual files can be
viewed and compared as a composite one. In this regard, a typical project team
creates, uses, coordinates, and assembles all these different models into one
conglomerate that can be used for overall building uses.
The benefits of BIM for a building project were briefly concluded by Sattineni and
Mead (2013). Firstly, it has considerable process implications for the architect.
Because of the parametric nature of BIM, when an architect models a building,
sections and details are instantly drafted. This means that labor efforts are added to
the schematic design phase, and that at the completion of the schematic design phase,
the team has a model that is rich in information. In practice, up to two out of three
architects consider the greatest value derived from BIM to be the reduction in
reworks during the design development phase. Besides, BIM provides the architect
with a means of communicating design intent in a way previously unavailable. BIM
software can produce photo-realistic renderings of both interior and exterior surfaces
and spaces, which allows the architect to communicate its design intent to the owner
to help inform design decisions, as well as to the contractors to ensure that pricing
and scheduling efforts are as accurate as possible. This ability has fundamentally
changed the way the contractors are able to understand and plan for architectural
design.
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Secondly, BIM provides the contractors with many other tools. Perhaps the most
important one is the ability to coordinate complex building systems that interface
with one another. For example, building services such as MEP and fire protection
piping are typically located within ceilings, walls, and other confined spaces. As
such, they usually require considerable coordination to ensure they all fit and function
properly. Prior to BIM implementation, this process occurred in the field, by the
contractors, and typically led to tremendous RFIs and supplemental instructions from
the design team. These RFIs led to reworks and delays that were costly to the project.
With BIM, interfacing trade contractors can proactively gather together to work these
conflicts out in the virtual environment. This avoids not only the inefficient process
of seeking clarifying information in the field, but also costly work stoppages and
redesigns (Sattineni and Mead, 2013).
Thirdly, BIM is used considerably by the contractors in 4D analysis which is
generally accepted as the ability to tie the sequencing of activities on, from
construction schedule to the objects in the model. This analysis enables construction
professionals to visualize exactly how the building will be erected in sequence,
allowing the general contractor to test its scheduling logic, critical path, and uncover
otherwise unforeseen conditions that may relate to site logistics or unique
environmental conditions. The analysis also avails the general contractor of the
ability to communicate its construction plan to the owner, architect, and specialty
contractors (Sattineni and Mead, 2013).
Lastly, BIM can also provide the operations and maintenance team with tools to
better manage the property. If stipulated by the contract, intelligent information can
be added to a working model produced by the design and construction team. This
model can replace the cumbersome “operations and maintenance” manuals.
Information including maintenance schedules, the manufacturer’s contacts,
64
warranties, and replacement parts can all be linked within the model (Sattineni and
Mead, 2013).
To summarize, the list of benefits is not exhaustive, but represents broad application
and significant power of BIM tools. BIM has many added benefits for the service
providers to improve design, preconstruction, construction, and operations and
maintenance processes.
In the VDC approach, project requirements are represented as performance models.
BIM models are integrated and multi-disciplinary, providing a good medium for the
project stakeholders to collaborate on a shared data platform. These models are also
performance models in the sense that they exhibit some level of capabilities to
analyze, evaluate, and predict project performance related to specified project
objectives. To enhance information management, a model fit for intended
downstream uses should be provided. This should ensure the necessary LODs that are
needed to be furnished to different team members at different stages of the project so
that they can correctly perform the functions required, and make the appropriate
decisions in a timely manner (Chua and Yeoh, 2015).
Gao and Fischer (2006) explored nine BIM uses (model functions, MFs) that were
manifested by 11 case projects in Finland. Such functions included:
· MF0: Establishment of design targets;
· MF1: Visualization/marketing;
· MF2: Simulation and analysis;
· MF3: Design checking (system design coordination or constructability checking);
· MF4: Construction drawings and schedules/BOM;
· MF5: QTO and cost estimation;
· MF6: Supply chain management/building product procurement;
65
· MF7: Construction planning/4D modeling; and
· MF8: Facility management.
3.4.2.3 VDC process
Based on the above analysis on VDC and BIM, the VDC process in the Singapore
construction industry was proposed, as shown in Figure 3.6. The phases named in this
figure were consistent with those in the traditional and current delivery processes.
BIM implementation in the Singapore construction industry is usually influenced by
policies. New initiatives including VDC have been encouraged by the local
government.
BD DD CD AP/FB/PC HC O&M
Agency
Owner
Architect
Engineers
General contractorConstruction
model
Trade contractors
Facility manager
Lifecycle phase1 CC SD CS
Architectural model
Note: CC=conceptualization, BD=bidding, SD=schematic design, DD=design development,
CD=construction documentation, AP=agency permit, FB=final buyout, PC=preconstruction,
CS=construction, HC=handover & closeout, O&M=operations and maintenance.
Structural/MEP models
Sta
keh
old
er (
Peo
ple
)
Figure 3.6 Stakeholder involvement in the proposed VDC process for the Singapore
construction industry
Kunz and Fischer (2012) advocated that in a typical VDC project, all key
stakeholders should be invited to a project kickoff meeting, including the owner
representative, architect, major contractors, and a potential user. Regulatory agencies
and a facility manager are also involved in the meeting. Definition of the product
(facility), organization (participants), and process (design and construction phases) of
the project should be achieved in the meeting. The proposed VDC approach is
described below.
66
1. Conceptualization. With the help of the principal architect, the owner sets project
requirements, including targeted schedule, cost, BIM use, and so on. The key
engineers as well as the general contractor and key specialty contractors are pre-
qualified (Khanzode et al., 2007).
2. Bidding. In this phase, the key engineers and contractors are engaged, either
separately or in a designer/contractor consortium.
3. Schematic design. This phase involves the agencies, owner, architect, structural
engineer, building systems designers (MEP engineers), and general contractor. Key
trade contractors (Khanzode et al., 2007) and the facility manager (Kunz and Fischer,
2012) should also be involved to contribute their knowledge. This phase mainly
includes the following activities (Gao and Fischer, 2006; Porwal and Hewage, 2013):
· The architect, together with the structural and MEP engineers, develops an
architectural massing model (usually selecting the best one from several
alternatives);
· The structural and MEP engineers work with the architect side by side even
though only architectural modeling is underway. They take advantages of
renderings or quantity and location information from the 3D architectural model
for their own analyses. The structural engineer normally uses the architect’s
model as a base to make strength calculations for preliminary framing plan,
evaluate the appropriateness of the architectural design, and compare different
options for the structural frame. MEP engineers conduct a computerized analysis
of the 3D spatial model, firstly simulating the architectural space model to
compare the architect’ concepts, and setting realistic targets such as sizing for
building services design. They also review the architectural model and give
feedbacks with respect to the more complicated systems. Then, they develop a
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structural model and MEP models when the architectural massing model is
almost fixed;
· The general contractor prepares gross square feet estimate and volume take-off
early and compares them to the owner’s cost target;
· The key trade contractors such as MEP subcontractors contribute construction
knowledge to help the MEP engineers develop their design models (Khanzode et
al., 2007; Fischer, 2008); and
· The design team applies for and obtains planning approval of one selected BIM
massing model at the end of this phase (BCA, 2013b).
4. Design development. This phase mainly includes the following activities (Gao and
Fischer, 2006):
· The architect simulates, compares, and selects building components, and
completes the architectural design model. This model is shared with the engineers
who have only to adjust, not re-create, the model to allow accurate outputs of
plans, details, and drawings;
· The structural engineer develops the structural design model based on the
architectural design model;
· The MEP engineers develop detailed MEP design models when the systems
specification is in place and the best solution of the MEP systems has been
already chosen, based on the shared architectural design model and structural
design model;
· All the building systems are engineered and coordinated by the project team. This
includes the final system coordination that in the traditional delivery approach
was usually deferred until the construction phase because the trade contractors’
inputs were not available until then;
· The general contractor uses the architectural, structural, and MEP design models
shared by the design team as bases to build a construction model. It is possible for
68
the general contractor to integrate standard building products into this model so
that more off-site prefabrication and assembling will be available.
Constructability checking is also completed;
· The trade contractors can produce their shop models and fabrication models
based on the construction model and specific design models; and
· Specifications are developed based on the agreed and prescribed systems.
5. Construction documentation. This phase mainly includes the following activities
(Gao and Fischer, 2006):
· The design team shares its design models and 2D drawings with the owner, other
designers, and contractors where needed;
· Generating documents where needed for processes such as procurement (BOM),
permitting, and so on; and
· The project team develops specifications to provide narrative documentation of
the design intent wherever necessary;
6. Agency permit/Final buyout/Preconstruction. The following activities are
mainly included in this phase (Gao and Fischer, 2006):
· The design team applies for and obtains regulatory approvals of all building plans
of different disciplines (BCA, 2013b);
· The project team prepares submittals to meet local legal requirements where
needed (AIACC, 2014);
· The general contractor extracts quantity information of the items documented
earlier and sends accurate material lists to subcontractors to acquire their pricing;
· Prefabrication of some systems and procurement of long lead items can
commence as early as necessary since the design is fixed;
· The general contractor extracts QTOs and estimates cost; and
69
· The general contractor simulates schedule options and finalizes the construction
schedule.
7. Construction. In this phase, the general contractor can use 3D models to control
the logistics of engineering, manufacturing, and construction. The design team can
update its design models as the project proceeds and possible changes are solved.
However, some construction administration processes in the VDC project remain
similar to traditional practices. For example:
· Quality control, inspection, and testing will be relatively unchanged;
· Change orders, particularly for owner directed changes, must be formally
negotiated, solved, and documented; and
· Scheduling and progress will be periodically reviewed.
8. Handover/Closeout. The general and specialty contractors, together with the
design team, maintain and update the construction model with the latest information.
Laser scanning technology can be used to capture the existing (as-built) conditions
that can be combined with the BIM models. Finally, a reliable as-built BIM model
that incorporates the as-built information of all the major systems and equipment in
the BIM model is created and will be moved to the operations and maintenance team.
9. Operations and maintenance. The operations and maintenance team, with
reference to the as-built model, manages the building and relevant utilities and makes
better informed operations and maintenance decisions.
Sattineni and Mead (2013) found that similar to how a carpenter needs a square to
complete a successful woodworking project, the designers and contractors need BIM
to complete a successful VDC project. Firms have many powerful tools to choose
from, and industry standards on how to use BIM technology continue to be rolled out.
70
The construction industry is shifting towards an industry where the VDC process is
on its way to adoption, judging by user statistics (Jung and Joo, 2011). Khanzode et al.
(2007) used VDC tools to coordinate the MEP systems of a large healthcare project.
The owner, along with the architect, mechanical engineer, and general contractor,
pre-qualified the MEP subcontractors based on their abilities to coordinate and
collaborate their work with the work of other subcontractors using 3D/4D tools. The
detailed process and a pull schedule for the coordination of the MEP systems were
developed collectively by the design team, general contractor, and MEP
subcontractors. The general and specialty contractors were involved at the beginning
of the schematic design phase so that they could provide inputs into the
constructability and operations issues in the design. The MEP subcontractors were
responsible for modeling their portion of work using 3D tools in the design
development phase, and completing a fully coordinated design in 3D at the end of the
construction documentation phase. Finally, all the coordination work of the MEP
systems was done in a “Big Room”, and all the construction documents were also
generated from this room. As a result, the benefits of this VDC project in MEP
disciplines included labor savings ranging from 20% to 30% for all the MEP
subcontractors, 100% prefabrication for the plumbing contractor, less than 0.2%
rework for the mechanical subcontractor, zero conflict in the field installation of the
systems, and only a handful of RFIs for the coordination of the MEP systems. The
overall benefits to the owner included about six months’ savings on the schedule.
Kim and Fischer (2013) developed a decision-support system (DSS) using 4D model-
based analysis tools for tunnel construction. Because actual ground conditions were
much worse than those predicted in the preconstruction phase, the project schedule
delayed and much unnecessary cost overrun was caused to the general contractor.
However, following the analysis process of the DSS, the tunnel construction was
simulated. Visualization of the tunnel construction enabled more stakeholders to
71
participate in the project review. Metrics (such as the number of trucks, amount of
concrete, hours of labor, and quantity of excavation) analyzed by the DSS speeded up
the decision-making of allocating resources to other stakeholders. Consequently, the
schedule was accelerated and overall schedule compliance was maintained.
Cho and Fischer (2010) developed an integrated system using VDC tools for supply
chain management in door, frame, and hardware installation process. Each status of
materials in a supply chain was scanned, 4D color-coded, quantified, and then
reported. Then both time logs from real-time data capturing tools and quantities
completed from VDC models were combined to create an as-built progress of the
supply chain. This progress would be compared with as-planned work plans. The
work plans were accordingly updated both daily and weekly for better alignment
between demand and supply. All the supply chain members (suppliers, field teams,
project managers, and owner) had access to the project website to see the status
reports and visualizations. This enhanced visual coordination and communication
between field crews and off-site personnel, and brought a high level of accountability.
3.4.3 DfMA
3.4.3.1 Overview of DfMA using BIM
According to McFarlane and Stehle (2014), in general, DfMA is the design and
manufacture of discrete sections of a product (or structure) which are then assembled
at one location, typically a factory for mass-production. It has been used in the
automotive and aerospace industries for many years and has been applied to many
other industries (Selvaraj et al., 2009). Nevertheless, the uptake of DfMA in the
construction industry is slow, because it is either sporadic or serves as a partial
solution, such as precast concrete elements and structural steelwork componentry.
72
When applied to the construction industry, DfMA focuses on developing a design that
is optimized for off-site manufacture (OSM) of discrete sections of the final facility
and on-site assembly of them after being transported to site, essentially moving site-
based activities into a controlled factory environment. Figure 3.7 shows a DfMA
envelope, which consists of three major components.
Geometry
Metadata
Production
Digital engineering
3D models- visualization- finite element- numerical control- 2D drawing
production
OSM- small-scale items- panelized systems- large-scale modules- fully enclosed space:
individual rooms to complete buildings
BIM- program (time & cost)- quality & performance- safety & maintenance- environmental impacts: noise pollution, carbon footprint,
disruption to businesses & residents
Figure 3.7 DfMA envelope (McFarlane and Stehle, 2014)
Firstly, the geometry model is a 3D virtual reality model. It allows both technical and
non-technical project team members to visually understand and interrogate the design
intent. This model mainly includes the engineers’ finite element models and CNC
fabrication models that enable automated production of building elements. The
models are also used to produce 2D drawings for non-automated processes such as
the regulatory approvals and third party manufacture of small-scale items (McFarlane
and Stehle, 2014).
Secondly, production represents OSM in a factory environment. Modular
construction or OSM is part of the DfMA process (McFarlane and Stehle, 2014).
Blismas and Wakefield (2009) regarded OSM as a contributor to progress in the UK
construction industry, within the term “Modern Methods of Construction”. Modules
73
can range from small-scale items such as electrical fittings, through large-scale items
such as precast concrete floors and panelized systems in steelwork, precast concrete
or timber, to fully enclosed spaces such as individual rooms or complete buildings
(Ross et al., 2006). The entire fit-out process, namely the manufacture and assembly
of structural and MEP modules of different scales as well as decorative elements, can
be carried out in a factory. A higher level of quality control and improved overall
quality assurance can be achieved through factory production (Blismas et al., 2006).
In addition, the metadata model is a multi-dimensional database. This model contains
all relevant project parameters. Multiple design analyses can be conducted, such as
calculating and predicting the impacts of time, sequencing, scheduling, costs,
sustainability, constructability, and so on, allowing the team to assess different design
options and select the best one (McFarlane and Stehle, 2014).
The basis of DfMA is virtual reality modeling of the building, which includes four
significant elements, namely the discretization of the construction, 3D design
collaboration, 4D construction planning, and five-dimensional (5D) costing. All of
them should be interrogated and improved by the project team until the optimum
solution is reached (McFarlane and Stehle, 2014). As indicated in Figure 3.7, BIM is
part of DfMA. To adopt the DfMA approach, two of the major components, namely
3D geometry and metadata model, need full implementation of BIM.
Nevertheless, the design consultants also need to know how they will source what
they are drawing and ensure “fit” confidence on site. DfMA is a key driver of these
capabilities (Chandler, 2015). By using DfMA, a virtual reality project is constructed
and improved by the project team through several iterations. It allows all the key
stakeholders to participle interactively in the design and planning processes (Gibb and
74
Isack, 2003), and ensures that all the project parameters are met prior to commencing
actual construction on site.
Some key advantages of DfMA include (Gibb and Isack, 2003; Blismas et al., 2006;
Blismas and Wakefield, 2009; McFarlane and Stehle, 2014):
· Interactive participation in the design and planning processes by all key
stakeholders, leading to optimum design solutions, such as rapid implementation
of design changes with the parametric nature of BIM;
· Minimizing on-site operations/activities;
· Reduced the number of site personnel;
· Reduced congested work areas and multi-trade interfaces;
· Reduced construction time and testing and commissioning time;
· Accurate project completion date;
· Reduced wastage (factory wastage is reduced to near-zero and on-site wastage is
significantly reduced);
· High quality or very predictable quality finishes;
· Enabling inspection and controlling off-site works; and
· Providing certainty of project cost outcomes.
As a subset of DfMA, OSM has long been recognized internationally as offering
numerous benefits to most parties in the construction process. It is further recognized
as a key vehicle for driving process improvements. For example, the Australian
construction industry had identified OSM as a key vision for improving the industry
over the next decade (Hampson and Brandon, 2004). This echoed sentiments overseas,
especially the UK. Like the UK, the Australian construction industry had been
characterized as adversarial and inefficient; therefore, structural and cultural reforms
were urgently needed. This call for efficiency and productivity improvements across
these industries suggested that OSM be a major role to play. Indeed, the UK
75
government has proposed OSM as an important contributor to change the local
construction industry (Blismas and Wakefield, 2009).
Similar to the UK and Australia, the Singapore government has identified similar
initiatives, trying to achieve a quantum leap in productivity improvement. The BCA
and Singapore Press Holdings Limited (BCA and SPH, 2015) suggested that local
construction practitioners need to change their mindsets to focus on designing for
labor-efficient construction and moving as much construction work to off-site as
possible. To support the DfMA approach, an additional S$450 million has been set
aside to support the firms that adopt impactful construction technologies, and to train
and upgrade their workers to keep updated with technological advancements.
In October 2017, the BCA formulated a construction industry transformation map
(CITM), which envisions an advanced and integrated construction industry with
widespread adoption of leading technologies, led by progressive and collaborative
firms, and supported by a skilled and competent construction workforce. Key global
trends and challenges, such as digital revolution, rapid urbanization, and climate
change, increasingly impact the construction industry. To tackle these challenges, the
CITM identifies three key transformation areas, including Integrated Digital Delivery
(IDD), DfMA, and green buildings (BCA, 2017a).
However, currently the use of OSM or DfMA is not widespread, even though it may
be intuitively appealing. A major reason posited for the reluctance among owners and
contractors to adopt OSM is that they have difficulty ascertaining the benefits that
such an approach would add to a project (Pasquire and Gibb, 2002; Gibb and Isack,
2003). According to McFarlane and Stehle (2014), the greatest benefit of DfMA can
be realized subject to the combination of all the three components in the envelope
including the use of BIM. Thus, using advanced technologies such as BIM is
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encouraged by the Singapore government to drive the local construction industry to
transit to a higher degree of DfMA adoption (BCA and SPH, 2015).
3.4.3.2 DfMA process
Blismas and Wakefield (2009) reported that the real advantages of OSM or DfMA
can only be realized through a thorough understanding of the principles underpinning
manufacturing. Therefore, DfMA adoption requires fundamental structural changes to
the construction industry, compared to the manufacturing industry. The DfMA
approach changes the way people in the building industry work, both in terms of the
process and the product. However, there is little information about how the DfMA
approach may be used in the project lifecycle in the Singapore construction industry.
Based on the above analysis on DfMA and BIM, a proposed DfMA process for use in
the Singapore construction industry was figured out in this study (see Figure 3.8).
Manu-
facture
Substr-
ucture
Supers-
tructure
Fit-
out
Agency
Owner
Architect
Engineers
General contractor2
Manufacturer3
Subcontractors4
Facility manager
Notes: 1. CC=conceptualization, BD=bidding, SD=schematic design, DD=design development,
CD=construction documentation, AP=agency permit, FB=final buyout, CS=construction,
HC=handover & closeout, O&M=operations and maintenance; 2. General contractor: may be
manufacturer if conventional construction is not included; 3. Manufacturer: manufacturing team,
including factory based operatives, site erection teams, and so on; 4. Subcontractors: mainly for
traditional construction, including ground works, substructure, and so on; 5. Final buyout: the
engagement of the subcontractors that will complete traditional construction.
CD AP/FB5 CS HC O&M
Sta
keh
old
er (
Peo
ple
)
Lifecycle phase1 CC BD SD DD
Figure 3.8 Stakeholder involvement in the proposed DfMA process for the Singapore
construction industry (adapted from Ross et al. (2006) and McFarlane and Stehle
(2014))
77
The project phases named in this figure were in line with their counterparts in the
traditional and current delivery processes, but the construction phase was divided into
four stages, namely manufacture, substructure, superstructure, and fit-out. As
mentioned in Section 1.2, BIM promotion in Singapore adopts a top-down approach.
The local government has been driving the incorporation of BIM into the DfMA
process.
McFarlane and Stehle (2014) advocated that to obtain an optimum solution, all key
stakeholders (both technical and non-technical) should be interactively involved in
the design and planning process. According to Figure 3.8, project phases and key
activities in a typical DfMA project are described below.
1. Conceptualization. With the help of the prime architect, the owner sets project
requirements, including schedule, cost, BIM uses, and so on. Key regulatory agencies
in Singapore also participate in the phase.
2. Bidding. Key engineers such as structural and MEP engineers are engaged. Since
all key stakeholders are required to contribute in the design stage, the general
contractor and the manufacturer should also be engaged to avoid them working at risk
in a financial manner before the construction phase. A two-stage contract, instead of
the contract that was traditionally awarded after the design stage, should be used
(Gibb and Isack, 2003; Ross et al., 2006). The manufacturing team will also act as the
general contractor if conventional construction is not included in this project.
3. Schematic design. This phase involves the agencies, owner, architect, structural
engineer, building systems designers (MEP engineers), general contractor, and
manufacturer. The higher the level of OSM, the more important it is to get the
manufacturer involved as early as possible (Ross et al., 2006). The facility manager
78
should also be involved to contribute operations knowledge ahead of time. This phase
mainly includes the following activities (Gibb and Isack, 2003; Ross et al., 2006;
McFarlane and Stehle, 2014):
· Fixing key project parameters such as project scope, basic design (massing,
elevations, floor plans, and so on), system selection (structural, skin, HVAC, and
so on), and building components to be prefabricated;
· The architect, together with the structural and MEP engineers, general contractor,
and manufacturer, develops an architectural massing model (usually selecting the
best one from several alternatives);
· The structural and MEP engineers work with the architect side by side even
though only architectural modeling is underway. They take advantages of
renderings or quantity and location information from the architectural model for
their own analyses, which is similar with those of the VDC approach. Apart from
this, the design team also gets inputs from the general contractor and
manufacturer to develop the designs that are suitable for off-site manufacturing
and assembly in downstream phases. The engineers and contractors review the
architectural model and give feedbacks with respect to the more complicated
systems. Then, the engineers develop a structural model and MEP models when
the architectural massing model is almost fixed; and
· The design team applies for and obtains planning approval of one selected BIM
massing model at the end of this phase (BCA, 2013b).
4. Design development. During this phase, all the design decisions that are necessary
to ensure that changes during construction will not be necessary are finalized. The
design is fully and unambiguously fixed otherwise changes will be very costly after
manufacture of building elements and modules begins (Blismas and Wakefield, 2009).
This phase mainly includes the following activities (Gann, 1996; Gibb and Isack,
2003; Ross et al., 2006):
79
· All building elements are defined;
· The LOD required is determined;
· The architect simulates compares, and selects building components, and
completes the architectural design model, which is shared with the engineers who
only need to adjust, not re-create, the model to allow accurate outputs of plans,
details, and drawings;
· The structural engineer develops the structural design model based on the
architectural design model;
· The MEP engineers develop the detailed MEP design models when the systems
specification is in place and the best solution of the MEP systems has been
already selected, based on the architectural design model and the structural design
model shared upfront;
· All building systems are fully engineered and coordinated, which includes final
system coordination;
· Specifications are developed based on the agreed and prescribed systems;
· The general contractor uses the architectural, structural, and MEP design models
shared by the design team as bases to build a construction model. Constructability
checking is completed; and
· The manufacturer creates its fabrication models in different disciplines based on
the models shared upfront.
5. Construction documentation. This phase mainly includes the following activities:
· The design team shares its design models and 2D drawings with the owner, other
designers, contractors, and manufacturer where needed;
· Generating documents where needed for processes such as procurement (BOM),
permitting, and so on;
· The manufacturer creates shop drawings if necessary from the shared models
(design models and construction model) or directly uses these models;
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· The project team develops specifications to provide narrative documentation of
the design intent wherever necessary; and
· The construction (including manufacture and assembly) team documents
information for fabrication, assembly, site layout, detailed schedule, and testing
and commissioning procedures.
6. Agency permit/Final buyout. Since the general contractor and manufacturer are
engaged before the design phase, this phase refers to the engagement of the
subcontractors that will complete the conventional construction, including ground
works, substructure, and so on. Thus, this phase mainly includes the following
activities (Ross et al., 2006):
· The project team applies for and obtains regulatory approvals of all building
plans of different disciplines (BCA, 2013b);
· The project team prepares submittals to meet the local legal requirements where
needed;
· The general contractor extracts QTOs and estimates cost;
· The general contractor extracts quantity information of the building components
documented earlier and sends accurate BOM to the subcontractors to acquire
their pricing. This is similar with the traditional process; and
· The general contractor simulates schedule options and finalizes the construction
schedule.
7. Construction. Since the DfMA approach is designed mainly for those parts that
can be prefabricated off site, including superstructure and fit-out. The construction
phase is divided into four phases (see Figure 3.8), and mainly consists of the
following activities (Ross et al., 2006):
· Manufacturing and assembling of the building systems and modules, and
transporting them to the site for installation;
81
· The subcontractors complete the ground works and substructure on site; and
· The superstructure and fitting out processes can take place in the factory before
or while the ground works and substructure are being done on-site, leading to the
overall effect of compressing the on-site phase and activities.
8. Handover/Closeout. The general contractor, manufacturer, and design consultants
constantly maintain and update the construction model and fabrication models with
the latest information. With support from the laser scanning technology, a reliable as-
built BIM model that incorporates the existing (as-built) information of all the
systems and equipment is created and provided for the operations and maintenance
team. Besides, many other aspects of the closeout of the DfMA project are also
similar to those of traditional projects, including (AIACC, 2014):
· Punch list correction;
· Warranty obligations; and
· Occupancy and completion notification.
9. Operations and maintenance. The operations and maintenance team, with
reference to the as-built model which contains much intelligent information, manages
the building and relevant utilities and makes better informed operations and
maintenance decisions. The cumbersome “operations and maintenance” manuals are
discarded. The intelligent information includes, but not limited to maintenance
schedules, manufacturers’ contacts, warranties, and replacement parts (Sattineni and
Mead, 2013).
McFarlane and Stehle (2014) suggested a new procurement method to better
implement the DfMA approach. To develop an optimal design that suits for
manufacture and assembly, the owner may establish a contract with a single party
(either a contractor or a designer/contractor consortium) which assumes the full
82
responsibility for both the design and construction of the building. The owner gives
the freedom to the winning contractor (or consortium) to propose and realize an
innovative design, including the use of new materials, and production and assembly
techniques. The only requirement is that the design should meet the owner’s
functional requirements. This may be ideal in the future once more and more firms
are able to take responsibilities in both the design and construction work.
3.5 Comparisons among Project Delivery Processes
3.5.1 Differences among project delivery processes
Based on the above review and analysis of the traditional, current, and full BIM-
enabled collaborative processes, the key activities related to BIM in these processes
are presented in Table 3.5. The major differences that make them uniquely defined in
a building project in the Singapore context can be concluded (see Table 3.6). It can be
seen that both the traditional process and current process are unproductive. Even
though the Singapore government has mandated BIM submissions of all building
plans, BIM collaboration between the design phase and downstream phases is not
widely achieved currently. Thus, process transformation is imperative in the local
construction industry.
In terms of the full BIM-enabled processes, IPD, VDC, and DfMA are commonly
recognized and used internationally. IPD is driven by the owner and satisfying the
owner’ demands is at the heart of the IPD process (Autodesk, 2008). It relies on the
collective expertise of all the key stakeholders throughout the project process,
particularly in the early stages. The resulting increase in project knowledge creates a
better understanding of the project earlier in the design process, enabling the IPD
team to more effectively assess their project options and consider how to align them
with the owner’s goals. Thus, all the key stakeholders in IPD are on the same boat,
83
Table 3.5 Summary of the key activities related to BIM in the current process and the
proposed IPD, VDC, and DfMA processes in the Singapore construction industry
Phases Processes Key activities related to BIM
CC Current The owner and the architect set project requirements
IPD The key stakeholders are engaged, form an IPD team by a
multi-party contract, jointly set project goals and benchmarks,
outline BIM goals and potential BIM uses (such as 4D
modeling and model updating) based on the project
characteristics, stakeholders’ goals and capabilities, and desired
risk allocations, and identify the responsible stakeholders for
the BIM uses and information exchanges;
The team agrees on the reward/risk sharing arrangements
VDC
/DfMA
The owner and the architect set project requirements and outline
the BIM goals and potential BIM uses (such as 3D site analysis
and code validation) based on the project characteristics
SD
(criteria
design for
IPD)
Current The architect and engineers create their design models with
little collaboration and without the construction, fabrication,
and operations and maintenance inputs from the downstream
people
IPD All key project parameters (such as scope, basic design, system
selection, schedule, cost target, quality levels, prefabricated
components, buildability, and constructability) are set;
All the key trade contractors are engaged to input their site
expertise
VDC The design team and construction team agree on the multi-party
collaboration contracts to share data, identify the data
exchanges between the key firms, and define the exchange
procedures and formats;
The engineers and contractors work together with the architect
to develop architectural model; the facility manager may also
contribute to the design
DfMA The engineers, general contractor, and manufacturer are
engaged with two-stage contracts, further outline the BIM goals
and potential BIM uses (such as 3D coordination and digital
fabrication) according to their goals and capabilities as well as
risk allocations, and identify the responsible parties for the BIM
uses and the collaboration methods (such as regular meetings)
of data exchanges;
The engineers, general contractor, and manufacturer contribute
in the architectural modeling
DD
(detailed
design for
IPD)
Current The building elements may not be well defined;
The building systems may not be coordinated until the
construction phase
IPD All the building systems are fully engineered and coordinated,
with required LOD
VDC
/DfMA
The engineers build the structural and MEP models based on
the architectural model;
The general contractor creates the construction model and
fabrication model (if any) using the design models as bases
CD
(implemen
-tation
documents
for IPD)
Current The design team produces 2D drawings and specifications for
the downstream uses such as regulatory submissions and tender
documents preparation;
Prefabrication of some building components cannot commence
due to design uncertainties
84
IPD
/VDC
/DfMA
The contractors and manufacturer document the construction
intent of the building systems and components to produce shop
and/or fabrication drawings;
The fabrication of the building systems, especially those long-
lead items, begins as the design is fixed;
The team generates documents for permitting, assembly,
detailed schedule, and so on
AP
(agency
review for
IPD)
Current
/VDC
/DfMA
The designers apply for planning approvals in BIM format
IPD Since the regulatory agencies participate actively in the design
stage, the team applies for planning approvals and responds to
the agency comments in parallel with the design stage
BD Current The designers only pass 2D drawings or incomplete design
models to potential contractors
IPD Since the key contractors have been engaged either in the
conceptualization phase or in the design stage, this phase entails
the buyout of remaining contracts such as the trade contractors
not involved in design and materials without long lead time
VDC The trade contractors that were not involved in the design stage
are engaged
DfMA Since the general contractor and manufacturer have been
engaged in the early design stage, this phase engages the
subcontractors to complete the conventional construction such
as the ground works and substructure
CS Current The general contractor re-builds the BIM models for
construction uses and their own submissions;
Due to the disconnection between the upfront and downstream
parties, RFIs may be frequently raised for the designers to
respond, and reworks need to be completed where necessary;
A low percentage of building components would be
prefabricated
IPD
/VDC
The team uses the models to guide construction, and reviews
and updates the models until completion
DfMA The manufacturer produces building systems and modules in
the factory environment before or while the ground works and
substructure are being done on-site
HC Current The team may spend much time and many resources to resolve
the disputes between the stakeholders
IPD The team finalizes the as-built BIM models and specifications
for operations and maintenance and resolves the risk and reward
sharing arrangements
VDC
/DfMA
The team finalizes the as-built BIM models and specifications
O&M Current The operations and maintenance team uses 2D as-built
drawings to manage the building unless the owner pays for the
3D as-built models
IPD
/VDC
/DfMA
The operations and maintenance team uses 3D as-built models
as planned in the project beginning to manage the building
Note: CC=conceptualization; SD=schematic design; DD=design development;
CD=construction documentation; AP=agency permit; BD=bidding; CS=construction;
HC=handover/closeout; O&M=operations and maintenance.
85
(Source: Adapted from Gibb and Isack (2003); Gao and Fischer (2006); Ross et al.
(2006); AIA and AIACC (2007); Anumba et al. (2010); Kunz and Fischer (2012);
BCA (2013b); Porwal and Hewage (2013); AIACC (2014); Lam (2014); McFarlane
and Stehle (2014))
leading to an ideal approach of project delivery. However, the way of delivering
building projects may be still traditional now and it may not be changed in a short
time to get to the ideal IPD approach and forget about the existing way. Hence, IPD is
not very practical today due to several reasons. Among these are high degree of
concern regarding risk in relation to IPD and the close partnerships it necessitates,
and need for new legal frameworks to match the emerging IPD approach. Moreover,
many industry players feel there is a need for those within the industry to assimilate
new competencies and skills related to collaboration and information management to
support IPD. Azhar et al. (2014) found that very few projects have been delivered
under the IPD approach.
Table 3.6 Major differences among the proposed project delivery processes in
Singapore
Factors Traditional
process
Current
process
Collaborative processes
IPD VDC DfMA
Initiator None Government Owner Contractor Government
BIM use No Partially Not necessarily,
but highly
recommended
Fully, openly Fully, openly
Phase Linear,
distinct
Linear,
distinct
Lifecycle Design and
construction
Design and
construction
Party Silos,
Fragmented
Silos,
Fragmented
All the key
stakeholders
Architect,
engineers,
general
contractor,
key trade
contractors
Architect,
engineers,
general
contractor,
manufacturer
Financial
incentive
Individually
managed
Individually
managed
Shared reward Individually
managed
Individually
managed
Contract Silos Silos One multi-party
contract
Multi-party
collaboration
contracts
Multi-party
collaboration
contracts
Applica-
bility
Not
productive
Not
productive
Not practical
currently, but ideal
in the long run
Practical Practical
86
VDC is initiated by the general contractor who wants to detect problems virtually in
the design stage and hopes that during the construction stage there are fewer errors,
omissions, changes, and the like. Usually the owner frequently establish minimal
apparent risk and minimum first cost as crucial selection criteria for a new project
(Kunz and Fischer, 2012). Only the general contractor and key trade contractors
working side by side with the design consultants on the platform of BIM during the
design phase make the VDC approach more practical and affordable for building
projects.
In Singapore, DfMA is driven by the government as an enabler to on-site productivity
(BCA and SPH, 2015). This echoes sentiments in other countries, especially the UK
and Australia in which similar policies had been implemented (Blismas and
Wakefield, 2009). It will be better if the owner of a building project is enthusiastic on
this approach. Similar to the VDC process, the DfMA approach involves not only the
general contractor, but also the manufacturer in the design phase to develop the
design that maximizes OSM of those parts that can be prefabricated. It also leaves
conventional construction. Hence, the DfMA approach is also practical, especially
when the local government stresses on it.
Besides, Table 3.7 provides other differences between the traditional, current, and full
BIM-enabled delivery processes from the perspectives of risk management,
communication, design review and feedback, decision-making, dispute resolution,
change management, responsibility, and project team culture. Key points and related
statements are briefly described with support from previous studies.
87
Table 3.7 Differences and supporting statements in literature between the traditional, current, and full BIM-enabled processes in Singapore
Project factor Traditional process Current process IPD VDC DfMA
Focus 2D based;
information and
knowledge
fragmentation
Mandatory to use
BIM before
construction starts
Built on collaboration, all key
stakeholders work together
till project closeout towards
common project goals
Using 3D/4D modeling of
BIM to manage
data/information and
integrate processes and
different disciplines in
design and construction
phases
Develop designs that
optimize OSM and
on-site installation
Risk Individually
managed,
transformed to the
greatest extent
possible (AIACC,
2006; AIA and
AIACC, 2007)
Individually
managed,
transformed to the
greatest extent
possible (AIACC,
2006; AIA and
AIACC, 2007)
Collectively managed,
appropriately shared
(AIACC, 2006; AIA and
AIACC, 2007)
Sharing risk reports that
includes description of risks,
assessed probability of
occurrence, affected parties
who can help fix or who
must respond if it becomes
real (Kunz and Fisher, 2012)
Reduced on-site risk;
better controlled in a
factory environment
(Blismas and
Wakefield, 2009)
Communicati
on/technology
Paper-based, 2D;
analog (AIACC,
2006; AIA and
AIACC, 2007)
Paper-based, 2D
drawings or
incomplete design
BIM models passed
to downstream
stakeholders
Digitally based, virtual;
3D/4D/5D/six-dimensional
(6D) BIM, shared BIM
models; concise, open, and
trusting (AIACC, 2006; AIA
and AIACC, 2007)
Virtual, 3D/4D/5D/6D BIM,
shared BIM models that fit
for the intended downstream
use (Kunz and Fisher, 2012;
Chua and Yeoh, 2015)
Digitally based,
augmented reality,
3D/4D/5D BIM,
shared BIM models
(McFarlane and
Stehle, 2014)
Design
review
/feedback
2D, slowly,
fragmented in
different phases and
disciplines; often
many changes and
RFIs in later phases
(AIA and AIACC,
2007)
Slowly, fragmented
in different phases
and disciplines, 2D
(AIA and AIACC,
2007); 3D only
among designers or
incomplete 3D
models from design
team to downstream
parties
Rapid feedback from
teammates, within a few
days; team members view
their interactions from a
“customer-supplier” point of
view (Fischer et al., 2014);
designers fully understand the
ramifications of their
decisions (AIA and AIACC,
2007)
Rapid feedback from
teammates, within a few
days; team members can
learn to view their
interactions from a
“customer-supplier” point of
view (Fischer et al., 2014);
design decisions
communicated to the field
(Fischer, 2008)
Rapid feedback from
other disciplines and
the manufacturer
(Gibb and Isack,
2003)
Decision-
making
Challenging to make
group decisions,
inadequate
Challenging to make
group decisions,
inadequate
All team members agree on
decision-making method
predetermined; all decisions
Collaboratively making
design decisions; working
together to reduce decision-
Designers, together
with the manufacturer
and site experts, make
88
communication
between key
members (AIA and
AIACC, 2009)
communication
between key
members (AIA and
AIACC, 2007)
are measured against shared
goals about what’s best for
the project (AIA and AIACC,
2007)
making latency (Khanzode
et al., 2007)
decisions during the
design phase (Gibb
and Isack, 2003)
Dispute
resolution
Often claims,
thrusting the parties
into adversarial
positions; team is
crippled (AIA and
AIACC, 2007)
Often claims,
thrusting the parties
into adversarial
positions; team is
crippled (AIA and
AIACC, 2007)
Promptly resolved internally
without filing claims and
adopting adversarial positions
(AIA and AIACC, 2007)
– Agreed on the
tolerances and
standards of units by
mockups and
prototypes to avoid
disputes (Ross et al.,
2006)
Change
management
Managed and
updated in silos
when changes occur;
many change orders
and reworks after
construction starts
(AIACC, 2014)
Managed and
updated in silos
when changes occur;
many change orders
and reworks after
construction starts
(AIACC, 2014)
Avoiding changes by putting
much more efforts during
design; also involving owner
during design to avoid
owner’s changes in later
phases; limited RFIs
(AIACC, 2014)
Very few change orders
related to field conflicts due
to 4D visualization and
simulation (Khanzode et al.,
2007)
Avoiding changes by
fixing design together
with the general
contractor and
manufacturer and
using standard,
modular components
in design model
(Blismas and
Wakefield, 2009)
Responsibility Using guarantees,
penalties, and risk
transfers; excelling
in assessing liability
but doing very little
to avoid risks which
reinforces
individualistic
behavior (Fischer et
al., 2014)
Using guarantees,
penalties, and risk
transfers; excelling
in assessing liability
but doing very little
to avoid risks which
reinforces
individualistic
behavior (Fischer et
al., 2014)
Clearly defining
responsibilities in a no-blame
culture leading to
identification and resolution
of problems, not
determination of liability
(AIA and AIACC, 2009);
placing responsibility on the
most able person with
decisions being made on a
best-for-project basis
(AIACC, 2006; AIA and
AIACC, 2007)
– –
89
Team culture Individualistic
behavior for own
interests, easily turn
into adversarial
relationships (AIA
and AIACC, 2007);
since agreements are
made between two
firms, rather than
amongst the entire
team, they reinforce
individualism rather
than project
optimization
(Fischer et al., 2014)
Individualistic
behavior for own
interests, easily turn
into adversarial
relationships (AIA
and AIACC, 2007);
since agreements are
made between two
firms, rather than
amongst the entire
team, they reinforce
individualism rather
than project
optimization (Fischer
et al., 2014)
Trust and respect; open
sharing and transparent;
rewarding best-for- project
behavior (AIA and AIACC,
2007, 2009; AIACC, 2007);
working in a “Big Room”
(Fischer et al., 2014)
Open sharing; the general
contractor, key trade
contractors, and engineers
work side by side with the
architect in a “Big Room” to
model the project and
coordinate their designs
(Khanzode et al., 2007);
clustering, to identify
optimal organizational
configurations to implement
“Big Room” design
strategies (Chua and Yeoh,
2015)
Open sharing; the
general contractor,
manufacturer, and
engineers work side
by side with the
architect to develop
optimal designs that
suit for OSM and on-
site erecting (Ross et
al., 2006; Blismas and
Wakefield, 2009)
90
3.5.2 Relationships between full BIM-enabled processes
According to AIA and AIACC (2007, 2009), IPD is built on collaboration which
requires all the key stakeholders of a building project to be involved early and work
together before the construction phase. Without trust-based collaboration, IPD will
falter and participants will remain in the adverse and antagonistic relationships that
plague the construction industry today. IPD promises better outcomes, but outcomes
will not change unless people responsible for delivering those outcomes change. The
advent of IPD is influencing hiring practices toward people with collaboration skills,
rather than people capable of BIM. So it may not necessarily involve BIM, but the
use of BIM is strongly recommended as BIM facilitates collaboration (AIACC, 2007;
Autodesk, 2008; AIACC, 2014; Fischer et al., 2014).
As mentioned in Section 3.4.2, VDC tries to integrate various processes and multiple
disciplines involved in a project. The construction industry is shifting into the
integration stage and finally the automation stage. Both of them are based on data
sharing between the design and construction phases. Therefore, it stresses on
integration of processes. It is notable that even the terms “integration” and
“collaboration” are interchangeable, they focus on different contents. The former is
on data and process, and the latter on project participants.
Essentially, DfMA tries to move site-based activities into a controlled factory
environment. It requires very well-developed designs that encourage the efficient off-
site prefabrication of modular and portable elements for easy transportation and rapid
on-site assembly. So, the essence here is designs because it is very expensive for any
design changes once the manufacture process commences. Meanwhile, advanced
techniques are required to develop such designs.
91
The above analysis reveals the distinctive differences between the proposed IPD,
VDC, and DfMA approaches, with the use of BIM or the concept of IDD being the
key similarity of the approaches (see Figure 3.9). Enabled by BIM, IDD is a new
concept that was proposed by the Singapore government. This concept aims to
integrate various processes and stakeholders along the construction value chain
through advanced information and communications technology (ICT) and smart
technologies (BCA, 2017a). The early involvement of major stakeholders also serves
as an important similarity. In addition, all these approaches can be applied subject to
external environment, such as the local policies and constriction market situation.
People
Process Technology
Collaboration
IPD
Design
DfMA
Integration
VDCBIM (IDD)
Figure 3.9 Distinctive differences and similarities of IPD, VDC, and DfMA
On the other hand, provided that BIM technology is used in the project, VDC and
DfMA could be seen as subsets of IPD (see Figure 3.10). Table 3.6 indicates that
what VDC and DfMA stress on a project is also included in the IPD approach once
BIM is fully used. The is consistent with the finding of Porwal and Hewage (2013)
that IPD has materialized as a project delivery method that could most effectively and
fully facilitate BIM implementation in the construction industry.
IPD
VDC DfMA
Figure 3.10 Relationships between IPD, VDC, and DfMA when using BIM
92
Fischer et al. (2014) proposed a framework of using IPD (see Figure 3.11). It is best
understood by working backwards, from the product (integrated building systems)
which integrated project teams have agreed to deliver, to integrated information.
Sharing information is a lynchpin of the IPD team. Information must remain
consistent across all disciplines, and everyone must have access to all latest
information, at any time. BIM allows the team to explore many design options, and to
discuss how different designs will add value (or not) and how they will affect
performance targets. As mentioned in Section 3.4.2, visualization and simulation are
the engine of VDC, and serve as tools in this framework to connect integrated
information. Hence, it can be concluded that VDC is a subset of IPD if BIM is fully
used in the IPD process. Meanwhile, BIM can also help establish an appropriate off-
site fabrication strategy, and understand the operability and sustainability of an
intentional design. With BIM fully used in the whole process, off-site fabrication and
on-site installation are encouraged. Autodesk (2008) examined how BIM is central to
process changes that IPD would bring. Fabrication from BIM models is suggested in
the IPD approach to integrate the design and construction phases, resulting in
elimination of shop drawings, better tolerance and lead time, and faster field
assembly. Thus, DfMA is also a subset of IPD given that BIM is fully implemented.
High performance
building
Integrated building systems
Integrated processes
Integrated organization
Integrated information
(BIM+)
Collaboration, co-location
Visualization, simulation
Productionmanagement
Measurable value
Figure 3.11 How integrated information supports the creation of a high-performance
building (Fischer et al., 2014)
Therefore, IPD is too ideal to be implemented currently and may not necessarily use
BIM, while both VDC and DfMA are practical in the short term (see Table 3.6). VDC
and DfMA are somewhat similar and overlap to some extent (see Figure 3.10). Hence,
93
VDC, DfMA, or a hybrid of them can be a good solution in the short term and IPD
should be explored and gradually applied in the future in the Singapore construction
industry. Nevertheless, although BIM may not necessarily be incorporated in the IPD
process, it is recommended by researchers (AIACC, 2007; Autodesk, 2008; Succar,
2009; El Asmar et al., 2013; AIACC, 2014; Fischer et al., 2014). The IPD approach
has many benefits that VDC and DfMA do not provide, such as financial incentives
being shared with downstream contractors and risks being managed by the most able
parties. It is the downstream contractors that finally complete and monitor on-site
activities to build a facility. Hence, this study will investigate process transformation
strategies to the VDC, DfMA, and IPD processes.
3.6 Summary
This chapter reviewed the literature on five project delivery processes, namely the
traditional project delivery process, current process, and three full BIM-enabled
processes. The latter consists of IPD, VDC, and DfMA, which are either commonly
recognized in driving process improvement and increasingly used in the global
construction industry, or greatly encouraged and supported by the Singapore
government. All of them were adapted for use in the local construction industry. Key
activities, especially those related to BIM, in each project phase of these delivery
methods were identified. By comparing the traditional and current processes with the
full BIM-enabled processes, it is concluded that much should be done to shift the
local construction industry to be more collaborative and integrated with the use of
BIM. Moreover, the relationships between the IPD, VDC, and DfMA approaches
were explored. VDC, DfMA, or a hybrid of them was recommended to be
implemented in the short term, while the IPD approach should be explored and
gradually applied in the long term in the Singapore construction industry. Even
though recently the CITM has been released by the local government, it will take
94
years before the local industry widely adopts the DfMA approach or BIM at large in
practice. Thus, it is believed that this study was still novel and practically significant.
95
Chapter 4: Review of NVA Activities and Proposal of a BIMIR
Evaluation Model
4.1 Introduction
The majority of building project teams in the Singapore construction industry need to
operate in an environment that promotes the widespread use of BIM. It is therefore
necessary to understand to what extent the project teams are capable and ready to
implement BIM in their building projects. This chapter first provides a review on the
NVA activities in the project lifecycle by comparing the key activities between the
current delivery process and the full BIM-enabled delivery processes which were
discussed in Chapter three. Subsequently, the chapter reviews the resulting wastes of
the NVA activities, and the causes to these NVA activities in terms of the roles of the
major stakeholders. Finally, to evaluate the BIMIR status in building projects, a
model is developed based on the NVA activities, using the FSE approach.
4.2 NVA Activities and Their Causes and Resulting Wastes
4.2.1 Identifying NVA activities
The BIM uses currently adopted in the Singapore construction industry were partial.
Without knowing the downstream BIM uses in a building project, the design team
may not be able to identify the reusable project information and important
information exchanges (Anumba et al., 2010). Compared with the IPD, VDC, and
DfMA processes, this partial BIM adoption created major NVA practices in the
current project delivery process, which would result in various wastes and consume
time (Nikakhtar et al., 2015) and manpower, leading to productivity loss. These NVA
practices were depicted by project phasing and major stakeholders (see Table 4.1), and
could be translated into a total of 44 common NVA activities. The detailed descriptions
96
Table 4.1 Major NVA practices characterized by project stakeholders and project phasing in the current project delivery in Singapore
Phases Conceptualization Schematic
design
Design
development
Construction
documentation
Agency
permit/
Bidding/
preconstruction
Construction
(including
manufacture)
Handover/
Closeout/Operations
and maintenance
Agency
(AG)
Lack of
involvement
(AIACC, 2014)
Lack of
involvement
(AIACC,
2014)
Lack of
involvement
(AIACC, 2014)
– – – –
Owner
(OW)
Inadequate
project objectives
(Arain, 2005)
Resistance to use
BIM in the whole
project (Gibb and
Isack, 2003)
No reward/risk
sharing
arrangements are
set in beginning
(AIACC, 2014)
Lack of
involvement
(Arain, 2005)
Not joint
controlled by
OW, AR,
EGs, GC, and
all key TCs
(AIACC,
2014)
Lack of
involvement
(Arain, 2005)
Insufficient
design review
and feedback
Not engaged
GC, TCs, MF,
and FM early
(Ross et al.,
2006; AIA and
AIACC, 2007;
Kunz and
Fischer, 2012;
AIACC, 2014)
Changes from
owner
Insufficient LOD of
2D as-built
drawings
Not as-built BIM
model
No reward sharing
arrangements
between all key
participants
(AIACC, 2014)
Architect
(AR)
Unclear design
intent
Not working
side by side
with EGs and
contractors
(Gao and
Fischer, 2006)
Not sharing
BIM model
with EGs
Not applying
Not working side
by side with EGs
(Gao and Fischer,
2006)
Not sharing
complete BIM
model with EGs
(Gao and Fischer,
2006)
Not sharing
Not
coordinated
design model
(AIACC,
2014)
Insufficient
communication
with EGs
Only passing
2D drawings or
incomplete 3D
BIM model to
GC, TCs, and
MF
Design changes
Long RFIs
response time as
design model
cannot directly
guide site work
Not updated design
BIM model
–
97
for planning
approval
(BCA, 2013b)
complete BIM
model early
Engineers
(EG)
Lack of
involvement
Lack of
involvement
early in this
phase to
contribute in
architectural
modeling
(feedbacks)
(Gao and
Fischer, 2006)
Not applying
for planning
approvals
(BCA, 2013b)
Coordination of
building systems
deferred until the
construction phase
because TC input
is not available
until then (AIACC,
2014)
Not
coordinated
design model
(AIACC,
2014)
Insufficient
communication
with other
designers
Only passing
2D drawings or
incomplete 3D
BIM models to
GC, TCs, and
MF
Design changes
Long RFIs
response time
Not updated design
BIM models
–
General
contractor
(GC)
Lack of
involvement (not
appointed)
Lack of
involvement
(not
appointed)
Lack of
involvement to
contribute
construction
knowledge (AIA
and AIACC, 2007;
AIACC, 2014)
Construction
model not
developed due to
unwillingness of
the design team to
share BIM models
(Gao and Fischer,
2006)
Information
such as BOM,
assembly,
layout, detailed
schedule, test
and
commissioning
procedures not
documented
after the design
phase (AIACC,
2014)
Long-lead
items are not
identified and
defined during
Re-building
BIM model
due to
unavailable or
insufficient
documents (2D
drawings or
incomplete 3D
BIM models)
(Eastman et al.,
2011)
Extending 2D
drawings
(without BIM)
from designers
to guide
Too much
RFIs/OW
changes/design
changes/paperwork
(AIA and AIACC,
2007; Eastman et
al., 2011)
Insufficient
communication
with OW, AR,
EGs, and
subcontractors
(including TCs and
MF)
Low OSM rate
Much more disputes
with owner and
designers
Insufficient LOD of
2D as-built
drawings
Not as-built BIM
model with review
by AR
98
design so that
their
procurement is
not begun as
early as
possible
(AIACC,
2014)
construction
Not BIM-
based
construction
schedule
planning
(BCA and SPH,
2015)
Congested and
many interfaces on
site (Blismas and
Wakefield, 2009
Differences
between actual and
planned site
conditions without
3D visualization
(Kim and Fischer,
2013)
Key trade
contractor
(TC, such as
structural
and MEP
contractors)
Lack of
involvement (not
appointed)
Lack of
involvement
Lack of
involvement to
contribute
site/constructability
knowledge
Shop drawings
cannot be
merged into
this phase
(AIACC,
2014)
– Incomplete 2D
shop drawings or
BIM models
Too much
RFIs/OW
changes/design
changes
–
Manufacturer
/Supplier
(MS)
Lack of
involvement (not
appointed)
Lack of
involvement
Lack of
involvement to
contribute
knowledge of
material selection,
transportation, site
erection, and so on
(Gibb and Isack,
2003)
Shop drawings
cannot be
merged into
this phase
(AIACC,
2014)
Prefabrication
of some
systems cannot
commence
because the
– Incomplete 2D
shop drawings or
BIM models
Too much
RFIs/OW
changes/design
changes
Low OSM rate
(BCA and SPH,
2015)
–
99
design is not
fixed (AIACC,
2014)
Superstructure and
fitting out cannot
take place in the
factory totally
before or while the
ground works and
substructure are
being done on-site
(Ross et al., 2006)
Facility
manager
(FM)
Lack of
involvement (not
appointed)
Lack of
involvement
Lack of
involvement to
contribute
knowledge of
operations and
maintenance (Kunz
and Fischer, 2012)
– – – Insufficient design
and construction
information for
operations and
maintenance
100
of these NVA activities were presented in Section 7.2.2. It should be noted that in this
study, “architect” represents architectural consultancy firms and “engineers”
represent consultancy firms in respective disciplines to reflect the Singapore context.
4.2.2 Resulting wastes
Previous studies reported that NVA work held a considerable portion in most
construction processes (Agbulos and AbouRizk, 2003; Dunlop and Smith, 2004;
Farrar et al., 2004; Al-Sudairi, 2007; Nikakhtar et al., 2015). This work even
exceeded 50% of the total work in some cases (Al-Sudairi, 2007). To quantify the
effect of the NVA activities in the current process on productivity, a total of 13 kinds
of potential resulting wastes that would be more impactful have been identified from
the literature review (see Table 4.2). The identification and reduction of these wastes
would result in potential time savings, leading to enhanced productivity performance.
Table 4.2 Potential wastes affecting productivity more seriously
Code Wastes References
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
W01 Defects x x x x x x x x x
W02 RFIs x x x x x
W03 Reworks/abortive works x x x x x x x
W04 Waiting/idle time x x x x x x x x x
W05 Change orders x x x
W06 Activity delays x x x x x
W07 Overproduction/reproduction x x x x x x x x x x x
W08 Transporting/handling
materials
x x x x x x x x
W09 Unnecessary inventory x x x x x x x x
W10 Excess processing beyond
standard
x x x x x
W11 Unnecessary movement of
people and equipment
x x x x x x
W12 Design deficiencies (errors,
omissions, additions)
x x x x
W13 Safety issues (injuries) x x x x x
Note: (1) Abdel-Razek et al. (2007); (2) Alarcon (1997); (3) Alwi et al. (2002); (4)
Arayici et al. (2011); (5) Chua and Yeoh (2015); (6) Eastman et al. (2011); (7)
Ekanayake and Ofori (2004); (8) Fan et al. (2014); (9) Formoso et al. (1999); (10)
Forsberg and Saukkoriipi (2007); (11) Lee et al. (1999); (12) Nikakhtar et al. (2015);
(13) Ohno (1988); (14) Senaratne and Wijesiri (2008); (15) Teo et al. (2014); (16)
101
Wong et al. (2014); (17) Wu and Low (2011); (18) Wu and Low (2012). x indicates
the inclusion of the specific waste in the reference.
The wastes were analyzed in the construction industry. Among these potential wastes,
defects, waiting/idle time, overproduction, transporting materials, unnecessary
inventory, excess processing beyond standard, and unnecessary movement of people
and equipment are seven major wastes that stem from the Toyota production system,
while the remainder is raised by previous construction management studies. It should
be noted that some similar wastes have been combined, such as waiting time and idle
time, whereas some are overlapping with each other to some extent but may be used
in different situations, such as waiting time and activity delays, defects and reworks,
and overproduction and necessary inventory. Specifically, in the building project
context, the wastes can be interpreted as follows:
W01: Defects. Every defective part requiring repairs would add extra cost of time,
manpower, materials, and facilities. Paperwork would also be created.
W02: RFIs. RFIs usually result from incomplete design information and design
inefficiencies, and require extra communication for clarification between
individuals or parties, especially between the contractors and the specific
designers during construction.
W03: Reworks/abortive works. Abortive works due to poor quality require field staff
to demolish and build again. Redesigns may be needed if such works are
completed using wrong construction methods.
W04: Waiting time/idle time. Waiting or idle time is produced when two
interdependent work processes are not completely aligned, due to such reasons
as breakdowns, changeovers, poor site layout, and waiting for materials or the
owner’s or the designers’ approvals.
W05: Change orders, which are often driven by the designers and the owner. Such
orders would create field conflicts, re-designs, wasted products and work
processes, and reworks. With BIM implementation, change orders can be
102
drastically reduced because the owner’s intent would be better represented in
3D models (Fan et al., 2014; Fischer et al., 2014).
W06: Activity delays. Any violation to schedule compliance would cause wasted
time and efforts on site. This waste could happen when the work processes are
not well defined and completely synchronized, and can be reduced by quicker
project layout and detailed scheduling capability of BIM functions (Gao and
Fischer, 2006; Fan et al., 2014).
W07: Overproduction/reproduction, which describes producing more items than
required or producing earlier, due to reasons such as uncertain design
information. This is a detrimental waste because it causes other wastes such as a
high level of inventory.
W08: Transporting/handling materials. This waste describes the movement of
materials from one location to another, which is not directly related to a value
adding work process in the construction stage (Nikakhtar et al., 2015). Reasons
such as poor design and poor site layout would cause such a waste.
W09: Unnecessary inventory. This waste occurs when there is excess supply of raw
material, work in progress or finished goods, which requires extra labor and
equipment to handle during construction (Nikakhtar et al., 2015).
W10: Excess processing beyond standard, which occurs when performing unneeded
work processes. An engineer, for example, is wasting time and efforts if
trimming 1 mm of flash from a class C area of a window when 6 mm of flash is
acceptable. This often occurs due to poor design, unclear specifications, re-
entering data and duplicated data, lack of standards, poor communication,
unnecessary documentation, and so on.
W11: Unnecessary movement of people and equipment. This waste is characterized
by those movements of man or machine which are not as easy to achieve as
possible. Examples are unneeded travel between work stations, and excessive
machine movements from start point to work start point, which may be due to
103
poorly-designed layout. A good solution is to simulate the work processes
before actual construction in the virtual environment.
W12: Design deficiencies (errors, omissions, additions). Uncertainty or
incompleteness of design information in the design stage would seriously affect
productivity performance as it will inevitably cause many field problems in the
later stages.
W13: Safety issues (injuries). It is impossible in the building project to significantly
enhance productivity without considering safety issues. Dealing with any
injuries and accidents consume extra time and manpower.
Because of reduced wastes, time and manpower can be saved, leading to schedule
compliance and enhanced productivity. For example, Chelson (2010) explained the
relationships between RFIs, reworks, idle time, activity delays, change orders, and
affected productivity and cost in an inefficient project (see Figure 4.1 and Figure 4.2).
Due to poorly coordinated and unclear building plans being used on site in the current
practices, tremendous RFIs are raised from the contractors. Such RFIs mean
increased field conflicts which cause workers’ idle time waiting for the consultants’
responses and potential reworks where necessary, resulting in decreased productivity.
§ Poorly coordinated and unclear plans § Constructability
problems§ Construction delays
§ Contractor productivity rates decrease
§ Cost of construction increases
Number of RFIs increases
Rework increases
Change orders increase
Figure 4.1 Productivity and cost affected by RFI, rework, idle time, activity delay,
and change order (Chelson, 2010)
104
Plans coordination with BIM – number of RFIs decreases
Fewer field conflicts/confusion - reworks and idle time decreases, prefabrication increases
Contractor productivity rates increase - reduced owner-enbaled change orders, increased profits
RFI
Rework
Figure 4.2 How BIM coordination enhances productivity and cost performance by
reducing major wastes (Chelson, 2010)
However, with full BIM use, all building systems can be fully defined, engineered,
and coordinated so that these RFIs would occur earlier and informally during the
design stage. The requests for clarification are issued before field personnel are
situated in the field attempting to install work according to an imperfect plan. Thus,
the number of RFIs is greatly reduced. This means fewer conflicts and confusion can
be found during construction, further decreasing reworks and idle time. As a result,
productivity performance will be increased and fewer change orders are needed (see
Figure 4.2). Both the owner and the contractors benefit from this process. The
contractors keep profits they realized by their BIM implementation activities. If the
owner was the sponsor of BIM, then they can obtain productivity savings as well.
4.2.3 Causes of NVA activities
An effective analysis of the NVA activities produced in the current project delivery in
Singapore requires a comprehensive understanding of their root causes, including but
not limited to: (1) the design team is not required by the owner and is unwilling to
share its models with the contractors; (2) the contractors have not recognized the
potential value of BIM, or worry about high learning curve of BIM; and (3) the
fragmentation between the design phase and the construction phase. The contractors
105
are not engaged upfront to input their construction knowledge. In this study, the
causes were observed and categorized according to the roles of the major
stakeholders in a building project.
1. Agency (where “AG” represents “agency”):
AG01: Currently focusing on the design stage to move from 2D drafting practices to
3D working environment by developing a set of BIM submission templates
and guidelines to help professionals understand the new process of regulatory
submissions using BIM (Zahrizan et al., 2013);
AG02: Mandating the use of BIM (such as BIM submissions) in no way guarantees
that the primary principles of collaboration, and best-for-project thinking will
be followed (AIACC, 2014); and
AG03: Unclear legislation and qualifications for precasters (versus concreter) and
inadequate codes for OSM varieties, for example, to address tilt-up rather than
other precast products (Blismas and Wakefield, 2009).
2. Owner (where “OW” represents “owner”):
OW01: Owner inertia against the use of BIM or off-site prefabrication due to limited
knowledge and inexperience (Gibb and Isack, 2003; Blismas and Wakefield,
2009);
OW02: The owner frequently establishes minimal apparent risk and minimum first
cost as crucial selection criteria for new projects (Kunz and Fischer, 2012);
OW03: The owner’s financial problems (Arain, 2005);
OW04: Unaware of the benefits of BIM and building lifecycle management (BLM).
As indicated in a BIM survey, many owners are still unaware of BIM or BLM
and thus need for professional guidelines on leveraging BIM (Khosrowshahi
and Arayici, 2012);
106
OW05: Traditional contracts often tend to create incentives for individual firms to
protect their own interests at the expense of the project, rather than embrace
best-for-project thinking (AIA and AIACC, 2009);
OW06: The owner awards architectural and engineering contracts solely based on
qualification to provide the design services before the construction phase. The
lowest cost contractors then build such a project (Azhar et al., 2014);
OW07: Goals set between the owner and architect are vague and poorly defined, and
rarely passed on to people working in later stages of the project (Fischer et al.,
2014);
OW08: Using mechanisms such as guarantees, penalties, and risk transfers does not
address the fundamental causes of poor performance, and leads to focusing on
assessing liability but doing very little to avoid the risks (Fischer et al., 2014);
OW09: Perceiving design fees for OSM as more expensive than traditional process,
even though they are potentially lower by using standard products (Blismas
and Wakefield, 2009); and
OW10: The owner’s desire for particular structures or traditional finishes may inhibit
OSM and limit design options (Blismas and Wakefield, 2009).
3. Architect (where “AR” represents “architect”):
AR01: The final set of drawings and specifications must contain sufficient details to
facilitate construction bids. Because of potential liability, the architect may
choose to include fewer details in the drawings or insert language indicating
that the drawings cannot be relied on for dimensional accuracy. These
practices often lead to misinterpretation and disputes with contractors, as
errors and omissions are detected and responsibility and extra costs reallocated
(Arain, 2005; Eastman et al., 2011);
AR02: Its design model shows intent but does not show exact dimensions of every
component, while construction models/drawings show more at a higher LOD
107
and precision because the constructors have to build from them (AIACC,
2014);
AR03: Not required by contract to share design models with the contractors (Kiani et
al., 2015);
AR04: Its design model or drawings fit for mandatory BIM submissions, but not fit
for intended downstream use because of insufficient LOD and precision for
every component (Chua and Yeoh, 2015);
AR05: Does not understand field operations enough and lacks construction input in
design (Chelson, 2010);
AR06: Lack of skilled BIM experts in the local market to engage (Kiani et al., 2015);
AR07: Project design decisions are at the sole discretion of the designers, without
complete knowledge of the impact on construction (AIACC, 2014);
AR08: Does not model everything the contractors need for QTOs. For example,
architectural modeling usually does not differentiate between the walls that
stop at the ceiling and those that extend to the structural floor above (AIACC,
2014);
AR09: Spending much time and effort locating, recreating, or transferring fragmented
information when working on a fragmented team (Fischer et al., 2014);
AR10: Unless asked and encouraged, the architect will not even consider the lifecycle
value of or incremental changes to the project (Kunz and Fischer, 2012); and
AR11: Limited expertise of OSM and its processes in the marketplace, especially for
SMEs, with design philosophy based on traditional methods that are unsuited
to OSM (Blismas and Wakefield, 2009).
4. Engineers (where “EG” represents “engineers”):
EG01: Their design models show intent but do not show exact dimensions of every
component, while construction models/drawings show more at a higher LOD
108
and precision because the constructors have to build from them (AIACC,
2014);
EG02: Not required by contract to share design models with the contractors (Kiani et
al., 2015);
EG03: Their design models or drawings fit for mandatory BIM submission, but not fit
for intended downstream use because of insufficient LOD and precision for
every component (Chua and Yeoh, 2015);
EG04: Do not understand field operations enough and lack construction input in the
design (Chelson, 2010);
EG05: Lack of skilled BIM experts in the local market to engage (Kiani et al., 2015);
EG06: Project design decisions are at the sole discretion of the designers, without
complete knowledge of the impact on construction (AIACC, 2014);
EG07: Spending much time and effort locating, recreating, or transferring fragmented
information when working on a fragmented team (Fischer et al., 2014);
EG08: Unless asked and encouraged, engineers will not even consider the lifecycle
value of or incremental changes to the project (Kunz and Fischer, 2012);
EG09: Limited expertise of OSM and its processes in the marketplace, especially for
SMEs, with design philosophy based on traditional methods that are unsuited
to OSM (Blismas and Wakefield, 2009); and
EG10: Downstream designers have to make extra efforts to reconfigure or reformat
the data that are viewed differently by upstream designers (Gao and Fischer,
2006; Eastman et al., 2011).
5. General contractor (where “GC” represents “general contractor”):
GC01: Not required by the owner and agencies to adopt BIM (Kiani et al., 2015);
GC02: Lack of skilled BIM experts to engage to help construction/site manager and
unable to see how BIM benefits them (Fox and Hietanen, 2007; Chelson, 2010;
Zahrizan et al., 2013; Kiani et al., 2015);
109
GC03: Only 2D drawings or incomplete 3D models (such as insufficient LOD) are
shared from the design team (Chua and Yeoh, 2015);
GC04: Costly training and high learning curve (initial productivity loss) for the
general contractor to use BIM (Eastman et al., 2011);
GC05: Many firms are reluctant and inexperienced to use BIM, and still seem to be
happy to continue using the traditional CAD practices (Khosrowshahi and
Arayici, 2012);
GC06: Most construction players have little knowledge of BIM and do not know how,
when, and what to start to use it (Zahrizan et al., 2013);
GC07: Lack of national BIM standards and guidelines for the general contractor
(Zahrizan et al., 2013);
GC08: Doubt about the effectiveness of BIM because limited data have proven the
effectiveness (Zahrizan et al., 2013);
GC09: Afraid of the unknown and resistant to change from comfortable daily routine
to the new work process (Zahrizan et al., 2013);
GC10: Lack of legal support from local authorities such as software price subsidies
(Kiani et al., 2015);
GC11: Lack of tangible benefits of BIM and limited evidence to warrant its use
(Khosrowshahi and Arayici, 2012; Zahrizan et al., 2013; Kiani et al., 2015);
GC12: Not thinking of changing conventional working methods and no demand for
BIM uses (Khosrowshahi and Arayici, 2012; Kiani et al., 2015);
GC13: Having to make extra efforts to reconfigure or reformat the data that are
viewed differently by upstream designers (Gao and Fischer, 2006; Eastman et
al., 2011);
GC14: Reluctance to adopt OSM (Blismas and Wakefield, 2009); and
GC15: Limited expertise of OSM and its processes in the marketplace, especially for
SMEs, with design philosophy based on traditional methods that are unsuited
to OSM (Blismas and Wakefield, 2009).
110
6. Trade contractors (where “TC” represents “trade contractor”, such as structural
and MEP contractors):
TC01: Not required by the owner or general contractor or agencies to adopt BIM
(Kiani et al., 2015);
TC02: Lack of skilled BIM experts to engage to help site staff and unable to see how
BIM benefits them (Fox and Hietanen, 2007; Chelson, 2010; Zahrizan et al.,
2013; Kiani et al., 2015);
TC03: Only 2D drawings or incomplete 3D models (such as insufficient LOD) are
shared from the designers or general contractor (Chua and Yeoh, 2015);
TC04: Costly training and high learning curve (initial productivity loss) for trade
contractors to use BIM (Eastman et al., 2011);
TC05: Many firms are reluctant and inexperienced to use BIM, and still seem to be
happy to continue using the traditional CAD practices (Khosrowshahi and
Arayici, 2012);
TC06: Most construction players have little knowledge of BIM and do not know how,
when, and what to start to use it (Zahrizan et al., 2013);
TC07: Lack of national BIM standards and guidelines for the trade contractors
(Zahrizan et al., 2013);
TC08: Doubt about the effectiveness of BIM because limited data have proven the
effectiveness (Zahrizan et al., 2013);
TC09: Afraid of the unknown and resistant to change from comfortable daily routine
to the new work process (Zahrizan et al., 2013);
TC10: Lack of legal support from local authorities such as software price subsidies
(Kiani et al., 2015);
TC11: Lack of tangible benefits of BIM and limited evidence to warrant its use
(Khosrowshahi and Arayici, 2012; Zahrizan et al., 2013; Kiani et al., 2015);
TC12: Not thinking of changing conventional working methods and no demand for
BIM uses (Khosrowshahi and Arayici, 2012; Kiani et al., 2015);
111
TC13: Downstream contractors have to make extra efforts to reconfigure or reformat
data that are viewed differently by upstream stakeholders (Gao and Fischer,
2006; Eastman et al., 2011); and
TC14: Limited expertise of OSM and its processes in the marketplace, especially for
SMEs, with design philosophy based on traditional methods that are unsuited
to OSM (Blismas and Wakefield, 2009).
7. Manufacturer/Supplier (where “MS” represents “manufacturer/supplier”):
MS01: The design must be fixed early and fit for off-site prefabrication and on-site
assembly, and does not permit any changes as these are expensive once
fabrication has commenced (Gibb and Isack, 2003; Blismas and Wakefield,
2009; Selvaraj et al., 2009);
MS02: Not required by the owner or general contractor or agencies to adopt BIM in
manufacture (Kiani et al., 2015);
MS03: Lack of skilled BIM experts to engage and unable to see how BIM benefits
the manufacturer/supplier (Fox and Hietanen, 2007; Chelson, 2010; Zahrizan
et al., 2013; Kiani et al., 2015);
MS04: Only 2D drawings or incomplete 3D models (such as insufficient LOD) are
shared from the designers or general contractor (Chua and Yeoh, 2015);
MS05: Costly training and high learning curve (initial productivity loss) for the
manufacturer/supplier to use BIM (Eastman et al., 2011);
MS06: Many firms are reluctant and inexperienced to use BIM, and still seem to be
happy to continue using the traditional CAD practices (Khosrowshahi and
Arayici, 2012); and
MS07: Market protection from traditional suppliers/manufacturers to use BIM and
OSM (Blismas and Wakefield, 2009).
8. Facility manager (where “FM” represents “facility manager”):
112
FM01: Not required by the owner and/or subsequently not involved in the design
phase (AIA and AIACC, 2007; Kunz and Fischer, 2012).
In summary, the identified NVA activities are mostly caused by these reasons which
were translated into a total of 53 causes in six roles (government agency, owner,
architect/engineers, contractor, manufacturer/supplier, and facility manager). The
detailed descriptions of these causes were presented in Section 7.2.5.
4.3 BIMIR
To successfully implement a new technology or technological process (such as BIM),
many factors should be taken into consideration, such as personal attitudes,
relationships between project participants, specific project characteristics, legal
issues, and individuals’ resistance to change (O’Brien, 2000; Nitithamyong and
Skibniewski, 2003). The attitudes toward the new technology or technological
processes are affected by potential financial loss in the first projects and the
perception of peers’ attitudes. A survey in Taiwan indicated that about 93% of the
local architectural firms would be psychologically ready to implement BIM to
maintain competitiveness in the local industry if their competitors had already done
so (Juan et al., 2017). Thus, at the project level, the primary participants may not
participate equally even though the resources are in place.
Without a clear knowledge of benchmark, metrics, and guidance, project teams might
hesitate and the adoption rate of BIM implementation was not high. The firms usually
learned from successful BIM implementation cases. During the last decade, the firms
that planned to use BIM had to overcome technical and organizational difficulties and
did not clearly know where they were moving (Won et al., 2013). Lee (2007a)
113
proposed that BIM implementation of a building project could be classified into four
phases according to the level of organization involved:
(1) First phase: personal adoption
In this phase, there is only one BIM modeler who produces and maintains a BIM
model. Others may use the data produced from this model, but no collaboration
between the modeler and the users is involved. For example, a single architect
develops a design model and produces drawings purely from the model without
any data exchange with others. Another example is that a subcontracted BIM
modeler creates a BIM model that is not or cannot be actively used in the project,
and the model is only produced for presentation purposes or meeting the client’s
request.
(2) Second phase: adoption within a team of a party
A team of several people in a primary project participant may work
collaboratively using interoperable BIM software applications. For instance, in an
architectural consultancy firm, an architectural model is created and used only by
the architectural staff of this party.
(3) Third phase: adoption across different types of teams within a party
Several teams with different roles and responsibilities in a primary project
participant collaborate to complete their own scopes of work. For example, a BIM
model developed by one team is shared with estimators or schedulers within this
party to do planning.
(4) Fourth phase: adoption across parties
The major difference between this phase and the third phase is that this phase
involves much more complex coordination and collaboration issues across
different parties with different BIM capabilities and interoperability issues
between different BIM tools. In this phase, for example, BIM implementation
activities require BIM detailers from different teams or parties to openly share
their models, such as for consolidation and clash detection.
114
The level of BIM implementation varies by company. While early and aggressive
BIM implementers may have reached the fourth phase, other firms were still
struggling in the first phase. The lessons and experience revealed that fundamental
innovations and changes are expected both within individual parties and cross-
organization environments to drive widespread BIM implementation towards the
fourth phase and beyond.
In addition, Succar (2009) and Khosrowshahi and Arayici (2012) identified BIM
maturity and subdivided it into Pre-BIM status and three BIM maturity stages, which
could be depicted in Figure 4.3. Specifically, the Pre-BIM status described the
traditional project delivery using the traditional 2D CAD approach in the design,
detailing, and documentation. Besides, the BIM Stage One was characterized by
single-discipline models which did not have any modifiable parametric attributes.
Also, significant model-based data interchanges between different disciplines were
not involved, and the deliverables generated from the models were mostly CAD-like
documents. Moreover, compared with the stages mentioned above, the BIM Stage
Two involved necessary contractual amendments because of the need of sharing
models between disciplines. Such collaboration may occur within one project phase
or between two phases to encourage fast-tracking. Furthermore, the BIM Stage Three
created, shared, and maintained semantically-rich models across project phases
through model servers and common databases, which would enable virtual complex
analyses at an early stage. Since the contractual documents were reconsidered where
necessary, the downstream parties were involved upfront to activate concurrent
construction.
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Pre-BIM Status: traditional practice
BIM Stage 1: object-based modeling
BIM Stage 3: network-based integration
BIM Stage 2: model-based collaboration
· 2D drafting and detailing· Document-based and linear workflow· Asynchronous communication· Adversarial relationship· Risk avoidance· Lack of interoperability
· 3D object-oriented model· Single-disciplinary model· Automated and coordinated 3D visualizations· Basic data harvested from the model such as
2D plans, elevations, sections, and QTOs· Adversarial relationship· Asynchronous communication
· Multi-disciplinary data share and exchange· 4D and 5D analysis· Clash detection between disciplines· Contractual amendments· Asynchronous communication· Fast-tracking
· Semantically-rich nD model· Complex analysis at early stages· Multi-disciplines use the same model through
a common database· Concurrent construction· Major contractual reconsideration· Synchronize communication· Multi-user server for collaboration
Migration from 2D to 3D
Modeling to
collaboration
Collaboration to integration
Figure 4.3 BIM maturity stages in previous studies (Succar, 2009; Khosrowshiahi and
Arayici, 2012)
In this study, the implementation readiness of a project team that plans to use BIM
was defined as “the psychological willingness or the state of being prepared for
performing BIM implementation activities”. The implementation readiness described
the condition or situation of the team in the project planning and preparation stage.
Since BIM implementation practices in this project team were still in preparation at
this stage, the term “readiness” was used. To help the Singapore construction industry
move towards higher levels of BIM implementation, this study proposed four statuses
of BIMIR in the building project context:
(1) BIMIR status one (S1): no BIM implementation
In particular, this status does not involve any BIM implementation activities in the
project delivery, so it describes the traditional approach of project delivery. The
traditional 2D CAD approach is used in the design, detailing, and documentation.
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The industry suffers from low investment in technology and paper-based
information exchange. Many NVA activities will inevitably occur in the project
delivery. Thus, the Pre-BIM status proposed by Succar (2009) and Khosrowshahi
and Arayici (2012) pertains to this readiness status.
(2) BIMIR status two (S2): lonely BIM implementation
This is the first stage in implementing BIM, gradually moving from the traditional
2D drafting to object-based 3D parametric design (Forgues and Lejeune, 2015).
This readiness status is characterized by the situation that each discipline or
primary project participant may adopt BIM, and such adoption is in silos. In other
words, this status does not involve any significant model-based data sharing
between different disciplines or parties, which is also not required by the main
form of contract. Thus, the level of collaboration is weak, and NVA activities will
occur often. Based on these characteristics, the aforementioned first, second, and
third phases of BIM implementation proposed by Lee (2007a) as well as the BIM
Stage One (Succar, 2009; Khosrowshahi and Arayici, 2012) are closely related to
this readiness status. It should be noted that most BIM implementation activities
guided by the first Singapore BIM roadmap would typically lead to the lonely
BIM implementation. The term “lonely BIM” was widely used in previous BIM
implementation studies (Abbasnejad and Moud, 2013; Kuiper and Holzer, 2013;
Das et al., 2014; Forgues and Lejeune, 2015; Gibbs et al., 2015; Poirier et al.,
2015), which describes the opposite of “collaborative BIM”.
(3) BIMIR status three (S3): collaborative BIM implementation
The collaborative BIM implementation involves multidisciplinary or multi-
stakeholder collaboration in the design, detailing, analysis, and documentation.
The semantically-rich models are shared through proprietary or non-proprietary
formats and multi-user access platforms (such as BIM Server). Meanwhile, some
contractual documents need to be amended to allow data sharing and incentivize
downstream parties to actively participate upfront. In a building project under this
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readiness status, the level of collaboration may vary from project to project. In
such a project, the occurrence of the NVA activities is not very often. Generally
speaking, the more NVA activities occur or the NVA activities occur more
frequently, the lower the level of collaboration and readiness status would be.
Therefore, the fourth phase proposed by Lee (2007a) as well as the BIM Stage
Two and the BIM Stage Three proposed by Succar (2009) and Khosrowshahi and
Arayici (2012) are closely associated with this readiness status established in this
study. In particular, the BIM Stage Three involves closer collaboration than the
BIM Stage Two. The communication pattern tends to be synchronized in the
former stage and be asynchronous in the latter stage. Typically, the second
Singapore BIM roadmap has been encouraging the local construction value chain
to implement BIM in a more collaborative manner.
(4) BIMIR status four (S4): full BIM implementation
Specifically, this is the highest readiness status of BIM implementation
established in this study. Succar (2009) argued that IPD could be used to denote
an approach to or an ultimate goal of implementing BIM. Thus, a building project
under this readiness status should follow the six key characteristics or principles of
IPD (see Table 3.3).
The four BIMIR statuses were developed for the building project teams that operate
in the Singapore context. These statuses were different from the levels of the BIM
Maturity Model which is specifically used in the UK and consists of a large set of
well-defined UK-centric requirements and deliverables (standards and guidelines)
(Government Construction Client Group, 2011). Besides, the BIM Maturity Model
cannot be used to assess BIM capabilities of organizations or teams. This model
cannot use BIM levels to establish an organization’s ability to collaborate with others,
conduct model-based analysis, or deliver high-quality 4D construction scheduling.
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Instead, with the NVA activities being sub-criteria, BIMIR statuses can be measured
in building project teams.
4.4 A BIMIR Model for Building Projects
4.4.1 Existing BIM readiness models
To evaluate a project’s BIMIR, the project leadership team should make efforts to
understand its current BIM capabilities and practices in the project planning stage.
The efforts are necessary because this can help the leadership team to obtain a clear
view of the strengths and weaknesses of its BIM implementation. Based on the
evaluation result, the management staff can purposively figure out appropriate
strategies and prioritize resources to improve the weak areas.
The literature review showed that few studies by far have been conducted to assess
BIM capabilities and BIMIR status of a project team. Juan et al. (2017) developed a
model to assess the technology acceptance and organizational readiness of Taiwanese
architectural firms to adopt BIM and BIM-based building permit review process.
After recognizing the benefits of BIM implementation, the Taiwanese government
had been planning to enact a policy that would revolutionize the local construction
industry. BIM-based e-submissions would be incorporated into the local building
permit review process. The local architectural firms would be mostly affected as they
are involved in the early stages of building projects (Juan et al., 2017).
In particular, to study the local architects’ experience of and willingness to implement
BIM, Juan et al. (2017) identified 22 items to assess their BIM acceptance, and
categorized the items into four groups of attributes: BIM technology usage, external
environments, internal factors, and employees, based on the highly interrelated
technology acceptance model (Davis, 1989), knowledge management system (Zhang
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et al., 2009), and balanced scorecard (Ashurst et al., 2012). In addition, to assess
whether the architectural firms were ready to adopt BIM, Juan et al. (2017) also
established 18 assessment items. Based on the theory of readiness for workplace
change management (Becker, 2004), these assessment items were further classified
into six groups of attributes, namely leadership, business performance, operating
environment, organizational culture, technological environment, and workforce
demographics. This would help the architectural firms assess their organizational
readiness to adopt BIM and reflect upon their organizational work practices and
current technology capabilities.
Organizational readiness was contended to be highly related to the staff’s technology
acceptance within a firm (Venkatesh, 2000). With the organizational readiness
assessment, the Taiwanese architectural firms’ internal readiness to adopt a new
technology could be obtained. The staff would be willing to accept the new
technology when they are ready for organizational change (Tsikriktsis, 2004).
Therefore, organizational readiness plays a critical role in the successful adoption of
the new technology and technological process such as BIM.
Although the model proposed by Juan et al. (2017) can be used to assess the BIMIR,
it focused at the firm (the architectural firms) level in the Taiwanese context, rather
than at the project (building projects) level which includes the whole construction
value chain. Hence, this study proposes a BIMIR evaluation for evaluating the
BIMIR statuses of building projects in Singapore.
4.4.2 A fuzzy BIMIR model
This study assumed that BIMIR of a building project in Singapore could be evaluated
by a NVA index (NVAI). In this study, NVAI is defined as “the degree to which a
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project frequently produces critical NVA activities in the project lifecycle”. The
NVAI score could be measured by the frequency of occurrence of the critical NVA
activities in the project lifecycle. Generally speaking, the readiness was negatively
related to the frequency of occurrence of the critical NVA activities. As shown in
Section 7.2.2, a total of 44 common NVA activities were identified in a building
project. Among which, six, nine, nine, six, three, eight, and three common NVA
activities belonged to the project phases of conceptualization (P1), schematic design
(P2), design development (P3), construction documentation (P4), agency
permit/bidding/preconstruction (P5), construction (including manufacture) (P6), and
handover/closeout/operations and maintenance (P7), respectively. These NVA
activities enable the project leadership team to easily understand the criteria and
evaluate its BIMIR status according to its planning of BIM implementation activities.
If the critical NVA activities would have occurred more thoroughly and frequently in
their current implementation practices, BIMIR status of this project can be deemed as
lower. It should be noted that the importance of these critical NVA activities and
project phases varies, so they should be assigned with different weights.
The critical NVA activities were distributed in seven project phases. In this study, the
project phases were considered as evaluation criteria, and the NVA activities as sub-
criteria. Thus, the issue of evaluating the BIMIR status of this building project by
assessing the critical NVA activities became a multiple-criteria decision-making
process. Thus, in the three-layer BIMIR evaluation model, the first layer was the
BIMIR status (or NVAI score) of the building project, the second layer consisted of
the seven project phases, and each phase comprised of a few NVA activities (the third
layer). The literature review identified four commonly-used multi-criteria analysis
methods, namely the FSE, artificial neural network (ANN), preference ranking
organization method for enrichment evaluations (PROMETHEE), and analytic
hierarchy process (AHP). Since the PROMETHEE is not applicable to determine the
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weights, this method was not applied in this study. Although the AHP method can
determine the weights, its pairwise comparisons are not suitable to the seven project
phases. This is because: (1) these phases are interrelated along the project timeline,
rather than being considered in parallel; and (2) pairwise comparisons usually involve
inexact or incomplete information and are thus hard for decision makers to make
accurate expressions of relative preferences (Lin et al., 2008). Thus, this method was
also not taken into account.
The proposed BIMIR evaluation model in this study is used for self-assessment by
the project leadership team in the project planning and preparation stage, which could
be achieved by using the FSE or the ANN method. Following the establishment of the
technology acceptance assessment items and the organizational readiness assessment
items for the Taiwanese architectural firms, Juan et al. (2017) further categorized the
10 groups of attributes into five new assessment variables (project collaboration and
communication, technology investment and training, organizational structure and
operating environment, technical environment and support, and leadership and
executive power). A predictive model was developed to help the local architects
evaluate the readiness to implement BIM in their firms, which was based on the ANN
method and in particular, a back-propagation learning algorithm with feed-forward
architecture. The output of this model was BIM adoption decision (“yes” or “no”),
with a good overall prediction of 81.3%.
Nevertheless, the FSE method has advantages over the ANN method in terms of data
precision. The former, as pioneered by Zadeh (1965), can address decision problems
involving uncertainty (Chen and Hwang, 1992). This is because the FSE method is
able to deal with vague, imprecise, and ambiguous data, use natural language and
linguistic terms (Higgins and Goodman, 1993), and quantify the linguistic facet of
available data and preferences for individual or group decision-making. In particular,
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the decision-making process in the real world is often influenced by human factors,
such as subjective human thinking and preferences. In these circumstances, the
judgments are expressed by means of linguistic terms instead of real numbers. Such
problems related to the vagueness and imprecision in human judgments cannot be
solved by the ANN method. When the project leadership team self-assesses its
BIMIR in the project planning stage, subjective judgments will inevitably occur in
certain team members. For example, when required to rate the frequency of
occurrence of a NVA activity using a five-point or seven-point scale, a BIM expert
may be uncertain between “3” and “4”. Since it is impossible to rate “3.5”, the expert
can only provide a response, “3” or “4”. If the expert finally selects “3”, there is also
possibility for being “4”, and vice versa. In this case, the issue of uncertainty is
created, which can be solved by fuzzy set theory. Therefore, the FSE was adopted in
the development of the BIMIR evaluation model in this study.
In an universe of discourse 𝑈, a fuzzy subset 𝐴 of 𝑈 allows partial membership, and
can be defined as:
𝐴 = {(𝑢, 𝜇𝑎(𝑢))|𝑢 ∈ 𝑈} (4.1)
where 𝜇𝑎(𝑢) is the membership function of the fuzzy set 𝐴, which can also be written
as 𝐴(𝑢). 𝐴(𝑢) maps each element 𝑢 in 𝑈 to a real number (function value) ranging
from 0 to 1. This real number specifies the degree to which 𝑢 belongs to 𝐴. When
𝐴(𝑢) is large, its grade of membership of 𝑢 in 𝐴 is strong.
The key in fuzzy modeling is to define fuzzy numbers, and the result of calculations
would strongly depend on the shape of the membership function adopted. Ross (2010)
found that in practical applications, five types of membership functions had been
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widely used: (1) triangular; (2) trapezoidal; (3) S function; (4) Gaussian; and (5) Z
function. In this study, the triangular one (see Figure 4.4) was used, and fuzzy and
qualitative opinions expressed by BIM experts in Singapore were represented as
triangular fuzzy numbers (TFNs). The reasons of using TFN were: (1) it has been
most commonly used (Tah and Carr, 2000; Chou and Chang, 2008; Lam et al., 2010;
Xia et al., 2011; Nieto-Morote and Ruz-Vila, 2011, 2012; Zhao et al., 2013; Işik and
Aladağ, 2017); (2) it is easy to process qualitative information in a fuzzy environment
(Chou and Chang, 2008; Nieto-Morote and Ruz-Vila, 2012) because it is linear
(Mayor and Trillas, 1986); and (3) its representation with a simpler function shape
tends to be more intuitive (Chou and Chang, 2008) and more natural interpretation
(Nieto-Morote and Ruz-Vila, 2011, 2012).
U
1
A(u)
0a b cu
Figure 4.4 Triangular membership function
A TFN can be denoted as a triplet (𝑎, 𝑏, 𝑐) where 𝑎, 𝑏, and 𝑐 are real numbers, and
𝑎 < 𝑏 < 𝑐. Its membership function 𝐴(𝑢) is defined as equation (4.2):
𝐴(𝑢) = {
𝑢−𝑎
𝑏−𝑎, 𝑎 ≤ 𝑢 ≤ 𝑏
𝑐−𝑢
𝑐−𝑏, 𝑏 ≤ 𝑢 ≤ 𝑐
0, 𝑢 < 𝑎 𝑜𝑟 𝑢 > 𝑐
(4.2)
where 𝑎, 𝑏, and 𝑐 represents the lower bound, strongest grade membership, and upper
bound of 𝐴(𝑢), respectively, as shown in Figure 4.4. Meanwhile, the primary fuzzy
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arithmetic operations of any two TFNs, 𝐴1 = (𝑎1, 𝑏1, 𝑐1) and 𝐴2 = (𝑎2, 𝑏2, 𝑐2), are
indicated below (Chen and Hwang, 1992):
(1) Addition: 𝐴1+𝐴2 = (𝑎1+𝑎2, 𝑏1+𝑏2, 𝑐1+𝑐2);
(2) Subtraction: 𝐴1−𝐴2 = (𝑎1−𝑐2, 𝑏1−𝑏2, 𝑐1−𝑎2);
(3) Multiplication: 𝐴1×𝐴2 = (𝑎1×𝑎2, 𝑏1×𝑏2, 𝑐1×𝑐2);
(4) Multiplication of any real number and a TFN, 𝑘×𝐴 = (𝑘×𝑎, 𝑘×𝑏, 𝑘×𝑐);
(5) Division: 𝐴1∕𝐴2 = (𝑎1/𝑐2, 𝑏1/𝑏2, 𝑐1/𝑎2); and
(6) Division by any real number, 𝐴/𝑘 = (𝑎/𝑘, 𝑏/𝑘, 𝑐/𝑘).
The root of the FSE method lies in the concept of linguistic variables. Unlike the
numerical variables whose values are numbers, linguistic variables are the variables
whose values are linguistic terms such as words and sentences in a natural or artificial
language (Zadeh, 1975), and thus are not directly mathematically operable (Nieto-
Morote and Ruz-Vila, 2012). This concept plays a fundamental role in the decision-
making problems in which it may be difficult for decision makers to assign exact
numerical values to linguistic variables due to the unavailability or uncertainty of
information involved (Nieto-Morote and Ruz-Vila, 2012). Because of great
subjectivity, in these cases the decision makers tend to use linguistic variables instead
of numerical variables. In addition, to overcome the difficulty of mathematically
operating the linguistic variables, the linguistic terms should be transformed to fuzzy
numbers to characterize the meaning of the terms. In this study, the linguistic variable
was defined as the frequency of occurrence of each critical NVA activity within each
project phase. The determination of the number of conversion scales is usually
intuitive (Chen and Hwang, 1992). Following the scale of “seven plus or minus two”
(Miller, 1956), this study adopted the scale of five. This would generate large amount
of information from users and bring convenience to the users to make the subjective
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judgments. The linguistic values were defined as: never, rarely, sometimes, often, and
always. These fuzzy terms were transformed into TFNs, respectively.
A triangular fuzzy set usually needs to overlap with its neighboring fuzzy set by 25%
to 50% of the fuzzy set base (Cox, 1998). The intersection of two overlapping
membership functions should be at least 50% for control applications and a little
lower for classification (Driankov et al., 1996). Therefore, 50% was adopted in this
study (see Figure 4.5).
U
1
A(u)
0 0.25 0.5 0.75 1
Rarely Sometimes Often AlwaysNever
Figure 4.5 Membership functions of linguistic values
The Likert rating scale is suitable to obtain accurate data and the results are easy to
interpret (Ekanayake and Ofori, 2004). As shown in Table 4.3, a five-point Likert
rating scale (1=never, 2=rarely, 3=sometimes, 4=often, and 5=always) was used in a
survey to measure the frequency of occurrence of the critical NVA activities as
indicated by the BIM experts who had been implementing BIM in the Singapore
construction industry.
Table 4.3 Fuzzy numbers of linguistic terms
Linguistic term Range of percentile of possibility Fuzzy number
Never 0-25 (0, 0, 0.25)
Rarely 0-50 (0, 0.25, 0.50)
Sometime 25-75 (0.25, 0.50, 0.75)
Often 50-100 (0.50, 0.75, 1)
Always 75-100 (0.75, 1, 1)
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In this study, decision criteria and sub-criteria do not have the same importances so
they need to be weighted. The NVA activities that were not critically agreed upon by
the BIM experts in Singapore were subsequently excluded. The weighting could be
assigned to the critical NVA activities according to their respective mean scores. The
mean scoring method was recommended by Chan and Kumaraswamy (1996) who
calculated the relative importance of causes of delay in building projects based on the
views of the owners, consultants, and contractors in Hong Kong. In particular, the
mean score (𝑀𝑖) of a particular critical NVA activity can be calculated using equation
(4.3):
𝑀𝑖 =∑ (𝑓𝑖𝑠𝑖)𝑛
𝑖=1
𝑛 (4.3)
where:
𝑛 represents the total number of responses;
𝑀𝑖 represents the mean score of a particular critical NVA activity 𝑖;
𝑠𝑖 represents the score that the BIM experts rated the critical NVA activity; and
𝑓𝑖 represents the frequency of each rating on the critical NVA activity, and ∑ 𝑓𝑖 =𝑛𝑖=1
1.
Then, the mean score (𝑀𝑝) of a particular project phase can be calculated using
equation (4.4) which was also applied in previous construction management studies
(Xu et al., 2010b; Zhao et al., 2016a):
𝑀𝑝 = ∑ 𝑀𝑖𝑝𝑘
𝑖=1 (4.4)
where:
𝑘 represents the number of critical NVA activities within a project phase;
𝑀𝑝 represents the mean score of a particular project phase 𝑝;
𝑀𝑖𝑝
represents the mean score of critical NVA activity 𝑖 under project phase 𝑝; and
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∑ 𝑀𝑖𝑘𝑖=1 represents the summation of the mean scores of all the 𝑘 critical NVA
activities within a project phase.
In addition, to calculate the weighting of each project phase, the weighting of each
critical NVA activity within each project phase should be determined. The weights
assigned to the critical NVA activities within a project phase can be calculated using
equation (4.5):
𝑊𝑖 =𝑀𝑖
∑ 𝑀𝑖𝑘𝑖=1
(4.5)
where:
𝑘 represents the number of critical NVA activities within a project phase;
𝑊𝑖 represents the weighting of a particular critical NVA activity 𝑖 in the project phase,
and ∑ 𝑊𝑖 = 1𝑘𝑖=1 ;
𝑀𝑖 represents the mean score of critical NVA activity 𝑖; and
∑ 𝑀𝑖𝑘𝑖=1 represents the summation of the mean scores of all the 𝑘 critical NVA
activities within the project phase.
Similarly, to calculate the overall NVAI, the weighting of each project phase should
be determined. The weights assigned to the project phases can be calculated using
equation (4.6):
𝑊𝑝 =𝑀𝑝
∑ 𝑀𝑝𝑞𝑝=1
(4.6)
where:
𝑞 represents the number of project phases in a project (in this study, 𝑞=7);
𝑊𝑝 represents the weighting of a particular phase 𝑝, and ∑ 𝑊𝑝𝑞𝑝=1 = 1;
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𝑀𝑝 represents the mean score of phase 𝑝, which can be calculated by summing the
mean scores of all the critical NVA activities within this phase; and
∑ 𝑀𝑝𝑞𝑝=1 represents the summation of the mean scores of all the phases in the project
lifecycle.
Such weighted-mean methods of determining the weighting of critical factors and
factor groups were also adopted in previous construction management studies (Yeung
et al., 2007; Xu et al., 2010a; Xu et al., 2010b; Xia et al., 2011; Zhao et al., 2016a).
Subsequently, to evaluate the BIMIR status via the NVAI score of the building
project, the data related to the frequencies of occurrence of all the critical NVA
activities, which were rated by the BIM experts involved in the building project,
should be input to the proposed FSE model. In particular, the frequency of occurrence
of a NVA activity can be calculated using equation (4.7):
𝐹𝑖𝑝
= (𝑓𝑖1𝑝
, 𝑓𝑖2𝑝
, 𝑓𝑖3𝑝
) =1
𝑟× ∑ 𝐹𝑖𝑗
𝑝𝑟𝑗=1 (4.7)
where:
𝐹𝑖𝑝
represents the TFN of the frequency of occurrence of NVA activity 𝑖 within
project phase 𝑝;
𝑟 represents the number of the users who participated in evaluating the NVAI in a
building project; and
𝐹𝑖𝑗𝑝
represents the TFN of the frequency of occurrence of NVA activity 𝑖 within
project phase 𝑝 evaluated by user j; and 𝑓𝑖1𝑝
, 𝑓𝑖2𝑝
, and 𝑓𝑖3𝑝
represent the lower bound,
strongest grade membership, and upper bound of 𝐹𝑖𝑝
, respectively.
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Next, the frequency of occurrence of each project phase where the critical NVA
activities within this phase frequently occur in this building project can be calculated
using equation (4.8):
𝐹𝑝 = (𝑓1𝑝
, 𝑓2𝑝
, 𝑓3𝑝
) = ∑ (𝑊𝑖 × 𝐹𝑖𝑝
)𝑘𝑖=1 (4.8)
where:
𝐹𝑝 represents the TFN of the frequency of occurrence of project phase 𝑝;
𝑘 represents the number of critical NVA activities within project phase 𝑝;
𝑊𝑖 represents the weighting of NVA activity 𝑖, and ∑ 𝑊𝑖 = 1𝑘𝑖=1 ; and
𝑓1𝑃, 𝑓2
𝑃, and 𝑓3𝑃 represent the lower bound, strongest grade membership, and upper
bound of 𝐹𝑝, respectively.
Therefore, the NVAI (a fuzzy set 𝑁𝑉𝐴𝐼) of this building project can be calculated
using the following equations (4.9 and 4.10):
𝑁𝑉𝐴𝐼 = (𝑛𝑣𝑎𝑖1, 𝑛𝑣𝑎𝑖2, 𝑛𝑣𝑎𝑖3) = ∑ (𝑊𝑝 × 𝐹𝑝) =𝑞𝑝=1 ∑ {𝑊𝑝 × ∑ (𝑊𝑖 × 𝐹𝑖
𝑝)𝑘
𝑖=1 }𝑞𝑝=1 (4.9)
𝑛𝑣𝑎𝑖𝑡 = ∑ (𝑊𝑝 × 𝑓𝑡𝑝
), (𝑡 = 1, 2, 3)𝑞𝑝=1 (4.10)
where:
𝑛𝑣𝑎𝑖1, 𝑛𝑣𝑎𝑖2, and 𝑛𝑣𝑎𝑖3 represent the lower bound, strongest grade membership, and
upper bound of 𝑁𝑉𝐴𝐼, respectively;
𝑊𝑝 represents the weighting of phase 𝑝; and
𝑓𝑡𝑝 can be calculated using equation 4.8.
The operation of defuzzifying the fuzzy numbers is an important procedure for the
evaluation in a fuzzy environment. To transform the TFNs of the 𝑁𝑉𝐴𝐼 into a non-
fuzzy crisp number, a single value that adequately represents the TFNs and indicates
a final rating in the interval [0, 1] would be produced. Four methods for the
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defuzzification process, namely centroid method (or center of area), mean of maximal,
𝛼-cut method, and signed distance method, were commonly used (Yager, 1980; Klir
and Yuan, 1995; Chou and Chang, 2008).
The centroid method (see Figure 4.6) was used in this study because of the following
reasons: (1) it is relatively simple and most widely used (Chou and Chang, 2008; Lam
et al., 2010; Nieto-Morote and Ruz-Vila, 2011; Zhao et al., 2013; Işik and Aladağ,
2017); (2) the defuzzified value moves smoothly around output fuzzy region; and (3)
it could reflect the actual situation and the perceptions of the local BIM experts.
U
1A(u)
0a bu*
Figure 4.6 Central method of defuzzification
As shown in Figure 4.6, this method was intended to figure out the point (𝑢∗) which
represents the center of gravity of the fuzzy set using equation (4.11):
𝑢∗ = ∫ 𝐴(𝑢)𝑢𝑑𝑢𝑏
𝑎(∫ 𝐴(𝑢) 𝑑𝑢
𝑏
𝑎)⁄ (4.11)
Since this study adopted the triangular fuzzy set, the point indicating the center of
gravity (the crisp number of NVAI score) could be calculated using the following
equation:
NVAI score = 1/3 × (𝑛𝑣𝑎𝑖1 + 𝑛𝑣𝑎𝑖2 + 𝑛𝑣𝑎𝑖3) (4.12)
131
As indicated in Figure 4.7, the crisp number would be in the interval [0, 1] and fall
into two adjacent linguistic terms. Thus, the NVAI score could be interpreted as the
linguistic term with a higher membership value.
U
1
A(u)
0 0.25 0.5 0.75 1
Rarely Sometimes Often AlwaysNever
Figure 4.7 Translation of NVAI score into linguistic terms (frequency of occurrence)
Furthermore, it should be noted that the BIMIR status of the building project was
negatively associated with the NVAI score. The abovementioned translation of the
NVAI score into the frequency of occurrence of the critical NVA activities was
generic. However, because the number (5) of the frequency of occurrence of the
critical NVA activities was not equal to the number (4) of the previously-defined
BIMIR statuses (see Table 4.4), the translation of the NVAI score into the BIMIR
status needed to be adjusted.
Table 4.4 Generic translation of NVAI score to BIMIR status
Linguistic term NVAI score BIMIR status
Always 0.875≤ Index score S1 (no BIM implementation)
Often 0.625≤ Index score <0.875 S2 (lonely BIM implementation)
Sometimes 0.375≤ Index score <0.625 S3 (collaborative BIM implementation)
Rarely 0.125≤ Index score <0.375 S4 (full BIM implementation)
Never Index score <0.125 S4 (full BIM implementation)
Because the basic purpose of the FSE approach is to re-assign the responses of
discrete numbers (such as 1, 2, 3, 4, and 5) to a new continuous interval [0, 1],
adjustment to the translation based on this interval is justified and would not violate
the logic of the fuzzy set theory.
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Literally both linguistic terms “often” and “always” could signify BIMIR S1 (no BIM
implementation) because the critical NVA activities occurred very frequently. Among
the interval [0.625, 0.875] of the linguistic term “often”, the crisp numbers ranging
from 0.750 to 0.875 were the half whose membership values were higher than the
other half. As indicated in Table 4.3, such a range fell into two adjacent fuzzy regions
“often” and somewhat “always” before the defuzzification process, and were
translated to BIMIR S2 (lonely BIM implementation). However, literally speaking,
this range signified that the critical NVA activities occurred very frequently, and in
other words, almost all the critical NVA activities occurred frequently. Thus, it may
bias the reality that the NVAI scores in this range were classified into BIMIR S2
(lonely BIM implementation). Instead, it should be considered more logical and
rational to translate the NVAI scores in the range of 0.750–0.875 to a lower BIMIR
status (S1, no BIM implementation). Likewise, among the interval [0.125, 0.375]
indicating the linguistic term “rarely”, the crisp numbers between 0.250 and 0.375,
before the defuzzification process, belonged to two neighboring fuzzy regions “rarely”
and somewhat “sometimes”, and were translated to BIMIR S4 (full BIM
implementation). Literally speaking, however, this range indicated that the critical
NVA activities did not occur very frequently. In other words, there were a small
number of critical NVA activities that occurred quite frequently. Thus, it would
possibly reflect the real situation if the NVAI scores in the range of 0.250 to 0.375
would be translated to a lower BIMIR status, namely BIMIR S3 (collaborative BIM
implementation), rather than BIMIR S4 (full BIM implementation).
Therefore, based on the above semantic analysis, the translation of the crisp numbers
of NVAI scores into BIMIR statuses should be slightly adjusted to solve the problem
of unequal numbers between the BIMIR statuses and the linguistic terms. As shown
in Table 4.5, the NVAI score ranges were classified into four divisions. In particular,
compared with the NVAI scores of the original dividing lines (see Figure 4.7), the
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NVAI scores of the new dividing lines increased by 0.125 (one eighth of the whole
interval). For instance, together with the range of 0.875–1.000, the range of 0.750–
0.875 was grouped into BIMIR S1 (no BIM implementation); the range 0.500–0.625
was adjusted from BIMIR S3 (collaborative BIM implementation) to BIMIR S2
(lonely BIM implementation); the range 0.250–0.375 was moved from BIMIR S4
(full BIM implementation) to BIMIR S3 (collaborative BIM implementation).
Table 4.5 Adjusted translation of NVAI score to BIMIR status
BIMIR status NVAI score
S1 (no BIM implementation) 0.75≤ Index score
S2 (lonely BIM implementation) 0.50≤ Index score <0.75
S3 (collaborative BIM implementation) 0.25≤ Index score <0.50
S4 (full BIM implementation) Index score <0.25
Overall, such adjustments were considered logical and reasonable and would not
violate the rationale and basis of the fuzzy set theory because: (1) the crisp numbers
were not translated to the original rating scale (frequency of occurrence), but
translated to BIMIR statuses; and (2) this study aimed to study BIMIR statuses in the
Singapore construction industry. Instead, the adjustments would make the evaluation
results more accurate and closer to the reality.
Therefore, the proposed fuzzy BIMIR model could provide a tool for project
leadership teams to evaluate and understand the extent to which their project teams
are capable and ready to implement BIM towards higher levels of BIM
implementation. The evaluation results can be either a crisp number or a linguistic
term to indicate the NVAI score and the BIMIR status.
4.5 Summary
The chapter reviewed the literature on the NVA activities in the current project
lifecycle process in Singapore. A total of 13 resulting wastes and 53 potential causes
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contributed by the key stakeholders were identified from the literature review. Four
BIMIR statuses were defined in this study, with support from previous studies, and
the definition was consistent with the local government’s BIM policies. Using the
critical NVA activities as evaluation criteria, a fuzzy BIMIR evaluation model for
building projects was developed. In this model, the BIMIR status of a building project
could be assessed by the NVAI, which could be measured by the frequency of
occurrence of the critical NVA activities in the project lifecycle. The BIMIR was
assumed to be negatively related to the frequency of occurrence of such activities.
135
Chapter 5: Review of Factors Affecting BIM Implementation
and Proposal of an Organizational Change Framework
5.1 Introduction
This chapter identifies, from the literature review, the factors that hinder and drive the
transformation towards full BIM implementation in the Singapore construction
industry. As no theory on the process transformation using BIM is found in the
literature, this chapter reviews the literature on the theories of intra- and inter-
organizational change, including Leavitt’s diamond model and MIT90s framework as
well as their derivatives. These theories are then adapted to propose an organizational
change framework for building projects using BIM, which sets the foundation for
interpreting the factors affecting change towards full BIM implementation. Since the
main blocks of the proposed framework (people, process, technology, and external
environment) consist of a number of attributes that can guide the changes, this
framework can also serve as a theoretical model for process transformation.
5.2 Factors Affecting BIM Implementation
5.2.1 Hindrances to full BIM implementation
Previous studies have reported that many factors hindered BIM implementation in the
construction industry. Through the literature review analyzing 31 previous global
studies on BIM implementation, this study has identified 47 hindrances which would
increase project teams’ difficulty in implementing BIM collaboratively, as shown in
Table 5.1 (where “H” represents “hindrance”). However, these previous studies failed
to identify and understand the 47 hindrances comprehensively. It would be difficult
for project teams to implement BIM without considering all these hindrances. More
importantly, there was little information about how the hindrances may influence BIM
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Table 5.1 Hindrances to BIM implementation
Code Hindrances to full BIM implementation References
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
H01 Executives failing to recognize the value of BIM-based processes
and needing training
√ √ √ √ √ √
H02 Concerns over or uninterested in sharing liabilities and financial
rewards
√ √ √ √ √ √
H03 Construction lawyers and insurers lacking understanding of
roles/responsibilities in new process
√ √ √ √
H04 Lack of skilled employees and need for training them on BIM and
OSM
√ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √
H05 Industry’s conservativeness, fear of the unknown, and resistance
to change comfortable routines
√ √ √ √ √ √ √ √ √ √
H06 Employees still being reluctant to use new technology after being
pushed to training programs
√ √ √
H07 Unfamiliarity to use BIM and entrenchment in 2D drafting √ √ √ √ √ √ √ √ √ √ √
H08 Financial benefits cannot outweigh implementation and
maintenance costs
√ √ √ √ √
H09 Lack of sufficient evidence to warrant BIM use √ √ √ √ √
H10 Liability of BIM such as the liability for common data for
subcontractors
√ √ √ √
H11 Resistance to changes in corporate culture and structure √ √ √ √ √ √ √ √
H12 Need for all key stakeholders to be on board to exchange
information
√ √ √ √ √ √ √ √ √ √ √
H13 Lack of trust/transparency/communication/partnership and
collaboration skills
√ √ √ √ √ √ √ √ √ √ √ √ √
H14 BIM operators lacking field knowledge √ √ √ √
H15 Field staff dislike BIM coordination meetings looking at a screen √ √ √ √
H16 Lack of consultants’ feedbacks on subcontractors’ model
coordination
√
H17 Few benefits from BIM go to designers while most to contractors
and owners
√
H18 Lack of legal support from authorities √ √ √ √
H19 Lack of owner request or initiative to adopt BIM √ √ √ √
H20 Decision-making depending on relationships between project
stakeholders
√ √
H21 Owners set minimal risk and minimum first cost as crucial
selection criteria
√ √ √ √
137
H22 Poor knowledge of using OSM and assessing its benefits √
H23 Requiring higher onsite skills to deal with low tolerance OSM
interfaces
√
H24 OSM relies on suppliers to train contractors to install correctly √
H25 Owners’ desire for particular structures or finishes when
considering OSM
√
H26 Market protection from traditional suppliers/manufacturers and
limited OSM expertise
√
H27 Contractual relationships among stakeholders and need for new
frameworks
√ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √
H28 Traditional contracts protect individualism rather than best-for-
project thinking
√ √ √ √
H29 Lack of effective data interoperability between project
stakeholders
√ √ √ √ √ √ √ √ √ √ √
H30 Owners cannot receive low-price bids if requiring 3D models √
H31 Firms’ unwillingness to invest in training due to initial cost and
productivity loss
√ √ √ √
H32 Assignment of responsibility/risk to constant updating for broadly
accessible BIM information
√ √ √
H33 Lack of standard contracts to deal with responsibility/risk
assignment and BIM ownership
√ √ √ √ √ √ √
H34 BIM model issues (e.g., ownership and management) √ √ √ √
H35 Poor understanding of OSM process and its associated costs √
H36 OSM requires design to be fixed early using BIM √ √
H37 Seeing design fees of OSM as more expensive than traditional
process
√
H38 Difficulty in logistics and stock management of OSM √ √
H39 Unclear legislations and qualifications for precasters and
inadequate codes for OSM varieties
√
H40 Interpretations resulted from unclear contract documents √
H41 Using monetary incentive for team collaboration results in
blaming rather than resolving issues
√
H42 Costly investment in BIM hardware and software solutions √ √ √ √ √ √ √ √ √
H43 Interoperability issues such as software selection and insufficient
standards
√ √ √ √ √
H44 Need for increasingly specialized software for specialized
functions
√ √ √ √
H45 Difficulty in multi-discipline and construction-level integration √ √
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H46 Technical needs for multiuser model access in multi-discipline
integration
√ √ √
H47 Firms cannot make most use of IFC and use proprietary formats √ √ √
Total number of hindrances studied 3 6 8 5 3 2 11 3 9 14 7 18 5 3 9 1 4 6 4 15 15 13 10 6 2 4 5 3 4 7 13
Note: (1) AIA and AIACC (2007); (2) AIA and AIACC (2009); (3) Aranda-Mena et al. (2009); (4) Arayici et al. (2011); (5) Autodesk (2008); (6) Autodesk
(2012); (7) Azhar et al. (2014); (8) Bernstein and Pittman (2004); (9) Bernstein et al. (2012); (10) Blismas and Wakefield (2009); (11) Chelson (2010); (12)
Eastman et al. (2011); (13) Fischer et al. (2014); (14) Fischer (2008); (15) Forsythe et al. (2015); (16) Fox and Hietanen (2007); (17) Gao and Fischer
(2006); (18) Ghaffarianhoseini et al. (2017); (19) Gibb and Isack (2003); (20) Juan et al. (2017); (21) Kent and Becerik-Gerber (2010); (22) Khosrowshahi
and Arayici (2012); (23) Kiani et al. (2015); (24) Kunz and Fischer (2012); (25) McFarlane and Stehle (2014); (26) Miettinen and Paavola (2014); (27)
Porwal and Hewage (2013); (28) Ross et al. (2006); (29) Sattineni and Mead (2013); (30) Turk (2016); (31) Zahrizan et al. (2013). √ indicates the inclusion
of the specific hindrance in the reference.
139
implementation in the Singapore context. For example, Eastman et al. (2011)
investigated many hindrances but did not study governments’ active participation in
the design stage to specify BIM use. Khosrowshahi and Arayici (2012) identified the
hindrances to BIM implementation at high maturity levels for the contractors in the
UK, but failed to investigate the factors for other roles in the construction value
chain. Autodesk (2012) revealed that executives’ failure to recognize the value of
BIM would reduce employees’ willingness and enthusiasm to work with BIM, while
Zahrizan et al. (2013) reported that the employees may entrench themselves into the
traditional way of working even after being pushed by the management to attend
training programs. Sattineni and Mead (2013) found that commonly-used contractual
structures would inevitably lead to duplicate efforts made by different parties to
create digital information models, but rarely examined the influence of cultural
factors and individuals’ competencies on BIM implementation. Azhar et al. (2014)
explored legal, cultural, and technological issues of BIM implementation, but failed
to identify the hindrances related to the new work processes. Zahrizan et al. (2013)
and Kiani et al. (2015) identified unsupportive culture such as the unwillingness to
change that hindered the BIM implementation in Malaysia and Iran, respectively, but
did not study the governments’ active roles in the design process to specify BIM uses.
Juan et al. (2017) studied the factors hindering the Taiwan construction industry to be
ready to adopt BIM, but was limited to the architectural firms.
Confront with these hindrances, the projects teams tend to find it difficult to
implement BIM openly and collaboratively. The percentage of building projects
implementing BIM with a relatively high collaboration level was not high. For
example, Lam (2014) reported that almost all consultancy firms in Singapore had
implemented BIM, but 80% of such BIM implementation was firm-based, rather than
based on project-wide collaboration. The duplicate efforts for the designers and the
contractors to create building information models respectively are not uncommon
140
bothin Singapore (Lam, 2014) and overseas (Sattineni and Mead, 2013). Actually,
people seek change, but do not want to be changed (Senge, 1990). Hence, it is critical
to get major stakeholders to understand the potential value and benefits of full BIM
implementation (Arayici et al., 2011; Khosrowshahi and Arayici, 2012). Once the
major stakeholders change to implement their part of BIM, an integrated process of
delivering the project using shared BIM models can be realized. This study intends to
fill the gap by investigating the hindrances with significant influence on BIM
implementation in building projects in Singapore, and reveal the theoretical rationale
behind these hindrances, extending the relevant literature.
5.2.2 Drivers for full BIM implementation
In addition to the hindrances, BIM implementation has been motivated by driving
factors. In this study, a total of 32 factors driving for full BIM implementation have
been identified from 35 previous studies related to BIM implementation in various
countries, as listed in Table 5.2 (where “D” represents “driver”) which also shows the
number of the drivers studied by each reference. These previous studies usually
investigated only a few specific drivers that enhanced BIM implementation in
particular countries rather than studying all these drivers comprehensively. For
example, among the references that involved 10 or more drivers, Gao and Fischer
(2006) and Kunz and Fischer (2012) focused on driving the contractors and designers
to work collaboratively on design models so that construction issues can be identified
and solved virtually before actual construction commences, but the involvement of
owners and facility managers was limited in this process. Eastman et al. (2011)
studied the majority of the identified factors but did not identify the key role of
government agencies in terms of their financial support such as subsidizing BIM
implementation cost (infrastructure purchase and upgrading, training, and
consultancy costs). Khosrowshahi and Arayici (2012) proposed a roadmap for BIM
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Table 5.2. Drivers for full BIM implementation
Code Drivers for full BIM implementation References
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
D01 BIM vision and leadership from the management √ √ √ √ √ √ √ √ √
D02 Changes in organizational structure and culture √ √ √ √ √ √ √ √
D03 Stakeholders seeing the value of adopting their own part of
BIM
√ √ √ √ √ √ √ √
D04 Training on new skillsets and new ways of working √ √ √ √ √ √ √ √ √ √ √ √ √ √ √
D05 Owner’s requirement and leadership to adopt BIM √ √ √ √ √ √ √
D06 Regulatory agencies’ early participation to BIM use √ √ √ √
D07 Gaining competitive advantages from full BIM use √ √ √
D08 All disciplines sharing models in a “Big Room” √ √ √ √ √ √ √ √ √ √ √ √ √
D09 Government support such as subsidizing training, software,
and consultancy costs
√ √ √ √ √ √
D10 Enabling subcontractors to use lower-skilled labor on site √ √ √
D11 OSM lowering safety risks by controlling work in factory √ √ √ √ √
D12 Alignment of the interests of all stakeholders √ √
D13 Governance of BIM-related policies and standards √ √ √ √ √
D14 Data sharing and access on BIM platforms √ √ √ √ √ √ √ √ √
D15 3D visualization enabling design communication √ √ √ √ √ √ √ √ √
D16 Four-dimensional simulation before construction √ √ √ √ √ √ √ √
D17 Design coordination between disciplines through clash
detection and resolution
√ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √
D18 Complex design analysis in sustainability, material
selection, and constructability
√ √ √ √ √ √ √ √ √
D19 Project lifecycle costing √ √ √ √
D20 Producing models and drawings for construction and
fabrication
√ √ √ √ √ √ √ √
D21 High accuracy of model-based documentation √ √ √
D22 More off-site fabrication and assembly of standard
elements
√ √ √ √ √ √ √ √ √
D23 Automatic model updating and drawing production to deal
with design changes and their implications
√ √ √
D24 Lifecycle information management improving operations
and maintenance
√ √ √ √ √ √ √
D25 Increasing use of design-build and fast-track approach √ √
D26 On-site work proceeds in parallel with off-site production √ √ √ √
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D27 OSM standardizes design and manufacturing processes,
simplifying construction and testing and commissioning
processes
√ √ √ √ √
D28 OSM enabling better quality and consistency of building
elements
√ √ √
D29 OSM reduces building wastes, especially on-site wastes √ √ √ √
D30 Integrating model management tools with enterprise
systems to share data
√
D31 Increasing complexity in buildings, project delivery, and
marketplace
√ √ √ √
D32 New technologies such as CNC machines √ √ √ √ √
Total number of drivers studied 6 4 5 7 4 3 3 8 5 8 5 12 1 5 5 11 3 4 11 1 5 10 4 10 2 11 4 12 4 5 3 2 3 11 4
Note: (1) AIACC (2014); (2) Aranda-Mena et al. (2009); (3) Arayici et al. (2011); (4) Autodesk (2012); (5) Azhar et al. (2014); (6) BCA (2013b); (7)
Bernstein and Pittman (2004); (8) Blismas and Wakefield (2009); (9) Blismas et al. (2006); (10) Cheng and Lu (2015); (11) Chua and Yeoh (2015); (12)
Eastman et al. (2011); (13) Fischer et al. (2014); (14) Fischer (2008); (15) Forsythe et al. (2015); (16) Gao and Fischer (2006); (17) Ghaffarianhoseini et al.
(2017); (18) Gibb and Isack (2003); (19) Juan et al. (2017); (20) Kent and Becerik-Gerber (2010); (21) Khanzode et al. (2007); (22) Khosrowshahi and
Arayici (2012); (23) Kiani et al. (2015); (24) Kunz and Fischer (2012); (25) Li et al. (2009); (26) McFarlane and Stehle (2014); (27) Miettinen and Paavola
(2014); (28) Oo (2014); (29) Porwal and Hewage (2013); (30) Ross et al. (2006); (31) Sattineni and Mead (2013); (32) Selvaraj et al. (2009); (33) Turk
(2016); (34) Won et al. (2013); (35) Zahrizan et al. (2013). √ indicates the inclusion of the specific driver in the reference.
143
implementation in the UK construction industry but did not drive off-site
prefabrication which facilities BIM use. Won et al. (2013) investigated the critical
factors that were commonly considered to enhance BIM implementation in a firm,
which was different from the present research studying the factors at the project level.
Without the consensus of all the major stakeholders in the project to implement BIM
collaboratively, the project-wide implementation of BIM cannot be realized based on
individual parties’ efforts. This previous study also lacked the consideration of
government aspects and new construction methods such as off-site prefabrication.
McFarlane and Stehle (2014) concentrated on incorporating the OSM process into
BIM implementation. Juan et al. (2017) studied the factors that would increase
architectural firms’ motivation and readiness to implement BIM, such as the pressure
from their competitors, which was limited in the architectural firms in Taiwan. Oo
(2014) identified the critical cultural and individual factors for the architectural firms
in Singapore to shift from the traditional work practices towards BIM work processes,
but the implementation was also firm-based; meanwhile, this said study failed to
identify the factors driving the new construction methods and motivating the
collaborative relationships among the primary participants. Other previous studies
investigated even fewer drivers. Therefore, none of the previous studies had provided
a comprehensive understanding of the 32 drivers.
Because of these drivers, the percentage of overall BIM adoption in the Singapore
construction industry has been growing (Lam, 2014; BCA, 2016). Perhaps the most
important economic driver for BIM systems and their adoption will be the intrinsic
value that their quality of information will provide to building owners. Improved
information quality, building products, visualization tools, cost estimates, and
analyses lead to better decision-making during the design stage and fewer wastes in
the downstream phases, reducing both first costs for construction and lifecycle
costs. Together with the value of building models for operations and maintenance, a
144
snowball effect is likely, where clients demand the use of BIM on their projects
(Eastman et al., 2011). Overall, a holistic view of all the identified drivers should be
established for the project teams to implement BIM openly and collaboratively. This
study would extend the relevant literature by identifying the drivers with significant
influence on changing towards full BIM implementation in building projects in
Singapore and demonstrating the theoretical rationale behind these drivers.
5.3 A Proposed Organizational Change Framework for BIM
Implementation
5.3.1 Organizational change theories
Organizational change is defined as “an empirical observation of difference in form,
quality or long term state of an organizational entity, coming out of the deliberate
introduction of new styles of thinking, acting or operating, and looking for the
adaptation to the environment or for a performance improvement” (Pardo-del-Val et
al., 2012). Michel et al. (2013) advocated that organizations operating in a changing
environment need sustainable organizational changes for their own survival,
development, and success. Among existing organizational change theories, Leavitt’s
diamond theory was selected in this study because it assesses organizations’ current
level of functioning and activities for designing better strategies of implementing new
technologies (Dahlberg et al., 2016), which is consistent with the Singapore
government’s encouragement to use BIM in the local industry. BIM has been
emerging as a new technology or technological process which has been proven to be
beneficial to building projects (Eastman et al., 2011; Singh et al., 2011).
Nevertheless, many firms may not know how to implement BIM and deal with the
changes brought to their organizations (Zahrizan et al., 2013).
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5.3.1.1 Leavitt’s diamond model and its derivatives
Leavitt’ diamond model, also known as Leavitt’s system model, was developed as a
mechanism for analyzing the organization-wide effects when changes take place
(Leavitt, 1965). This model regards an organization as an interdependent system, and
identifies four interrelated major components, namely task, people, technology, and
structure (see Figure 5.1). The definitions of the four components are generic. Firstly,
“task” refers to what the organization tries to achieve and how things are being done
to get close to goals, which should be considered in a qualitative way. Secondly,
“people” refer to the staff of the organization who carry out the “task”. The
organization should not only look into the individuals’ roles and responsibilities, but
also their characteristics, such as skillsets, knowledge, and efficiency. Thirdly,
“structure” refers to the hierarchy, relationships, communication patterns, and
collaboration between different management levels, departments, and employees.
Last but not least, “technology” enables “people” to perform the “task”, such as
computers, equipment, working methods, and software applications.
People
Task
Structure Technology
Figure 5.1 Leavitt’s diamond model (Leavitt, 1965)
The “technology” applied now in the construction industry has evolved since 1965
when the diamond model was established. The younger generation is usually IT
savvy and more used to incorporate ICT into the project delivery process. While this
model served as the fundamental theory of organizational change in this study, the
digital technology would be described at the later paragraphs of this section.
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The organization can be thought of as maintaining a balance of the four areas. It is
crucial to understand the connection between the four components to successfully
implement an integrated change. One common mistake when preparing an
organization for a change initiative is to treat the initiative in isolation from the rest of
the organization. This is because it is almost impossible to implement a change
strategy without considering its impact on other processes, departments, or
individuals. Hence when a change happens in any one of the four areas it affects the
entire system. A changed technology will necessarily affect the people involved in it,
the structure in which they work, and the task they perform. Similarly, changes to
task, structure, and people will have similar knock-on effects. Thus, Leavitt’s theory
can be helpful to firms that plan to apply new technology to the workplace in a way
that lessens stress and encourages teamwork (Smith et al., 1992).
Leavitt and Bahrami (1988) further developed the diamond model to emphasize the
inter-relationships between these components: (1) people issues, including
motivation, skill, and rewards; (2) business structure, including processes,
organization, and job definition; (3) control mechanisms, including performance
indicators and management information; and (4) technology, including operational
systems and information delivery.
The model has been widely recognized and applied in previous studies in novel and
critical ways. Smith et al. (1992) used the diamond model to assess how
organizational change had been managed in the Management Information Center of
the British Institute of Management over a 10-year period. This result showed that it
was not satisfactory to manage change reactively nor to attempt to manage a flatter
structure in the same way as a multi-layered structure.
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By drawing on the notions of objective and subjective realities from the arena of
sociology of knowledge, Sarker (2000) further developed the diamond model (see
Figure 5.2) and used it as a frame for informing the implementation of code-
generators. Broad guidelines were formulated for managing the implementation of
“interpretively flexible” information technologies in four steps: (1) self-understanding
through self-reflection; (2) identifying and understanding all important stakeholder
groups; (3) identifying the stakeholders who may resist; and (4) modifying the
objective or subjective realities as appropriate.
Structure
(e.g. power,
influence)
Tasks
(e.g. important,
satisfying)
People
(e.g. qualified,
useless)
Technology
(e.g. facilitating
communication
Structure
(e.g. organization
charts)
Tasks
(e.g. typing,
programming)
People
(e.g. manager,
programmer)
Technology
(e.g. mainframe,
code generator)
C
U
L
T
U
R
E
The Domain of Subjective Reality The Domain of Objective Reality
Figure 5.2 An enhanced diamond model (Sarker, 2000)
Hoff and Scheele (2014) argued that Leavitt’s diamond model did not explain how
the four components are interrelated, other than by stating that everything affects
everything else. Wigand (2007) examined the impact of IT (e-mail) on structure,
people, and tasks, as well as the interaction of these components with other
organizational factors and external forces. An organizational interaction diamond
model was established, as shown in Figure 5.3, which was built on the studies of
Chandler (1962), Leavitt and Bahrami (1988), and Scott Morton (1991). The
interaction model illustrated how an organization redesigns itself by concentrating on
management processes, structure, strategies, people, and tasks, to meet the demands
of external forces (such as new technology and changing market). This said study
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found that IT efforts would be opportunities to create new ways of working by
redesigning the tasks, changing the roles of the individuals, and spanning
organizational boundaries.
Figure 5.3 An organizational interaction diamond model (Wigand, 2007)
Chang et al. (2009) identified 21 critical factors for mobile commerce adoption and
arranged the factors into a model which incorporated the four components in
Leavitt’s diamond model. The results revealed that the majority of the top 10 critical
success factors could be categorized as “technology” and “task” areas. Meanwhile, it
was found that the support capabilities of IT vendor, senior management support, and
capabilities of the project team were the top three factors for the mobile commerce
adoption. Similarly, Ranjbari (2013) studied 69 factors that would affect the
implementation of information management systems and further divided these factors
into 29 factors which could be linked to different aspects of Leavitt’s diamond model.
This formed the foundation for the integrated multi-perspective framework of
implementing information management systems.
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Wilfling and Baumoel (2011) argued that although models and methods for managing
change have been widely adopted, business change projects were not always as
successful as intended. One possible reason was the missing integration of the
cultural and emotional aspects of change in the models. This said study developed an
advanced enterprise architecture model by extending the existing meta model of
enterprise architecture with the core artifact “cultural and emotional specification”.
The organizational diamond model established by Leavitt and Bahrami (1988) served
as the primary construct and conceptual base. This advanced model not only
represented a strategic and organizational as well as IT view on the structures of the
information system, but also offered a cultural and emotional view by characterizing
the organizational culture and people’s behavior. Thus, the comprehensive and
advanced model provided a holistic understanding of the change projects and thereby
filled the gaps between strategy, processes, and IT.
Dahlberg et al. (2016) argued that the four basic components in Leavitt’s diamond
model articulated the basis of component interrelations, but the factors were generic
and lacked contemporary constructs. This is because new constructs such as business
models, corporate governance, and IT became established after the time Leavitt’s
diamond model was built. This previous study then modified the wording of some
factors in the theory by: (1) replacing “technology” with “technology, IT services,
and information”; (2) modifying “structure” into “strategy, business model, and
governance”; and (3) replacing “task” into “tasks and processes”, as shown in Figure
5.4. It should be noted that such modifications were regarded as updates of Leavitt’s
theory to reflect contemporary constructs, not as changes to the logic of the original
theory. Dahlberg et al. (2016) remarkably applied the modified diamond model to
investigate the determinants of chief information officers (CIOs)’ roles and tasks in
an organizational context. It was found that the factors of the modified model could
be used to understand, describe, and/or classify the findings of both evolutionary and
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CIO role studies. For example, Weill and Woerner (2013) proposed four roles
(embedded, IT services, external people, and enterprise process) for CIOs based on
how they allocated their time between various tasks, and found that the “strategy,
business model, and governance” component appeared as the main determinant for
the embedded CIO’s role because the biggest part of their time was allocated to the
tasks related to this component. In the meantime, the modified diamond model was
also validated through interviews with 36 CIOs within six industries covering the
time period from 1960s to present times. All the components had significant effect on
the CIOs’ roles and tasks. This provided a clear indication that the modification of
Leavitt’ diamond model was a useful description of the factors that defined CIOs’
role and tasks at any particular time in any specific organization, and showed how
those tasks changed.
Strategy, business model, governance
Tasks, processesTechnology,
IT services, information
People
Figure 5.4 A modified Leavitt’s system model (Dahlberg et al., 2016)
5.3.1.2 MIT90s framework
It was predicted that the advent of computer and management science would
significantly change the structure and processes of most corporations. Chandler
(1962) found that changes in an organization’s structure followed changes in the
firm’s strategy and that the organizational structure often had to be modified
continuously until it was effective in supporting the firm’s strategy. By incorporating
this finding, Rockart and Scott Morton (1984) constructed a conceptual model to help
people understand the impact of adopting new technologies on their organizations
(see Figure 5.5). The conceptual model of technology impact particularized Leavitt’s
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diamond model by: (1) changing its generic “task” into the broader concept of “the
organization’s strategy”. This would not violate to Leavitt’s model, because
“strategy” represents a summing of the organization’s tasks; (2) adding “corporate
culture” to expand organization structure; (3) adding a box for “management
processes”, such as plans creation, meetings, discussions, and evaluations; and (4)
adding two driving forces in the external environment which were separated from the
four boxes characterizing the organization with a permeable membrane. This
membrane allowed the five internal elements to be exposed to the external driving
forces. “Management processes” was placed in the middle because it was seen as part
of the glue that holds the organization together. As shown in Figure 5.5, the external
socioeconomic environment and the newly-developed technology are two principal
driving forces external to the organization, which put the internal elements (its
technology, strategy, processes, people, and structure) into motion. Consequently, the
change in any of the internal elements required equilibrating changes in other
elements to maintain the balance required for the organization to be effective, such as
the changed relationships and communication patterns among the individuals. Thus, it
is crucial to find the link between strategic ideas and the implementation of new
technologies.
Organization structure and the corporate culture
The organization’s strategy
Technology
Individuals and roles
Management processes
Externalsocio-economic
environment
External technological environment
Figure 5.5 A conceptual model of technology impact (Rockart and Scott Morton,
1984)
In addition, this conceptual model paved the way for the development of MIT90s
framework (see Figure 5.6). This framework was designed to encourage
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organizations to understand the dynamics of transformation when acquiring new
technologies (Scott Morton, 1991). It helped the managers of educational
organizations to understand the impact that ICT would have on institutional missions,
organizational structures, and operating practices. This framework assumed that an
institution’s effectiveness in using ICT for teaching and learning was a function of six
inter-related elements: external environment, structure, strategy, management
processes, technology, and individuals and roles. Other assumptions included: (1)
managing change was about taking heed of the interaction of the six elements and
their configurations rather than just about managing the elements themselves; (2) the
fit between internal configuration and the external environment was important; and
(3) cultural issues mediated the strategy-technology relationship. The major
difference between the aforementioned conceptual model of technology impact and
the MIT90s framework is the scope of organizational culture. The former regarded
organizational culture as a very important dimension of organizational structure
(Schein, 1982), while the latter recognized organizational culture as an integral part
of the organization, including organizational structure, management processes, and
individuals and roles.
Structure
Strategy Technology
Individuals and roles
Management processes
Externalsocio-economic
environment
External technological environment
CultureOrganizational
border
Figure 5.6 MIT90s framework (Scott Morton, 1991)
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Mistry (2008) applied the MIT90s framework to benchmark e-learning as a separate
entity to more conventional learning and teaching practices. A total of 21 e-learning
themes were identified and then fitted into the six elements of the framework. This
encouraged the institutions to understand the evolutionary and revolutionary
transformation from conventional learning and teaching practices to e-learning
practices, and evaluate the acquisition of e-learning tool-kits over a period of six
months.
Despite the fact that the MIT90s framework was developed for individual enterprise
contexts, researchers have justified its application in the cross-enterprise
environment. In particular, Verdecho et al. (2012) conceptualized the collaborative
inter-enterprise context in the renewable energy sector as an organization that pursues
common objectives. This previous study adapted the main blocks of the MIT90s
framework to propose a conceptual framework of collaboration (see Figure 5.7). This
framework classified the factors that influenced the cross-enterprise collaboration into
four groups: strategy, business processes and infrastructure, organizational structure,
and culture. It should be noted that the strategic factors were in an upper level
because strategic aspects (such as the need of being competitive) could drive the
collaboration among enterprises. The collaboration pyramid in Figure 5.7 indicated
that in order for the collaboration to be effective and sustainable in the cross-
enterprise environment, it was necessary to manage and balance the four groups of
factors. In addition, the application of this collaboration framework was validated
through a case study in a project in the renewable energy sector. Most of the primary
project participants, namely raw material suppliers, sub-assembly suppliers,
engineering enterprise, and promoter enterprise, worked within different business
sectors. The application was performed at photovoltaic solar energy business unit
dedicated to the design, construction, operation, and maintenance of photovoltaic
solar energy plants. This application substantiated the argument that the
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establishments of intra- and inter-organizational technology deployment are similar
(Croteau and Bergeron, 2009; De Haes et al., 2012).
· Process alignment· IS/ICTs interoperability· Complementary skills· Coordination between activities
· Collaboration leadership· Compatibility of management styles· Joint decision-making· Multidisciplinary teams
· Trust· Commitment· Cooperation· Information shared· Conflict management
· Joint vision· Design of the inter-enterprise
supply chain/network· Equity· Top management support
Business processes
and infrastructure
Organizational
structure
Culture
Balance
Collaboration
Strategy
Figure 5.7 Conceptual framework of collaboration adapted from MIT90s framework
(Verdecho et al., 2012)
5.3.2 A proposed organizational change framework for building projects
Leavitt’s organization model was also applied in construction management studies.
For example, Kasimu et al. (2012) adapted the model’s four interrelated factors to
outline key variables in the development of a knowledge management
implementation framework to create, capture, acquire, update, transfer, store, share,
and use knowledge in construction firms. It was found that successful implementation
of this framework would rely on the commitment, attitude, behaviors, dedication, and
personal interest of the top management or knowledge experts in the construction
firms.
Although Leavitt’s model and the MIT90s framework have been applied to study the
effects of the changes within individual organizations, little is known about the
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changes to inter-organization contexts when implementing BIM other than the
traditional CAD. The building project context in the construction industry is also
representative in a cross-enterprise environment. A building project team that
implements BIM is a collaborative inter-enterprise environment, which can be seen as
a large project organization in which the project participants (business units)
collectively work to achieve common project goals within constraints (Verdecho et
al., 2012). Thus, BIM implementation in building projects is justified as an
organizational change (Azhar et al., 2014), because individual participants may be
entrenched in the traditional drafting practices or in fragmented BIM adoption, and
need to adapt to a new project delivery process using BIM.
Lyytinen and Newman (2008) stated that Leavitt’s model’s four-factor classification
was good because it is simple, extensive, and sufficiently well defined. If needed, it
can be easily extended with other categories to obtain richer vocabulary. For
example, Kwon and Zmud (1987) augmented the model with the concept of an
environment; while other studies had included also culture (Davis et al., 1992).
This study proposed an organizational change framework for managing the
transformation towards full BIM implementation in the Singapore construction
industry (see Table 5.3). The four-component classification was structured by
adapting the main blocks of the aforementioned models and frameworks, and a total
of 11 organizational change factors and 29 change attributes have been established
based on the literature review analyzing 20 previous studies. The conceptual
constructs were interpreted in the subsequent sections.
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Table 5.3 Proposed organizational change framework for building projects implementing BIM
Components Factors Attributes Code References
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
People Inter-enterprise
structure
Contractual relationship PeS1 √ √ √
Leadership PeS2 √ √ √ √ √
Reward arrangement PeS3 √ √ √
Involvement PeS4 √ √ √ √
Risk allocation PeS5 √ √
Conflict management PeS6 √ √ √ √
Corporate
culture
Sharing PeC1 √ √ √ √ √ √
Willingness to change PeC2 √ √ √ √
Commitment on new ways PeC3 √ √ √ √ √
Trust and transparency PeC4 √ √ √
Individuals and
roles
Mindset and attitude PeI1 √ √ √ √ √ √
Knowledge, skills and experience PeI2 √ √ √ √ √ √
Training and education PeI3 √ √ √ √
Process Management
processes
Communication PrM1 √ √ √ √ √ √ √ √
Controlling and decision-making PrM2 √ √ √ √ √ √
Corporate
strategy
Goals and requirements setting PrS1 √ √ √
Vision and mission PrS2 √ √ √ √ √ √
Top management support PrS3 √ √ √
Processes alignment PrS4 √ √ √
Task Coordination and simulation PrT1 √ √ √ √
Documentation PrT2 √ √ √
Production PrT3 √ √ √
Model management PrT4 √ √ √
Technology Infrastructure Hardware and software solutions TI √ √ √ √ √ √ √
Data exchange Interoperability TD √ √ √ √ √ √
Construction
method
Prefabrication TC √ √
External
environment
Socioeconomic
environment
Policy ES1 √ √ √ √
Changing market ES2 √ √
Technological
environment
New technological solutions ET √ √ √ √
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Note: (1) Alshaher (2013); (2) Bikson and Eveland (1990); (3) Bobbitt and Behling (1981); (4) Croteau and Bergeron (2009); (5) Dahlberg et
al. (2016); (6) Dahlberg (2016); (7) Higgins (2005); (8) Juan et al. (2017); (9) Kasimu et al. (2012); (10) Lyytinen and Newman (2008); (11)
Mitchell (2013); (12) Oraee et al. (2017); (13) Price and Chahal (2006); (14) Rockart and Scott Morton (1984); (15) Sarker (2000); (16)
Smith et al. (1992); (17) Teo and Heng (2007); (18) Verdecho et al. (2012); (19) Wigand (2007); (20) Wilfling and Baumoel (2011). √
indicates the inclusion of the specific change attribute in the corresponding reference.
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5.3.2.1 People
In this component, a total of 13 organizational change attributes pertaining to three
people-related factors (inter-enterprise structure, corporate culture, and individuals
and roles) were identified. A project team that implements BIM is a relational system
of BIM-based network (Oraee et al., 2017). In particular, “inter-enterprise structure”
refers to the contractual structure, business model, and governance (Rockart and Scott
Morton, 1984; Scott Morton, 1991; Verdecho et al., 2012; Dahlberg et al., 2016) of
the collaborative project organization, which defines the authorities, roles,
responsibilities, tasks, and business interests of the team members (Verdecho et al.,
2012). The inter-enterprise network relies on the collaboration between the team
members. Accordingly, five attributes, namely contractual relationship (PeS1),
reward arrangement (PeS3), stakeholder involvement (PeS4), risk allocation (PeS5),
and conflict management (PeS6) should be included in this factor (see Table 5.3). The
project team is a hierarchical structure, in which the leadership (PeS2) team and
personnel should be in place to make final decisions when problems occur. Besides,
“corporate culture” (Rockart and Scott Morton, 1984; Scott Morton, 1991; Verdecho
et al., 2012) describes the values and beliefs that provide rules of behavior (Smircich,
1983) of the individual project participants. Verdecho et al. (2012) found that
information sharing (PeC1), executive commitment (PeC3), and trust (PeC4) in other
stakeholders could facilitate the inter-enterprise collaboration in the project team, and
therefore these attributes should be comprised in the cultural factor. Willingness to
change (PeC2) should also be included in this factor as it directly influences the
behaviors of the stakeholders (Teo and Heng, 2007), such as to implement BIM alone
or share models with others. In addition, “individuals and roles” (Rockart and Scott
Morton, 1984; Scott Morton, 1991; Wigand, 2007) characterizes the thinking,
attitudes, and competencies of the employees. Thus, the mindset and personal
attitudes (PeI1) as well as the knowledge and skills (PeI2) of the individuals should
159
be coved in this factor. Since the individuals tend to lack sufficient knowledge of the
new way of working, it is reasonable to also take account in training and education
(PeI3) to provide technology support (Juan et al., 2017).
5.3.2.2 Process
In this component, a total of 10 organizational change attributes were established,
which were associated with three organizational change factors, namely management
processes, corporate strategy, and tasks. Process refers to various work practices with
or without BIM uses to convert resources into products and services in the project
lifecycle. As part of it, the “management processes” (Rockart and Scott Morton,
1984; Scott Morton, 1991) is considered as an important factor, which describes the
communication (such as meetings and discussions) among the stakeholders, and the
collective decision-making process in the building project context. Thus,
communication (PrM1) and controlling/decision-making (PrM2) should be included
in the attributes of this factor. Meanwhile, the “task” in Leavitt’s model was updated
with “tasks and processes” (Dahlberg et al., 2016) to reflect the contemporary
constructs, without changing the model’s logic. This categorized the “task” (Leavitt,
1965) into the process component to represent the characteristics of routine work
(BIM activities to be completed) for adequate process development, such as
coordinating models of specific disciplines (PrT1), providing feedbacks, documenting
design and construction intent (PrT2), producing drawings and building elements
(PrT3), and managing and updating models (PrT4). More importantly, the “task” in
Leavitt’s model was replaced by the organizational strategy which represents a
summing of an organization’ tasks. In this study, “corporate strategy” (Rockart and
Scott Morton, 1984; Scott Morton, 1991; Dahlberg et al., 2016) was proposed to
represent the stakeholders’ strategic tasks in the higher level. This factor provides a
common understanding of what needs to be achieved and how to achieve the goals,
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identifies each party’s role, and formalizes the commitment of the executive
management (Verdecho et al., 2012). Thus, four attributes, namely goals and
requirements setting (PrS1), vision and mission (PrS2), top management support
(PrS3), and process alignment (PrS4) should be taken into consideration in this factor.
5.3.2.3 Technology
This component consisted of three technology-related factors (infrastructure, data
exchange, and construction method) in the proposed organizational change
framework. BIM-related technologies, which have been counted among the most
critical IT innovations worldwide (Juan et al., 2017), can be seen as evolutionary and
revolutionary changes to the IT, ICT, and CAD that have been widely established and
adopted in the literature. “Infrastructure” includes powerful hardware and software
and external applications (TI). “Data exchange” provides necessary support to
execute all kinds of tasks and management processes. Thus, the “interoperability”
(TD) between disparate disciplines, models, applications, and management systems
should be ensured. In the meantime, “construction method” is a critical determinant
of productivity performance. Off-site fabrication and on-site installation (TC) has
been recognized to reduce waste and improve productivity in projects and studies
(Blismas and Wakefield, 2009), which can be facilitated by advancing technologies.
The technology component (Rockart and Scott Morton, 1984; Leavitt and Bahrami
1988; Scott Morton, 1991; Wigand, 2007; Dahlberg et al., 2016) in the proposed
organizational change framework does not change its function and logic in Leavitt’s
model and the MIT90s framework. This is because the BIM-related tools, with
emphasis on information exchange, are more advanced than ever and reflect the
contemporary situation.
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5.3.2.4 External environment
The external environment component describes the external driving forces that may
put the internal components of the project organization into motion, including
external socioeconomic and technological environments. The motion would result in
changes to the internal factors until a new balance is established. The inter-enterprise
context, therefore, needs to change or redesign itself by focusing on the
abovementioned people, process, and technology components to meet the demands of
the external forces (Wigand, 2007). In terms of “socioeconomic environment”, policy
(ES1) and changing market (ES2) are regarded as two impactful external forces in
this study. This is because changes in these two aspects would have a more important
role in a city-state (Singapore) than in a big country (Guo, 2006). The national
standards and legislations were formulated rapidly in Singapore (Cheng and Lu,
2015; Juan et al., 2017), which all the local project teams must comply with. In
addition, technologies related to BIM have been constantly improving; more powerful
hardware and a wide range of software and external applications become available to
choose from. Thus, the attribute “new technological solutions” (ET) is coved in the
“technological environment” factor. It should be noted that the external forces are
regarded more as change triggers to the internal components, rather than changes
occurred in the project organizations.
The proposed four-component classification (see Table 5.3) echoes sentiments in
previous studies conducted in Singapore (Teo and Heng, 2007; Teo, 2008) which
focused on deploying automated QTO system in terms of people, process, and
technology in the local construction industry. Therefore, it is believed that the
proposed organizational change framework expands the global body of knowledge
related to organizational change and BIM implementation, and can be appropriately
used in the Singapore construction industry.
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Based on the literature review, this research found that the 29 attributes of the
proposed organizational change framework were able to interpret the hindrances to
change and the drivers for change towards full BIM implementation identified in
Section 5.2. The detailed interpretations would be presented in Section 7.3.4.
5.3.3 Conceptual model
This section presents the derivation of the conceptual model of this study. This study
aims to develop the BBPT model that can propose managerial strategies for building
projects to move from the current project delivery approach towards higher levels of
BIM implementation, and thus improve their productivity performance. The key of
this BBPT model is the four-component (people, process, technology, and external
environment) structure, which is consistent with the proposed organizational change
framework. The model would be developed based on the assumption that BIM
implementation in the building project context can be conceptualized as a big
organization, which has been justified (Croteau and Bergeron, 2009; De Haes et al.,
2012; Verdecho et al., 2012). Since the main blocks of the proposed framework
(people, process, technology, and external environment) consist of a number of
attributes that can guide the industry to change, the proposed organizational change
framework (see Table 5.3) forms a foundation of the BBPT model and therefore can
theoretically serve as the conceptual model for process transformation in this study.
5.4 Summary
In this chapter, 47 hindrances to change and 32 drivers for change towards full BIM
implementation were identified. Based on the existing theories of organizational
change, an organizational change framework for building projects using BIM was
proposed, which consists of 29 change attributes on people, process, technology, and
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external environment aspects. Since the main blocks (people, process, technology,
and external environment) are four key areas that need to be changed in BIM
implementation and process transformation, this framework can also serve as the
theoretical model for the BBPT model.
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Chapter 6: Research Methodology
6.1 Introduction
This chapter aims to address research design, methods of data collection, and
methods of data analysis in this research. It is recommended that combining multiple
methods be used in the research field of project management, because this approach
can overcome some inherent limitations of using a single method and facilitate a
comprehensive understanding of a given project management research phenomenon
(Love et al., 2002). Besides, although combining both qualitative and quantitative
approaches may need more time, money, and energy, it has been suggested to be used
in the research design and data collection due to its greater utility (Tashakkori and
Teddlie, 1998; Abowitz and Toole, 2010).
Figure 6.1 illustrates the overall research methodology in this study. The literature on
project delivery processes with different levels of BIM implementation (see Chapter
three), potential factors affecting BIM implementation, and theories of organizational
change (see Chapter five) were reviewed. Based on the literature review, the
similarities and differences of these delivery processes were examined. By comparing
the industry practices in the current, partial BIM process with their counterparts in the
full BIM-enabled processes, plenty of NVA activities in the current process were
identified. Using these NVA activities as evaluation attributes, a fuzzy BIMIR model
was proposed (see Chapter four). With the data related to the frequency of occurrence
of these activities collected from the first survey (coded as Survey I), the BIMIR
statuses of building projects in Singapore were evaluated. The differences of resulting
wastes and leading causes in building projects with different BIMIR statuses were
examined. Follow-up interviews were conducted with the practitioners who originally
participated in Survey I. In addition, the theories of organizational change were
165
reviewed and modified to propose an organizational change framework in Chapter
five. This framework is suited for studying process transformation in building
projects that plan to implement BIM. In addition, factors driving and hindering the
change towards full BIM implementation were identified from the literature review.
The data related to the significance of these factors were collected in the second
survey (coded as Survey II). Based on the interpretation of the hindrances and drivers
with the organizational change framework, managerial strategies were tailored to help
move towards higher BIMIR statuses.
Need for research: Suboptimal productivity performanceIncreasing recognition of BIM potential
Problem statement: Partial BIM implementation in current project delivery
Possible solution: Transforming current project delivery into full BIM-enabled delivery
Literature review
Construction industry
Potential wastes
Construction value chain
Productivity-related policies
Organizational readiness
Full BIM-based delivery:§ IPD/VDC/DfMA
Survey I
An in-depth understanding of full BIM implementationCase study
BBPT model
Managerial strategies for changing from current BIMIR status towards higher BIMIR statuses
Research scope:Building projects in Singapore
Leading causes for surveyed projects of different BIMIR
A fuzzy BIMIR evaluation model
Widely-agreedNVA activities
Frequency of occurrence of NVA activities
Questionnaires
Interviews
Proposed definition of four BIMIR statuses
Survey IIQuestionnaires
Interviews
NVA index scores
Analysis of past documents
Participant observations
Critical CHCs and CDCs
Wastes reduction between projects of various BIMIR
Organizational change theories:· Leavitt’s diamond theory· MIT90s framework
A proposed organizational change framework
NVA activities in current industry practices
Potential causes
Factors driving and hindering change towards full BIM use
Existing classifications of BIM implementation and BIM maturity
Productivity measurement
Technology adoption
BIMIR statuses
Significantly important causes
Conclusions and recommendations
BIM
Figure 6.1 Research methodology
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6.2 Research Design
The research design is to develop a plan for testing the hypotheses formulated in
Section 1.7. There are four common types of research designs, namely case studies,
surveys, experiments, and regression (Tan, 2012). Case studies are used to understand
or interpret particular instances with a small number of cases; surveys are used to
infer broad population characteristics, opinions, attitudes, or reasons for certain
actions or preferences from a sample; an experiment is used to test cause-effect
relationships by manipulating variables; a regression design is used to examine the
associations between variables when the above experimental design becomes
cumbersome.
6.2.1 Survey
As a systematic method of collecting data based on a sample, the survey technique
has been widely used to collect professional views on critical factors in previous
construction management studies (Teo et al., 2007; Zhao et al., 2013; Hwang et al.,
2017). This study involves to collect professional views on the NVA activities in the
current project delivery process in building projects in Singapore and their resulting
wastes and potential causes as well as the factors hindering and driving BIM
implementation. Hence, a survey would be conducted in this research.
Since the number of questions in the survey was large, this survey was arranged into
two parts which were coded as Survey I and Survey II (see Figure 6.1). The basic
purpose was to increase response rate and avoid any confusion or unwillingness of
potential respondents. Survey I was expected to collect the key stakeholders’ views
on the level of agreement on and the frequency of occurrence of the NVA activities
after BIM implementation had been mandated by the local government since July
2015, on the frequency of occurrence of the resulting wastes and their impact on
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productivity, and on the relative importance of the potential causes to the NVA
activities according to their past or ongoing building projects. These views could be
used to verify the NVA activities identified in the literature review, to examine the
severity of the resulting wastes on productivity, and to figure out more important
causes. Survey II was intended to gain a fundamental understanding of the critical
factors that had drove or hindered BIM implementation in their projects.
The population for this study was comprised of all the organizations in the Singapore
construction industry. Initially, the sampling frame consisted of 1318 organizations
that had registered in the government agencies or institutes at the time of this
research, including the BCA, the URA, the Housing and Development Board (HDB),
the building developers registered with the Real Estate Developers’ Association of
Singapore, the architectural consultancy firms registered with the Singapore Institute
of Architects, the structural and MEP consultancy firms registered with the
Association of Consulting Engineers Singapore, the larger contractors registered with
the BCA, and the facility management firms registered with the Association of
Property and Facility Managers. Among the contractors, it is considered logical to
select only the larger ones because they tend to have adequate resources for BIM
implementation. The registered contractors were classified into two categories based
on their businesses: construction workheads and specialist workheads. The tendering
limits for the contractors with different financial grades in both workheads were
presented in Table 6.1. Previous studies (Zhao et al., 2014b, 2015) that had surveyed
the construction firms in Singapore considered A1, A2, Single grade, and L6
contractors as large firms, and others as SMEs. Meanwhile, another study (Teo et al.,
2007) classified A2–C3 contractors as SMEs recommended by SPRING Singapore.
In this study, the contractors of B2 and L5 grades and below were excluded in the
sample frame, while B1 contractors were included because their tendering limits are
large and about three times as large as those of B2 and L5 contractors. The
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contractors with multiple financial grades were calculated according to their highest
tendering limits, rather than being calculated repeatedly. In addition, the larger
construction firms of a few workheads that are not related to BIM (such as SY01b,
SY02, and SY10) were also excluded. Hence, a total of 570 registered contractors
were obtained in the survey.
Table 6.1 Tendering limits of contractors registration system (S$ million)
Time
Period
Construction Workheads
(CW01 and CW02)
Specialist Workheads
(CR, ME, MW, and SY)
A1 A2 B1 B2 C1 C2 C3 Single
grade L6 L5 L4 L3 L2 L1
1 July
2015-30
June 2016
unlimited 90 42 14 4.2 1.4 0.7 unlimited unlimited 14 7 4.2 1.4 0.7
1 July
2016-30
June 2017
unlimited 85 40 13 4 1.3 0.65 unlimited unlimited 13 6.5 4 1.3 0.65
Note: CW01=General building; CW02=Civil engineering.
Source: BCA (2017b) https://www.bca.gov.sg/ContractorsRegistry/contractors_tend
ering_limits.html.
Since the survey was arranged in two stages. The 1318 organizations were equally
and randomly divided in all the disciplines into two sub-sampling frames for the two
surveys. To logically link Survey I and Survey II, a question was added at the end of
Survey I, asking if the potential respondents would be willing to participate in the
next stage (Survey II) of this study. Thus, after the first half (659) of these
organizations was selected for Survey I, the rest 659 organizations, along with the
organizations that had been involved in Survey I and were willing to participate in
Survey II, were obtained for Survey II. This was deemed as appropriate for the
distribution of the potential respondents. Since there were sampling frames,
probability samples should be adopted. There are four common types of probability
sampling, including simple sampling, systematic sampling, stratified sampling, and
cluster sampling. Simple random sampling was used in the data collection because
each organization was as likely to be drawn as the others.
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According to Gao and Fischer (2006), BIM implementation usually follows two kinds
of strategies, namely top-down approach (from company-wide research and
development to project-based use of BIM models) and bottom-up approach (from
project-based use of BIM models to company-wide research and development). The
former should be prioritized since corporate vision and executive leadership are
crucial (Autodesk, 2012). Hence, the senior management from the sampled
organizations would be first contacted prior to the middle management. On the other
hand, BIM is ultimately driven and successfully implemented through efforts on the
“shop floor” by the individuals. They apply BIM in their day-to-day execution of
projects. Therefore, some experienced staff who were not at the management level,
such as site experts, would also be approached.
6.2.2 Case study
According to Yin (2014), a case study is an empirical inquiry that investigates a
contemporary phenomenon in depth and within its real-life context. It may lead to
new and creative insights, development of new theories, and have high validity with
practitioners (Voss et al., 2002). Yin (2014) suggested that case studies focus on
questions about “what, why, and how”. Because this study attempted to investigate
how BIM could be incorporated into the project delivery processes to transform the
current industry practices and thus assist in productivity improvement in Singapore, a
case study would be adopted.
The case study intended to provide an in-depth understanding of how a specific
building project in Singapore actually moved from the current delivery process to a
more collaborative or integrated one, and of what the result was in terms of
productivity growth. It could be best understood by working backwards. The
implementation of managerial strategies for strengthening the drivers for change and
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overcoming the hindrances to change were tracked, which resulted in changes in the
BIM implementation activities contributed by the key stakeholders in the project
lifecycle. Once the implementation activities had been changed, the potential wastes
might be reduced, achieving productivity improvement.
Voss et al. (2002) recommended that for a given set of available resources, the fewer
the case studies, the greater the opportunity for in-depth observations. In this study,
given the resource limitation, one case study was conducted in a large construction
and development firm operating in Singapore, which had been delivering many
building projects simultaneously. One of the projects of higher BIMIR status was
selected, which would be compared with one of the typical projects of relatively
lower BIMIR status in this firm. Since firms are usually participating in more than
one project at a time, it is expected that the findings in this case study could be
generalized to other firms. The investment in BIM implementation is meant to
enhance the capabilities of a firm in the process transformation. Thus, the
generalization was justified.
6.3 Methods of Data Collection
In this study, questionnaires, interviews, observations, and analysis of past documents
were used to collect both qualitative and quantitative data. No single method of data
collection is ideal and using a combination of methods has been highly recommended
(Abowitz and Toole, 2010).
6.3.1 Questionnaires and interviews
Among various methods of data collection, questionnaire has been recognized as the
most cost-effective and popular way to collect information (Gravetter and Forzano,
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2012). Questionnaires and interviews have been widely used by researchers in
previous studies related to BIM implementation (Arayici et al., 2011; Khosrowshahi
and Arayici, 2012; Wong et al., 2014). Thus, questionnaires and personal interviews
were designed to collect data in this research. Terminologies used were explained in
the questionnaires to ensure that the potential respondents were clear about the
questions.
Two preliminary questionnaires were developed based on the literature review.
Specifically, the causes identified in Section 4.2.3 may be merged in the preliminary
questionnaire if they had similar statements. The causes in the “Architect” group and
“Engineers” group were merged into the “Architect/Engineers” group, and so did
those in the “General contractor” group and “Key trade contractors” group. The
questionnaires were revised based on the comments from five BIM experts who
participated in a pilot study. Interviews were conducted with these experts in the pilot
study to solicit their comments on the readability, accuracy, and comprehensiveness
of the questionnaires. The profile of the experts could be found in Table 6.2. All the
experts, who were from large firms and had at least three years’ experience of
implementing BIM in building projects in Singapore, were selected out of
convenience to pretest the questionnaires. Three of them were project manager,
corporate BIM manager, and technical manager of large construction and
development firms with over 10 years’ experience in this field; the other two included
one quantity surveying in charge from a general construction firm and one senior
architectural associate from a large architectural consultancy firm, with more than
five years’ work experience. Based on their comments, new NVA activities, wastes,
causes, drivers for change, and hindrances to change were added to the questionnaires.
For example, “the general contractor’s BIM team does modeling but not coordination
for trade contractors” and “the general contractor requires but does not train the trade
contractors to use BIM” were added as two new causes for “general contractor”;
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while two new causes (“high cost to engage BIM experts or outsource to BIM
drafters” and “the trade contractors use CAD and cannot integrate BIM models from
the general contractor to their site models”) were added for “key trade contractors”.
In addition, revisions were made to improve the readability and accuracy of the
statements of these items. Footnotes were added to explain the terminologies used.
Table 6.2 Summary of the interviews in the pilot study
Method BIM
expert
Work
experience
Designation Firm Duration
time
Face to
face
E1 16-20 years Project manager Construction and
development
1 hour
E2 16-20 years Technical manager Construction and
development
E3 11-15 years Corporate BIM
manager
Construction and
development
E4 5-10 years Quantity surveying
in charge
General
construction firm
1 hour
Over
telephone
E5 5-10 years Senior architectural
associate
Architectural
consultancy firm
25
minutes
The questionnaires were sent to the organizations in the respective sub-sampling
frames through emails or handed to them personally. The final questionnaire of
Survey I included five sections (see Appendix 1). The first section presented the
research objectives and the author’s contact details in a brief introductory letter. The
second section solicited the profile of the respondents and their organizations, such as
their affiliations, working experience, main businesses, and project characteristics. In
the third section, the NVA activities were listed. Because these activities were
collected from the literature, their validity of being NVA should be checked. The
respondents were requested to rate the level of agreement and the frequency of
occurrence of the NVA activities according to one of their past or ongoing building
projects, using two five-point Likert scales (1 = strongly disagree, 2 = disagree, 3 =
unsure, 4 = agree, and 5 = strongly agree; 1 = never, 2 = rarely, 3 = sometimes, 4 =
often, and 5 = always). The measurement of the level of agreement was believed to be
reliable because the Likert scale system has been deemed effective in measuring the
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attitudes of the respondents (Albaum, 1997). In addition, open-ended questions were
also presented to invite the respondents to suggest other NVA activities that they
deemed critical and reasonable. The fourth section asked the respondents to rate the
frequency of occurrence and the impact on productivity of the resulting wastes from
the abovementioned NVA activities in the same project mentioned in the third section.
Regarding the rating scale for the impact on productivity, 1 = insignificant effect, 2 =
minor detrimental effect, 3 = moderate detrimental effect, 4 = significant detrimental
effect, and 5 = catastrophic effect. The last section requested the respondents to rate
the importance of each potential cause in the same project, using another five-point
Likert scale (1 = not important, 2 = slightly important, 3 = moderately important, 4 =
very important, and 5 = extremely important). In this section, open-ended questions
were also presented to ask for suggesting other important causes. According to the
“seven plus or minus two” principle (Miller, 1956), the scale of five was adopted,
which is convenient for respondents to judge. The problem of poor response rate
could potentially be lessened by follow-up emails. As mentioned in Section 6.2.1, at
the end of this survey, the respondents were asked whether they were willing to be
involved in the next stage of this study.
After closing the questionnaire survey and data analysis, post-survey interviews were
conducted with four experts who had originally participated in this survey and
possessed BIM implementation experience in Singapore. In the interviews, the
experts were presented with the survey results to seek their comments. To gain an in-
depth understanding, they were also invited to provide possible explanations for the
NVA activities and their causes that were agreed upon as critical ones.
In Survey II, the final questionnaire consisted of four sections (see Appendix 2). The
relevant research objectives related to this survey and the author’s contact details
were presented in the beginning, followed by the questions to profile the respondents
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and their organizations. Next, the potential respondents were requested to rate the
significance of the 47 hindrances to change and 32 drivers for change towards full
BIM implementation, using a five-point Likert scale (1 = very insignificant, 2 =
insignificant, 3 = neutral, 4 = significant, and 5 = very significant). Similar to Survey
I, new respondents should make judgments based on the status quo in one of their
past or ongoing building projects. Those who participated in both surveys should
provide their answers with reference to the same projects they used in Survey I.
Lastly, new drivers or hindrances, which they deemed rational and significant, were
allowed to be added to complement the factors identified from the literature review.
Besides, five experts who were originally included in the data sample were
interviewed for their comments on relevant analysis results of Survey II. They were
also asked to provide possible explanations for the results.
In addition, having conducted the two surveys, face to face interviews were
performed with the management staff and a BIM coordinator from a large
construction and development firm based in Singapore in the case study. These
interviewees were working in a building project (Project A) and also had experience
in delivering another project (Project B) of a lower BIMIR status in the same firm.
During the interviews, after collecting the basic information related to the
interviewees, the projects, and the firm, open-ended questions were raised for the
interviewees. This allowed them to express what and how they changed their BIM
implementation activities in Project A, compared with those in Project B, other than
being constrained by a fixed set of possible responses.
Finally, after the BBPT model was developed, a total of six professionals from six
different building projects in Singapore were interviewed to validate the model. It
should be noted that these experts were not involved in the data collection of the two
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surveys mentioned in Section 6.2.1. The reason of using an even number of the
experts rather than an odd one was that this study would use statistics to test the
validity of the BIMIR evaluation model, instead of using a median response to ensure
reproducibility. During the validation process, these professionals were first requested
to estimate the NVAI scores and the BIMIR statuses of their building projects based
on their experience and judgments, and then rate the frequency of occurrence (or
implementation level) of the critical NVA activities according to the actual
circumstances of the projects (see Appendix 3). To improve the accuracy of the
estimation, the NVAI scores and the frequency of occurrence were assigned in the
form of percentage. Thus, there were at least two decimal places in the fractional part
of the scores. Then, to test the validity of the proposed fuzzy BIMIR evaluation
model in the BBPT model, the NVAI scores and BIMIR statuses estimated by the
experts were compared with those calculated by the BBPT model. In addition, the
experts were invited to comment on the BBPT model in terms of user-friendliness of
the model and the usefulness of the managerial strategies to help their leadership
teams make decisions to move towards higher BIMIR statuses.
6.3.2 Observations
In the case study, passive participant observations were conducted. The author sat in
weekly project meetings in the construction site office. Information related to the
behavioral patterns (such as body language, verbal expressions, meeting rules, data
exchange procedures, and communication patterns with others) of the key
stakeholders of Project A were recorded. The author also visited the construction site
at times to gain a knowledge of background factors, such as site layout and filed
staff’s feelings.
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6.3.3 Analysis of past documents
To gain a good understanding of the project and reduce observer bias, past documents
were collected and analyzed. The documents in Project A included their internal
documents (such as minutes of meetings of the weekly project meetings, construction
schedule, construction drawings produced from their building information models,
RFI documents and responses, and productivity figures), academic literature
regarding BIM adoption in the case firm projects, and media coverage. The internal
documents were collected by interpersonal networking, while the literature and media
coverage were available on the Internet. Analysis of past documents helped in the
conduct of the case study, which intended to understand how the case firm moved
from Project B towards the more productive Project A in terms of BIM
implementation.
6.4 Methods of Data Analysis
To begin with, Cronbach’s alpha coefficient was calculated to test the reliability and
internal consistency of the data collected from the surveys. The alpha coefficient
ranges from 0 to 1 and should exceed 0.7 for a scale to be reliable (Nunnally, 1978).
The threshold may decrease to 0.6 in exploratory research (Robinson et al., 1991)
Many studies have advocated that Likert scale data could be analyzed using
parametric statistical methods, such as t-test (Binder, 1984; Hwang et al., 2014).
Allan (1976) stated that the power and flexibility derived from parametric methods
can outweigh possible small biases. Allen and Seaman (2007) found that conclusions
and interpretations drawn from the parametric methods could be easier and more
informative. Thus, t-test was used to analyze the data in this study. Specifically, in
terms of the data collected in Survey I, one-sample t-test, which can test the null
hypothesis that the population mean (usually 3 in a five-point Likert scale) is equal to
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a specified value, was used to test whether all the NVA activities were significantly
agreed as critical NVA activities by the respondents and whether their potential
causes were significantly important.
In addition, the ranking technique was used. The NVA activities were ranked overall
and internally in each project phase according to the mean scores of the level of
agreement. These scores were also used to calculate the weights of the evaluation
criteria and sub-criteria, using equations 4.3 to 4.6. Likewise, the potential causes to
the NVA activities were also ranked overall and internally under each project role
according to their importance mean scores.
The surveyed building projects were classified into four groups by their BIMIR
statuses which were assessed based on the frequency of occurrence data, using
equations 4.7 to 4.12 and Table 4.5. To measure the mean score difference of the
resulting wastes and important causes between the four groups of building projects,
one-way analysis of variance (ANOVA) were performed. Moreover, to measure the
degree of agreement associated with the severity ranking of the wastes and the
importance ranking of the causes between the four groups of projects, Spearman’s
rank correlation coefficients were calculated and statistically tested. The Spearman’s
rank correlation computes the correlation between the rankings among multiple
groups, and has been widely used in project management research (Arain, 2005;
Hwang et al., 2009). A significance level of 0.05 (two-tailed) was used for this
analysis. The multi-group comparisons results were expected to reveal positive
changes as BIMIR status increased.
Likewise, as for the data related to the drivers for and hindrances to change towards
full BIM implementation that were collected in Survey II, the one-sample t-test was
also adopted to check whether these factors had statistically significant influence on
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BIM implementation in the Singapore constriction industry, and these factors were
ranked. Besides, independent-samples t-test was used to check whether there were
differences in the significance mean scores of the critical hindrances and drivers
between different groups of surveyed organizations. Regarding the responses of those
who completed both surveys, the one-way ANOVA and the Spearman’s rank
correlation were also applied to check the changes between the four groups of
building projects. The Statistical Package for the Social Sciences (SPSS) software
was used to conduct the quantitative data analysis for this study.
Furthermore, conversation analysis and content analysis were adopted to analyze the
data collected in the case study. The former could study the weekly project meetings,
and the latter dealt with the interviews data.
6.5 Summary
This study combined multiple methods in the research design, data collection, and
data analysis. Two surveys were conducted to validate the NVA activities and their
resulting wastes and potential causes as well as the factors affecting change towards
full BIM implementation in building projects in Singapore. The first questionnaire
was expected to obtain the data related to the level of agreement and frequency of
occurrence of the NVA activities, the frequency of occurrence and impact on
productivity of the wastes, and the importance of the causes. The second
questionnaire intended to obtain the significance of the hindrances to change and
drivers for change. Post-survey interviews were conducted to better understand the
survey results. In addition, a case study was performed in a large construction and
development firm to gain an in-depth understanding of its process transformation and
technology adoption. Moreover, professionals were contacted to validate the BBPT
model. Personal interviews, passive observations, and analysis of past documents
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were also used to collect data. A variety of statistical analysis methods would be used
to analyze the data.
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Chapter 7: Data Analysis and Discussions
7.1 Introduction
This chapter presents the analysis of the data collected from the two surveys. All the
five hypotheses are tested in this chapter. Among which, Hypotheses 1, 4, and 5 were
tested in a conventional way (inferential statistics), which involved tests of
significance (p-values) at the 0.05 level. Hypotheses 2 and 3 were tested in a new
way, which involved evaluation of BIMIR status using the proposed fuzzy BIMIR
model, and observation of surveyed building projects and their wastes in different
BIMIR statuses.
Specifically, Survey I received 73 completed responses from the AEC service
providers in Singapore. The analysis results indicated that 38 of the 44 NVA
activities identified from the literature review were validated by the respondents as
critical NVA activities. The six activities that were not significantly agreed by the
industry were excluded, with support from post-survey interviews. Using the data
related to the level of agreement of 38 critical NVA activities, the weighting of the
fuzzy BIMIR model was determined. The BIMIR statuses of the 73 surveyed building
projects were evaluated using the fuzzy BIMIR model. Among which, 15, 47, and 11
projects were in BIMIR S1, S2, and S3, while no projects were in BIMIR S4. Thus, it
was observed that Hypothesis 2 that “the BIMIR statuses of building projects in
Singapore are low” was supported (see Section 7.2.3.2). Five tests that randomly
excluded 3 responses suggested that this model was stable and thus could be used to
predict the BIMIR status of any other building project in similar contexts. Besides,
the criticality of the 13 wastes was analyzed. The mean scores and rankings of the
wastes in project groups of different BIMIR statuses were checked. It was found that
as BIMIR increased, productivity was less influenced by the 13 wastes. Therefore, it
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was observed that Hypothesis 3 that “the higher the BIMIR status, the lower the
criticality of the wastes and the higher the productivity performance” was also
supported (see Section 7.2.4.2). Moreover, all the 53 causes to the NVA activities
were found to be significantly important. Similarly, the mean scores and rankings of
the causes among the projects of various BIMIR statuses were also tested.
Survey II was completed with 89 data sets collected from practitioners in the
Singapore construction industry. The analysis results suggested that 44 out of the 47
hindrances and 31 out of the 32 drivers had significant influence on the overall lonely
BIM implementation in Singapore. In addition, these significant factors were
interpreted with the proposed organizational change framework. Accordingly,
managerial strategies were formulated on people, process, technology, and external
environment aspects.
7.2 Analysis Results and Discussions of Survey I
7.2.1 Profile of respondents and their organizations
Questionnaire Survey I was expected to investigate the level of agreement on and the
frequency of occurrence of the NVA activities, the frequency of occurrence of the
resulting wastes and their impact on productivity, and the relative importance of the
potential causes to these NVA activities in building projects in Singapore. From April
to August 2016, a total of 659 questionnaires were sent out, and 73 completed
questionnaires were received. It has been considered as appropriate that these
questionnaires were obtained based on the respondents’ willingness to participate in
the study (Wilkins, 2011). A response rate of 11.08% was acceptable because it fell
within the general response rate of 10%–15% for Singapore surveys (Teo et al.,
2007). The profile of the respondents and their organizations is presented in Table
7.1.
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Table 7.1 Profile of the respondents and their organizations in Survey I
Characteristics Categorization Frequency Percentage (%)
Organization
Main business Architectural firm 11 15.1
Structural engineering firm 9 12.3
MEP engineering firm 9 12.3
General construction firm 23 31.5
Trade construction firm 1 1.4
Facility management firm 1 1.4
Others 19 26.0
BCA financial grade A1 22 30.1
B1 2 2.7
C3 2 2.7
Single grade 1 1.4
L6 6 8.2
L4 1 1.4
L2 1 1.4
L1 2 2.7
Not applicable 36 49.3
Years of BIM adoption 0 year 9 12.3
1-3 years 34 46.6
4-5 years 17 23.3
6-10 years 11 15.1
Over 10 years 2 2.7
Respondent
Discipline Government agent 4 5.5
Developer 3 4.1
Architect 15 20.5
Structural designer 11 15.1
MEP designer 8 11.0
General contractor 19 26.0
Trade contractor 5 6.8
Supplier/Manufacturer 2 2.7
Facility manager 6 8.2
Years of experience 5-10 years 31 42.5
11-15 years 14 19.2
16-20 years 5 6.8
21-25 years 4 5.5
Over 25 years 19 26.0
In terms of the responding organizations, 23 (31.5%) of the organizations were
general construction firms, and 11 (15.1%), nine (12.3%), nine (12.3%), one (1.4%),
and one (1.4%) were architectural firms, structural engineering firms, MEP
engineering firms, a trade construction firm, and a facility management firm,
respectively. Moreover, the main businesses of the 19 organizations listed in the
“others” category included the BCA, the URA, the HDB, developers, precasters, a
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facility management firm, and other consultancy firms such as multidisciplinary
consultancy firms and a BIM consultancy firm. This indicated a good balance of the
distribution of the industry players.
Based on the organizations’ financial grades, over half (50.7%) of the organizations
were registered contractors with the BCA. Among which, 22 (30.1%) were A1
contractors, followed by six (8.2%) L6 contractors, two (2.7%) B1 contractors, and
one (1.4%) single grade contractor. The inclusion of small- and medium-sized
contractors (two for C3, two for L1, one for L4, and one for L2) was because some
contractors were subsidiaries of larger contractors, or their grades were renewed by
the BCA during the five-month survey. The remaining 36 (49.3%) organizations
comprised of government agencies, developers, consultancy firms, and a facility
management firm.
As for experience of implementing BIM, 34 (46.6%) of the organizations started to
adopt BIM in their building projects in last one to three years, and 17 (23.3%) had
four to five years’ BIM implementation experience. Only two firms had implemented
BIM over 10 years. Because BIM implementation had been mandated by the local
government since July 2015, it was reasonable that over half (58.9%) had no more
than three years’ experience. The nine (12.3%) organizations that had not
implemented BIM at the time of this research were not excluded in the subsequent
data analysis. The reason was that the Singapore government had made huge efforts
in promoting BIM implementation. For instance, the aforementioned mandate was
accompanied by a new BIM fund to grow the collaboration capabilities of the
practitioners beyond just modelling within their own firms, which would defray part
of the initial costs in training, consultancy, software, and hardware (BCA, 2016).
Because of these efforts, all the respondents should have been equipped with BIM
knowledge, skills, or experience. Even though the nine organizations had not
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implemented BIM in their current projects, it did not mean that the respondents did
not have BIM knowledge. In the Singapore context, the respondents should have
insights into BIM implementation; they must had plans to implement BIM in the near
future projects as mandated. Thus, their responses were used subsequently. This
result indicated that the local construction industry had been moving from the
traditional delivery approach into the new approach of building planning, design,
construction, and operations using BIM.
With respect to the 73 respondents’ disciplines, 19 (26.0%) of them worked as
general contractors, while 15 (20.5%), 11 (15.1%), 8 (11.0%) were architects,
structural designers, and MEP designers, respectively. As for working experience, 31
(42.5%) of the respondents had worked for five to 10 years in the Singapore
construction industry, implying that they were more interested in the emerging new
way of planning, designing, building, and operating building projects. In addition, 19
(26.0%) of the respondents had over 25 years’ work experience in this field. Most of
them were at the management level.
According to the profile, it could be concluded that the respondents and their
organizations could be well representative of key BIM implementers in the local
construction value chain, thus assuring the response quality. It was expected that the
data collected from them were reliable.
7.2.2 Level of agreement of NVA activities
The reliability of the 73 responses was tested by calculating the Cronbach’s alpha
coefficient. As recommended by Nunnally (1978), the generally agreed upon lower
limit for the Cronbach’s alpha is 0.7 for a scale to be reliable and 0.6 being
questionable. The coefficient for all the 44 NVA activities was 0.934, indicating high
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data reliability. The results in Table 7.2 indicated that the Cronbach’s alpha
coefficients for the NVA activities met such requirement in all project phases, except
the conceptualization phase (P1). The coefficient of 0.632 was questionable.
Nevertheless, Robinson et al. (1991) stated that the threshold may decrease to 0.6 for
newly-developed measures in exploratory research, which was consistent with the
NVA activities scale in this study. Thus, this coefficient was considered acceptable.
Table 7.2 Level of agreement ranking and t-test results of the NVA activities
Code NVA activities Mean Overall
rank
Internal
rank
p-
value
P1: Conceptualization (α = 0.632)
# Lack of involvement by government agency 3.18 40 – 0.224
N1.1 Inadequate project objectives and performance
metrics set by owner
3.51 25 3 0.000*
# Owner resists to use BIM in the whole project 2.81 44 – 0.137
N1.2 No reward/risk sharing arrangements among
major stakeholders are set by owner
3.85 11 1 0.000*
N1.3 Lack of involvement by engineers (not
appointed)
3.41 31 4 0.003*
N1.4 Lack of involvement by general contractor (not
appointed)
3.73 17 2 0.000*
P2: Schematic design (α = 0.799)
# Lack of involvement by government agency 2.89 43 – 0.392
N2.1 Lack of joint control and agreement on project
targets and metrics by major stakeholders
3.51 25 5 0.000*
N2.2 Architect, engineers, and contractors do not
work together in design modeling
3.66 20 4 0.000*
# Architect does not share its complete model with
engineers
3.16 41 – 0.255
# Architect and engineers do not submit their
schematic design models for regulatory
approvals
3.21 39 – 0.083
N2.3 Engineers not involved early in this phase to
contribute in architectural modeling
3.49 27 6 0.001*
N2.4 Lack of involvement by general contractor and
key trade contractors to contribute site
knowledge (not appointed)
3.92 6 1 0.000*
N2.5 Lack of involvement by manufacturer/supplier
(not appointed) to contribute fabrication
knowledge
3.92 6 1 0.000*
N2.6 Lack of involvement by facility manager (not
appointed) to contribute operations and
maintenance knowledge
3.92 6 1 0.000*
P3: Design development (α = 0.772)
# Lack of involvement by government agency 2.93 42 – 0.567
N3.1 Insufficient design review and feedback by
owner
3.26 38 8 0.032*
186
N3.2 Architect, engineers, and contractors do not
work together in design modeling
3.56 22 5 0.000*
N3.3 Architect does not share its complete model with
engineers and contractors
3.27 37 7 0.047*
N3.4 Coordination of building systems is deferred
until construction phase due to unavailable trade
contractor input until then
4.10 2 2 0.000*
N3.5 Lack of involvement by general contractor and
key trade contractors to contribute site
knowledge (not appointed)
4.21 1 1 0.000*
N3.6 Construction model is not developed due to
unwillingness of architect and engineers to share
their BIM models
3.30 36 6 0.044*
N3.7 Lack of involvement by manufacturer/supplier
(not appointed) to contribute knowledge of
material selection, transportation, site erection,
and so on
4.03 3 3 0.000*
N3.8 Lack of involvement by facility manager (not
appointed) to contribute operations and
maintenance knowledge
3.92 6 4 0.000*
P4: Construction documentation (α = 0.876)
N4.1 Not fully defined and coordinated between
architectural, structural, and MEP design models
3.75 16 3 0.000*
N4.2 Insufficient communication between architect
and engineers
3.45 29 5 0.001*
N4.3 Information such as bill of materials, assembly,
layout, detailed schedule, testing and
commissioning procedures is not documented
after design
3.52 23 4 0.000*
N4.4 Long-lead items are not identified and defined
during design for early procurement
3.45 29 5 0.001*
N4.5 Shop drawing process is not merged into design
as contractors and manufacturer/supplier cannot
document construction intent
3.82 12 2 0.000*
N4.6 Prefabrication of some systems cannot start as
design is not fixed
3.97 4 1 0.000*
P5: Agency permit/Bidding/Preconstruction (α = 0.748)
N5.1 Architect and engineers only pass 2D drawings
or incomplete 3D BIM models to contractors and
manufacturer/supplier
3.88 10 1 0.000*
N5.2 General contractor has to re-build BIM model
based on insufficient documents from designers
3.79 13 2 0.000*
N5.3 General contractor extends 2D drawings
(without BIM) from designers to guide
construction
3.79 13 2 0.000*
P6: Construction (including Manufacture) (α = 0.787)
N6.1 Owner and designers enable changes during
construction
3.93 5 1 0.000*
N6.2 Architect and engineers need long time to
respond to contractors’ RFIs as their design
models cannot directly guide site work
3.70 19 4 0.000*
N6.3 Architect and engineers do not update their
design models
3.60 21 5 0.000*
N6.4 Contractors and manufacturer/supplier have 3.77 15 2 0.000*
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excessive RFIs and paperwork
N6.5 General contractor communicates insufficiently
with other key stakeholders
3.36 34 8 0.005*
N6.6 Low proportion of building components in
superstructure and fitting out using OSM
3.40 32 7 0.000*
N6.7 Congestion and many interfaces on site 3.73 17 3 0.000*
N6.8 Incomplete 2D drawings or 3D BIM models for
trade contractors and manufacturer/supplier
3.49 27 6 0.000*
P7: Handover/Closeout/Operations and maintenance (α = 0.772)
N7.1 As-built BIM models are not handed to facility
manager who uses insufficient levels of detail
2D as-built drawings
3.52 23 1 0.000*
N7.2 Many disputes/claims/litigations between
general contractor and owner and designers
3.37 33 2 0.001*
N7.3 Facility manager does not have sufficient BIM-
based design and construction information for
operations and maintenance
3.33 35 3 0.008*
*The one-sample t-test result was significant at the 0.05 level (two-tailed).
#The NVA activity was not significantly agreed upon by the respondents as a critical
NVA activity.
The NVA activities were ranked according to their level of agreement mean scores
which ranged from 2.81 to 4.21. To test whether each NVA activity was significantly
agreed upon by the local professionals, the one-sample t-test was performed. The test
value of 3.00 and the significance level of 0.05 (two-tailed) were adopted in this
study. The activities that obtained mean scores above 3.00 and p-values below 0.05
were deemed as critical NVA activities. The analysis results (see Table 7.2)
suggested that 38 activities were widely-agreed upon NVA activities in the current
industry practices in the Singapore construction industry. Thus, Hypothesis 1 that
“the construction industry agrees upon frequent NVA activities in the current project
delivery in the Singapore context” was supported.
The top 10 critical NVA activities were highlighted and discussed. Among which, the
top three overall rankings were occupied by the NVA activities in the design
development phase (P3), namely “lack of involvement by general contractor and key
trade contractors to contribute site knowledge (not appointed)” (N3.5, mean = 4.21,
ranked first), “coordination of building systems is deferred until construction phase
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due to unavailable trade contractor input until then” (N3.4, mean = 4.10, ranked
second), and “lack of involvement by manufacturer/supplier (not appointed) to
contribute knowledge of material selection, transportation, site erection, and so on”
(N3.7, mean = 4.03, ranked third). This substantiated the argument by Gao and
Fischer (2006) that the participation of the contractors and the manufacturer/supplier
in the detailed design stage is of great importance in a building project that
implements BIM. Without their early involvement, detailed off-site manufacture and
site activities cannot be well coordinated in the virtual design environment before
actual construction commences (AIACC, 2014). Upfront architect and engineers may
not have sufficient construction knowledge and experience to support the detailed
design coordination. As a result, the problems that traditionally would happen on site
remain unsolved until the construction stage where these problems would inevitably
take place. In addition, another highly-ranked NVA activity in this phase was
obtained by “lack of involvement by facility manager (not appointed) to contribute
operations and maintenance knowledge” (N3.8, mean = 3.92, ranked sixth),
suggesting that the operations and maintenance team should also be appointed and
involved no later than the design development phase (Kunz and Fischer, 2012). Their
proactive participation would significantly improve the flow of information
throughout the design, construction, and operations and maintenance phases, and
enrich operations and maintenance information which tended to be unavailable in the
current industry practices. Currently, data in the BIM model tended to be less relevant
for operations and maintenance.
The fourth-ranked NVA activity was “prefabrication of some systems cannot start as
design is not fixed” (N4.6, mean = 3.97) in the construction documentation phase
(P4), implying that in the current project delivery, design was usually not fixed even
after the design stage. Consequently, off-site production work that would potentially
enhance the efficiency of carrying out construction activities cannot start proactively.
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Apart from the unavailable contractor input in the design stage, not fixing design
early could be attributed to unclear owner conception from the beginning, which
would affect the design consultants’ understanding of the owner’s brief. As a result,
there would be frequent change orders enabled by the owner and the design
consultants in the later stages of the project, significantly affecting project
productivity. Therefore, the NVA activity “owner and designers enable changes
during construction” (N6.1, mean = 3.93) in the construction phase (P6) received the
fifth highest overall rating.
Three NVA activities in the schematic design phase (P2) obtained the sixth most
agreement, including “lack of involvement by general contractor and key trade
contractors to contribute site knowledge (not appointed)” (N2.4, mean = 3.92), “lack
of involvement by manufacturer/supplier (not appointed) to contribute fabrication
knowledge” (N2.5, mean = 3.92), and “lack of involvement by facility manager (not
appointed) to contribute operations and maintenance knowledge” (N2.6, mean =
3.92). This was in line with AIACC (2014) and Azhar et al. (2014) which found that
the building project changing to fully implement BIM should engage the downstream
parities from the early design stage. Although their proactive involvement would be
most important in the detailed design phase when a great number of construction
details are required, the participation even earlier would have a larger impact on the
finalization of project targets and metrics as well as key deign parameters.
Another highly ranked NVA activity was “architect and engineers only pass 2D
drawings or incomplete 3D BIM models to contractors and manufacturer/supplier”
(N5.1, mean = 3.88) in the bidding and preconstruction phase (P5). A common
situation was that the design consultants are usually not required by the owner and are
unwilling to share their models with the contractors, which would pose extra costs to
the latter for re-building the design models (Sattineni and Mead, 2013; Lam, 2014).
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In addition, the design models may be of poor quality and sharing such models would
expose the consultants to liability issues.
In terms of the internal rankings within each phase, apart from the top 10 critical
NVA activities that were distributed from the schematic design phase to the
construction phase, “no reward/risk sharing arrangements among major stakeholders
are set by owner” (N1.2, mean = 3.85) and “as-built BIM models are not handed to
facility manager who use insufficient levels of detail 2D as-built drawings” (N7.1,
mean = 3.52) received the highest ratings in their respective phases. Reward and risk
sharing arrangements in the project team ensure the team members to work in a best-
for-project manner and build trust-based collaboration throughout the project
completion. This was because they would be on the same boat; their corporate
benefits were subject to the success of this project. Meanwhile, using BIM in the
operations and maintenance phase drawn much attention in previous studies (Aranda-
Mena et al., 2009; Khosrowshahi and Arayici, 2012).
Nevertheless, six NVA activities obtained either mean scores below 3.00 or p-values
above 0.05, indicating that such activities did not obtain wide agreement from the
local BIM experts in the current industry practices, despite their occurrence in some
projects in Singapore. The activities included: (1) “lack of involvement by
government agency” (mean = 3.18; p-value = 0.224) and “owner resists to use BIM in
the whole project” (mean = 2.81; p-value = 0.137) in the conceptualization phase; (2)
“lack of involvement by government agency” (mean = 2.89; p-value = 0.392),
“architect does not share its complete model with engineers” (mean = 3.16; p-value =
0.255), and “architect and engineers do not submit their schematic design models for
regulatory approvals” (mean = 3.21; p-value = 0.083) in the schematic design phase;
and (3) “lack of involvement by government agency” (mean = 2.93; p-value = 0.567)
in the design development phase.
191
Post-survey interviews were conducted with four experts who had originally
participated in this survey and possessed BIM implementation experience in
Singapore. The profile of these experts were presented in Table 7.3. In the post-
survey interviews, the experts were presented with the survey results. They
commented that the findings of this survey were reasonable and in agreement with
their expectations. To gain an in-depth understanding, they were also invited to
provide possible explanations for the six NVA activities that were not widely agreed
upon as critical NVA activities. There comments were used to support the exclusion
of such NVA activities.
Table 7.3 Profile of the interviewees in Survey I
Interviewee Work experience Designation Firm
1 15 years Project manager General construction firm
2 10 years Senior engineer MEP consultancy firm
3 8 years Quantity surveying in
charge
General construction firm
4 11 years Deputy contracts manager Construction and
development firm
Firstly, the experts participating in the post-survey interviews argued that the
Singapore government has been proactive on BIM implementation, so the NVA
activities “lack of involvement by government agency” in the conceptualization phase
and the design phases were not significantly agreed upon. In addition, even the owner
may have a cost-beneficial thinking and does not have experience in implementing
BIM (Lam, 2014), the post-survey interviewees highlighted that BIM implementation
has been considered definitely beneficial to the owner in the long-term (Smith, 2014).
Thus, “owner resists to use BIM in the whole project” was contradicted by the local
circumstances. Moreover, “architect does not share its complete model with
engineers” was not perceived critical in the Singapore construction industry. The
experts involved in the post-survey interviews stated that the architect usually shared
CAD-like documents with the engineers because the latter tended to be customized
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into the traditional way of working. It is possible that the architect would share its
design model if both of them were using compatible BIM tools in the design. Thus,
this NVA activity was not deemed critical in the local construction industry.
Furthermore, the government agencies’ strict review process on the building plans e-
submissions in BIM format now made it impossible that “architect and engineers do
not submit their schematic design models for regulatory approvals” before the project
could proceed to the next phases.
7.2.3 BIMIR of building projects in Singapore
7.2.3.1 Weights of project phasing and NVA activities
Using equations 4.5 and 4.6 described in Section 4.2.2 and the data related to the
level of agreement of the critical NVA activities, this study calculated the weights of
all the seven project phases and 38 critical NVA activities, as shown in Table 7.4. An
example of demonstrating the calculation process was presented in Appendix 4. As
shown in Table 7.4, the project phases’ weights ranged from 0.073 to 0.213.
Table 7.4 Weighting for project phasing and its NVA activities
Project phasing NVA
activity
Mean score of
level of agreement
Mean of
phasing
Weight of
NVA activity
Weight of
phasing
P1: Conceptualization N1.1 3.51 14.49 0.242 0.104
N1.2 3.85 0.266
N1.3 3.41 0.235
N1.4 3.73 0.257
P2: Schematic design N2.1 3.51 22.41 0.156 0.161
N2.2 3.66 0.163
N2.3 3.49 0.156
N2.4 3.92 0.175
N2.5 3.92 0.175
N2.6 3.92 0.175
P3: Design development N3.1 3.26 29.64 0.110 0.213
N3.2 3.56 0.120
N3.3 3.27 0.110
N3.4 4.10 0.138
N3.5 4.21 0.142
N3.6 3.30 0.111
N3.7 4.03 0.136
N3.8 3.92 0.132
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P4: Construction
documentation
N4.1 3.75 21.97 0.171 0.158
N4.2 3.45 0.157
N4.3 3.52 0.160
N4.4 3.45 0.157
N4.5 3.82 0.174
N4.6 3.97 0.181
P5: Agency permit/
Bidding/Preconstruction
N5.1 3.88 11.47 0.338 0.082
N5.2 3.79 0.331
N5.3 3.79 0.331
P6: Construction
(including Manufacture)
N6.1 3.93 28.97 0.136 0.208
N6.2 3.70 0.128
N6.3 3.60 0.124
N6.4 3.77 0.130
N6.5 3.36 0.116
N6.6 3.40 0.117
N6.7 3.73 0.129
N6.8 3.49 0.121
P7: Handover/Closeout/
Operations and
maintenance
N7.1 3.52 10.22 0.345 0.073
N7.2 3.37 0.330
N7.3 3.33 0.326
Sum – – 139.18 – 1.000
The average weights of the seven project phases (the weight of a phase divided by the
number of critical NVA activities included in this phase) and rankings were 0.0260
(5), 0.0268 (2), 0.0266 (3), 0.0263 (4), 0.0275 (1), 0.0260 (5), and 0.0245 (7),
respectively. This was consistent with the results in Table 7.2 that the critical NVA
activities in the agency permit/bidding/preconstruction phase were essential to BIM
implementation, and that the activities in the schematic design phase and the design
development phase occupied the most positions in the top 10 overall rankings.
7.2.3.2 BIMIR of the surveyed building projects
Using the proposed fuzzy BIMIR model described in Section 4.4.2 and the data
related to the frequency of occurrence of the 38 critical NVA activities collected from
Survey I, this study calculated the NVAI scores of all the 73 building projects (coded
in chronological order) in Singapore. As shown in Table 7.5, the NVAI scores ranged
from 0.323 to 0.905. An example is presented to demonstrate the calculation process
of the proposed fuzzy BIMIR model (see Appendix 4). According to Figure 4.7, the
194
NVAI scores were translated into the linguistic terms (frequency of occurrence). The
overall average NVAI score of the 73 surveyed building projects in Singapore was
0.634 (“often”), suggesting that the critical NVA activities very frequently occurred
in the Singapore construction industry. These NVAI scores could serve as a ballpark
benchmark, with which local project building projects could compare.
Table 7.5 NVAI scores of the surveyed building projects in Singapore
Project code NVAI score Linguistic
term
Project code NVAI score Linguistic
term
01 0.694 often 38 0.837 often
02 0.608 sometimes 39 0.760 often
03 0.527 sometimes 40 0.677 often
04 0.352 rarely 41 0.851 often
05 0.602 sometimes 42 0.763 often
06 0.341 rarely 43 0.630 often
07 0.608 sometimes 44 0.369 rarely
08 0.571 sometimes 45 0.551 sometimes
09 0.664 often 46 0.730 often
10 0.905 always 47 0.521 sometimes
11 0.755 often 48 0.669 often
12 0.685 often 49 0.488 sometimes
13 0.680 often 50 0.738 often
14 0.577 sometimes 51 0.323 rarely
15 0.572 sometimes 52 0.790 often
16 0.711 often 53 0.759 often
17 0.490 sometimes 54 0.688 often
18 0.496 sometimes 55 0.819 often
19 0.538 sometimes 56 0.736 often
20 0.669 often 57 0.500 sometimes
21 0.741 often 58 0.791 often
22 0.632 often 59 0.621 sometimes
23 0.438 sometimes 60 0.614 sometimes
24 0.729 often 61 0.404 sometimes
25 0.605 sometimes 62 0.541 sometimes
26 0.790 often 63 0.685 often
27 0.681 often 64 0.513 sometimes
28 0.739 often 65 0.792 often
29 0.796 often 66 0.651 often
30 0.503 sometimes 67 0.621 sometimes
31 0.590 sometimes 68 0.668 often
32 0.494 sometimes 69 0.635 often
33 0.720 often 70 0.629 often
34 0.643 often 71 0.521 sometimes
35 0.833 often 72 0.424 sometimes
36 0.811 often 73 0.500 sometimes
37 0.705 often – – –
Note: the average NVAI score of the surveyed building projects in Singapore was
0.634 (“often”).
195
According to the adjusted translation method in Table 4.5, these NVAI scores were
also translated into BIMIR statuses. As Table 7.6 indicates, while only 11 (15.07%)
surveyed building projects in Singapore had implemented BIM in a collaborative
manner (BIMIR S3), 62 (84.93%) projects had lower BIMIR statuses (15 for BIMIR
S1 and 47 for BIMIR S2), implying that either no BIM (BIMIR S1) or lonely BIM
(BIMIR S2) was implemented in these building projects. It was notable that no
project surveyed had implemented BIM fully (BIMIR S4) in Singapore. In addition,
the overall average BIMIR status (translated from the overall average NVAI score of
0.634) was S2 (lonely BIM implementation). These findings were consistent with the
Singapore context that most building projects were adopting fragmented, firm-based
BIM uses (Lam, 2014). Since the mandatory BIM implementation took effect in July
2015, the local construction industry had been moving from the traditional project
delivery into BIM-based project delivery and was shifting to collaborative BIM
implementation. Thus, Hypothesis 2 that “the BIMIR statuses of building projects in
Singapore are low” was supported.
Table 7.6 BIMIR statuses of the surveyed building projects
NVAI score N % BIMIR status
0.75≤ Index score 15 20.55 S1 (No BIM implementation)
0.50≤ Index score <0.75 47 64.38 S2 (Lonely BIM implementation)
0.25≤ Index score <0.50 11 15.07 S3 (Collaborative BIM implementation)
7.2.3.3 Stability tests of the proposed FSE model
The stability of the proposed fuzzy BIMIR model was tested five times. In each test,
three random numbers were generated from the random numbers table and set aside.
The proposed model was then re-developed by changing the weights of the evaluation
criteria (project phases) and sub-criteria (critical NVA activities). The NVAI scores
of the 73 projects were re-calculated. The level of agreement mean scores of the
remaining 70 responses were used to re-calculate the weights.
196
The generation of the five groups of random numbers were explained. Firstly, the 73
surveyed building projects were coded from 01 to 73 (see Table 7.5). Secondly, the
five tests used five groups of random numbers which were generated from the first
five rows of the random numbers table. In each row, the selection started from the
first random number and moved rightwards, and stopped when a list of three random
numbers were generated. Those numbers that are either greater than 73 or repeated
were ignored. For example, the first three random numbers (03, 47, and 43) in the
first row were used in the first test. Consequently, five groups of random numbers
were generated, as shown in Table 7.7.
Table 7.7 Generation of five groups of random numbers
Test Random numbers Location in random numbers table
1 03, 47, 43 #1, #2, and #3 (1st row)
2 24, 67, 62 #3, #4, and #5 (2nd
row)
3 16, 02, 27 #1, #3, and #4 (3rd
row)
4 12, 56, 26 #1, #2, and #5 (4th row)
5 55, 59, 35 #1, #2, and #4 (5th row)
According to the fuzzy BIMIR model described in Section 4.4.2 and the frequencies
of occurrence of the 38 critical NVA activities from Survey I, new NVAI scores of all
the 73 building projects in Singapore were calculated, as shown in Table 7.8.
Compared with the results presented in Table 7.5, the new NVAI scores obtained in
all the five stability tests either remained the same or only slightly changed by 0.001.
It should be noted that due to decimal representation, some NVAI scores (such as the
fourth test score of Project 02) seemed to change by 0.001 in Table 7.8, but actually
the score changes were 0.000. Thus, such test scores were not noted in the table. Also,
some NVAI scores (such as the third test score of Project 16) seemed to remain
unchanged, but actually the scores changed by 0.001. Thus, such test scores were
noted in the table. In addition, BIMIR statuses in the five tests remained unchanged.
Therefore, it could be concluded that the fuzzy BIMIR model proposed in this study
197
showed stable evaluation results and could be used for prediction in other building
projects in Singapore.
Table 7.8 New NVAI scores and changes of the surveyed building projects in five
stability tests
Project
code
NVAI
score
NVAI score
in test #1
NVAI score
in test #2
NVAI score
in test #3
NVAI score
in test #4
NVAI score
in test #5
01 0.694 0.694 0.694 0.695a 0.694 0.693
b
02 0.608 0.608 0.608 0.608 0.607 0.608
03 0.527 0.527 0.527 0.528 0.527 0.527
04 0.352 0.352 0.352 0.351 0.352 0.352
05 0.602 0.602 0.602 0.603 0.602 0.602
06 0.341 0.341 0.341 0.341 0.341 0.341
07 0.608 0.608 0.608 0.608 0.608 0.608
08 0.571 0.571 0.571 0.572a 0.571 0.570
b
09 0.664 0.664 0.664 0.664 0.664 0.664
10 0.905 0.905 0.905 0.905 0.905 0.905
11 0.755 0.755 0.755 0.755 0.755 0.755
12 0.685 0.685 0.685 0.685 0.685 0.685
13 0.680 0.680 0.680 0.681 0.680 0.680
14 0.577 0.577 0.578 0.578 0.577 0.577
15 0.572 0.572 0.572 0.572a 0.572 0.571
16 0.711 0.711 0.711 0.711b 0.711 0.711
17 0.490 0.490 0.490 0.490 0.490 0.490
18 0.496 0.496 0.496 0.496 0.496 0.496
19 0.538 0.538 0.539 0.538 0.539 0.539
20 0.669 0.669 0.669 0.669 0.670 0.670
21 0.741 0.741 0.741 0.741 0.741 0.741
22 0.632 0.632 0.632 0.631 0.632 0.632
23 0.438 0.438 0.438 0.438 0.438 0.438
24 0.729 0.729 0.728b 0.729 0.729 0.728
25 0.605 0.606 0.605 0.605 0.605 0.605
26 0.790 0.790 0.790 0.790 0.790 0.790
27 0.681 0.681 0.681 0.680b 0.681 0.681
28 0.739 0.739 0.739 0.739 0.739 0.739
29 0.796 0.797 0.796 0.797 0.797 0.797
30 0.503 0.503 0.503 0.502 0.503 0.504a
31 0.590 0.590 0.590 0.590 0.591 0.591
32 0.494 0.494 0.494 0.494 0.494 0.494
33 0.720 0.720 0.719 0.720 0.720 0.720
34 0.643 0.643 0.643 0.643 0.643 0.643
35 0.833 0.833 0.833 0.833 0.833 0.833
36 0.811 0.811 0.811 0.811 0.811 0.811
37 0.705 0.705 0.705 0.705 0.705 0.705
38 0.837 0.837 0.837 0.837 0.837 0.837
39 0.760 0.760 0.760 0.760 0.760 0.760
40 0.677 0.677 0.677 0.677 0.677 0.677
41 0.851 0.851 0.851 0.850 0.851 0.851
42 0.763 0.763 0.763 0.763 0.763 0.763
43 0.630 0.629 0.629 0.629 0.630 0.630
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44 0.369 0.369 0.369 0.369 0.369 0.369
45 0.551 0.551 0.551 0.551 0.551 0.551
46 0.730 0.730 0.731 0.730 0.730 0.730
47 0.521 0.521 0.521 0.521 0.521 0.521
48 0.669 0.670 0.669 0.670a 0.669 0.669
b
49 0.488 0.488 0.488 0.488 0.488 0.489
50 0.738 0.738 0.738 0.738b 0.738 0.738
51 0.323 0.323 0.323 0.323 0.323 0.323
52 0.790 0.790 0.790 0.791a 0.790 0.790
53 0.759 0.759 0.759 0.759 0.759 0.759
54 0.688 0.688 0.688 0.688 0.688 0.688
55 0.819 0.819 0.819 0.820 0.819 0.819
56 0.736 0.736 0.736 0.736 0.736 0.736
57 0.500 0.500 0.500 0.500 0.500 0.500
58 0.791 0.791 0.791 0.791 0.791 0.791
59 0.621 0.621 0.621 0.622a 0.621 0.620
b
60 0.614 0.614 0.615 0.614 0.614 0.615
61 0.404 0.404 0.405 0.404 0.404 0.405
62 0.541 0.541 0.541 0.541 0.541 0.541
63 0.685 0.684 0.685 0.685 0.685 0.685
64 0.513 0.513 0.513 0.513 0.513 0.513
65 0.792 0.792 0.792 0.792 0.792 0.793
66 0.651 0.651 0.652 0.652a 0.651 0.651
67 0.621 0.621 0.620 0.620 0.621 0.621
68 0.668 0.668 0.668 0.668 0.668 0.668
69 0.635 0.635 0.635 0.635 0.635 0.635
70 0.629 0.629 0.629 0.629 0.629 0.629
71 0.521 0.521 0.521 0.521 0.521 0.521
72 0.424 0.424 0.424 0.424 0.424 0.424
73 0.500 0.500 0.500 0.500 0.500 0.500
Note: BIMIR statuses in the five tests remained the same. aThe NVAI score increased by 0.001.
bThe NVAI score decreased by 0.001.
7.2.4 Resulting wastes
The Cronbach’s alpha coefficient value was 0.901, indicating that the data related to
the resulting wastes of the critical NVA activities in building projects in Singapore
had high reliability.
7.2.4.1 Overall ranking
It was not necessary to conduct the one-sample t-test to check whether a waste was
statistically significant in affecting productivity performance (issue of “yes or no”).
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Unlike the NVA activities, all the 13 wastes identified from the literature review were
deemed significant. As explained in Section 4.2.2, these wastes indeed consume time
and efforts and thus hurt productivity performance. Instead, it would be helpful to
study the criticality of these wastes in influencing productivity (issue of “degree”).
In this study, waste criticality (WC) was defined to measure how critical to
productivity performance a waste is in the partial BIM-based current industry
practices. The WC of waste 𝑙 rated by respondent 𝑗 was calculated as the root square
of the product of the waste’s impact on productivity (𝐼𝑃) and frequency of occurrence
(𝐹𝑂), which keeps the scale of 𝑊𝐶 consistent with 𝐼𝑃 and 𝐹𝑂, as shown in equation
(7.1). The criticality of a factor has been used in previous construction management
studies (Zhao et al., 2016a).
𝑊𝐶𝑙𝑗
= √𝐹𝑂𝑙𝑗
× 𝐼𝑃𝑙𝑗 (7.1)
As Table 7.9 indicates, the WC mean scores of the 13 resulting wastes ranged from
2.81 to 3.60. These wastes were ranked based on their overall mean scores. The mean
scores of frequency of occurrence and impact on productivity ranged from 2.68 to
3.89 and from 2.77 to 3.66, respectively. It is notable that the largest and smallest
mean score belonged to W02 and W13 for frequency of occurrence as well as W03
and W09 for impact on productivity. Thus, none of the 13 resulting wastes had a very
high frequency of occurrence and a very low impact on productivity, or vice versa. In
the subsequent paragraphs, the top five critical wastes were analyzed and discussed.
The local BIM experts’ comments collected in the post-survey interviews were also
used to gain a clear understanding of the most critical wastes.
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Table 7.9 Mean and ranking of resulting wastes
Code Resulting wastes Frequency of
occurrence
Impact on
productivity
WC
Mean Rank Mean Rank Mean Rank
W01 Defects 3.71 2 3.34 6 3.49 4
W02 RFIs 3.89 1 3.29 7 3.53 2
W03 Reworks/abortive works 3.62 3 3.66 1 3.60 1
W04 Waiting/idle time 3.56 5 3.47 4 3.48 5
W05 Change orders 3.58 4 3.41 5 3.46 7
W06 Activity delays 3.51 7 3.48 3 3.47 6
W07 Overproduction/reproduction 3.19 8 3.05 8 3.10 8
W08 Transporting/handling materials 3.05 10 2.97 10 2.98 10
W09 Unnecessary inventory 2.99 12 2.77 13 2.83 12
W10 Excess processing beyond standard 3.08 9 2.95 11 2.99 9
W11 Unnecessary movement of people and
equipment
3.04 11 2.90 12 2.94 11
W12 Design deficiencies (errors, omissions,
additions)
3.53 6 3.53 2 3.51 3
W13 Injuries/safety issues 2.68 13 3.05 9 2.81 13
“Reworks/abortive works” was recognized as the most critical waste (mean = 3.60),
indicating that building projects in Singapore had suffered from a great deal of
abortive works. This result echoed the post-survey interviewees who reported that
abortive works usually happened throughout the construction phase and influenced a
lot of trades on site. Actually, the trade contactors rarely used BIM tools (Lam, 2014)
and tended to arrange their construction activities ahead of time, which may not be
updated, planned, and reflected in the design models. Consequently, clashes were
often detected during the construction stage, leading to abortive works and extra time
and manpower to redo the works.
“RFIs” received the second highest rating in the waste ranking (mean = 3.53). This
result indicated that productivity performance of building projects in Singapore was
seriously affected by frequent enquiries and clarifications between downstream and
upfront key stakeholders. In the post-survey interviews, the experts stated that plenty
of time and efforts were wasted in paperwork. Since the design models of different
disciplines were not well coordinated, design issues were postponed until the
construction stage where the contractors often needed to request for clarifications or
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confirmations of verbal instructions. Nevertheless, the design consultants may be
wary of providing early and incomplete information to the contractors because of
potential liabilities (Eastman et al., 2011; AIACC, 2014). This process of requesting
and responding would waste huge efforts, and even affect the project progress.
“Design deficiencies (errors, omissions, additions)” occupied the third position (mean
= 3.51), suggesting that the collaboration was poor between the design consultants
and downstream parities in the design stage. This result substantiated the finding of
Nikakhtar et al. (2015) that a productive delivery should prevent deficiencies from
being made through mistake-proofing in the planning stage. Because of unclear
owner conception and poorly coordinated design models, design errors and changes
were common in the later stages of the project. As a result, construction activities
prone to errors could not be identified in time and thus delaying the construction
progress.
“Defects” was ranked fourth in the waste ranking (mean = 3.49), implying that
defective products were often produced in the construction phase, seriously affecting
productivity performance in the project. As the most obvious waste, every defective
item would require repairs and reworks as well as paperwork.
Another highly ranked waste was “waiting/idle time” (mean = 3.48), suggesting that
the field staff often spent much time waiting for instructions and confirmation from
the design consultants, materials supply, and so on. The professionals participating in
the post-survey interviews also highlighted that it was not uncommon that due to poor
planning and coordination among various trades on the construction site, construction
activities were suspended until getting responses, and the field staff were idle.
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7.2.4.2 Comparison among different BIMIR statuses
As shown in Table 7.6, out of the 73 surveyed building projects in Singapore, 15, 47,
and 11 belonged to BIMIR S1 (no BIM implementation), S2 (lonely BIM
implementation), and S3 (collaborative BIM implementation), respectively. This
section investigated the differences in the mean scores and rankings of the resulting
wastes between the three BIMIR groups.
As shown in Table 7.10, the WC mean scores ranged from 2.80 to 3.89 in the BIMIR
S1 group of building projects, from 2.70 to 3.69 in the S2 group of projects, and from
2.83 to 3.52 in the S3 group of projects. Overall, the mean values gradually decreased
as BIMIR status increased. The overall mean scores of the 13 wastes in the three
groups declined from 3.56 (S1) to 3.17 (S2) and further 3.13 (S3). Therefore, it was
concluded that BIM implementation could prevent the wastes from occurring, which
definitely enhanced productivity performance in the Singapore construction industry.
Thus, Hypothesis 3 that “the higher the BIMIR status, the lower the criticality of the
wastes and the higher the productivity performance” was supported.
Table 7.10 ANOVA results of the WC between BIMIR statuses
Code S1 (no BIM) S2 (lonely BIM) S3 (collaborative BIM) ANOVA
Criticality mean Rank Criticality mean Rank Criticality mean Rank p-value
W01 3.75 5 3.45 5 3.30 3 0.321
W02 3.81 4 3.45 4 3.52 1 0.364
W03 3.69 7 3.69 1 3.11 8 0.123
W04 3.72 6 3.49 2 3.14 5 0.200
W05 3.89 1 3.33 7 3.42 2 0.105
W06 3.81 2 3.45 6 3.08 9 0.045*
W07 3.44 9 2.98 8 3.11 7 0.141
W08 3.27 11 2.90 9 2.96 11 0.268
W09 3.25 12 2.70 13 2.83 13 0.047*
W10 3.59 8 2.77 12 3.12 6 0.007*
W11 3.41 10 2.80 10 2.89 12 0.060
W12 3.81 3 3.47 3 3.25 4 0.272
W13 2.80 13 2.77 11 3.01 10 0.757
Note: criticality = root square of the product of the impact on productivity and the
frequency of occurrence. *The mean score difference was significant at the 0.05 level.
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To test whether the mean scores differed between the three BIMIR status groups, the
one-way ANOVA were performed using SPSS. There are three main assumptions for
the ANOVA: (1) the wastes were normally distributed in each status group; (2) the
samples were independent in all the groups; and (3) homogeneity of variances in all
the groups. The first assumption was not an issue because the one-way ANOVA can
tolerate the data that are non-normal. Since the data were collected through an online
survey, the second assumption was also achieved. In the test of homogeneity of
variances, p-values of all the 13 wastes were greater than 0.05, so the null hypothesis
that the three groups had equal variances was accepted. Thus, the homogeneity of
variances was ensured in the three groups, and the third assumption was achieved.
It should be noted that with SPSS, unequal sample sizes in the three groups would not
harm the one-way ANOVA. Calculating sums of squares required different formulas
if the sample sizes were unequal, but SPSS could automatically use right ones. A
practical issue was that very unequal sample sizes might affect the “homogeneity of
variances” assumption. The ANOVA was considered robust to moderate departures
from this assumption, but the departure should be small when the sample sizes were
very different. According to Keppel and Wickens (2004), there was not a good rule of
thumb for the point at which unequal sample sizes made the heterogeneity of
variances a problem.
In addition, a post hoc test of the ANOVA could reveal detailed multiple comparisons
between any two groups. In SPSS, if equal variances (homogeneity of variances)
were assumed, post hoc tests such as least significant difference (LSD) could be used;
otherwise, tests such as Games-Howell should be used. In this study, the LSD method
was adopted because this method applied standard t-tests to all possible pairs of group
means. The results were presented in Table 7.11. The p-values below 0.05 indicated
that significant differences in the mean scores existed. The ANOVA results in Table
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7.10 showed that the mean scores of three wastes (W06, W09, and W10) significantly
differed between the three groups at the 0.05 level, and the multiple comparisons (see
Table 7.11) revealed that another four wastes (W03, W05, W07, and W11) also had
different mean scores between different groups. Since three groups were involved in
this study, this section would analyze and discuss the differences based on the
multiple comparison results.
Table 7.11 Post hoc test results for the wastes different between BIMIR statuses
Waste Multiple
comparisons
Mean
difference
p-
value
Waste Multiple
comparisons
Mean
difference
p-
value
W01 S1 S2 0.299 0.210 W08 S1 S2 0.371 0.107
S3 0.446 0.163 S3 0.310 0.312
S2 S3 0.147 0.583 S2 S3 -0.061 0.812
W02 S1 S2 0.357 0.157 W09 S1 S2 0.551 0.014*
S3 0.291 0.387 S3 0.417 0.157
S2 S3 -0.067 0.814 S2 S3 -0.134 0.587
W03a S1 S2 0.002 0.995 W10 S1 S2 0.822 0.002
*
S3 0.584 0.090 S3 0.466 0.179
S2 S3 0.583 0.046* S2 S3 -0.356 0.223
W04 S1 S2 0.228 0.340 W11a S1 S2 0.613 0.018
*
S3 0.578 0.074 S3 0.519 0.132
S2 S3 0.349 0.198 S2 S3 -0.094 0.744
W05a S1 S2 0.557 0.035
* W12 S1 S2 0.335 0.215
S3 0.467 0.182 S3 0.562 0.121
S2 S3 -0.090 0.759 S2 S3 0.227 0.456
W06 S1 S2 0.364 0.097 W13 S1 S2 0.029 0.918
S3 0.732 0.014* S3 -0.211 0.583
S2 S3 0.368 0.136 S2 S3 -0.240 0.459
W07a S1 S2 0.453 0.049
* – – – – –
S3 0.322 0.291 – – –
S2 S3 -0.131 0.610 – – – – aAdditional waste that was not statistically significant in the ANOVA (Table 7.10).
*The mean difference was significant at the 0.05 level.
The WC mean score of “reworks/abortive works” decreased by 0.583 from the
BIMIR S2 group (mean = 3.69, ranked top) to the S3 group (mean = 3.11, ranked
eighth). This was because that compared with lonely BIM implementation, a project
team that implemented BIM collaboratively targeted not only the design consultants
and the general contractor, but also plenty of trade contractors. Thus, the construction
planning of the trade contactors was incorporated into the team’s planning using
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BIM, and the site status could be reflected and continuously updated in the design
models virtually. As a result, clashes were often detected before actual construction
activities were carried out, and the quality of the activities was improved because of
fewer modifications and compromises on the project site (Fan et al., 2014),
significantly reducing the abortive works on site.
“Change orders” was ranked top in the S1 group (mean =3.89) and seventh in the S2
group (mean = 3.33). The difference of the mean scores between the two groups was
significant (0.557), implying that even lonely BIM implementation could drastically
reduce the influence of change orders on productivity performance. This was
probably because the owner’s intent was better represented in 3D design models
(Fischer et al., 2014), and thus fewer design errors, omissions, and additions were
needed in the construction stage.
“Activity delays” obtained a higher mean score in the S1 group (mean = 3.81, ranked
second) than that in the S3 group (mean = 3.08, ranked ninth), suggesting that as
BIMIR status became much higher, the WC score decreased by 0.732. Apart from
fewer reworks as mentioned earlier, collaborative BIM adoption involved key
contractors early, which facilitated quicker project layout planning, a higher
proportion of work using prefabrication, and more detailed scheduling. Thus,
schedule compliance could be guaranteed (Chelson, 2010; Fan et al., 2014).
“Overproduction/reproduction” received the mean scores of 3.44 (ranked ninth) and
2.98 (ranked eighth) in the S1 and S2 groups, respectively. This result indicated that
the WC score of this waste decreased by 0.453 when the project changed to
implement BIM. Accuracy of building objects is one of the most important
advantages in the 3D design models (Eastman et al., 2011). Given that the project
206
schedule was well complied with, the team could have a better understanding of the
timing and the quantity of production.
The mean score of “unnecessary inventory” in the S1 group (mean =3.25, ranked
twelfth) was significantly higher than that in the S2 group (mean = 2.70, ranked
bottom), by 0.551. As mentioned above, the timing and the quantity of finished
elements were better understood with BIM implementation to avoid overproduction,
which would in return control the procurement of materials and the work under
production. As a result, an appropriate level of inventory was ensured.
“Excess processing beyond standard” obtained a significantly higher score in the S1
group (mean = 3.59, ranked eighth) than that in the S2 group (mean = 2.77, ranked
twelfth), decreasing by 0.822. Although the information fragmentation still existed
across the design stage and the construction stage in lonely BIM implementation, the
design models that represented the project outcomes as needed could provide clearer
specifications and more accurate documentation of construction intent. This ensured
that unneeded work processes may be reduced to some extent.
The mean scores of “unnecessary movement of people and equipment” in the S1
group (mean = 3.41, ranked eighth) was significantly higher than that in the S2 group
(mean = 2.80, ranked twelfth). When the contractors changed to use BIM, the better
project planning, such as quicker and optimized site layout, could be done ahead of
actual construction. Thus, field personnel were better guided in carrying out activities
on site, substantially reducing unneeded movements.
Furthermore, the Spearman’s rank correlation was conducted to check the WC
ranking difference between the three BIMIR status groups of building projects. As
shown in Table 7.12, the results indicated that despite significant differences in the
207
WC mean scores and rankings of the seven resulting wastes, the correlation
coefficients between any two groups were greater than 0.5 and significant at the 0.05
level. Therefore, there were statistically significant agreement on the rankings of all
the 13 wastes between the three readiness groups. The BIMIR S1 group and S2 group
shared nine common wastes in their respective top 10 wastes, and the S2 group and
the S3 group shared eight common wastes.
Table 7.12 Spearman’s rank correlation results of the WC between BIMIR statuses
BIMIR Test statistics S1 (no BIM) S2 (lonely BIM) S3 (collaborative BIM)
S1 (no BIM) Correlation
coefficient
1.000 0.643 0.758
p-value – 0.018* 0.003
*
S2 (lonely BIM) Correlation
coefficient
– 1.000 0.582
p-value – – 0.037*
S3
(collaborative
BIM)
Correlation
coefficient
– – 1.000
p-value – – – *Correlation was significant at the 0.05 level (two-tailed).
7.2.5 Causes to NVA activities
The overall Cronbach’s alpha coefficient value of the data related to the importance
of the causes to the critical NVA activities was 0.971, implying high data reliability.
It was notable that the coefficient value of the “government agency” group was 0.635.
This was acceptable in this study because: (1) the threshold of 0.7 could decrease to
0.6 for newly-developed measures in exploratory research (Robinson et al., 1991);
and (2) the scale only included three items. Hair et al. (2009) revealed that positive
relationship to the number of items in the scale should be considered in assessing the
Cronbach’s alpha coefficient, and that decreasing the number of items will decrease
the coefficient value.
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7.2.5.1 Overall ranking
As shown in Table 7.13, the mean scores of the 53 causes to NVA activities ranged
from 3.27 to 4.00. These causes were ranked based on their overall mean scores.
Similar to the analysis of the 13 resulting wastes, the one-sample t-test was performed
to test whether these factors were statistically important causes to the critical NVA
activities identified earlier. The analysis results indicated that all the causes obtained
mean scores above 3.00, with p-values below 0.05. Thus, these factors significantly
caused the critical NVA activities in the current industry practices in Singapore.
Table 7.13 Importance ranking and t-test results of the causes to the NVA activities
Code Causes to NVA activities Mean Overall
rank
Internal
rank
p-
value
R1: Government agency (α = 0.635)
C1.01 Focusing on design stage by developing BIM
submission templates and guidelines
3.55 44 2 0.001*
C1.02 Mandating BIM submissions cannot guarantee
collaboration and best-for-project thinking
3.60 38 1 0.000*
C1.03 Unclear legislations and qualifications for
precasters (versus concreter) and inadequate
codes for OSM varieties
3.44 50 3 0.000*
R2: Owner (α = 0.870)
C2.01 Inertia against use of BIM or off-site
prefabrication
3.64 34 2 0.000*
C2.02 Establishing minimal apparent risk and
minimum first cost as crucial selection criteria
3.58 42 4 0.000*
C2.03 Unaware of the benefits of BIM and lifecycle
management
3.59 40 3 0.000*
C2.04 Creating incentives for individual firms to
protect their own interests
3.42 52 8 0.000*
C2.05 Awarding architectural and engineering design
contracts solely based on qualification
3.51 47 6 0.000*
C2.06 Setting vague goals with architect and rarely
passing them on to downstream parties
3.78 15 1 0.000*
C2.07 Focusing on assessing liability and risk transfers
using mechanisms such as guarantees and
penalties
3.53 45 5 0.000*
C2.08 Perceiving design fees for OSM as more
expensive than traditional process
3.44 50 7 0.000*
C2.09 Desire for particular structures or traditional
finishes
3.27 53 9 0.010*
R3: Architect/Engineers (α = 0.887)
C3.01 Because of potential liability, architect includes
fewer details in drawings or indicates that the
drawings cannot be relied on for dimensional
accuracy
3.66 31 10 0.000*
209
C3.02 Architect does not model what contractors need
for QTOs
3.74 19 6 0.000*
C3.03 Not required by contract to share design models
with contractors
3.77 16 4 0.000*
C3.04 Design models/drawings fit for mandatory BIM
submissions, but not fit for intended
downstream use
4.00 1 1 0.000*
C3.05 Architect and engineers do not understand field
operations enough and lack construction input in
design
3.89 4 2 0.000*
C3.06 Lack of skilled BIM experts to engage 3.89 4 2 0.000*
C3.07 No complete knowledge of their design
decisions’ impact on construction
3.77 16 4 0.000*
C3.08 Architect and engineers spend much time and
effort locating, recreating, or transferring
fragmented information
3.71 22 7 0.000*
C3.09 Unless asked and encouraged, architect and
engineers do not consider lifecycle value of or
incremental changes
3.70 27 8 0.000*
C3.10 Limited expertise of OSM and its processes in
the market for architect and engineers
3.67 29 9 0.000*
C3.11 Downstream designers have to make extra
efforts to reconfigure or reformat data
3.66 31 10 0.000*
R4: General contractor/Key trade contractors (α = 0.962)
C4.01 General contractor not required by owner and
government to adopt BIM
3.62 37 18 0.000*
C4.02 General contractor only has 2D drawings or
incomplete 3D model shared from designers
3.71 22 11 0.000*
C4.03 General contractor has to make extra efforts to
reconfigure or reformat data
3.92 3 1 0.000*
C4.04 General contractor’s reluctance to adopt OSM 3.52 46 21 0.000*
C4.05 General contractor’s BIM team does modeling
but not coordination for trade contractors
3.71 22 11 0.000*
C4.06 General contractor requires but does not train
trade contractors to use BIM
3.60 38 19 0.000*
C4.07 Lack of skilled BIM experts to engage to help
construction manager and unable to see how
BIM benefit them
3.89 4 2 0.000*
C4.08 Training cost and high learning curve (initial
productivity loss) to use BIM
3.85 9 5 0.000*
C4.09 Reluctant and inexperienced to use BIM and
happy to continue using traditional CAD
3.88 7 3 0.000*
C4.10 Having little knowledge of BIM and do not
know how, when, and what to use it
3.75 18 9 0.000*
C4.11 Lack of national BIM standards and guidelines
for contractors
3.81 13 8 0.000*
C4.12 Doubt about the effectiveness of BIM because
of limited evidence
3.74 19 10 0.000*
C4.13 Afraid of the unknown and resistant to change
from comfortable daily routine
3.67 29 14 0.000*
C4.14 Lack of legal support from authority 3.49 48 22 0.000*
C4.15 Lack of tangible benefits of BIM to warrant its use 3.64 34 16 0.000*
C4.16 Not thinking of changing conventional methods
and no demand for BIM use
3.58 42 20 0.000*
210
C4.17 Limited expertise of OSM and its processes in
the market for contractors
3.66 31 15 0.000*
C4.18 Trade contractors not required by general
contractor/owner/government to adopt BIM
3.64 34 16 0.000*
C4.19 High cost for trade contractors to engage BIM
experts or outsource to BIM drafters
3.71 22 11 0.000*
C4.20 Trade contractors only have 2D drawings or
incomplete 3D model shared from designers or
general contractor
3.84 10 6 0.000*
C4.21 Trade contractors have to make extra efforts to
reconfigure or reformat data
3.88 7 3 0.000*
C4.22 Trade contractors use CAD and cannot integrate
BIM models from general contractor into their
site models
3.84 10 6 0.000*
R5: Manufacturer/Supplier (α = 0.916)
C5.01 Does not permit design changes as these are
expensive once fabrication has commenced
3.95 2 1 0.000*
C5.02 Not required by owner/general
contractor/government to adopt BIM in
manufacture
3.71 22 5 0.000*
C5.03 Lack of skilled BIM experts to engage and
unable to see how BIM benefit them
3.82 12 2 0.000*
C5.04 Only 2D drawings or incomplete 3D model
shared from designers or general contractor
3.74 19 4 0.000*
C5.05 Training cost and high learning curve (initial
productivity loss) to use BIM
3.68 28 6 0.000*
C5.06 Reluctant and inexperienced to use BIM and
still happy to continue using CAD
3.81 13 3 0.000*
C5.07 Market protection from traditional
suppliers/manufacturers
3.49 48 7 0.000*
R6: Facility manager
C6.01 Not required by owner to use BIM and not
involved in design phase to contribute
knowledge
3.59 40 1 0.000*
*The one-sample t-test result was significant at the 0.05 level (two-tailed).
The causes with top 10 statistical significance were analyzed. In the
“Architect/Engineers” (R3) group, there were three leading causes with high
importance. Specifically, “design models/drawings fit for mandatory BIM
submissions, but not fit for intended downstream use” (C3.04, mean = 4.00) received
the highest overall rating among the 53 causes, and “architect and engineers do not
understand field operations enough and lack construction input in design” (C3.05,
mean = 3.89) was ranked fourth. These results indicated that the design consultants in
Singapore tended to focus on the regulatory submissions and did not have enough
time or construction knowledge to input sufficient precision and details in the design
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modeling or coordination for the contractors (Lam, 2014). In the post-survey
interviews, the professionals highlighted on-going change requests in their projects
and the importance of trade contractors in the design detailing and coordination.
Considering that the critical NVA activities related to “lack of involvement by
various downstream parties to contribute their insights in the design stage” were most
agreed upon by the local BIM experts, it was common that the design models were
purely created by the design consultants, and the high scores of such causes supported
the lonely BIM implementation in the construction industry. In addition, the cause
“lack of skilled BIM experts to engage” (C3.06, mean = 3.89) was also ranked fourth.
Similar to the cases studies conducted overseas (Chelson, 2010), BIM experts in
Singapore were not enough, and BIM operators were typically young, did not have
much site knowledge, and were not able to consider the downstream uses in the
design. The post-survey interviewees also emphasized that the design consultants
tended to lack enough fees that could allow them to engage BIM experts from the
market.
The cause “does not permit design changes as these are expensive once fabrication
has commenced” (C5.01, mean = 3.95) under the “Manufacturer/Supplier” (R5)
group obtained the second highest importance, indicating that large-scale
prefabrication of building components either commenced at a later stage or was not
based on the 3D design model. The professionals participating in the post-survey
interviews stated that the collaboration with manufacturers/suppliers led to productive
construction processes. However, due to the tight project schedule and costly
changes, the design was not fixed until the construction stage in the current project
delivery, which produced a number of NVA activities, such as “lack of involvement
by manufacturer/supplier” and “insufficient construction documentation”.
212
The remaining highly ranked causes were under the “General contractor/Key trade
contractors” (R4) group. Among the top 10 causes, the third most important cause
was “general contractor has to make extra efforts to reconfigure or reformat data”
(C4.03, mean = 3.92). Based on the analysis of the aforementioned two causes (C3.04
and C3.05), the design consultants’ models were not created as the contractors had
usually built the building. Thus, it was not uncommon that the general contractor
needed to make repeated efforts (Sattineni and Mead, 2013). Moreover, “lack of
skilled BIM experts to engage to help construction manager and unable to see how
BIM benefit them” (C4.07, mean = 3.89) also occupied the fourth position in the
overall ranking, “training cost and high learning curve (initial productivity loss) to
use BIM” (C4.08, mean = 3.85) was ranked ninth, and “reluctant and inexperienced
to use BIM and happy to continue using traditional CAD” (C4.09, mean = 3.88)
received the seventh highest rating. As mentioned earlier, although some young
practitioners were equipped with BIM skillsets, the construction market was short of
experienced BIM experts who could use BIM to guide construction activities on site.
Also, the professionals involved in the post-survey interviews reported that such
experts were expensive in the current market. Due to the constraint of project budget,
the management tried to balance training cost and the benefits that BIM
implementation could add to the project. Therefore, confronted with the high costs
and initial productivity loss in first projects, the management may decide to use the
traditional CAD approach in most of the planning and construction processes. Hence,
these causes resulted in many NVA activities in the construction stage.
On the other hand, three leading causes were related to the trade contractors,
including “trade contractors have to make extra efforts to reconfigure or reformat data
” (C4.21, mean = 3.88, ranked seventh), “trade contractors only have 2D drawings or
incomplete 3D model shared from designers or general contractor” (C4.20, mean =
3.84, ranked tenth), and “trade contractors use CAD and cannot integrate BIM models
213
from general contractor into their site models” (C4.22, mean = 3.84, occupied the
tenth position). Most of the trade contractors were SMEs who were usually still using
the traditional design tools and practices and did not care about the collaboration with
other trades on site, as advised by the interviewees. Meanwhile, since BIM was still
relatively new in the market and the cost was limited, it was not possible that the
trade contractors could train up enough BIM experts in the short term. Hence, the
constraints faced by the trade contractors would produce quite a number of NVA
activities in the current project delivery practices.
In terms of the internal rankings under each project role, apart from the top 10
important causes that were under three groups (R3 to R5), “mandating BIM
submissions cannot guarantee collaboration and best-for-project thinking” (C1.02,
mean = 3.60) received the highest rating under “Government agency” (R1).
Singapore is currently the only country that has mandated almost all public and
private building projects to implement BIM (Cheng and Lu, 2015; McAuley et al.,
2017). A local BIM expert participating in the post-survey interviews argued that
such mandate may lead some practitioners to implement BIM only for regulatory
approvals and continue to act as before. Thus, NVA activities were still produced.
Besides, “setting vague goals with architect and rarely passing them on to
downstream parties” (C2.06, mean = 3.78) was ranked top under the group of
“Owner” (R2), indicated that uncertainty of information and unwillingness to share
would create NVA activities, no matter whether BIM tools were used or not. Last but
not least, “not required by owner to use BIM and not involved in design phase to
contribute knowledge” (C6.01, mean = 3.59) was the only important cause identified
under “Facility manager” (R6) in this study. The contract with the operations and
maintenance team was directly responsible for those NVA activities related to
operations and maintenance.
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7.2.5.2 Comparison among different BIMIR statuses
Similar to the resulting wastes, the importance of the leading causes to the critical
NVA activities was likely to differ between different BIMIR groups. Thus, this
section investigated the differences in the mean scores and rankings of the leading
cause between the three BIMIR status groups of building projects.
As shown in Table 7.14, the importance mean scores ranged from 3.13 to 4.33 in the
BIMIR S1 group of building projects, from 3.34 to 4.11 in the S2 group of building
projects, and from 2.64 to 3.82 in the S3 group of building projects. Overall, the
higher the BIMIR status, the lower the mean scores. Thus, the causes became less and
less important when BIM was increasingly implemented in the surveyed building
projects in Singapore.
Table 7.14 ANOVA results of the causes between BIMIR statuses
Causes to
NVA
activities
S1 (no BIM) S2 (lonely BIM) S3 (collaborative
BIM)
ANOVA
Mean Overall
rank
Internal
rank
Mean Overall
rank
Internal
rank
Mean Overall
rank
Internal
rank
p-value
R1: Government agency
C1.01 3.33 51 3 3.66 40 2 3.36 10 1 0.616
C1.02 3.40 47 2 3.72 30 1 3.36 10 1 0.482
C1.03 3.67 31 1 3.45 52 3 3.09 25 3 0.297
R2: Owner
C2.01 3.80 24 3 3.68 38 3 3.27 15 3 0.308
C2.02 3.60 35 4 3.60 47 5 3.45 6 1 0.865
C2.03 3.53 40 5 3.70 34 2 3.18 18 4 0.322
C2.04 3.33 51 8 3.53 49 6 3.09 25 7 0.383
C2.05 3.47 44 6 3.66 40 4 2.91 42 9 0.028*
C2.06 3.87 15 1 3.85 22 1 3.36 10 2 0.202
C2.07 3.87 15 1 3.51 51 8 3.18 18 4 0.211
C2.08 3.47 44 6 3.53 49 6 3.00 36 8 0.253
C2.09 3.13 53 9 3.34 53 9 3.18 18 4 0.689
R3: Architect/Engineers
C3.01 4.00 11 6 3.66 40 10 3.18 18 8 0.142
C3.02 4.20 3 3 3.81 28 5 2.82 49 11 0.002*
C3.03 4.20 3 3 3.70 34 8 3.45 6 4 0.141
C3.04 4.33 1 1 4.04 3 1 3.36 10 6 0.014*
C3.05 3.87 15 7 3.91 15 3 3.82 1 1 0.955
C3.06 3.73 26 10 4.04 3 1 3.45 6 4 0.141
C3.07 3.67 31 11 3.85 22 4 3.55 4 2 0.478
215
C3.08 4.07 10 5 3.72 30 6 3.18 18 8 0.031*
C3.09 3.87 15 7 3.68 38 9 3.55 4 2 0.620
C3.10 3.80 24 9 3.72 30 6 3.27 15 7 0.220
C3.11 4.27 2 2 3.64 44 11 2.91 42 10 0.002*
R4: General contractor /Key trade contractors
C4.01 3.60 35 13 3.62 46 22 3.64 2 1 0.996
C4.02 3.87 15 6 3.83 25 16 3.00 36 11 0.036*
C4.03 4.20 3 1 3.94 11 8 3.45 6 2 0.106
C4.04 3.53 40 16 3.66 40 21 2.91 42 14 0.080
C4.05 3.73 26 9 3.87 21 15 3.00 36 11 0.027*
C4.06 3.87 15 6 3.70 34 19 2.82 49 19 0.021*
C4.07 4.20 3 1 3.98 8 5 3.09 25 5 0.002*
C4.08 3.73 26 9 4.06 2 2 3.09 25 5 0.011*
C4.09 3.87 15 6 4.11 1 1 2.91 42 14 0.001*
C4.10 3.40 47 20 4.00 5 3 3.18 18 4 0.007*
C4.11 3.60 35 13 4.00 5 3 3.27 15 3 0.096
C4.12 3.73 26 9 3.94 11 8 2.91 42 14 0.005*
C4.13 3.40 47 20 3.94 11 8 2.91 42 14 0.008*
C4.14 3.40 47 20 3.72 30 18 2.64 53 22 0.012*
C4.15 3.47 44 19 3.91 15 11 2.73 52 21 0.003*
C4.16 3.67 31 12 3.70 34 19 2.91 42 14 0.083
C4.17 3.53 40 16 3.89 19 13 2.82 49 19 0.003*
C4.18 3.53 40 16 3.83 25 16 3.00 36 11 0.044*
C4.19 3.60 35 13 3.89 19 13 3.09 25 5 0.059
C4.20 4.00 11 5 3.96 10 7 3.09 25 5 0.020*
C4.21 4.13 8 3 3.98 8 5 3.09 25 5 0.016*
C4.22 4.13 8 3 3.91 15 11 3.09 25 5 0.017*
R5: Manufacturer/Supplier
C5.01 4.20 3 1 3.94 11 2 3.64 2 1 0.261
C5.02 3.87 15 4 3.74 29 6 3.36 10 2 0.408
C5.03 3.73 26 5 4.00 5 1 3.18 18 3 0.035*
C5.04 3.93 14 3 3.83 25 5 3.09 25 4 0.041*
C5.05 3.60 35 7 3.85 22 4 3.09 25 4 0.079
C5.06 4.00 11 2 3.91 15 3 3.09 25 4 0.025*
C5.07 3.67 31 6 3.55 48 7 3.00 36 7 0.239
R6: Facility manager
C6.01 3.87 15 1 3.64 44 1 3.00 36 1 0.093 *The mean difference was significant at the 0.05 level.
To check the differences in the mean scores between the three BIMIR status groups,
the one-way ANOVA and its post hoc test were conducted. The p-values below 0.05
indicated the significant differences in the mean scores. Similar to the ANOVA
conducted in Section 7.2.4.2, out of the three main assumptions, the first and second
assumptions were achieved with no doubt. The third assumption (homogeneity of
variances in the three groups) should be checked. In the test of homogeneity of
variances, 51 out of the 53 causes obtained p-values over 0.05 and two (C1.02 and
216
C4.07) below 0.05. Thus, the null hypothesis that the three groups had equal
variances could not be determined, and more actions needed to be performed.
In SPSS, two types of post hoc tests can be performed. In this one-way ANOVA, the
LSD method was selected under “Equal Variances Assumed”, and the Games-Howell
method was adopted under “Equal Variances Not Assumed”. It was found that the
statistically significant differences resulted from the Games-Howell method were
completely included in those resulted from the LSD method. Therefore, the post hoc
test results of the LSD method were used in the subsequent data analysis, as shown in
Table 7.15. It was concluded that the homogeneity of variances was ensured in the
three groups and the third assumption was achieved.
Table 7.15 Post hoc test results for the causes different between BIMIR statuses
Cause Multiple comparisons Mean difference p-value
R2: Owner
C2.05 S1 S2 -0.193 0.431
S3 0.558 0.092
S2 S3 0.750 0.008*
R3: Architect/Engineers
C3.01a S1 S2 0.340 0.269
S3 0.818 0.049*
S2 S3 0.478 0.170
C3.02 S1 S2 0.391 0.170
S3 1.382 0.000*
S2 S3 0.990 0.003*
C3.04 S1 S2 0.291 0.239
S3 0.970 0.004*
S2 S3 0.679 0.017*
C3.08 S1 S2 0.343 0.166
S3 0.885 0.009*
S2 S3 0.542 0.055
C3.11 S1 S2 0.628 0.028*
S3 1.358 0.001*
S2 S3 0.729 0.024*
R4: General contractor/Key trade contractors
C4.02 S1 S2 0.037 0.899
S3 0.867 0.028*
S2 S3 0.830 0.013*
C4.03a S1 S2 0.264 0.314
S3 0.745 0.036*
S2 S3 0.482 0.106
217
C4.04a S1 S2 -0.126 0.665
S3 0.624 0.113
S2 S3 0.750 0.025*
C4.05 S1 S2 -0.139 0.620
S3 0.733 0.054
S2 S3 0.872 0.007*
C4.06 S1 S2 0.165 0.584
S3 1.048 0.011*
S2 S3 0.884 0.011*
C4.07 S1 S2 0.221 0.362
S3 1.109 0.001*
S2 S3 0.888 0.002*
C4.08 S1 S2 -0.330 0.243
S3 0.642 0.092
S2 S3 0.973 0.003*
C4.09 S1 S2 -0.240 0.390
S3 0.958 0.012*
S2 S3 1.197 0.000*
C4.10 S1 S2 -0.600 0.026*
S3 0.218 0.538
S2 S3 0.818 0.008*
C4.11a S1 S2 -0.400 0.211
S3 0.327 0.443
S2 S3 0.727 0.046*
C4.12 S1 S2 -0.203 0.450
S3 0.824 0.024*
S2 S3 1.027 0.001*
C4.13 S1 S2 -0.536 0.082
S3 0.491 0.231
S2 S3 1.027 0.004*
C4.14 S1 S2 -0.323 0.310
S3 0.764 0.075
S2 S3 1.087 0.003*
C4.15 S1 S2 -0.448 0.139
S3 0.739 0.070
S2 S3 1.188 0.001*
C4.16a S1 S2 -0.035 0.910
S3 0.758 0.075
S2 S3 0.793 0.028*
C4.17 S1 S2 -0.360 0.193
S3 0.715 0.055
S2 S3 1.075 0.001*
C4.18 S1 S2 -0.296 0.315
S3 0.533 0.178
S2 S3 0.830 0.015*
C4.19a S1 S2 -0.294 0.329
S3 0.509 0.207
S2 S3 0.803 0.020*
C4.20 S1 S2 0.043 0.878
S3 0.909 0.016*
S2 S3 0.867 0.007*
C4.21 S1 S2 0.155 0.592
S3 1.042 0.008*
218
S2 S3 0.888 0.008*
C4.22 S1 S2 0.218 0.438
S3 1.042 0.007*
S2 S3 0.824 0.011*
R5: Manufacturer/Supplier
C5.03 S1 S2 -0.267 0.337
S3 0.552 0.140
S2 S3 0.818 0.011*
C5.04 S1 S2 0.104 0.704
S3 0.842 0.023*
S2 S3 0.739 0.018*
C5.05a S1 S2 -0.251 0.402
S3 0.509 0.205
S2 S3 0.760 0.027*
C5.06 S1 S2 0.085 0.759
S3 0.909 0.016*
S2 S3 0.824 0.010*
R6: Facility manager
C6.01a S1 S2 0.228 0.452
S3 0.867 0.036*
S2 S3 0.638 0.065 aAdditional cause that was not statistically significant in the ANOVA (Table 7.14).
*The mean difference was significant at the 0.05 level.
As indicated in Table 7.15, the post hoc tests revealed that the mean scores of 32
causes significantly differed between the three groups at the 0.05 level. Since a great
number of causes were identified in the tests, this study explained the causes in
categories rather than discussed them one by one.
It is notable that out of the 32 causes, only two causes (C3.11 and C4.10) were
different between the BIMIR S1 group and S2 group of building projects.
Specifically, “downstream designers have to make extra efforts to reconfigure or
reformat data ” (C3.11) received the second highest rating (mean = 4.27) and a low
score (mean = 2.98, ranked 44th) in the BIMIR S1 and S2 groups, respectively. This
result was consistent with the NVA activity “architect does not share its complete
model with engineers in the schematic phase” that according to the survey results,
was not perceived critical in the Singapore construction industry. The architect
usually shared CAD-like documents and probably its design model with the
engineers, while the engineers may tend to complete their design work using the
219
traditional way. Thus, the need for the engineers to reconfigure data was not closely
associated with BIM implementation, but with the collaboration within the design
consultant team. Meanwhile, “having little knowledge of BIM and do not know how,
when, and what to use it” (C4.10) was ranked 47th in the S1 group (mean = 3.40) and
fifth in the S2 group (mean = 4.00). The difference of the mean scores was significant
(-0.600), implying that compared with years ago, local firms now already had some
knowledge and experience of BIM implementation. Therefore, the issues regarding
how, when, and what to perform BIM work processes had been somewhat addressed,
resulting in fewer NVA activities. Furthermore, since there was still weak
collaboration in terms of the whole project team, the importance ratings of other
causes did not significantly change. In contrast, because of the collaboration among
the major stakeholders that implemented their part of BIM, the majority of the causes
(the remaining 30 causes) had statistically become less important between the S1
group and the S3 group as well as between the S2 group and the S3 group.
The one-way ANOVA results were also supported by the Spearman’s rank
correlation between the three BIMIR groups. As shown in Table 7.16, the correlation
coefficient value between the BIMIR S1 group and the BIMIR S2 group was 0.277,
significant at the 0.05 level. The statistical significance indicated that overall the
rankings in the two groups were correlated and agreed upon, although there were still
differences in between suggested by the minor correlation coefficient (0.277). On the
contrary, the coefficient values between the S1 group and the S3 group as well as
between the S2 group and the S3 group were not significant, implying that most of
the causes were no longer important in the S3 group of building projects.
BIMIR status is a “snapshot” of BIM implementation which would be influenced by
the interactions between the hindrances to and drivers for BIM implementation.
220
Therefore, the analysis of the hindering and driving factors of BIM implementation
are presented in the subsequent sections.
Table 7.16 Spearman’s rank correlation of the causes between BIMIR statuses
BIMIR status Test statistics S1 (no BIM) S2 (lonely BIM) S3 (collaborative BIM)
S1 (no BIM) Correlation
coefficient
1.000 0.277 0.180
p-value – 0.045* 0.196
S2 (lonely
BIM)
Correlation
coefficient
– 1.000 0.035
p-value – – 0.802
S3
(collaborative
BIM)
Correlation
coefficient
– – 1.000
p-value – – – *Correlation was significant at the 0.05 level (two-tailed).
7.3 Analysis Results and Discussions of Survey II
7.3.1 Profile of respondents and their organizations
Survey II intended to identify the critical factors that hindered and drove BIM
implementation in building projects in Singapore. From May to August 2016, the
final questionnaires were sent to the remaining 659 out of the 1318 organizations as
well as the 33 organizations that responded to survey I and were willing to participate
in Survey II. Finally, 89 completed questionnaires were received, yielding a response
rate of 12.86% which fell within the general response rate of 10%–15% for Singapore
surveys (Teo et al., 2007). The profile of the 89 respondents and their organizations is
presented in Table 7.17.
Table 7.17 Profile of the respondents and their organizations in Survey II
Characteristics Categorization Frequency Percentage (%)
Organization
Main business Architectural firm 18 20.2
Structural engineering firm 6 6.7
MEP engineering firm 13 14.6
General construction firm 30 33.7
Trade construction firm 3 3.4
Facility management firm 3 3.4
Others 16 18.0
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BCA financial grade A1 27 30.3
A2 2 2.2
B1 1 1.1
C1 1 1.1
C3 3 3.4
Single grade 2 2.2
L6 5 5.6
L3 1 1.1
Not applicable 47 52.8
Years of BIM adoption 0 year 10 11.2
1-3 years 42 47.2
4-5 years 22 24.7
6-10 years 13 14.6
Over 10 years 2 2.2
Respondent
Discipline Government agent 2 2.2
Developer 6 6.7
Architect 22 24.7
Structural designer 9 10.1
MEP designer 9 10.1
General contractor 28 31.5
Trade contractor 6 6.7
Supplier/Manufacturer 2 2.2
Facility manager 5 5.6
Years of experience 5-10 years 40 44.9
11-15 years 11 12.4
16-20 years 8 9.0
21-25 years 9 10.1
Over 25 years 21 23.6
In terms of the respondent organizations, 30 (33.7%) of the organizations were
general construction firms, followed by 18 (20.2%) architectural firms, 13 (14.6%)
MEP engineering firms, six (6.7%) structural engineering firms, three (3.4%) trade
construction firms, and three (3.4%) facility management firms, respectively. In
addition, the 16 organizations listed in the “others” category included the BCA, the
HDB, developers, precasters, and other consultancy firms such as multidisciplinary
consultancy firms, a project management consultancy firm, and a BIM consultancy
firm. Thus, the sample had a good balance of different industry players, and the
respondent organizations were representative of the key BIM implementers in the
construction value chain.
222
When the organizations were measured by the financial grades in the contractors
registry of the BCA, 42 (47.2%) of the surveyed organizations were registered
contractors. Among which, 27 (30.3%), five (5.6%), two (2.2%), two (2.2%), and one
(1.1%) held the grades of A1, L6, A2, single grade, and B1, respectively. Because of
similar reasons with the questionnaire Survey I, there were also small- and medium-
sized contractors (three for C3, one for C1, and one for L3). The rest 47 (52.8%)
organizations included government agencies, developers, and various consultancy
firms.
Regarding the BIM implementation experience, about half (47.2%) of the
organizations had one to three years’ experience in their building projects in
Singapore, followed by 22 (24.7%) and 13 (14.6%) organizations having four to five
years and six to ten years’ experience, respectively. Only two firms had implemented
BIM in their building projects for more than 10 years. Since the mandatory BIM
implementation took effect in July 2015, it was considered reasonable that over half
(58.4%) of the organizations had just started to implement BIM in last three years.
Those organizations that had no BIM implementation experience in Singapore were
included in the following data analysis. In addition to the reason stated in Section
7.2.1, the 10 professionals should have a say in “which factors among the 47 potential
hindrances and 32 potential drivers should be considered when implementing BIM in
the near future projects”. Thus, their ratings were also important. This provided clear
evidence that the local construction industry had been moving from traditional project
delivery into BIM-based project delivery, and was shifting to industry-wide BIM
implementation. Thus, the data could reflect the views of key BIM users on the
hindrances to and drivers for change towards full BIM implementation in the
Singapore construction industry, thus assuring the response quality.
223
In terms of the respondents, the top three disciplines were: general contractor
(31.5%), architect (24.7%), and structural designer (10.1%) and MEP designer
(10.1%), while there were only two (2.2%) government agents and manufacturers
(suppliers), respectively. In addition, 55.1% of the respondents had over 10 years’
experience in the construction industry, and 30 (33.7%) of them had worked over 20
years.
Similar to the interviews conducted after Survey I, post-survey interviews were
performed to solicit comments on the hindrances to and drivers for BIM
implementation, especially those that were not found statistically significant. The
profile of the interviewees who had originally participated in Survey II and had more
than three years’ BIM implementation experience in Singapore was presented in
Table 7.18. The experts commented that overall the findings of this survey were
reasonable. There comments were used to support the explanation of the hindering
and driving factors as well as the exclusion of insignificant factors.
Table 7.18 Profile of the interviewees in Survey II
Interviewee Work experience Designation Firm
1 15 years Project manager General construction firm
2 5 years Quantity surveyor General construction firm
3 10 years Senior engineer MEP consultancy firm
4 8 years Quantity surveying in
charge
General construction firm
5 11 years Deputy contracts manager Construction and
development firm
7.3.2 Hindrances to change towards full BIM implementation
7.3.2.1 Overall ranking
As shown in Table 7.19, the data related to the respondents’ perceptions of the
hindrances’ influence on BIM implementation in building projects in Singapore
obtained the Cronbach’s alpha coefficient value of 0.974, showing high data
224
reliability. The mean scores of the 47 hindrances to BIM implementation ranged from
3.17 to 3.79, and these hindrances were ranked based on their mean scores. To test
whether the hindrances had statistically significant influence on BIM implementation,
the one-sample t-test was performed. The test results implied that BIM
implementation in building projects in Singapore was negatively affected by 44
critical hindrances to change (CHCs). The ranking of the CHCs could enable the
practitioners to understand which areas of activities of BIM implementation are
worthwhile to pay more attention to and to prioritize for resource investments.
Table 7.19 Significance ranking and t-test results of the hindrances to change
Code Hindrances to change towards full BIM implementation Mean Rank p-
value
H01 Executives failing to recognize the value of BIM-based
processes and needing training
3.64 8 0.000*
H02 Concerns over or uninterested in sharing liabilities and
financial rewards
3.48 21 0.000*
H03 Construction lawyers and insurers lacking understanding of
roles/responsibilities in new process
3.26 41 0.028*
H04 Lack of skilled employees and need for training them on
BIM and OSM
3.69 3 0.000*
H05 Industry’s conservativeness, fear of the unknown, and
resistance to change comfortable routines
3.69 3 0.000*
H06 Employees still being reluctant to use new technology after
being pushed to training programs
3.42 28 0.001*
H07 Entrenchment in 2D drafting and unfamiliarity to use BIM 3.69 3 0.000*
H08 Financial benefits cannot outweigh implementation and
maintenance costs
3.37 33 0.004*
H09 Lack of sufficient evidence to warrant BIM use 3.54 13 0.000*
H10 Liability of BIM such as the liability for common data for
subcontractors
3.33 37 0.010*
H11 Resistance to changes in corporate culture and structure 3.43 23 0.001*
H12 Need for all key stakeholders to be on board to exchange
information
3.79 1 0.000*
H13 Lack of trust/transparency/communication/partnership and
collaboration skills
3.33 37 0.009*
H14 BIM operators lacking field knowledge 3.62 11 0.000*
H15 Field staff dislike BIM coordination meetings looking at a
screen
3.43 23 0.000*
H16 Lack of consultants’ feedbacks on subcontractors’ model
coordination
3.34 35 0.005*
H17 Few benefits from BIM go to designers while most to
contractors and owners
3.17 46 0.178
H18 Lack of legal support from authorities 3.20 45 0.083
H19 Lack of owner request or initiative to adopt BIM 3.43 23 0.001*
H20 Decision-making depending on relationships between 3.28 40 0.019*
225
project stakeholders
H21 Owners set minimal risk and minimum first cost as crucial
selection criteria
3.35 34 0.003*
H22 Poor knowledge of using OSM and assessing its benefits 3.34 35 0.002*
H23 Requiring higher onsite skills to deal with low tolerance
OSM interfaces
3.42 28 0.000*
H24 OSM relies on suppliers to train contractors to install
correctly
3.26 41 0.037*
H25 Owners’ desire for particular structures or finishes when
considering OSM
3.17 46 0.152
H26 Market protection from traditional suppliers/manufacturers
and limited OSM expertise
3.42 28 0.001*
H27 Contractual relationships among stakeholders and need for
new frameworks
3.71 2 0.000*
H28 Traditional contracts protect individualism rather than best-
for-project thinking
3.65 7 0.000*
H29 Lack of effective data interoperability between project
stakeholders
3.43 23 0.000*
H30 Owners cannot receive low-price bids if requiring 3D
models
3.31 39 0.012*
H31 Firms’ unwillingness to invest in training due to initial cost
and productivity loss
3.63 10 0.000*
H32 Assignment of responsibility/risk to constant updating for
broadly accessible BIM information
3.52 17 0.000*
H33 Lack of standard contracts to deal with responsibility/risk
assignment and BIM ownership
3.54 13 0.000*
H34 BIM model issues (e.g., ownership and management) 3.53 15 0.000*
H35 Poor understanding of OSM process and its associated costs 3.51 19 0.000*
H36 OSM requires design to be fixed early using BIM 3.49 20 0.000*
H37 Seeing design fees of OSM as more expensive than
traditional process
3.45 22 0.000*
H38 Difficulty in logistics and stock management of OSM 3.40 31 0.001*
H39 Unclear legislations and qualifications for precasters and
inadequate codes for OSM varieties
3.25 43 0.024*
H40 Interpretations resulted from unclear contract documents 3.39 32 0.000*
H41 Using monetary incentive for team collaboration results in
blaming rather than resolving issues
3.22 44 0.041*
H42 Costly investment in BIM hardware and software solutions 3.66 6 0.000*
H43 Interoperability issues such as software selection and
insufficient standards
3.52 17 0.000*
H44 Need for increasingly specialized software for specialized
functions
3.43 23 0.000*
H45 Difficulty in multi-discipline and construction-level
integration
3.53 15 0.000*
H46 Technical needs for multiuser model access in multi-
discipline integration
3.64 8 0.000*
H47 Firms cannot make most use of IFC and use proprietary
formats
3.56 12 0.000*
Note: Cronbach’s alpha coefficient value = 0.974. *The one-sample t-test result was significant at the 0.05 level (two-tailed).
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The top rank of “need for all key stakeholders to be on board to exchange
information” (H12, mean = 3.79) echoed previous studies (El Asmar et al., 2013;
Forsythe et al., 2015) which found that the early involvement of all the major
stakeholders in a building project, especially the contractors, in the design stage to
share expertise and information is critical to creating and fixing optimal design
models early. Such models help solve the potential issues that would traditionally
occur during construction, and pave the way for the collaboration among the
stakeholders in later stages. The widely-accepted definition of BIM proposed by the
NBIMS committee stated that “a basic premise of BIM is collaboration by different
stakeholders at different phases of the life cycle of a facility to insert, extract, update
or modify information in the BIM to support and reflect the roles of that stakeholder”
(NBIMS, 2007). This survey result was also consistent with the finding of Kent and
Becerik-Gerber (2010) that the involvement of the manufacturers (suppliers) and
trade contractors was limited in the design stage. BIM implementation would be
efficient if the entire team ranging from the owner, the design consultants, to the
specialty contractors could actively participate and contribute from the early design
stage throughout project completion.
“Contractual relationships among stakeholders and need for new frameworks” (H27,
mean = 3.71) was the second most significant hindrance, indicating that the
temporary contractual structure in building projects in Singapore was not
collaborative and posed challenges in collaborative BIM implementation. This
finding was in line with the finding of Fischer et al. (2014) that as disputes raised, the
lack of new frameworks would easily thrust the primary participants into adversarial
positions. Often the parties’ only recourse was to claim, which would force them to
act in their own best interests, crippling the project team (AIA and AIACC, 2007).
For example, the downstream parties, if involved upfront, would work at risk upfront
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in a financial manner and lack motivation and enthusiasm to work collaboratively
with others (Ross et al., 2006).
“Lack of skilled employees and need for training them on BIM and OSM” (H04,
mean = 3.69) was ranked third in the overall ranking, implying that the Singapore
construction industry still suffered from the lack of skilled personnel who could lead
BIM modeling and management teams. The employees tended to be reluctant to
adopt the new technology and participate in the new workflow (Zahrizan et al., 2013).
One example of such reluctance was that many firms could not take advantage of the
commonly-used data exchange format (IFC) and still used proprietary formats, which
would not enable smooth data exchange with other team members. In addition, the
experts involved in the post-survey interviews highlighted the concern about the
management’s willingness to train their employees.
“Industry’s conservativeness, fear of the unknown, and resistance to change
comfortable routines” (H05, mean = 3.69) occupied the third position in the overall
ranking, suggesting that such negative mindsets and behaviors were rooted in the
local construction industry. This value proposition established conservative and
unsupportive culture of most firms, significantly hindering local BIM
implementation. This finding was consistent with previous studies (Khosrowshahi
and Arayici, 2012; Zahrizan et al., 2013) which found inadequate marginal utility to
be realized by using BIM. The stakeholders tended to be conservative and reluctant to
change their customized ways of working and blaming. Although the executives of
many firms changed to use 3D tools, their leadership style appealed to continue to
keep a 2D mindset.
“Entrenchment in 2D drafting and unfamiliarity to use BIM” (H07, mean = 3.69)
were also ranked third, revealing that many firms in Singapore had little expertise and
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experience in implementing BIM. This was in agreement with the survey result that
58.4% of the responding firms had no more than three years’ BIM experience, and
with the finding of Kiani et al. (2015) that many firms were satisfied with their
conventional work methods to complete their work and therefore saw BIM uses as
extra efforts. For example, upfront BIM operators tended to lack enough field
knowledge to know what they were modeling and its constraint in the actual
construction; as a result, the digital models may not be developed correctly.
“Costly investment in BIM hardware and software solutions” (H42, mean = 3.66)
received the sixth highest rating, implying that costly BIM infrastructure significantly
hindered BIM implementation. In the post-survey interviewees, the experts reported
that currently the hardware in offices may not be powerful enough to run relevant
BIM software at an efficient speed. For example, huge file sizes and required storage
space as well as high speed data transmission between users would pose challenges to
the current office environment. Although half of the initial purchase cost of the
hardware and software solutions were subsidized by the local government,
subsequent upgrading or subscription fees would be no longer funded.
“Traditional contracts protect individualism rather than best-for-project thinking”
(H28, mean = 3.65) was ranked seventh among the 47 hindrances. As mentioned
earlier, even BIM technology has been used by the design consultants and possibly
the contractors, the technological process has been suffering from physical and
information fragmentation in different stages of the project. The creation, integration,
and use of digital design models would potentially raise many liability issues under
the traditional contracts. This was because little collaboration was built within the
project team (Lam, 2014). For example, the upfront parties were cautious about
providing incomplete or wrong design information to the downstream parties
(Eastman et al., 2011), whereas the latter was anxious about providing professional
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advice in the design modeling due to potential liabilities (AIACC, 2014). Such issues
urged the roles and responsibilities to be established in standard contracts. Although
the BIM Particular Conditions in Singapore has been drafted (BCA, 2015b), the main
form of contract in Singapore was still based on the adversarial system, leading to
individualism and isolated working environment.
“Executives failing to recognize the value of BIM-based processes and needing
training” (H01, mean = 3.64) occupied the eighth position in the overall ranking. This
result substantiated the finding of Khosrowshahi and Arayici (2012) that the
executives of the primary participants may be unwilling to change when they had
long been psychologically entrenched in the traditional drafting practices. BIM
implementation requires not only powerful infrastructure, but also competent and
experienced personnel that could be trained or engaged from the market (Anumba et
al., 2010). The local practitioners needed training and technical support in practice as
most of them were not knowledgeable and experienced about a higher level of BIM
implementation. In most cases, the executives can determine the allocation of the
capital investment to purchase and upgrade the infrastructure, and the sponsorship of
training programs (Zhao et al., 2014a). However, the experts participating in the post-
survey interviews revealed that the management of many firms tended to ensure
things under control as they previously did, because the cost and benefits of BIM
implementation were difficult to estimate and foresee. Thus, without the executive
support, training sessions could not be arranged, and a higher resource allocation
priority could not be obtained, leading to the lack of supporting resources and
expertise among the major stakeholders.
“Technical needs for multiuser model access in multi-discipline integration” (H46,
mean = 3.64) was ranked eighth. This result was consistent with previous studies
(Azhar et al., 2014; Juan et al., 2017) which found that multi-discipline model
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integration would require technical expertise, protocols, and advanced infrastructure
for multiuser model access. The post-survey interviewees also pointed out that
different parties tended to use various software or software versions, which created a
difficulty in integrating digital models across disciplines.
“Firms’ unwillingness to invest in training due to initial cost and productivity loss”
(H31, mean = 3.63) obtained the tenth position, indicated that the local practitioners
did not actively invest in training their staff and adopting new technologies. The post-
survey interviewees emphasized the importance of financial capabilities. While the
biggest firms were able to ride on the BIM wave, a huge number of SMEs and foreign
firms based in Singapore faced adoption challenges such as lacking the capital
investment for BIM tools and trainings to build up BIM expertise (Lam, 2014;
Forsythe et al., 2015).
In addition, the three insignificant hindrances were analyzed. It has been verified in
numerical studies and projects that BIM implementation have many benefits, such as
facilitating information integration across the project lifecycle as well as close
collaboration among the primary participants (Rezgui et al., 2013). It may be still
biased or ignored that such benefits could reduce plenty of wastes such as RFIs. In
this regard, less manpower would be needed in the whole project team, which could
support the exclusion of “few benefits from BIM go to designers while most to
contractors and owners” (H17). Moreover, Singapore has been one of the leading
countries in terms of developing BIM implementation standards (Cheng and Lu,
2015), specifying the local industry to use BIM by mandates, and equipping the firms
with BIM capabilities by subsidizing part of their purchasing, training, and
consultancy fees (BCA, 2016). Thus, “lack of legal support from authorities” (H18)
was not a significant hindrance. Furthermore, one possible explanation for excluding
“owners’ desire for particular structures or finishes when considering OSM” (H25)
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was that this hindrance was related to the owner’s special requirement of OSM,
which was drastic. Also, the experts suggested that such particular desire would not
seriously affect the design modeling as long as the owner expresses clearly and early.
7.3.2.2 Comparison among different stakeholders
This section investigated the differences in the mean scores and rankings of the CHCs
among different stakeholders. In this study, based on the BCA financial grades in
Table 7.17, the responding organizations were categorized into two groups: upfront
stakeholders and downstream stakeholders. The former consisted of government
agencies, developers, and consultancy firms; the latter included construction firms,
precasters, and facility management firms. Among the 89 responding organizations,
42 had BCA financial grades, and three were facility management firms. Since one of
the facility management firms also had a BCA financial grade, the numbers of the
organizations in the upfront group and the downstream group were 45 and 44,
respectively. The reason of this categorizing was that the upfront stakeholders were
either policymakers, owners, or the firms required to submit building plans in BIM
format for regulatory approvals at earlier stages, while the downstream firms were not
or less affected by this policy. The BCA found that almost all the local consultants
had used BIM, but only large contractors were likely to adopt BIM (Lam, 2014).
To check whether there were differences in the significance mean scores of the CHCs
between the two groups of responding organizations, the independent-samples t-test
was performed. The p-values below 0.05 represented statistically significant
differences in the mean scores. Besides, the Spearman’s rank correlation was
conducted to test whether there was agreement on the rankings of the CHCs between
the two groups. A summary of the mean scores and rankings as well as the test results
are presented in Table 7.20.
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Table 7.20 Mean scores and ranking of the CHCs between upfront and downstream
stakeholders
Code Upfront stakeholders
(N=45)
Downstream stakeholders
(N=44)
Independent-
samples t-test
Mean Rank Mean Rank p-value
H01 3.71 12 3.57 4 0.550
H02 3.62 19 3.34 26 0.242
H03 3.36 39 3.16 38 0.399
H04 3.84 2 3.52 9 0.201
H05 3.80 5 3.57 4 0.338
H06 3.56 24 3.27 31 0.221
H07 3.82 3 3.55 7 0.268
H08 3.49 28 3.25 32 0.339
H09 3.76 7 3.32 29 0.075
H10 3.44 35 3.20 34 0.334
H11 3.49 28 3.36 23 0.606
H12 4.00 1 3.57 4 0.067
H13 3.47 32 3.18 36 0.243
H14 3.78 6 3.45 14 0.220
H15 3.67 17 3.18 36 0.033*
H16 3.64 18 3.02 44 0.007*
H19 3.47 32 3.39 20 0.751
H20 3.44 35 3.11 40 0.160
H21 3.31 42 3.39 20 0.744
H22 3.31 42 3.36 23 0.808
H23 3.33 41 3.50 11 0.417
H24 3.27 44 3.25 32 0.946
H26 3.36 39 3.48 12 0.606
H27 3.73 8 3.68 1 0.831
H28 3.69 14 3.61 2 0.763
H29 3.69 14 3.16 38 0.019*
H30 3.51 26 3.11 40 0.105
H31 3.82 3 3.43 17 0.085
H32 3.62 19 3.41 19 0.307
H33 3.60 21 3.48 12 0.583
H34 3.71 12 3.34 26 0.078
H35 3.49 28 3.52 9 0.884
H36 3.53 25 3.45 14 0.737
H37 3.51 26 3.39 20 0.564
H38 3.47 32 3.34 26 0.584
H39 3.40 37 3.09 42 0.152
H40 3.58 22 3.20 34 0.080
H41 3.38 38 3.07 43 0.153
H42 3.73 8 3.59 3 0.568
H43 3.58 22 3.45 14 0.603
H44 3.49 28 3.36 23 0.596
H45 3.73 8 3.32 29 0.074
H46 3.73 8 3.55 7 0.444
H47 3.69 14 3.43 17 0.232
Note: The Spearman’s rank correlation coefficient is 0.474 (p-value=0.000). *The hindrance was statistically significant at the 0.05 level (two-tailed).
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The mean scores of the upfront stakeholders were found to be generally higher than
those of the downstream ones. This result revealed that in a building project, the
upfront stakeholders usually gave more weight to the CHCs than the downstream
participants. The experts participating in the post-survey interviews reported that
teamwork among the design consultants was in an urgent need. They argued that
compared with the contractors who were headed by the general contractor, the
consultants usually could not develop a “frozen” set of models at an appropriate time
for all the stakeholders to work on it. Besides, the independent-samples t-test results
indicated that the means of three CHCs significantly differed between the two groups
of organizations, which would be analyzed and discussed below.
“Field staff dislike BIM coordination meetings looking at a screen” (H15) received a
significantly higher mean score from the upfront stakeholders (mean = 3.67) than
from the downstream stakeholders (mean = 3.18). This result implied that the upfront
stakeholders thought that the field staff were not ready to implement BIM. The
experts participating in the post-survey interviewees stated that the coordination of
work among the key stakeholders using BIM models, whether in face-to-face
meetings or via video conferencing, would have greater lifecycle impact. But in
reality, experienced and skilled field staff usually hesitated to learn new ways of
working and could not see how they could benefit from such models (Zahrizan et al.,
2013). The staff even fell burdened, because BIM implementation was seen not as a
mainstream activity on site but rather as add-ons to the existing meetings and site
work on call.
“Lack of consultants’ feedbacks on subcontractors’ model coordination” (H16) had a
large mean difference between the upfront stakeholders (mean = 3.64) and the
downstream firms (mean = 3.02). This result indicated that the upfront stakeholders
were not well prepared to use BIM and collaborate with the downstream parties. In
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the project that a post-survey interviewee participated, the consultants were focusing
only on the mandatory submissions in BIM format (Lam, 2014), and the BIM models
they created were not accurate enough for downstream uses. In addition, building a
property in the virtual platform is as tough as building the property on an actual site.
The other professional pointed out that the contractors did not have a suitable
candidate that could lead an in-house modeling team, and few resources of the
contractors were put into the modelling. To reduce potential clashes and paperwork,
the contractors’ modeling team and site engineers expected that the owner and design
consultants could provide useful feedbacks to help their model coordination at the
construction level, but at times the upfront parties were not able to give adequate
useful feedbacks in the coordination meetings (Chelson, 2010). In this case, BIM
implementation may not really be a helping hand to guide construction activities, but
rather be additional work or burden to the contractors.
The mean of “lack of effective data interoperability between project stakeholders”
(H29) was significantly distinct between the upfront parties (mean = 3.69) and the
downstream participants (mean = 3.16), suggesting that the upfront stakeholders had
difficulties in exchanging data (Arayici et al., 2011; Azhar et al., 2014). In the post-
survey interviews, the professionals highlighted that although the design consultants
used 3D software to design and produce submittals, different consultants concentrated
on their own submittals rather than the collaboration among disciplines. On the other
hand, the consultants still kept a 2D mindset. For example, pipes were represented by
lines in the 3D models. Consequently, the designs could not match among different
disciplines. In the meantime, most interviewees argued that the IT infrastructure in
their offices were not powerful enough to run the software at high speeds. Also, not
all the stakeholders used the same software or the same versions of the software.
Moreover, due to the lack of standards to follow, the parties came up with their own
235
models using proprietary formats. Thus, the models were not compatible, creating
difficulties in interoperating effectively among different stakeholders.
Furthermore, despite the statistically significant differences in the mean scores of the
three CHCs, the Spearman’s rank correlation coefficient of 0.474 with a p-value of
0.000 indicated significant agreement on the rankings of the 44 CHCs between the
two groups of organizations.
7.3.3 Drivers for change towards full BIM implementation
7.3.3.1 Overall ranking
As shown in Table 7.21, the Cronbach’s alpha coefficient value of the data related to
the influence of the drivers on BIM implementation in building projects in Singapore
was 0.968, implying that the data had high reliability. The mean scores of the 32
drivers for BIM implementation ranged from 2.88 to 3.99. These drivers were ranked
based on the overall mean scores. Similar to the analysis of the hindrances, the one-
sample t-test was also performed to check whether the influence of the drivers was
statistically significant. The test results suggested that 31 out of the 32 drivers
obtained p-values below 0.05, indicating that their mean scores were significantly
different from the test value of 3.00. Thus, the 31 critical drivers for change (CDCs)
had significantly driven BIM implementation in building projects in Singapore.
Table 7.21 Significance ranking and t-test results of the drivers for change
Code Drivers for change towards full BIM implementation Mean Rank p-
value
D01 BIM vision and leadership from the management 3.99 1 0.000*
D02 Changes in organizational structure and culture 3.64 11 0.000*
D03 Stakeholders seeing the value of adopting their own part of
BIM
3.71 9 0.000*
D04 Training on new skillsets and new ways of working 3.82 4 0.000*
D05 Owner’s requirement and leadership to adopt BIM 3.90 3 0.000*
D06 Regulatory agencies’ early participation to BIM use 3.76 6 0.000*
D07 Gaining competitive advantages from full BIM use 3.79 5 0.000*
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D08 All disciplines sharing models in a ‘Big Room’ 3.63 12 0.000*
D09 Government support such as subsidizing training, software,
and consultancy costs
3.69 10 0.000*
D10 Enabling subcontractors to use lower-skilled labor on site 2.88 32 0.316
D11 OSM lowering safety risks by controlling work in factory 3.30 26 0.008*
D12 Alignment of the interests of all stakeholders 3.44 21 0.000*
D13 Governance of BIM-related policies and standards 3.57 16 0.000*
D14 Data sharing and access on BIM platforms 3.62 13 0.000*
D15 3D visualization enabling design communication 3.75 7 0.000*
D16 4D simulation before construction 3.45 19 0.000*
D17 Design coordination between disciplines through clash
detection and resolution
3.92 2 0.000*
D18 Complex design analysis in sustainability, material
selection, and constructability
3.45 19 0.000*
D19 Project lifecycle costing 3.26 30 0.016*
D20 Producing models and drawings for construction and
fabrication
3.75 7 0.000*
D21 High accuracy of model-based documentation 3.58 15 0.000*
D22 More off-site fabrication and assembly of standard
elements
3.53 17 0.000*
D23 Automatic model updating and drawing production to deal
with design changes and their implications
3.48 18 0.000*
D24 Lifecycle information management improving operations
and maintenance
3.29 27 0.007*
D25 Increasing use of design-build and fast-track approach 3.35 23 0.004*
D26 On-site work proceeds in parallel with off-site production 3.28 28 0.007*
D27 OSM standardizes design and manufacturing processes,
simplifying construction and testing and commissioning
processes
3.26 30 0.019*
D28 OSM produces building elements with better quality and
consistency
3.31 24 0.006*
D29 OSM reduces building wastes, especially on-site wastes 3.27 29 0.024*
D30 Integrating model management tools with enterprise
systems to exchange data
3.60 14 0.000*
D31 Increasing complexity in buildings, project delivery, and
marketplace
3.40 22 0.001*
D32 New technologies such as CNC machines 3.31 24 0.003*
Note: Cronbach’s alpha coefficient value = 0.968. *The one-sample t-test result was significant at the 0.05 level (two-tailed).
“BIM vision and leadership from the management” (D01, mean = 3.99) was
recognized as the most significant factor in driving BIM implementation. This result
substantiated the argument of Autodesk (2012) and Miettinen and Paavola (2014) that
BIM implementation starts with a well-articulated vision sponsored in the project.
Autodesk (2012) advocated that top-down approaches are very important in
individual organizations who serve as part of the project leadership team. Thus,
without the vision and mission from the management and the executive leadership
237
behind it, dedicated resources assigned to the adoption of the new way of working
would be probably wasted.
“Design coordination between disciplines through clash detection and resolution”
(D17, mean = 3.92) received the second position in the driver ranking, substantiating
the value of fully coordinated 3D data. This result was in line with the findings of
previous studies (Porwal and Hewage, 2013; Sattineni and Mead, 2013) that full BIM
implementation would enable the development of a composite design model in the
design stage. Such a well-coordinated model could enable multiple downstream
disciplines to document the construction intent and collaborate with other trades on
site in the later stages of the project.
The third most significant driver was “owner’s requirement and leadership to adopt
BIM” (D05, mean = 3.90), indicating that the contractual requirement and active
participation of the owner would motivate its service providers to implement BIM.
Successful BIM implementation also requires the top-down approaches in the project.
Therefore, as the leader of the project team, the owner plays a key role in requiring its
service providers, via certain contract documents, to implement BIM work practices
(Arayici et al., 2011; Azhar et al., 2014). This result was consistent with the findings
of Arayici et al. (2011) and Azhar et al. (2014) that without the requirement and
leadership from the owner, the service providers may continue to deliver their scopes
of work in the accustomed ways, hindering the project-wide collaboration required by
successful BIM implementation.
“Training on new skillsets and new ways of working” (D04, mean = 3.82) was ranked
fourth. The top-down approaches mentioned earlier must be accompanied by bottom-
up approaches such as training the staff to carry out specific work processes to truly
reap the advantages of BIM implementation (Autodesk, 2012). Indeed, it is
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challenging to change the way that the staff carried out various work activities, such
as entrenching themselves in the traditional CAD drafting due to their poor BIM
skills or psychological resistance to change. The adoption of BIM requires many
resources, such as costly infrastructure and skilled personnel either by training the
employees or engaging experts in the market, which were big challenges for many
firms, especially SMEs (Kiani et al., 2015). Thus, only the senior-level support such
as arranging training programs on the new knowledge and skillsets can enable
changes to the existing practices (Arayici et al., 2011; Azhar et al., 2014; Zhao et al.,
2014a; Kiani et al., 2015). The project and organizational context need to be changed
first, followed by changed attitudes and associated behaviors.
“Gaining competitive advantages from full BIM use” (D07, mean = 3.79) was ranked
fifth. It should be noted that firms with successful experience of implementing BIM
would surely gain a competitive advantage in meeting qualification requirements and
win bids in future construction market, which ensured the long-term viability of the
firms and drove them to enhance the capability of implementing their part of BIM in
the current project in return.
“Regulatory agencies’ early participation to BIM use” (D06, mean = 3.76) occupied
the sixth position in the ranking. This results echoed Juan et al. (2017) which found
that in Singapore, the government is the dominant force to promote BIM
implementation. In such a top-down approach, the early involvement of the local
government through mandating building plans e-submissions and standardizing
building review procedures in the design phases could minimize agency comments
and required changes to the design thereafter (AIA and AIACC, 2007).
“3D visualization enabling design communication” (D15, mean = 3.75) was ranked
seventh, indicated enhanced communication patterns in the Singapore construction
239
industry. The functions of accurate 3D models, such as visualization, rendering,
walkthrough, and simulation, enable the project team to communicate the design
intent more clearly and effectively with each other, and with the owner. In particular,
many owners prefer 3D models and cannot understand clearly complex 2D shop
drawings because they are not trained architects. Besides, the visualization and
simulation also facilitate the design coordination across the design models from
different disciplines. Similar findings were also reported by previous studies
(Sattineni and Mead, 2013; Fischer et al., 2014; Wong et al., 2014). Moreover, the
construction impact can be easily studied when any change occurs in the later stages,
enabling the team to select the optimal design option. This is because these functions
can show how close the design comes to the expected outcomes and allow the team to
see the consequence of their decisions (Fischer et al., 2014).
“Producing models and drawings for construction and fabrication” (D20, mean =
3.75) also occupied the seventh position. Computer-based design integration enables
the project team to share data among disparate modelling and analysis applications
reliably by using exchange standards such as IFC (Kunz and Fischer, 2012).
Specifically, in the design stage, where key stakeholders including contractors
physically co-locate in a “Big Room”, the structural engineer can use the initial
architectural model as a base to do structural analysis, and adjust, not re-create, the
model to create and analyze a structural model, while the MEP engineers can create a
MEP model on the same design. The design team can then produce a composite
model by linking the structural and MEP models back into the original architectural
model (Gao and Fischer, 2006; Porwal and Hewage, 2013). In addition, based on the
high-accuracy models shared from the design team, the contractors can document the
construction intent, produce construction models and fabrication models as well as
required drawings, especially for off-site manufacturing, and constantly update the
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models during the construction period till the project is completed and an as-built
model is created.
“Stakeholders seeing the value of adopting their own part of BIM” (D03, mean =
3.71) obtained the ninth highest rating. This result was consistent with a previous
study (Khosrowshahi and Arayici, 2012) which advocated that getting the key
stakeholders, especially their top management, to understand the model-based
advantages over the 2D drafting practices and the competitive edge derived from
successful BIM adoption would drive them to be keen on the new way of delivering
this project. Although all key stakeholders team together, they remain responsible for
individual scopes of work and associated deliverables. The collaboration between the
designers and the contractors does not inherently result in the integration between
disciplines. If not all key stakeholders are keen on their work processes using BIM,
discipline-specific models cannot be integrated and shared openly for high-accuracy
documentation and drawings generation (Gao and Fischer, 2006; Porwal and
Hewage, 2013; Rezgui et al., 2013). For instance, it is common today for the design
team to produce one model, and for the contractors to develop their own model based
on the information provided to them (Sattineni and Mead, 2013, Lam, 2014).
Furthermore, as mentioned earlier, the top-down approach of promoting BIM is
critical in each key party. Thus, if the architect, engineers, contractors, fabricators,
and many other related practitioners do not see the value in implementing their part of
BIM work processes in the whole process, BIM implementation in this project will
likely be stunted (Khosrowshahi and Arayici, 2012; Kunz and Fischer, 2012; Kiani et
al., 2015). Thus, both the owner’ requirement and the service providers’ self-
motivations are vital to enhance BIM implementation in the project.
“Government support such as subsidizing training, software, and consultancy costs”
(D09, mean = 3.69) was ranked tenth in the driver ranking. In Singapore, part of the
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initial implementation costs in training, consultancy, software, and hardware would
be subsidized by the local government in a new BIM fund (BCA, 2016). Such efforts,
together with the active government participation and leadership from the early
design stage, had significantly push the local industry players to adopt BIM. In turn,
the successful BIM application would give the competitive advantage to the major
stakeholders as well as motivate the local government to provide further leadership
and support to BIM implementation in future projects, compared with the lonely BIM
implementation of many individual firms.
Furthermore, “enabling subcontractors to use lower-skilled labor on site” (D10, mean
= 2.88) was not perceived as a significant driver for BIM implementation. The
experts participating in the post-survey interviews reported that although BIM model
functions, especially the visualization of the design intent and the simulation of the
detailed scheduling, could enable the field staff to understand the design intent more
easily (Fischer et al., 2014), currently only relatively skilled workers could ride the
wave of BIM implementation. On the other hand, the local industry still did require
skilled workers for better quality and workmanship because the use of BIM could not
address all nuts and bolts in the actual construction activities (AIA and AIACC,
2009).
7.3.3.2 Comparison among different stakeholders
Similar to the CHCs, the significance of the 31 CDCs might differ among different
responding organizations. This section investigated the differences in the mean scores
and rankings of the CDCs between the upfront stakeholders and the downstream
stakeholders.
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To check whether the significance mean scores of the CDCs were distinct between
the two groups of responding organizations, the independent-samples t-test was
carried out. The p-values below 0.05 represented statistically significant differences
in the mean scores. In addition, the Spearman’s rank correlation was conducted to
examine whether there was agreement on the rankings of the CDCs between the two
groups of stakeholders. A summary of the mean scores and rankings as well as the
test results are presented in Table 7.22.
Table 7.22 Mean scores and ranking of the CDCs between upfront and downstream
stakeholders
Code Upfront stakeholders
(N=45)
Downstream stakeholders
(N=44)
Independent-samples t-test
Mean Rank Mean Rank p-value
D01 4.07 1 3.91 2 0.551
D02 3.78 9 3.50 16 0.273
D03 3.89 6 3.52 14 0.144
D04 3.91 4 3.73 5 0.431
D05 3.98 2 3.82 4 0.508
D06 3.91 4 3.61 10 0.171
D07 3.64 12 3.93 1 0.179
D08 3.53 16 3.73 5 0.391
D09 3.82 7 3.55 12 0.284
D11 3.27 25 3.34 26 0.741
D12 3.56 15 3.32 27 0.281
D13 3.62 13 3.52 14 0.680
D14 3.73 11 3.50 16 0.316
D15 3.78 9 3.73 5 0.827
D16 3.47 19 3.43 21 0.880
D17 3.96 3 3.89 3 0.764
D18 3.44 21 3.45 20 0.966
D19 3.38 22 3.14 31 0.255
D20 3.80 8 3.70 8 0.673
D21 3.62 13 3.55 12 0.750
D22 3.49 18 3.57 11 0.733
D23 3.47 19 3.50 16 0.887
D24 3.36 23 3.23 30 0.544
D25 3.27 25 3.43 21 0.481
D26 3.24 28 3.32 27 0.721
D27 3.11 31 3.41 23 0.168
D28 3.27 25 3.36 25 0.669
D29 3.24 28 3.30 29 0.829
D30 3.51 17 3.68 9 0.433
D31 3.31 24 3.50 16 0.405
D32 3.22 30 3.41 23 0.373
Note: The Spearman’s rank correlation coefficient is 0.787 (p-value=0.000). *The hindrance was statistically significant at the 0.05 level (two-tailed).
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The analysis results indicated that none of the 31 CDCs obtained significantly
different mean scores between the upfront stakeholders and the downstream
stakeholders. Besides, the overall mean scores of the upfront group (3.57) and the
downstream group (3.53) were roughly equal. This result was reasonable because
successful BIM implementation in a building project needed the entire project team,
both the upfront and downstream parties, to participate and collaborate with each
other, such as staying in close communication and exchanging data of different
disciplines (Rezgui et al., 2013). The post-survey interviewees found that although
project stakeholders remained responsible for their respective deliverables, working
on the same platform was essential for a more effective delivery.
Furthermore, the high Spearman’s rank correlation coefficient of 0.787 (p-value =
0.000) indicated significant agreement on the rankings of the 31 CDCs between the
two groups of stakeholders. This substantiated the statistically insignificant
differences.
7.3.4 Interpreting the CHCs and CDCs with the organizational change
framework
Since BIM implementation in the building project context can be conceptualized as
an organizational change, the project team is then recognized a project organization.
All the major stakeholders serve as the business units that work collaboratively with
each other in this organization. This section would interpret the significant hindrances
to and drivers for BIM implementation from the perspective of organizational change.
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7.3.4.1 People
Figure 7.1 shows the overall linkages between the people-related organizational
change attributes and the critical factors influencing BIM implementation in building
projects in Singapore. All the linkages between the CHCs, organizational change
attributes, and CDCs (CHC–organizational change attribute–CDC) in this figure were
analyzed point for point in this section.
H01
H02
H05
H06
H07
H10
H03
H04
H11
H12
H15
H21
H22
H13
H14
H23
H24
H32
H27
H28
PeS4
PeS5
PeC1
PeC2
PeC3
PeC4
PeS1
PeS3
PeI1
PeI2
PeI3
H33
PeS2
PeS6
H41
D02
D06
D08
D03
D04
D11
D12
D14
D25
CHCs
Organizational
change attributes
CDCs
Figure 7.1 Framework of people management from the organizational change
perspective
Inter-enterprise structure plays a key role in organizational change because it
determines the relationships among the primary project participants. Specifically,
“contractual relationships among stakeholders and need for new frameworks” (H27)
and “lack of standard contracts to deal with responsibility/risk assignment and BIM
ownership” (H33) are closely associated with “contractual relationship” (PeS1) in the
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attributes of organizational change, while two critical hindrances (H12 and H24) can
represent “involvement” (PeS4). As an organizational change, BIM implementation
requires all the major stakeholders in the project organization to work collaboratively
from early design through multi-party collaboration contracts or physical colocation
in a “Big Room”, enabling data sharing and optimizing the design (Kunz and Fischer,
2012; Azhar et al., 2014). In the collaborative team, the stakeholders remain
responsible for individual scopes of work and may not understand enough the work of
other disciplines, and therefore the participation and collaboration of the key
stakeholders is necessary. For example, design-build approach has been advocated by
the BCA (2013b) and a professional body (Anumba et al., 2010) to drive the
collaboration between the design team and construction team in BIM implementation.
Thus, “increasing use of design-build and fast-track approach” (D25) is pertaining to
“contractual relationship” (PeS1) and “all disciplines sharing models in a ‘Big
Room’” (D08) can represent “involvement” (PeS4). The project-wide collaboration
was plagued with the adversarial relationships among the key participants in the
existing contractual framework (Fischer et al., 2014). One possible explanation is that
the standard contracts to deal with the roles, responsibilities, and benefits of the
parties in BIM-based project delivery have not been well developed and proven to be
efficient (Eastman et al., 2011). Nevertheless, the owner requirement and continuous
participation would somewhat overcome the hostile relationship. The regulatory
agencies in Singapore would provide high-level compliance information and funds,
guiding the project team to purposefully design, build, and manage the building using
BIM (BCA, 2016). Hence, the critical driver “regulatory agencies’ early participation
to BIM use” (D06) can represent “leadership” (PeS2) in the organizational change
attributes. It was noteworthy that “leadership” (PeS2) were considered as a significant
attribute in other studies (Wigand, 2007; Kasimu et al., 2012; Verdecho et al., 2012;
Dahlberg, 2016) and can be represented by H25 which, in this study, was not deemed
as a critical hindrance. This is because this hindrance related to the owner’s special
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requirement of OSM was drastic given that the team is bound by multi-party
collaboration agreements.
“Traditional contracts protect individualism rather than best-for-project thinking”
(H28) can be closely linked to “reward arrangement” (PeS3) in the organizational
change attributes. In Singapore, the BIM Particular Conditions has been drafted to
guide the industry to address the procedures of handling digital data, roles and
responsibilities, intellectual property rights, each party’ extent of reliance on 3D
models, and contractual privity (BCA, 2015b). However, the main form of contract
currently used is still based on the adversarial system that prohibits collective benefits
and encourages individualism, and does not change the contractual relationships or
risk transfers in the principal agreements. The sharing of risks and rewards are not
included in the local forms of contract. Thus, another significant hindrance “concerns
over or uninterested in sharing liabilities and financial rewards” (H02) can also be
associated with “reward arrangement” (PeS3), and four hindrances related to risk and
responsibility (H02, H10, H21, and H32) can present “risk allocation” (PeS5) in the
attributes of organizational change. In addition, Kent and Becerik-Gerber (2010)
argued that monetary initiative would be a poor motivator to force the team to work
together because it might result in blaming rather than resolving issues. Thus, H41
can be linked to “conflict management” (PeS6). Since incorrect information providers
may be blamed for potential liability issues, the parties tend to be conservative in
providing information or advice to other parties (AIACC, 2014). To remove such
liability issues, the interests of the major stakeholders should be aligned (Azhar et al.,
2014). Therefore, “alignment of the interests of all stakeholders” (D12) can be
associated with “reward arrangement” (PeS3), “risk allocation” (PeS5), and “conflict
management” (PeS6) in the organizational change attributes.
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In addition, because of the pervasive nature, corporate culture also plays a critical role
in organizational change (Austin and Ciaassen, 2008). Removing the potential
liability concerns would facilitate open and continuous data sharing within the project
team; therefore, two significant hindrances (H02 and H12) and three drivers (D08,
D12, and D14) can be linked to “sharing” (PeC1). The team members’ willingness to
change depends largely on their awareness of new ways of design and construction,
understanding of BIM process, experience of using BIM, and level of reliance on the
traditional CAD approach. Low (1998) found that people tend to respond to change in
their accustomed ways when confronted with change, and hold a biased view of
change that fits most comfortably into their own perceptions of the reality. The staff
of the primary participants may be unwilling to change towards using BIM along with
OSM when they have been psychologically entrenched in the traditional drafting
practices (Khosrowshahi and Arayici, 2012). Thus, three critical hindrances (H05,
H07, and H11) are closely associated with “willingness to change” (PeC2), which
urges cultural change in the key stakeholders (“changes in organizational structure
and culture”, D02). Meanwhile, Zahrizan et al. (2013) reported that many firms
thought of adapting to new ways of working as extra efforts and cannot understand
the value of BIM over 2D drafting. Blismas and Wakefield (2009) found that the
owner is reluctant to understand the OSM process and worries about the cost for
unconventional design. Thus, H01 and H22 are associated with “commitment on new
ways” (PeC03). Nevertheless, the OSM approach moves more labor-intensive on-site
activities to a factory environment and creates many benefits, such as lowering injury
rate (Ross et al., 2006). In this regard, three critical drivers (D3, D08, and D11) are
related to “commitment on new ways” (PeC03) and may help to overcome the
widespread unwillingness. It should be noted that if the key parties cannot implement
their part of BIM, this project would not successfully implement BIM. The key
stakeholders’ early involvement in the “Big Room” to build trust-based collaboration
is crucial to adapt to organizational cultural change required by BIM implementation
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(AIA and AIACC, 2009; Autodesk, 2012; El Asmar et al., 2013). Thus, the
significant factors “lack of trust/transparency/communication/partnership and
collaboration skills” (H13) and “all disciplines sharing models in a ‘Big Room’”
(D08) can be linked to “trust and transparency” (PeC4) in the attributes of the
organizational change framework.
Furthermore, to implement an organizational change, the project organization should
ensure that the relevant individuals can adapt to the change. One challenge is to
change the thinking of both the executives and the employees of the major
stakeholders from considering only their own work to considering how the work can
affect the entire project. For example, although the employees are pushed by the
executives to attend training programs on BIM, they may still be ensnared to the
comfortable routines thereafter (Zahrizan et al., 2013). Thus, three critical hindrances
(H01, H06, and H15) can represent “mindset and attitude” (PeI1) in the
organizational change attributes. It is suggested that the negative mindsets of the
individuals toward change may be overcome by getting them to understand and
visualize the advantages of BIM work practices over the traditional drafting practices
(Khosrowshahi and Arayici, 2012). Thus, the significant driver “stakeholders seeing
the value of adopting their own part of BIM” (D03) can also be associated with this
attribute. It is worth reiteration that people tend to responds to change in their
accustomed ways, which may be influenced by their abilities and experience. The
individuals would unconsciously think whether they are qualified to be involved in
the BIM-based work practices in terms of their knowledge, skills, and experience. If
they feel that they are not competent and experienced or have not yet learnt about
similar success stories, they would not actively participate in relevant work practices,
or even undermine it. Therefore, three hindrances (H03, H14, and H23) can be linked
to “knowledge, skills, and experience” (PeI2). For example, due to the lack of
relevant expertise in using OSM together with BIM in the past projects, the
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employees would still be stuck to the traditional way in their first projects. Actions
should be taken to change the passive mindsets and behaviors, such as introducing
training and education programs (D04) to remove the resistance to change towards
the BIM and OSM work processes (Kiani et al., 2015). Thus, the critical hindrance
“lack of skilled employees and need for training them on BIM and OSM” (H04) and
critical driver “training on new skillsets and new ways of working” (D04) can be
related to “training and education” (PeI3). In turn, building the trust-based
collaboration between the key stakeholders as well as between the management and
the employees could ensure the effectiveness of training (Zhao et al., 2014a).
7.3.4.2 Process
Figure 7.2 presents the overall connections between the process-related
organizational change attributes and the critical factors influencing BIM
implementation in building projects in the Singapore context. All the linkages
between the CHCs, organizational change attributes, and CDCs (CHC–organizational
change attribute–CDC) in this figure were analyzed point for point in this section.
Firstly, management processes are part of the glue that holds the project organization
together (Rockart and Scott Morton, 1984). The hindrance “interpretations resulted
from unclear contract documents” (H40) represents “communications” (PrM1) in the
organizational change attributes. This is because unnecessary interpretations created
by any errors in the documents would probably harm the trust and communication
pattern between the project participants. Besides, three critical hindrances (H20, H34,
and H37) are closely associated with “controlling and decision-making” (PrM2) in
the proposed organizational change framework. “Decision-making depending on
relationships between project stakeholders” (H20) is stumbling as this causes
individualism other than the best-for-project thinking and behaviors (AIA and AIACC,
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CHCs
H08
H09
H16
H19
H20
H29
H30
H31
H36
H37
H38
H40
PrM2
PrS1
PrS2
PrS4
PrS3
PrT1
PrM1
PrT2
PrT3
PrT4
H34
D01
D05
D07
D13
D16
D17
D18
D19
D20
D21
D15
D23
D26
D27
D28
D24
Organizational
change attributes CDCs
Figure 7.2 Framework of process management from the organizational change
perspective
2009). Besides, the ambiguity in model authorship and ownership requirements (H34)
would inevitably cause inefficient work processes, duplicate efforts, and liability
anxieties in the lifecycle model management, which reduce the efficiency of project
management in the organization (Sattineni and Mead, 2013). Nevertheless, in the
design stage, the use of accurate 3D models enables the service providers to
communicate more clearly and effectively with each other, and with the owner.
Fischer et al. (2014) found that it is not uncommon that many owners can only
understand 3D models because they have little or no experience building anything.
Thus, the significant driver “3D visualization enabling design communication” (D15)
can solve the communication issues. Meanwhile, the driver “governance of BIM-
related policies and standards” (D13) can be linked to “controlling and decision-
making” (PrM2) since the BIM standards, guides, and best practices issued by the
local government would help the project team to make better decisions and control
the project throughout the project lifecycle (Cheng and Lu, 2015).
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Moreover, corporate strategy is crucial in organizational change (Dahlberg et al.,
2016). It is the owner that makes decisions whether or not to implement BIM to
achieve the project goals, which influence the service providers’ interest and
willingness to use BIM in practice. Thus, the hindrance “lack of owner request or
initiative to adopt BIM” (H19) can represent “goals and requirements setting” (PrS1)
in the attributes of organizational change. It should be noted that no established
standard fits the situation of every project and its participating firms due to the wide
variety of project types and strategic goals. Such governance of standards is not
adequate for BIM implementation in every project. Thus, the owner’s proactive
requirement, active participation, and leadership to adopt BIM (D05) is essential. In
addition, the change agent should allow the major stakeholders and their staff to
understand the vision and the impact of the change. Four critical hindrances (H08,
H09, H30, and H31) are therefore linked to “vision and mission” (PrS2). Specifically,
if the owner recognized that potential gains in productivity, quality, asset
management, and so on would outweigh the initial costs, it would push the service
providers to use BIM, ensure the quality and relevance of building information in
project requirements, and even pay for the training. However, the results indicated
that many parties lack the insights into training their staff and changing their work
processes, largely hindering BIM implementation. Cost and manpower invested in the
training as well as efforts made in adapting to the new work processes would
probably reduce productivity initially. This agrees with the finding of Eastman et al.
(2011) that many firms believe that the potential benefits of BIM are not tangible and
cannot outweigh the investments. Thus, if the firms do not have a long-term vision of
equipping their employees with new skills or if the investments cannot be subsidized
by the owner, they would still provide AEC services in the old way. The Singapore
government incentivizes BIM-ready firms to reap the full benefits of BIM and grow
their collaboration capabilities beyond just modelling by offering a new BIM fund
since July 2015 (BCA, 2016). Despite that, some survey respondents reported that the
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costs for subsequent upgrades or subscriptions are not funded and that the manpower
cost of carrying out BIM work practices remains a big issue. It should be noted that
any corporate strategy changes are subject to the owner and the senior management of
the participants, and thus the willingness to invest in training (H31) can also be
associated with “top management support” (PrS3) in the organizational change
attributes. As advocated by Autodesk (2012), BIM implementation is an
organizational transformation which starts with executive vision and sponsorship. In
order to avoid the pitfalls in a large-scale, radical change in the building project
context, a solid vision should be built. One example of such vision is the long-term
competitive advantages that successful BIM implementation in this project can give
to the service providers to win bids in the future market (Verdecho et al., 2012). Thus,
“gaining competitive advantages from full BIM use” (D07) can strengthen the change
attribute “vision and mission” (PrS2). Ideally, this BIM vision stems from the
executives; however, it is common for the mid-tier management to strive to put BIM
in the direct focus of the executives and seek for their sponsorship (Autodesk, 2012).
Therefore, the critical driver “BIM vision and leadership from the management”
(D01) can be associated with two attributes, PrS2 and PrS3. In the meantime,
although BIM facilitates OSM, the potential costly design changes after building
elements production require the design to be fixed early (Blismas and Wakefield,
2009). Thus, the hindrance related to the OSM process (H36) can be linked to
“process alignment” (PrS4). By moving on-site activities to a factory environment,
the standard design and production processes as well as the simplified construction
process would benefit the project team, such as reduced construction activities, site
disruptions, hazard exposures, site costs, and simplified inspection and test and
commissioning (Blismas and Wakefield, 2009; McFarlane and Stehle, 2014). Hence,
two significant drivers (D26 and D27) related to the adoption of BIM along with
OSM can be closely associated with “process alignment”’ (PrS4) in the
organizational change attributes.
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Furthermore, the changes in corporate strategy make sense only when specific tasks
are ultimately carried out on the shop floor (Autodesk, 2012). Two significant
hindrances (H29 and H36) can represent “coordination and simulation” (PrT1),
whereas the hindrances H40, H38, and H16 can be linked to “documentation” (PrT2),
“production” (PrT3), and “model management” (PrT4), respectively. Full BIM
implementation requires insights across multiple parties and aspects of the project.
Although a construction manager manages communication and reviews project
documentation to ensure the data quality and relevance across multi-disciplines,
changes inevitably occur. The impacts of the changes need the ongoing participation
and corresponding tasks of the relevant participants to respond to the changes. Thus,
managing this process and the related management of the model become critical to
the project (Eastman et al., 2011). On the other hand, the functions of digital
information models and the management of the models can facilitate the individuals
to carry out the day-to-day tasks, driving full BIM implementation in the project.
Specifically, four critical drivers (D16-D19) related to the development of optimal
design models can be associated with “coordination and simulation” (PrT1). The
project team can coordinate the models shared by specific disciplines and perform
analysis for sustainability, material selection, constructability, operations, and so on
(Eastman et al., 2011; Kunz and Fischer, 2012; Chua and Yeoh, 2015). Downstream
parties can document the design intent and construction intent from the fully
coordinated design models. Thus, the significant driver “high accuracy of model-
based documentation” (D21) can represent “documentation” (PrT2). In the meantime,
key contractors and manufacturers are able to use the design models shared by the
design team as bases for producing their construction models and fabrication models
as well as required drawings (Gao and Fischer, 2006; Porwal and Hewage, 2013).
Thus, two critical drivers (D20 and D28) can be associated with the change attribute
“production” (PrT3). In addition, “automatic model updating and drawing production
to deal with design changes and their implications” (D23) and “lifecycle information
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management improving operations and maintenance” (D24) can be linked to “model
management” (PrT4) which describes the functional and organizational relationships
of design, construction, and operations and maintenance. Whenever changes
(especially unexpected and late scope changes) take place, all these models can be
easily updated. More importantly, the project lifecycle implications of the changes
can be predicted in the digital models and thus better managed before changes would
traditionally occur in the later stages of the project (Gao and Fischer, 2006;
Khosrowshahi and Arayici, 2012).
7.3.4.3 Technology
Figure 7.3 presents the overall connections between the technology-related
organizational change attributes and the critical factors influencing BIM
implementation in the Singapore construction industry. All the linkages between the
CHCs, organizational change attributes, and CDCs (CHC–organizational change
attribute–CDC) in this figure were analyzed point for point in this section.
D22
D29
D30
H35
H42
H43
H44
H45
H46
H47
TD
TC
TI
CHCs
Organizational
change attributes CDCs
Figure 7.3 Framework of technology management from the organizational change
perspective
As an organizational change, full BIM implementation requires constantly advancing
technologies to improve the efficiency of carrying out the tasks (Azhar et al., 2014).
“Costly investment in BIM hardware and software solutions” (H42) and “need for
increasingly specialized software for specialized functions” (H44) are closely
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associated with “hardware and software solutions” (TI), implying that the lack of
capital investment for high-end infrastructure as well as relevant training programs is
still a big barrier for many firms, especially a great number of subcontractors,
although part of the costs would be funded by the local government (Lam, 2014;
Kiani et al., 2015). Meanwhile, four significant hindrances (H43 and H45-H47) can
represent “interoperability” (TD). Multidisciplinary integration is difficult for most
firms due to the lack of interoperability standards, the limited expertise of using IFC,
the multiuser access needed to a building information model, and the suboptimal
environment of one-or two discipline integration. In addition, OSM has been
recognized as a new construction method (Blismas and Wakefield, 2009; McFarlane
and Stehle, 2014), so the critical hindrance “poor understanding of OSM process and
its associated costs” (H35) is associated with “prefabrication” (TC). As mentioned
earlier, OSM requires design models to be fixed early to avoid costly design changes.
In this regard, detrimental resources including suppliers and contractors should be in
place in the design stage. Thus, the design cost may be perceived as higher than that
in the traditional process even though it is potentially lower by using standard
products. While the local BIM implementation was confront with various
technological challenges, motivations appear to be well recognized. Specifically,
“integrating model management tools with enterprise systems to exchange data”
(D30) can be associated with two attributes (TI and TD) of the adapted organizational
change framework. This is because such integration not only facilitates the data
sharing in individual parties, but also enables the parties to access the models and
exchange data conveniently with each other. Meanwhile, “more off-site fabrication
and assembly of standard elements” (D22) and “OSM reduces building wastes,
especially on-site wastes” (D29) may help address the “prefabrication” (TC) issues.
OSM has emerged as a new construction method and has gained wide recognition in
previous studies (Blismas and Wakefield, 2009; McFarlane and Stehle, 2014). Kunz
and Fischer (2012) argued that the project may use automated method to carry out
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routine design tasks or to help fabricate more standard building products in a factory.
On-site activities would be compressed and result in fewer workers and less waste on
sites, reducing costs (McFarlane and Stehle, 2014). Therefore, these drivers would
motivate the project organization to implement BIM appropriately.
7.3.4.4 External environment
Figure 7.4 illustrates the overall linkages between the organizational change attributes
on external environment aspect and the critical factors affecting BIM implementation
in building projects in Singapore. Changes in the external environment may drive the
internal components (people, process, and technology) of the project organization
into motion until reaching a rebalance (Rockart and Scott Morton, 1984; Wigand,
2007). All the linkages between the CHCs, organizational change attributes, and
CDCs (CHC–organizational change attribute–CDC) in this figure were analyzed
point for point in this section.
D09
D31
D32
H26
H39
ES1
ES2
ET
CHCs
Organizational
change attributes CDCs
Figure 7.4 Framework of external environment management from the organizational
change perspective
The significant hindrance related to legislations (H39) obviously represents “policy”
(ES1) with which the project team must comply. Moreover, due to the inadequate
expertise of OSM and its process in the local construction market, and the market
protection from large numbers of traditional suppliers in the small market, designs
tend to be unsuited to the use of off-site production and on-site assembly (Blismas
and Wakefield, 2009). Thus, “market protection from traditional
suppliers/manufacturers and limited OSM expertise” (H26) can be linked to
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“changing market” (ES2). As the construction market becomes increasingly complex
and requires new skill sets and increasing specializations, the Singapore government
has been offering the second BIM fund to the local construction industry to subsidize
part of the initial implementation costs, with the aim of motivating the BIM-ready
firms to grow collaboration capabilities beyond just modelling (BCA, 2016). Thus,
“increasing complexity in buildings, project delivery, and marketplace” (D31) can be
linked to “changing market” (ES2) and “government support such as subsidizing
training, software, and consultancy costs” (D09) can be closely associated with
“policy” (ES1) in the attributes of organizational change. Meanwhile, since the
technologies related to BIM have been constantly improving, more powerful
hardware and a wide range of software applications can be selected to help the project
participants to implement BIM openly. For example, the CNC machines can be used
to automate the manufacturing of standard building products for field installation
(Kunz and Fischer, 2012), driving the team to increase the use of OSM. Hence, D32
can be associated with “new technological solutions” (ET) in the organizational
change attributes which is meant to capture the constantly advancing hardware and
software applications, rather than hindering full BIM implementation (Wigand, 2007;
Dahlberg et al., 2016).
Therefore, Hypothesis 4 that “moving towards higher levels of BIM implementation
is hindered by a set of critical hindrances which can be interpreted from the
organizational change perspective” and Hypothesis 5 that “moving towards higher
levels of BIM implementation is driven by a set of critical drivers which can be
interpreted from the organizational change perspective” were supported.
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7.3.4.5 Importance of organizational change attributes
Based on the above analysis, among the top 10 CHCs in the overall ranking in Table
7.19, the top five and overall seven CHCs (H12, H27, H05, H07, H04, H28, and H01)
can be interpreted by some of the eight organizational change attributes on people
aspect, namely “involvement” (PeS4), “sharing” (PeC1), “contractual relationship”
(PeS1), “willingness to change” (PeC2), “training and education” (PeI3), “reward
arrangement” (PeS3), “commitment on new ways” (PeC3), and “mindset and
attitude” (PeI1) in order (see Figure 7.1). In contrast, the remainder (H42, H46, and
H31) of the top 10 CHCs can be linked to “vision and mission” (PrS2) and “top
management support” (PrS3) on process aspect as well as “hardware and software
solutions” (TI) and “interoperability” (TD) on technology aspect, respectively. None
of the top-ranked CHCs represents the organizational change attributes on external
environment aspect.
This result implied that people aspect is the key to changing successfully towards full
BIM implementation, which was consistent with the findings of Lee et al. (2005) and
Teo (2008) that the most significant problem in implementing new technologies is
people management. Thus, more collaboration needed to be built in the project teams
than those of the projects delivered using the traditional approach or using the lonely
BIM approach (Kiani et al., 2015).
Nonetheless, the project team should not overlook the two attributes (PrS2 and PrS3)
on process aspect. As the leadership team, the owner and the senior management of
service providers should have the insights into the potential and the value of BIM
over the traditional drafting practices and provide visible and continuous support to
the BIM work processes, such as building the trust among the team and arranging
training programs for the staff (Zhao et al., 2014a). It should be noted that the two
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attributes (TI and TD) on technology aspect would facilitate the more critical
attributes on people and process aspects. Without advances in technology, firms may
find it difficult to adapt to new workflow since such advances enable firms to share
expertise and data conveniently within the project organization (Zhao et al., 2015).
On the other hand, among the top 10 CDCs in the overall ranking of mean scores in
Table 7.21, the aforementioned top three CDCs and overall six positions (D01, D17,
D05, D07, D15, and D20) can be interpreted by some of the six organizational change
attributes on process aspect, including “vision and mission” (PrS2), “top management
support” (PrS3), “coordination and simulation” (PrT1), “goals and requirements
setting” (PrS1), “communication” (PrM1), and “production” (PrT3), respectively;
three significant drivers (D04, D06, and D03) are linked to “knowledge, skills, and
experience” (PeI2), “training and education” (PeI3), “leadership” (PeS2), “mindset
and attitude” (PeI1), and “commitment on new ways” (PeC3) on people aspect; D09
is associated with “policy” (ES1) on external environment aspect. It is notable that
none of these CDCs represents the attributes on technology aspect.
Hence, the six organizational change attributes on process aspect are more critical
areas in the successful change towards full BIM implementation in the Singapore
construction industry. This result substantiated the argument of Eastman et al. (2011)
that the most important driver would be the good information quality provided by the
fully coordinated design and construction models. These models enhance
visualization and design analyses, facilitate the use of standard building products, and
allow for maintenance and operations.
Meanwhile, the project team should not neglect the five attributes (PeI2, PeI3, PeS2,
PeI1, and PeC3) on people aspect. In order to complete the project more efficiently,
the owner has to build the knowledge and skills of its service providers such as by
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introducing training programs, which facilitates them to adapt to the new ways of
designing, building, and managing the building. In addition, the significant attribute
“policy” (ES1) on external environment aspect would catalyze the more critical
attributes on process and people aspects. It had been no more than five years since the
building planning submissions in BIM format became mandatory in building projects
in Singapore. Thus, many firms, especially SMEs, were still not experienced in using
the BIM technology, incorporating the BIM process into their work practices, and
delivering their scopes of BIM work (Kiani et al. 2015). So, more incentives from the
local government would drive the industry to enhance their BIM implementation.
To implement a successful organizational change towards full BIM implementation,
the project management team should prioritize their efforts and resources to the areas
related to the more critical attributes. Meanwhile, as Leavitt’s diamond theory and the
MIT90s framework indicate, the project team should understand that the interaction
and integration between these areas would facilitate more successful change in the
construction industry.
7.3.5 Proposed managerial strategies for reducing the CHCs and
strengthening the CDCs
It had been no more than five years since the building planning submissions in BIM
format became mandatory in Singapore. Hence, it was not uncommon that many
firms were still work in silos in the design, construction, and operations processes,
and remained inertial in changing their current ways of working. The theoretical
rationale behind the critical hindrances and drivers as well as the relative importance
of these factors and their respective change attributes provide a clear indication that
specific management strategies can be drawn for enhancing BIM implementation in
the building project.
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7.3.5.1 People
A total of eight people management strategies (PeMSs) were identified and discussed
in this section. The organizational change attributes and CHCs that were potentially
targeted by the management strategies would be elaborated.
Government support (PeMS1). The willingness of the major stakeholders to
implement BIM is influenced by government policies, competitor motivation,
financial incentives, and technological support (Juan et al., 2017). Thus, in addition to
the existing BIM fund which defrays part of infrastructure, training, and consultancy
cost (BCA, 2016), the government may lead or influence (PeS2) the industry’s
progress to change by further providing funds, especially to SMEs, to subsidize a
portion of manpower cost of carrying out BIM-related construction activities.
Furthermore, incentives such as additional GFA for the owner and a series of
objective performance milestones for the designers and contractors can be formulated
to help them get out of the conservative industry culture. Additional GFA may
motivate the owner to adopt new contractual solution to reduce the reluctance of the
designers and the financial risk of the downstream parties being involved in an earlier
stage. This strategy echoes the recommendation of the post-survey interviewees.
Standard contract (PeMS2). As mentioned earlier, under the current contractual
framework, the relationships among the project team tend to be adversarial. For
instance, the design team and the construction team tend to act as individuals by
creating different models. Thus, the BIM Particular Conditions has been partly
completed (Wickersham, 2009). An updated version should be developed and
established as the standard contract to incorporate the BIM work processes into the
contractual framework in Singapore. Such a contract should be agreed upon by all the
primary project participants (PeS4; H12 and H24) of the project, including the owner
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and the key designers and contractors. The manufacturers should also be bound if the
OSM approach would be adopted in this project. This helps build the collaborative
relationships and incentivize open data sharing (PeC1; H12) within the team. With
the multi-party contract, the major stakeholders will trust each other (PeC4; H13) and
act as a collaborative team (PeS1; H27 and H33).
Early involvement of major participants (PeMS3). The survey respondents cited
“need for all key stakeholders to be on board to exchange information” as their top
hindrance, substantiating the finding of Lam (2014) that most projects in Singapore
were plagued with inadequate project-wide collaboration. The involvement of the
manufacturers (suppliers) and trade contractors was limited in the design stage (Kent
and Becerik-Gerber, 2010). In the early design stage, the regulatory agencies would
provide high-level compliance information which specifies the project team to use
BIM to plan, design, build, and manage the building. The participation would
significantly avoid the required changes to the design as submitted for permit
(AIACC, 2014). Moreover, the key contractors and facility manager can input their
expertise in design modeling, allowing for coordination and constructability to be
built into the design rather than applied after problems occur in the later stages (Kunz
and Fischer, 2012). Without their knowledge and experience input upfront (PeC1;
H12), the design cannot be fixed early with sufficient constructability (PeC3; H01);
problems may occur during construction and operations and maintenance where
design changes would be costly. The standard contract should incentivize the
involvement of the key engineers and contractors from the early design stage or
before (PeS4; H12), which paves the way for the trust-based collaboration (PeC4;
H13) at the later stages of the project (Liao et al., 2017).
Sharing interests and risks (PeMS4). Sharing interests and risks should be agreed on
by all the key stakeholders in the standard contract (PeC1; H02). With this strategy,
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the corporate goals of the project participants are bound with the project outcomes
(PeS3; H02 and H28), which will build the necessary trust for the collaboration
among the participants (El Asmar et al., 2013). All the participants should be clearly
aware of the opportunities and responsibilities associated with the incorporation of
BIM into the project workflow. When problems occur, the sharing of risks forces the
team members to be responsible for the project (PeS5; H02, H10, H21, and H32)
rather than transferring risks or blaming others (PeS6; H41). This reduces the
designers’ potential liability due to providing inaccurate information (AIACC, 2014).
The sharing of rewards creates an environment that the team behaves in a best-for-
project manner rather than for individual benefits or even corruption. These avoid the
downstream parties from working at risk upfront. It is worth noting that individual
parties can leverage BIM only when it is successfully implemented in the project.
Removing inertia of the management and employees (PeMS5). The planning, design,
construction, and operation processes increasingly rely on the information models
(Eastman et al., 2011) which have been mandated or encouraged by the local
agencies. Keeping this in mind, the individuals of the major stakeholders should
recognize that change is inevitable to adapt to the information-oriented project
delivery (PeI1; H01, H06, and H15), and therefore break out of the conservative
culture. If the key parties cannot implement their part of BIM, this project would not
successfully implement BIM. Thus, the management should embrace possible
changes (PeC2; H05, H07, and H11), commit on the new way of working, and set the
tone of changing (PeC3; H01). With the tone at the top, the employees who carry out
day-to-day planning, design, construction, and operations work on the shop floor
have to change their passive mindsets and behaviors.
Providing project-wide and in-house training (PeMS6). The BIM vision and promise
should be complemented with a more realistic view of the adoption status (Miettinen
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and Paavola, 2014). As the mandatory BIM implementation started in July 2015, over
half (58.4%) of the responding organizations had no more than three years’
experience of implementing BIM. Meanwhile, the individuals’ anxiety about their
competencies should be removed. Once the project team is built, the owner should
lead BIM implementation by providing trainings (PeI3; H04) to the participants on
how to use new software applications, reinvent the workflow, assign responsibilities,
and model the construction process (PeI2; H03, H14, and H23), which would reduce
their misunderstanding and fear of the unknown. Moreover, individual parties can
provide constant in-house trainings to their employees to adapt to new policies,
procedures, and operations.
Highlighting short-term wins (PeMS7). After the project organization has perceived
BIM implementation as a priority and allocated resources in the training and BIM
implementation activities, short-term performance gains versus the traditional work
practices should be highlighted to convince the leadership team that BIM
implementation adds value to the project (PeI1; H01, H06, and H15). Such milestones
can generate energy and overcome the staff’s initial paralysis of continually carrying
out their part of BIM work practices (Autodesk, 2012). From a strategy point of view,
BIM implementation is a long-term journey spanning many years, so those who
change successfully and achieve enhanced BIM implementation will gain a
competitive advantage to win bids in the future market (Miettinen and Paavola,
2014). Thus, the short-term improvements in turn justify long-term investment such
as guaranteeing the sufficiency of the resources required in full BIM implementation
(PeC2; H05, H07, and H11) and gain confidence to adapt to the new way of working
(PeC3; H01 and H22) (Teo and Heng, 2007).
Cultivating trade contractors (PeMS8). The trade contractors may not know how to
deal with the changes brought to its staff. The on-site activities tend to be labor-
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intensive, and the field staff often lack sufficient skills of using digital design models
and prefer to submit their drawings in traditional CAD format. Although being
pushed to attend training programs on BIM working practices, they may still be
ensnared to the comfortable routines thereafter (Zahrizan et al., 2013). This can be
attributed to their psychological contradiction to the new processes and the shortage
of skilled personnel (Khosrowshahi and Arayici, 2012). Thus, the project leadership
team should cultivate the specialty contractors, such as by spearheading the design
modeling and coordination for them. The strategy helps incorporate trade contractors’
site knowledge to the design, build up their competencies (PeI2; H14 and H23), and
remove their entrenchment on the traditional drafting method.
7.3.5.2 Process
A total of 10 process management strategies (PrMSs) were identified and discussed
below. The organizational change attributes and CHCs that were potentially targeted
by these management strategies would be elaborated.
Owner management (PrMS1). It is the owner that makes the decision on whether to
implement BIM and on the pace of changing towards full BIM implementation in the
project organization. The lack of owner request or initiative would limit the interest
and willingness of the service providers to implement BIM in practice (Arayici et al.,
2011; Kunz and Fischer, 2012; Zahrizan et al., 2013). Managerially considering the
initial cost and apparent risks straightly forward rather than the potential value as
crucial selection criteria would limit the owner’s insight into the BIM implementation
in the project. Moreover, without the use of BIM in the design, owner-elected
changes and their lifecycle implications tend to be costly. Therefore, the owner
should rebuild its selection criteria when starting a new project, which strengthens the
positive influence from the owner’s requirement and leadership (PrS1; H19). In
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addition, because the designers tend to lack time and fees to allocate sufficient
resources to adopt BIM, the owner may need to require the design team to consider
the downstream uses when creating their design models, with more financial
incentives.
Government effort (PrMS2). The Singapore government has built up a BIM steering
committee to propose strategies for the local construction industry. For example, the
second version of the Singapore BIM Guide was released by the BCA in 2013 to
outline the roles and duties of the major stakeholders in implementing BIM at
different stages of the project (BCA, 2013b). This guide can help local BIM
implementers incorporate BIM uses into their day-to-day workflow, understand the
information flow, and manage information models, leading to better project control
(PrM2; H34). Meanwhile, the local government has been incentivizing the industry to
adopt productive technologies such as PPVC to manage the requirements of the
changing industry. A greater extent of OSM has been stipulated as part of the tender
conditions for industrial GLS sites (MOF, 2014), which paves the way for widespread
use of OSM (PrM2; H37).
Long-term vision and support (PrMS3). Autodesk (2012) advocated that BIM
implementation should start with a well-articulated vision supported by the project
leadership team, which was consistent with the top-ranked driver for BIM
implementation (see Table 7.21) in this study. As mentioned earlier, the management
should recognize the inevitable change towards the information-oriented project
delivery. The executives should have a visible and continual commitment on BIM
implementation. Moreover, BIM implementation is a long-term journey. Although
initial productivity loss and high implementation costs may pose financial risk in the
short term (Eastman et al., 2011), those who achieve enhanced BIM uses will gain a
competitive edge to win bids in the future market. This advantage in turn justifies the
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long-term investment (PrS2; H08, H09, H30, and H31) and convinces them to
guarantee the sufficient support (PrS3; H31) such as resources (Teo and Heng, 2007).
The solid vision of the team can be built through the high-profile communication
among the major stakeholders (Autodesk, 2012). In addition, the management needs
to convey the vision to the staff working on the shop floor, who otherwise cannot get
energy in the organizational transformation.
Lifecycle value proposition (PrMS4). In the project organization, the architect,
engineers, and contractors tend to be stuck to a culture and methods that minimize
cost in delivering their scopes of work, but rarely try to maximize the value of their
work (Kunz and Fischer, 2012). Previous studies (Sattineni and Mead, 2013; Lam,
2014) noted that the project delivery currently adopted in the construction industry
would cause many issues such as inefficient processes, repeated efforts, and liability
anxieties; for example, it is common for the architecture and engineering team to
create one design model, and for the main contractor and specialty contractors to
develop their own models based on the information from the design team. This is
because the design model is created without considering downstream uses and the
contractors are not involved in the design process. The value of the design work is not
maximized. In contrast, full BIM implementation requires the architectural model to
be shared with the engineers for creating their models on the same design; the key
contractors are also involved in the design stage and get the design models as bases
for creating their construction models (Gao and Fischer, 2006; Porwal and Hewage,
2013). This process would create constant value throughout the project. In addition,
periodical project meetings can be arranged to enhance the BIM use in the day-to-day
activities on the shop floor to realize the vision. Thus, this strategy can strengthen the
positive influences associated with management processes (PrM1; H40) and daily
tasks (PrT1, H29 and H36; PrT2, H40; PrT3, H38).
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Multi-party agreement (PrMS5). Currently, the design processes may not be
collaborative because the design team and the construction team tend to act as
individuals by creating different models (Lam, 2014). Under the lifecycle value
proposition, the new multi-party contract should allow and ideally incentivize the
primary participants to openly share data and act as a collaborative team (Kunz and
Fischer, 2012). The information flow is then ensured when model management tasks
are collaboratively completed throughout the project lifecycle (PrT4; H16), beyond
the design and construction team (PrT2; H40).
Collective decision-making (PrMS6). The major stakeholders that have shared risks
and rewards in the project organization should be involved in project management.
The contractors are no longer chary of providing advice about the design that may
benefit the whole project. The owner can empower the key service providers to
jointly set project goals and control the project. Once a problem raises, the decisions
can be collectively made according to the sources of expertise and information
(PrM2; H20 and H34), rather than the relationships with the owner (AIA and AIACC,
2009).
Moving towards IFC (PrMS7). Interoperability issues were recognized as a
significant challenge in BIM work practices (Zahrizan et al., 2013). The continual
development of IFC can tackle these issues if the participating firms use IFC
compliant software and share models using IFC rather than their proprietary formats.
In the post-survey interviews, the experts reported that different team members often
used different software or versions of software. Therefore, this strategy could
improve data interoperability efficiency in the design modeling and coordination
(PrT1; H29 and H36).
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Cultivating subcontractors (PrMS8). If the design consultants and general contractor
spearhead the model development, visualization, and simulation for the specialty
contractors, many management processes (PrM1; H40) and day-to-day work practices
(PrT1, H29 and H36; PrT2, H40; PrT3, H38) can be efficiently completed to truly
realize the solid vision of full BIM implementation in the project. For example, MEP
specialty contractors can contribute their construction expertise in the creation and
coordination of MEP design models, which in turn guides their construction work on
site (Khanzode et al., 2007). Without trade contractor input, the design would not be
fixed.
Building a multiuser access data platform (PrMS9). To strengthen the information
exchange and management, the project team may build a data platform that allows all
the key participants to access and link it with their data management systems (Succar,
2009). For instance, this platform would facilitate to upload submittals by the main
contractor and specialty contractors, provide feedbacks by the owner and the design
team (PrT4; H16), update the design models to deal with changes and their lifecycle
implications, and retrieve the latest models and documents.
Design for fabrication (PrMS10). The early involvement of the key participants,
especially the manufacturers or suppliers, enables to maximize the use of OSM and
fix the design early before fabrication (PrS4; H36). The benefits of OSM can be
reaped if standard building components are used in the design and production (PrT3;
H38) (McFarlane and Stehle, 2014). The components need to be carefully transported
and stored with support from best practices in lean construction.
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7.3.5.3 Technology
Five technology management strategies (TMSs) were identified. The organizational
change attributes and CHCs that were potentially targeted by these strategies would
be elaborated as follows.
Government effort (TMS1). The lack of national standards and guidelines remains a
concern (Zahrizan et al., 2013). Cheng and Lu (2015) observed that Singapore is one
of the leading countries for standards development in Asia because the local
government have developed 12 of the 35 BIM standards in Asia by the time of this
said study. For example, the BIM Particular Conditions has been drafted to address
the procedures of handling digital data and the extent of reliance on BIM models by
each party (BCA, 2015b). In addition to this document, a comprehensive
interoperability standard should be developed for the local industry (TD; H43).
Meanwhile, BIM software have been constantly improving to enable the cross-
discipline integration at the construction level. The post-survey interviewees,
however, reported that the hardware in many firms cannot support the advanced
software applications efficiently. Thus, apart from the primary participants’
investments to improve their infrastructures, the local government should provide
further capital support, especially for the large number of SMEs to subsidize part of
the cost of subsequent infrastructure upgrades or subscriptions (TI; H42).
Design for fabrication (TMS2). Changing the design philosophy that was currently
based on traditional methods is essential to the organizational change (Blismas and
Wakefield, 2009). The early involvement of contractors and suppliers facilitates the
design for maximizing off-site production and assembly and leaving minimum
assembly work on site (McFarlane and Stehle, 2014). This would optimize
manufacturing functions, standardize design and manufacturing processes, reduce
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safety risks in a factory environment, ensure maximum quality, and simplify
construction processes (PrT3; H35) (Belay, 2009).
Moving towards IFC (TMS3). Technically, data exchange among disparate modeling
and analysis applications using proprietary formats can work well for those
interoperable applications made by particular vendors. In the project involving many
parties, other applications may also be useful to this project (TI; H44). In this case,
cross-vendor data exchange standard should be agreed on. The IFC format has been
reliably to enable this process (TD; H43 and H45-H47) if the team members can
collaborate with others using it (Khosrowshahi and Arayici, 2012).
Building a multiuser access data platform (TMS4). Technically, setting up such a data
platform shifts the management of the BIM models in a single party or between a few
parties to an integrated management paradigm (Succar, 2009). Maintaining integrity
across different design models is imperative in practice (TD; H43 and H45-H47),
because changes are made to different models by their respective disciplines. Both
manual updates using IFC and smart automated transactions in BIM servers require
specialized expertise (TI; H44), which urges the development of a multi-access
platform which allows all the team members to access and link it with their data
management systems.
Providing project-wide and in-house training (TMS5). Apart from the awareness and
willingness to implement BIM, technical knowledge and ability is also needed
(Forsythe et al., 2015). For example, onsite assembly has become a new construction
method (TC; H35). In the manufacturing industry, the principles behind this method
has been widely proven to be productive (Blismas and Wakefield, 2009; McFarlane
and Stehle, 2014). Thus, training programs should be provided for the team members
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to understand the design, manufacture, construction, and management practices of the
OSM process and how to collaborate with other team members in the process.
7.3.5.4 External environment
Two management strategies from the external environment were proposed. The
organizational change attributes and CHCs that were potentially targeted by the
external environment management strategies (EMSs) would be elaborated below.
Government effort (EMS1). From the external environment point of view,
government efforts can be the new legislations, which add on the existing policies
and standards. For example, the guide of incorporating the use of BIM in the design,
planning, construction, and operations processes of the DfMA approach should be
developed (ES1; H39). In the meantime, apart from providing training courses for the
practitioners in the BCA Academy, encouragement and support to the relevant
education programs in local universities and colleges should also be in place (BCA,
2016). Such programs would build a talent pool of BIM implementation (ES2; H26),
especially the management talents with BIM skills that can lead the BIM teams in the
local firms.
Continuous learning and training (EMS2). A project is a knowledge-intensive entity.
The project team’s knowledge bandwidth affects its ability to deal with emerging
challenges during project delivery. Such dynamics require ongoing learning and
augmentation of knowledge bandwidth. Full BIM implementation in the project is a
process of continual examination and improvement. It also requires continuous
learning and testing of sometimes new and misunderstood BIM concepts (Autodesk,
2012). In the changing market where buildings, organizational structures, and legal
structure become increasingly complex, the project team needs continuous training
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and learning to support the adoption of BIM and OSM in the daily work (ES2; H26).
With support from the management, individuals should also learn new technologies
such as the use of CNC rebar bending/cutting machines and casting systems that may
drive fabricators to use BIM (ET). Such efforts would motivate the participants and
build up valuable intellectual capital in the organization.
Overall, a summary of detailed influence paths (CHC–organizational change
attribute–CDC–managerial strategy) for each CHC would be further identified in
Section 9.2.4. In addition, as indicated in Section 6.3.1, as part of the validation of the
BBPT model developed in Chapter 9, six personal interviews were conducted with
the local BIM experts from six different building projects to solicit their comments on
the usefulness of the managerial strategies in helping their project leadership teams
make decisions to move towards higher BIMIR statuses (see Appendix 3). The
validation results would be analyzed and discussed in Section 9.4.
7.4 Summary
A total of 38 NVA activities and all their causes were validated by Survey I. Using
the data related to the frequency of occurrence of such activities, the fuzzy BIMIR
model was applied to evaluate the BIMIR statuses of the surveyed building projects
in Singapore, and the results reported an overall lonely BIM implementation status.
Besides, the wastes in the project groups of different BIMIR statuses were checked. It
was found that as BIMIR increased, the wastes became less severe to productivity
and the leading causes became less important to the NVA activities. In addition, 44
CHCs and 31 CDCs were found to be significantly influential. The differences in the
significance mean scores and rankings of these factors between the upfront and
downstream stakeholders were checked and explained. In addition, the proposed
organizational change framework could be used to interpret these significant factors
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from the perspectives of people, process, technology, and external environment,
which helped to figure out a set of managerial strategies for process transformation
towards higher levels of BIM implementation.
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Chapter 8: Case Study
8.1 Introduction
Since design consultants must submit their building plans for regulatory approvals
and actually they have somewhat overemphasize the submission policy (Lam, 2014),
full BIM implementation in building projects relies heavily on contractors’ BIM
implementation activities. This chapter presents a case study of BIM implementation
in a large construction and development firm based in Singapore, which was
conducted from May 2016 to August 2016. Specifically, two residential building
projects (coded as Project A and Project B) that were involved in Survey I were
selected as the subjects of the case study. The BIMIR statuses of the projects were S3
and S2, respectively. Table 8.1 shows the profile of the six interviewees who
provided information for the two projects.
Table 8.1 Profile of the interviewees in the case study
No. Experience Title Project BIMIR status
I1 16-20 years Project manager Project A S3 (collaborative BIM
implementation) I2 11-15 years Corporate BIM manager
I3 11-15 years BIM coordinator
I4 11-15 years Deputy project manager
I5 16-20 years Technical manager Project B S2 (lonely BIM
implementation) I6 5-10 years Quantify surveyor in charge
8.2 Background of Case Projects
The case firm was a Singapore-based subsidiary of a state-owned central enterprise in
China and has been a listed corporation. Since the foundation in 1992, the firm has
completed more than 150 projects in Singapore. Currently, the firm was a BCA-
registered contractor with a financial grade of A1 under the workhead of CW01
(general building), and with a financial grade of B1 under the workhead of CW02
(civil engineering). The tendering limits of A1 and B1 were unlimited and S$40
million, respectively. As shown in Table 8.1, Project A and Project B were involved
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in Survey I and selected to be further investigated. The firm served as general
contractors, with its subsidiaries being structural and MEP trade contractors in both
projects. Since the BIMIR status of Project A (S3) was higher than that of Project B
(S2), this study would discuss the dynamics of the firm’s process transformation
journey from Project B to Project A by enhanced BIM adoption.
Project A involved in the design and construction of a large ongoing residential
building project with a GFA of more than 100,000 m2. Because of the huge volume of
this project as well as the experience, skills, resources, and reputation of the case
firm, Project A was selected by the Singapore government as a sample project to
showcase BIM implementation in the local context. The local government agencies
visited the construction site regularly to oversee BIM uses in this project and
productivity figures at the time of the visits.
The data were collected through participant observations, personal interviews, and
analysis of past documents. Firstly, the author passively participated in the weekly
project meetings in the construction site office over two months (from May 2016 to
July 2016) when the basement construction was about to be completed and the
structure construction was ongoing. In each project meeting, there was a BIM session
(also called technical meeting, VDC meeting, or coordination meeting). The basic
purpose of the author’ attendance in the meetings was to observe how the project
team collaborated in the BIM work processes. Specifically, the behavioral patterns
(such as body language, verbal expressions, meeting rules, model management
procedures, and provide-wide communication) of the team members were directly
observed. Notes were taken promptly and supplemented by using a recorder. The
observations ended until a milestone event when the government agencies visited the
site and the project team reported and showcased its milestone BIM uses and
resulting performance improvement. Secondly, a project manager, a corporate BIM
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manager, a BIM coordinator, and a deputy project manager (see Table 8.1) were
interviewed, which allowed them to provide in-depth views on BIM implementation
activities in this project. In addition, information about the BIM implementation
practices were also collected from past documents about this project. The minutes of
meetings, milestone reports, and productivity figures were collected from the project
manager and deputy project manager through personal networking and were
analyzed. The websites of the firm and Project A were regularly reviewed. The author
also visited the construction site at times to understand the site layout and filed staff’s
behaviors and feelings.
By comparison, Project B was a typical residential building project that the case firm
had been working on. A technical manager and a quantity surveyor in charge were
interviewed. The key activities related to BIM implementation could be represented
by the typical current project delivery described in Table 3.5 and very often resulted
in many NVA activities and tremendous wastes such as design changes and reworks.
The two interviewees pointed out that the biggest issue was that the design
consultants were neither able nor required by the owner to build the design models of
good quality that the contractors could use. Specifically, the owner did not push hard
enough. Apart from the lack of time and resources (Lam, 2014), the consultants did
not get sufficient fees from the design contracts, which also caused reluctance of the
consultants to use BIM. Consequently, the designs created by the consultants were
messy. For example, green lines of the construction site were not at the same location
in the architectural design and structural design, some openings for air-conditioning
were not appropriately located, and columns were occasionally meters away from
where they should be located. All these should have been done in the design stage.
However, the technical manager reported that after winning the bid and entering this
project, the general contractor struggled for a few months for the designs, because the
contractor found many design errors and discrepancies and had to come back with the
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consultants to discuss and finalize the designs. Especially, most MEP designs were
re-developed. Furthermore, standards of the consultants and the contractors were
different. The manager stated that the consultants had their own standards (Zahrizan
et al., 2013), such as producing 2D drawings, and the general contractor could only
create its construction model and drawings by imaging how the consultants modeled
the designs. All these practices were NVA.
Thus, BIM implementation in this project was lonely and fragmented in individual
parties, rather than on a project-wide collaboration basis. In other words, the project
delivery process was not changed except that design tools of individual parties were
upgraded, but such tools were not interoperable and integrated across parties. Even
the production of construction shop drawings still relied heavily on the traditional
CAD approach. In addition, the operations and maintenance team tended to be
unwilling to use the 3D design models because the team neither trusted nor had
access to the as-built model unless the owner bought and shared the model. The
technical manager implied that to improve productivity performance, the consultants
side needed a lot of improvements (Sattineni and Mead, 2013). This was because they
not only created a number of NVA activities upfront, but also affected BIM
implementation activities downstream.
8.3 Critical Changes
Compared with Project B, critical changes were made in Project A in terms of BIM
implementation activities and were discussed and analyzed in this section. Project A
adopted a VDC-ish approach, with the general contractor as the leading party to
coordinate and compile BIM models and construction BIM execution plan in the
construction stage. The project manager of the general contractor indicated that
moving construction activities forward to the design stage was a good way of
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improving its productivity performance, because the virtually coordinated design
could enhance on-site work accuracy. The corporate BIM manager believed that
collaborative BIM implementation could create value in this journey. Because of the
distinctive differences between the typical current delivery process and the VDC
delivery process, this project team needed to change the typical way of working
which was not efficient enough. The key critical hindrances encountered included the
following aspects: (1) adaptability. All the key stakeholders had to change their
preferential working habits and lacked training on the new way of working
(Autodesk, 2012); (2) trade contractors’ capabilities. They preferred to use 2D
drawings for submissions and construction, and lacked BIM skillsets; and (3) smooth
communication of information. It was difficult to get the major stakeholders to
collectively communicate, review, and coordinate digital models (AIA and AIACC,
2009; Forsythe et al., 2015). Nevertheless, to obtain the benefits of BIM, the project
team overcame the hindrances by: (1) aligning all the key stakeholders from the
beginning and providing project-wide trainings when possible; (2) spearheading BIM
model development for all the key trade contractors; and (3) involving all the key
stakeholders in the weekly project meetings to contribute knowledge. Particularly, in
this project the owner’s representatives who were BIM experts participated in the
BIM sessions very actively. This helped the service providers understand their client’
s brief.
With these strategies, the project team had managed the design well in the
construction phase through the following main changes in the BIM work practices:
(1) Sharing design models between the owner, the design consultants, the general
contractor, and the key trade contractors (AIACC, 2014). Specifically, after
obtaining the regulatory approvals, the architect and structural engineer handed
over their design models to the general contractor for further design development
and coordination in the preconstruction stage. The general contractor used the
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architectural design model as a reference to integrate it with the structural design
model, and created a high-level (schematic) construction model which considered
the BIM uses of downstream subcontractors;
(2) Requiring and facilitating the trade contractors to use BIM. The top-down
approach was adopted on the contractors side (Autodesk, 2012). In the tendering
stage, the general contractor passed its coordinated design models to potential
subcontractors, instead of 2D drawings, and in subcontracts, required the
subcontractors to model in their fields. The subcontractors then created their
models based on the construction model, rather than 2D CAD drawings. This
result substantiated the argument of Autodesk (2012) that the top-down approach
should be accompanied by bottom-up approaches such as educating and creating
convenience for the subcontractors to carry out their day-to-day work on the shop
floor; and
(3) Driving collaboration and coordination among the team in a “Big Room”
(Khanzode et al., 2007). The project team focused on a “build twice” concept.
Every Monday, the key stakeholders worked together in the BIM sessions to do
design coordination, discussed on RFIs and technical issues, and commented on
the design together. The high-level construction model was then virtually
displayed, communicated, reviewed, and revised collectively by all the key
stakeholders, including precast contractors, in the weekly technical meetings in
the construction site office, greatly reducing design and construction
uncertainties. The corporate BIM manager of the general contractor believed that
if its BIM team created concrete values and benefits to the other parties of the
project team, these parties would simply follow and be cooperative. The values
could be as basic as highlighting clashes in reports and showcasing them in the
3D environment. For example, in a BIM session that focused on solving
discrepancies between the general contractor and the structural consultant, other
parties also actively participated and provided advice. Besides, the general
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contractor presented the corporation of PBUs into the design model in the virtual
environment for the whole team to discuss, which facilitated project-wide
decision-making on the use of the PBUs before actual construction. Finally, all
the models were approved by the owner and specific design consultants and
combined to guide construction activities (including off-site production) three
levels ahead versus the actual site progress. During the construction stage, a
central data platform was used to help the team members store, view, retrieve,
review, comment, and monitor the latest composite construction model and
relevant documents. This platform enabled the general contractor to upload
submittals, as well as facilitated the owner and the consultants to provide
feedbacks in a real time manner. Thus, all the key stakeholders could share
information and update their respective models to deal with changes and their
lifecycle implications. In addition, the general contractor would also share its as-
built constriction model to the operations and maintenance team.
Although these changes indeed solved some BIM implementation issues in this
project, there were also practices that remained the same with the traditional way of
working. According to the observations and interviews, some typical issues included:
(1) The design consultants only modeled in their fields for regulatory submissions.
No multidisciplinary coordination was completed. Consequently, the general
contractor’s BIM team could not use their design models, even they were willing
to do that for saving time and efforts. The project manager stated that to change
this situation, the owner must push the consultants to create good-quality models.
Such models need to be coordinated in multi-disciplines; otherwise, burden
would still be added to the contractors.
(2) Some team members were pushed, but may not be mentally ready and
subjectively willing to implement BIM (such as for achieving key performance
indicators required by the BCA). This result echoed the finding of Kiani et al.
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(2015) that firms in Iran were satisfied with their conventional methods to
complete the design and construction work and therefore saw BIM uses as extra
efforts. For example, in a BIM session, after the general contractor’s BIM team
showcased how 3D design environment could help detect clashes in the basement
design, an experienced architect still found a few clashes. She emotionally argued
that “people use 3D design models, which may be not as good as before when
they had to image according to 2D documents; BIM makes people lazy now”.
The management staff and its BIM team of the general contractor responded that
“it is people’s fault, not BIM’s”. The mindset needed to be constantly changed.
The way of people doing things really matters.
(3) During the BIM sessions, the owner’s representatives and the design consultants
could not understand clearly the general contractor who could not ask specific
questions at times. This was because the trade contractors, especially those with
much work experience, were reluctant to be involved in these sessions and did
not show much interest and enthusiasm in such sessions. In these cases, the
upfront parties needed to guess, affecting the successful communication.
(4) The contractors needed more details and often raised RFIs. For example, a trade
contractor did wrongly in wall construction because insufficient details were
available after the type of the wall was changed in the design model. Besides, the
contractors could not distinguish frame width from structural openings. Thus,
with the use of BIM, NVA activities and wastes, even substantially reduced, still
existed in this project.
8.4 Performance Assessment
The critical changes that had been made in Project A resulted in enhanced milestone
productivity performance by the time of this case study. As examples, productivity
improvements for the shop drawing preparation process and the RFIs were reported
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in this study. The total time spent on preparing the structural and architectural shop
drawings was projected from the following activities: (1) coordinating the structural,
architectural, and MEP design provisions; (2) preparing the shop drawings; (3)
virtually reviewing and revising the designs and drawings; and (4) approving the
drawings. Meanwhile, by driving the project-wide collaboration and coordination in
the weekly technical meetings, all interfacing issues were virtually resolved, thus
substantially reducing the number of RFIs. Consequently, about 60% of the RFIs
raised were related to material or specification clarifications, rather than design
issues. The estimated time spent for preparing the structural and architectural shop
drawings in Project A were 836 man hours and 1408 man hours, and saved by 40%
and 42%, respectively, compared with Project B. The numbers of RFIs in the
architecture, structure, and MEP disciplines were 126, 63, and 15, respectively,
substantially reduced by 70% in total. The results were in line with Nath et al. (2015)
which found that using BIM to re-engineer the precast shop drawing generation
process in public building projects in Singapore would result in a substantial time
saving of 380 man hours of producing the shop drawings, leading to an overall
productivity improvement of about 36% for processing time and 38% for total time.
This study only presented the results of the time savings for the work process of
preparing the coordinated structural and architectural shop drawings as well as the
reduced number of RFIs. The reasons were that the time saving statistics were not
fully documented as the residential project was not yet completed, and that the project
team tended to be wary of providing all the statistics of the enhanced productivity
performance. Thus, it was considered reasonable that this study only reported the
figures of the shop drawing preparation processes and the RFIs as examples to
illustrate the enhanced productivity performance resulted from the BIM-based
implementation and process changes.
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In addition, the change towards full BIM implementation for productivity gains was
also supported by previous studies. For example, Cohen (2010) reported that an
interior tenant improvement project was completed using the IPD approach within 8.5
months, an impossible schedule with the typical traditional delivery method used by
the owner. Critical changes in the work processes were made in the project team. The
owner actively participated in the design and construction phases. The contractors
and suppliers were involved during the design stage to share their expertise; for
instance, their virtual construction manager worked together with the architectural
design consultant two days a week. Meanwhile, building officials also participated
from the early design stage to ensure that the permitting process would not impede
the schedule, saving more than one week in the planning reviews. Thus, the
documents generated from the composite design model created by the whole team
could be used for permitting, analysis, bidding, fabrication, and so on. The
contractors could procure time- and cost-variable materials and services earlier. After
the detailed design phase, the composite model was moved from the architect to the
contractors, instead of being re-built by the contractors in the early construction stage
which was NVA. During construction, the architect moved to the construction site.
This close collaboration with the contractors made many NVA activities unnecessary
and freed the architect to spend much less time reviewing and responding the RFIs
and submittals from the contractors. Consequently, there were 125 RFIs in total on
the final cost of $13.34 million, 39.61% fewer than the average of 155 RFIs per $10
million recommended by Chelson (2010). The results suggested that compared with
the typical current project delivery process, following an IPD delivery process and
monitoring the project process with a predetermined BIM implementation plan would
largely avoid the occurrence of the critical NVA activities and their resulting wastes
in the project.
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The case study expanded the process re-engineering of the precast shop drawings
production in Singapore (Nath et al., 2015) to the project lifecycle perspective. It is
likely that collaborative BIM implementation would help remove critical NVA
activities and wastes in the design, construction, and operations and maintenance
processes and led to a more efficient project delivery. Even the contractual structure
and the BIM work activities in the design stage of Project A remained the same with
those of the typical current project delivery process adopted in Singapore, the project-
wide BIM collaboration built in the construction stage could also significantly reduce
the critical NVA activities and their resulting wastes, enhancing productivity
performance. This finding indicated short-term wins for the project team and
represented a benchmark for adopting an appropriate BIM-based delivery process to
identify and reduce the wastes in the Singapore construction industry. Nevertheless,
the collaboration and coordination in the earlier stages of the project were not yet
achieved, and therefore a collaborative contractual structure that governs the close
project-wide collaboration and multidisciplinary coordination from the beginning
throughout project completion remains urgently needed in Singapore (Fischer et al.,
2014). Besides the owner’s awareness of and insights into the value that full BIM
implementation can add to the project, the incentives from the government like
additional GFA may motivate the owner to adopt new contractual solution to reduce
the reluctance of the design consultants and the risk of the downstream parties being
involved in an earlier stage. In addition, because in most projects in Singapore the
designers tend to lack time and fees to allocate sufficient resources to adopt BIM, the
owner may need to give more fees to the design team and require them to consider
the downstream uses when creating their design models, with more financial
incentives.
Because of the large number of foreign workforce in the Singapore construction
industry, local building project teams may involve people of different races, ages, and
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ways of working. Because of this unique circumstance, local experimentation and
continuous learning play a central role in BIM implementation in Singapore
(Miettinen and Paavola, 2014). Although it is difficult to change people from diverse
backgrounds, they would accept and adopt BIM if they see the concrete values.
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Chapter 9: Developing a BIM-Based Process Transformation
Model for Building Projects in Singapore
9.1 Introduction
This chapter presents the development of the BBPT model for building projects in
Singapore. The BBPT model intends to evaluate the BIMIR status of a building
project in the planning stage, and provide managerial strategies in terms of people,
process, technology, and external environment. This model consists of two part-
models: a BIMIR evaluation model and a BIMIR improvement model. The former
uses the 38 critical NVA activities as evaluation sub-criteria to assess the BIMIR
status of the building project and investigate the leading causes to these NVA
activities, whereas the latter analyzes the critical factors that hinder this project to be
at the current BIMIR status and motivate it to change to a higher BIMIR status from
the organizational change perspective. Managerial strategies, with different priorities,
on people, process, technology, and external environment aspects are provided for the
project organization to move towards full BIM implementation. In particular, the 33
responding projects that completed both surveys were analyzed as an example to
illustrate the BIMIR movement model, and the analysis results also served as part of
the general BBPT model. Finally, the validation results of the BBPT model from six
different building projects were presented and discussed.
9.2 Comparing the CHCs and CDCs among BIMIR Statuses
9.2.1 Profile of respondents and their organizations involved in both
surveys
The profile of the 33 respondents who had participated in both surveys is presented in
Table 9.1. It was deemed as appropriate that the 33 completed questionnaires were
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obtained based on their willingness to participate in the study (Wilkins, 2011).
Regarding the main businesses of these organizations, 12 (36.4%) were general
construction firms, and five (15.2%), three (9.1%), two (6.1%), two (6.1%), and one
(3.0%) were architectural firms, MEP engineering firms, structural engineering firms,
trade construction firms, and a facility management firm, respectively. Moreover, the
eight organizations in the “others” category included four developers, two precasters,
and two other consultancy firms (one multidisciplinary consultancy firm and one
BIM consultancy firm). In terms of the organizations’ BCA financial grades, 18
(54.5%) were contractors registered with the BCA. Among which, 11 (33.3%) were
A1 contractors, followed by three (9.1%) L6 contractors, two (6.1%) C3 contractors,
one (3.0%) B1 contractor, and one (3.0%) single grade contractor. The remaining 15
(45.5%) organizations were not contractors. As for the experience of BIM
implementation, 18 (54.5%) of the organizations started to use BIM in their building
projects in last one to three years, and six (18.2%) had implemented BIM for four to
five years. Five firms had implemented BIM over five years, and none had over 10
years’ relevant experience. Because of the reasons stated in Sections 7.2.1 and 7.3.1,
the four (12.1%) responding organizations without BIM implementation experience
were also included in the subsequent data analysis.
Table 9.1 Profile of the respondents and their organizations involved in both surveys
Characteristics Categorization Frequency Percentage (%)
Organization
Main business Architectural firm 5 15.2
Structural engineering firm 2 6.1
MEP engineering firm 3 9.1
General construction firm 12 36.4
Trade construction firm 2 6.1
Facility management firm 1 3.0
Others 8 24.2
BCA financial grade A1 11 33.3
B1 1 3.0
C3 2 6.1
Single grade 1 3.0
L6 3 9.1
Not applicable 15 45.5
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Years of BIM adoption 0 year 4 12.1
1-3 years 18 54.5
4-5 years 6 18.2
6-10 years 5 15.2
Over 10 years 0 0
Respondent
Discipline Government agent 0 0
Developer 3 9.1
Architect 7 21.2
Structural designer 3 9.1
MEP designer 2 6.1
General contractor 11 33.3
Trade contractor 3 9.1
Supplier/Manufacturer 2 6.1
Facility manager 2 6.1
Years of experience 5-10 years 15 45.5
11-15 years 6 18.2
16-20 years 1 3.0
21-25 years 1 3.0
Over 25 years 10 30.3
With respect to the disciplines, 11 (33.3%) of the 33 respondents served as general
contractors, followed by seven (21.2%) architects, three (9.1%) developers, three
(9.1%) structural engineers, three (9.1%) trade contractors, two (6.1%) MEP
designers, two (6.1%) manufacturers/suppliers, and two (6.1%) facility managers.
None government agent was involved. Moreover, 15 (45.5%) of the respondents had
five to ten years’ working experience, indicating a large proportion of young BIM
implementers. Ten (30.3%) and six (18.2%) respondents had worked for over 25
years and 11 to 15 years in the Singapore construction industry.
Therefore, the profile indicated that the 33 respondents could well represent the key
BIM implementers in the local construction industry, assuring the response quality.
According to the generally-accepted rule, statistical analysis could be performed with
a sample size larger than 30, because the central limit theorem held true (Ott and
Longnecker, 2010).
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9.2.2 Linking Survey I and Survey II
The data of the 33 responding projects that participated in both surveys were analyzed
for two purposes: (1) to exemplify the application of the BIMIR movement model;
and (2) to use the analysis results to predict the performance of the overall 89
surveyed projects for generalization to some extent. Firstly, the achievement of the
first purpose either would not be influenced by sample size, or relied on qualitative
characteristics of fundamentals in the general model.
Secondly, the appropriateness and reliability of using the 33 responses to predict the
89 responses should be assessed. The Spearman’s rank correlation was performed. As
shown in Table 9.2, the correlation coefficients were 0.639 and 0.850 for the 44
CHCs and the 31 CDCs, respectively, with p-values of 0.000. These test statistics
provided clear indication that the mean score rankings of all the CHCs and the CDCs
were significantly agreed upon by the 33 respondents and the overall 89 respondents.
In addition, the two groups of respondents shared seven common CHCs and CDCs in
their respective top 10 rankings, despite differences in the rankings of some CHCs
and CDCs. Thus, the sample of the 33 respondents and their organizations could be
used to link Survey I and Survey II, and identify the top-ranked hindrances and
drivers. This method was deemed reasonable, because this study would identify the
top 10 CHCs and CDCs in terms of the mean score ranking rather than mean scores.
Table 9.2 Overall mean scores and rankings of the CHCs and CDCs in different
samples
CHC Code 89 data sets 33 data sets CDC Code 89 data sets 33 data sets
Mean Rank Mean Rank Mean Rank Mean Rank
H01 3.64 8 3.70 7 D01 3.99 1 3.82 6
H02 3.48 21 3.36 40 D02 3.64 11 3.85 4
H03 3.26 41 3.45 35 D03 3.71 9 3.58 16
H04 3.69 3 3.70 7 D04 3.82 4 3.76 7
H05 3.69 3 3.58 21 D05 3.90 3 3.91 1
H06 3.42 28 3.61 16 D06 3.76 6 3.76 7
H07 3.69 3 3.67 11 D07 3.79 5 3.85 4
H08 3.37 33 3.30 43 D08 3.63 12 3.76 7
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H09 3.54 13 3.73 4 D09 3.69 10 3.58 16
H10 3.33 37 3.52 28 D11 3.30 26 3.39 26
H11 3.43 23 3.73 4 D12 3.44 21 3.45 23
H12 3.79 1 3.85 2 D13 3.57 16 3.64 13
H13 3.33 37 3.42 37 D14 3.62 13 3.58 16
H14 3.62 11 3.58 21 D15 3.75 7 3.67 11
H15 3.43 23 3.39 38 D16 3.45 19 3.61 15
H16 3.34 35 3.36 40 D17 3.92 2 3.91 1
H19 3.43 23 3.70 7 D18 3.45 19 3.24 30
H20 3.28 40 3.36 40 D19 3.26 30 3.30 29
H21 3.35 34 3.48 30 D20 3.75 7 3.88 3
H22 3.34 35 3.55 24 D21 3.58 15 3.70 10
H23 3.42 28 3.55 24 D22 3.53 17 3.58 16
H24 3.26 41 3.45 35 D23 3.48 18 3.67 11
H26 3.42 28 3.61 16 D24 3.29 27 3.21 31
H27 3.71 2 3.73 4 D25 3.35 23 3.39 26
H28 3.65 7 3.88 1 D26 3.28 28 3.45 23
H29 3.43 23 3.48 30 D27 3.26 30 3.48 21
H30 3.31 39 3.61 16 D28 3.31 24 3.52 20
H31 3.63 10 3.76 3 D29 3.27 29 3.48 21
H32 3.52 17 3.64 13 D30 3.60 14 3.64 13
H33 3.54 13 3.55 24 D31 3.40 22 3.39 26
H34 3.53 15 3.61 16 D32 3.31 24 3.42 25
H35 3.51 19 3.52 28 – – – – –
H36 3.49 20 3.67 11 – – – – –
H37 3.45 22 3.39 38 – – – – –
H38 3.40 31 3.61 16 – – – – –
H39 3.25 43 3.48 30 – – – – –
H40 3.39 32 3.64 13 – – – – –
H41 3.22 44 3.21 44 – – – – –
H42 3.66 6 3.58 21 – – – – –
H43 3.52 17 3.64 13 – – – – –
H44 3.43 23 3.48 30 – – – – –
H45 3.53 15 3.55 24 – – – – –
H46 3.64 8 3.70 7 – – – – –
H47 3.56 12 3.48 30 – – – – –
Note: the Spearman’s rank correlation coefficient for the CHCs between the two
samples was 0.639 (p-value = 0.000); the Spearman’s rank correlation coefficient for
the CDCs between the two samples was 0.850 (p-value = 0.000).
9.2.3 Comparison among projects with different BIMIR
9.2.3.1 CHCs
Out of the 33 surveyed building projects in Singapore, eight, 20, and five received
BIMIR statuses of S1 (no BIM implementation), S2 (lonely BIM implementation),
and S3 (collaborative BIM implementation), respectively. This section investigated
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the differences in the mean scores and rankings of the 44 CHCs among the three
BIMIR status groups of projects.
As shown in Table 9.3, the mean scores ranged from 3.50 to 4.13 in the BIMIR S1
group of building projects, from 3.00 to 3.90 in the S2 group of building projects, and
from 3.00 to 4.20 in the S3 group of building projects. To test whether the mean
scores differed among the three BIMIR status groups, the one-way ANOVA were
performed. The analysis results indicated that none of the 44 CHCs significantly
differed among the three status groups at the 0.05 level (see Table 9.3).
Table 9.3 Mean scores and rankings of the CHCs: BIMIR S1 vs. BIMIR S2 vs.
BIMIR S3
Code S1 (no BIM) S2 (lonely BIM) S3 (collaborative BIM) ANOVA
Mean Rank Mean Rank Mean Rank p-value
H01 3.75 23 3.60 9 4.00 4 0.760
H02 3.88 12 3.20 39 3.20 37 0.277
H03 3.88 12 3.25 38 3.60 12 0.422
H04 3.75 23 3.75 3 3.40 22 0.863
H05 3.50 42 3.60 9 3.60 12 0.983
H06 3.63 33 3.60 9 3.60 12 0.998
H07 3.88 12 3.60 9 3.60 12 0.846
H08 4.13 1 3.00 43 3.20 37 0.070
H09 4.00 8 3.60 9 3.80 7 0.643
H10 3.88 12 3.30 34 3.80 7 0.341
H11 3.50 42 3.75 3 4.00 4 0.634
H12 3.88 12 3.75 3 4.20 1 0.696
H13 3.63 33 3.30 34 3.60 12 0.763
H14 3.88 12 3.30 34 4.20 1 0.326
H15 3.63 33 3.15 40 4.00 4 0.179
H16 4.13 1 3.05 42 3.40 22 0.096
H19 3.75 23 3.55 16 4.20 1 0.526
H20 3.50 42 3.30 34 3.40 22 0.900
H21 3.75 23 3.40 28 3.40 22 0.745
H22 3.75 23 3.45 23 3.60 12 0.695
H23 3.63 33 3.45 23 3.80 7 0.741
H24 3.75 23 3.40 28 3.20 37 0.677
H26 4.00 8 3.40 28 3.80 7 0.449
H27 4.13 1 3.65 8 3.40 22 0.402
H28 4.00 8 3.90 1 3.60 12 0.836
H29 3.63 33 3.50 20 3.20 37 0.789
H30 4.13 1 3.45 23 3.40 22 0.267
H31 3.63 33 3.80 2 3.80 7 0.933
H32 4.00 8 3.55 16 3.40 22 0.486
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H33 3.88 12 3.45 23 3.40 22 0.630
H34 3.88 12 3.55 16 3.40 22 0.682
H35 3.75 23 3.40 28 3.60 12 0.776
H36 4.13 1 3.50 20 3.60 12 0.431
H37 4.13 1 3.10 41 3.40 22 0.064
H38 4.13 1 3.50 20 3.20 37 0.160
H39 3.88 12 3.40 28 3.20 37 0.379
H40 3.63 33 3.70 6 3.40 22 0.794
H41 3.88 12 3.00 43 3.00 44 0.077
H42 3.75 23 3.55 16 3.40 22 0.892
H43 3.75 23 3.60 9 3.60 12 0.958
H44 3.63 33 3.45 23 3.40 22 0.929
H45 3.63 33 3.60 9 3.20 37 0.717
H46 3.88 12 3.70 6 3.40 22 0.774
H47 3.75 23 3.40 28 3.40 22 0.745
Besides, the Spearman’s rank correlation was conducted to check whether there were
ranking differences among the three BIMIR status groups of building projects. As
indicated in Table 9.4, the correlation coefficients among the three BIMIR status
groups of projects were neither high nor significant at the 0.05 level. This result was
consistent with the top 10 CHCs between the three status groups. Specifically, the
BIMIR S1 and S2 groups of projects shared three common CHCs, and the S1 and S3
groups shared two common CHCs, despite the S2 and S3 groups shared five in their
respective top 10 rankings. Thus, the mean scores and rankings of the CHCs were not
significantly agreed upon by the three BIMIR status groups of projects in which BIM
implementation was hindered by different top-ranked CHCs. It was concluded that
moving from BIMIR S1 to BIMIR S2 would need fundamental changes and moving
from BIMIR S2 to BIMIR S3 would need significant changes.
Table 9.4 Spearman’s rank correlation of the CHCs: BIMIR S1 vs. BIMIR S2 vs.
BIMIR S3
BIMIR status Test statistics S1 (no BIM) S2 (lonely BIM) S3 (collaborative BIM)
S1 (no BIM) Correlation
coefficient
1.000 -0.202 -0.187
p-value – 0.188 0.225
S2 (lonely
BIM)
Correlation
coefficient
– 1.000 0.261
p-value – – 0.087
S3
(collaborative
BIM)
Correlation
coefficient
– – 1.000
p-value – – –
294
9.2.3.2 CDCs
This section investigated the differences in the mean scores and rankings of the 31
CDCs among the three BIMIR status groups of building projects. As shown in Table
9.5, the mean scores ranged from 2.75 to 4.38 in the BIMIR S1 group of building
projects, from 3.20 to 4.10 in the S2 group of building projects, and from 3.20 to 4.00
in the BIMIR S3 group of building projects. The one-way ANOVA results suggested
that all the 31 CDCs did not have statistically significant differences among the three
groups at the 0.05 level (see Table 9.5).
Table 9.5 Mean scores and rankings of the CDCs: BIMIR S1 vs. BIMIR S2 vs.
BIMIR S3
Code S1 (no BIM) S2 (lonely BIM) S3 (collaborative BIM) ANOVA
Mean Rank Mean Rank Mean Rank p-value
D01 3.75 5 4.00 2 3.20 30 0.540
D02 4.00 2 3.85 6 3.60 8 0.845
D03 3.63 8 3.55 20 3.60 8 0.988
D04 3.75 5 3.85 6 3.40 20 0.775
D05 3.75 5 4.00 2 3.80 4 0.871
D06 3.88 3 3.80 8 3.40 20 0.653
D07 4.38 1 3.75 9 3.40 20 0.186
D08 3.63 8 3.90 5 3.40 20 0.688
D09 3.50 19 3.60 18 3.60 8 0.982
D11 3.63 8 3.30 26 3.40 20 0.764
D12 2.88 30 3.65 17 3.60 8 0.112
D13 3.50 19 3.75 9 3.40 20 0.788
D14 3.00 28 3.70 14 4.00 1 0.300
D15 3.50 19 3.75 9 3.60 8 0.885
D16 3.13 26 3.75 9 3.80 4 0.404
D17 3.63 8 4.10 1 3.60 8 0.559
D18 2.75 31 3.25 28 4.00 1 0.177
D19 3.13 26 3.30 26 3.60 8 0.695
D20 3.63 8 3.95 4 4.00 1 0.778
D21 3.63 8 3.70 14 3.80 4 0.969
D22 3.63 8 3.60 18 3.40 20 0.926
D23 3.63 8 3.70 14 3.60 8 0.979
D24 3.00 28 3.20 31 3.60 8 0.583
D25 3.63 8 3.25 28 3.60 8 0.669
D26 3.25 25 3.50 21 3.60 8 0.797
D27 3.63 8 3.45 23 3.40 20 0.897
D28 3.63 8 3.50 21 3.40 20 0.929
D29 3.50 19 3.40 24 3.80 4 0.811
D30 3.38 24 3.75 9 3.60 8 0.721
D31 3.50 19 3.40 24 3.20 30 0.890
D32 3.88 3 3.25 28 3.40 20 0.260
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As indicated in Table 9.6, the Spearman’s rank correlation coefficients were 0.416
(moderate correlation) between the BIMIR S1 and BIMIR S2 as well as -0.455
(moderate negative correlation) between the BIMIR S1 and BIMIR S3 groups of
projects, with p-values below 0.05. These results agreed with the fact that the two
pairs shared nine and seven common CDCs in the respective top 10 rankings,
respectively. Meanwhile, the rank correlation coefficient was 0.001 (no correlation)
between the BIMIR S2 and BIMIR S3 groups of projects, despite seven common
CDCs were shared in their respective top 10 rankings. Therefore, the top-ranked
factors driving the surveyed projects of BIMIR S1 and BIMIR S2 were similar, and
those driving the projects of BIMIR S1 and BIMIR S3 were significantly different.
Therefore, moving from BIMIR S1 towards BIMIR S2 was natural, whereas moving
towards BIMIR S3 would need structural changes in the project teams in all aspects.
Each team member should try to change their work practices and adapt to work in
collaborative networks.
Table 9.6 Spearman’s rank correlation of the CDCs: BIMIR S1 vs. BIMIR S2 vs.
BIMIR S3
BIMIR status Test statistics S1 (no BIM) S2 (lonely BIM) S3 (collaborative BIM)
S1 (no BIM) Correlation
coefficient
1.000 0.416 -0.455
p-value – 0.020* 0.010
*
S2 (lonely
BIM)
Correlation
coefficient
– 1.000 0.001
p-value – – 0.995
S3
(collaborative
BIM)
Correlation
coefficient
– – 1.000
p-value – – – *Correlation was significant at the 0.05 level (two-tailed).
9.2.4 Areas needing improvement
Based on the interpretation of the 44 CHCs and 31 CDCs with the proposed
organizational change framework in Section 7.3.4 and the explanation of the
managerial strategies for reducing the CHCs and strengthening the CDCs on people,
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process, technology, and external environment aspects in Section 7.3.5, the overall
influence paths of managing the CHCs and CDCs from the organizational change
perspective were figured out, as shown in Table 9.7. It can be seen from this table that
a comprehensive list of managerial strategies were prepared for project leadership
teams to choose from. Given a CHC, the associated change attribute(s) and CDC(s)
could be identified, and then the corresponding managerial strategies would be
determined. For example, if BIM implementation was hindered by one of the CHCs,
“executives failing to recognize the value of BIM-based processes and needing
training” (H01), in a building project, two organizational change attributes, namely
“commitment on new ways” (PeC3) and “mindset and attitude” (PeI1) were identified
as the key areas that required significant improvements (see Figure 7.1 and Table
9.7). Then, three strategies including “early involvement of major participants”
(PeMS3), “removing inertia of the management and employees” (PeMS5), and
“highlighting short-term wins” (PeMS7) could be used. The selection of these
strategies depended on the two CDCs (D03 and D08) by which the project team was
also motivated to implement BIM. The theoretical rationale and explanations of the
strategies could be found in Sections 7.3.4 and 7.3.5.
In addition, the local government’s leadership (PeMS1 and EMS1) and project
participants’ continuous learning and training (EMS2) were constant strategies that
every project organization needs to implement to develop, succeed, and survive in the
changing built environment.
To help building projects of lower BIMIR statuses move towards higher BIMIR
statuses, this study identified the key areas requiring improvements with priorities
from the organizational change perspective. If everything is important, nothing is
manageable. The top-ranked CHCs represented the most important areas of BIM
297
Table 9.7 Overall paths of reducing the CHCs and strengthening the CDCs from the
organizational change perspective
CHC
code
Organizational
change attribute
CDC
code
Proposed managerial strategy
H01 PeC3 D03 PeMS5: removing inertia of the management and
employees
PeMS7: highlighting short-term wins
D08 PeMS3: early involvement of major participants
PeMS7: highlighting short-term wins
PeI1 D03 PeMS5: removing inertia of the management and
employees
PeMS7: highlighting short-term wins
H02 PeS3; PeS5;
PeC1
D12 PeMS4: sharing interests and risks
H03 PeI2 D04 PeMS6: providing project-wide and in-house
training
H04 PeI3 D04 PeMS6: providing project-wide and in-house
training
H05 PeC2 D02 PeMS5: removing inertia of the management and
employees
PeMS7: highlighting short-term wins
H06 PeI1 D03 PeMS5: removing inertia of the management and
employees
PeMS7: highlighting short-term wins
H07 PeC2 D02 PeMS5: removing inertia of the management and
employees
PeMS7: highlighting short-term wins
H08 PrS2 D01, D07 PrMS3: long-term vision and support
H09 PrS2 D01, D07 PrMS3: long-term vision and support
H10 PeS5 D12 PeMS4: sharing interests and risks
H11 PeC2 D02 PeMS5: removing inertia of the management and
employees
PeMS7: highlighting short-term wins
H12 PeS4 D08 PeMS2: standard contract
PeMS3: early involvement of major participants PeC1 D08
D14
H13 PeC4 D08 PeMS2: standard contract
PeMS3: early involvement of major participants
H14 PeI2 D04 PeMS6: providing project-wide and in-house
training
PeMS8: cultivating trade contractors
H15 PeI1 D03 PeMS5: removing inertia of the management and
employees
PeMS7: highlighting short-term wins
H16 PrT4 D23 PrMS9: building a multiuser access data platform
D24 PrMS5: multi-party agreement
PrMS9: building a multiuser access data platform
H19 PrS1 D05 PrMS1: owner management
H20 PrM2 D13 PrMS6: collective decision-making
H21 PeS5 D12 PeMS4: sharing interests and risks
H22 PeC3 D11 PeMS7: highlighting short-term wins
H23 PeI2 D04 PeMS6: providing project-wide and in-house
training
PeMS8: cultivating trade contractors
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H24 PeS4 D08 PeMS2: standard contract
H26 ES2 D31 EMS1: government effort
EMS2: continuous learning and training
H27 PeS1 D25 PeMS2: standard contract
H28 PeS3 D12 PeMS4: sharing interests and risks
H29 PrT1 D16 PrMS4: lifecycle value proposition
PrMS7: moving towards IFC
PrMS8: cultivating subcontractors D17
D18
D19
H30 PrS2 D01, D07 PrMS3: long-term vision and support
H31 PrS2 D01, D07 PrMS3: long-term vision and support
PrS3 D01
H32 PeS5 D12 PeMS4: sharing interests and risks
H33 PeS1 D25 PeMS2: standard contract
H34 PrM2 D13 PrMS2: government effort
PrMS6: collective decision-making
H35 TC D22 TMS2: design for fabrication
TMS5: providing project-wide and in-house
training D29
H36 PrS4 D26, D27 PrMS10: design for fabrication
PrT1
D16 PrMS4: lifecycle value proposition
PrMS7: moving towards IFC
PrMS8: cultivating subcontractors D17
D18
D19
H37 PrM2 D13 PrMS2: government effort
H38 PrT3 D20 PrMS4: lifecycle value proposition
PrMS8: cultivating subcontractors
D28 PrMS10: design for fabrication
H39 ES1 D09 EMS1: government effort
H40 PrM1 D15 PrMS4: lifecycle value proposition
PrMS8: cultivating subcontractors
PrT2 D21 PrMS4: lifecycle value proposition
PrMS5: multi-party agreement
PrMS8: cultivating subcontractors
H41 PeS6 D12 PeMS4: sharing interests and risks
H42 TI D30 TMS1: government effort
H43 TD D30 TMS1: government effort
TMS3: moving towards IFC
TMS4: building a multiuser access data platform
H44 TI D30 TMS3: moving towards IFC
TMS4: building a multiuser access data platform
H45 TD D30 TMS3: moving towards IFC
TMS4: building a multiuser access data platform
H46 TD D30 TMS3: moving towards IFC
TMS4: building a multiuser access data platform
H47 TD D30 TMS3: moving towards IFC
TMS4: building a multiuser access data platform
Extra PeS2 D06 PeMS1: government support
ET D32 EMS1: government effort
EMS2: continuous learning and training
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implementation, and the top-ranked drivers represented the most important
motivations that the projects already beard in mind and could somewhat control.
Given resource constraints of a building project, the leadership team should allocate
resources to the most important areas (top 10 factors) rather than all the key areas (all
critical factors).
Table 9.3 and Table 9.5 presents the top 10 CHCs and CDCs for the eight surveyed
building projects of BIMIR S1, 20 surveyed projects of BIMIR S2, and five surveyed
projects of BIMIR S3. Based on the interpretation of the top 10 CHCs with the
proposed organizational change frameworks in Figures 7.1 to 7.4, the most important
organizational change attributes for the three BIMIR status groups of surveyed
building projects were obtained (see Table 9.8) and discussed below.
Among the most important areas, some areas should be further targeted with
emphasis. As shown in Table 9.8, the building projects of BIMIR S1 (no BIM
implementation) should primarily target “vision and mission” (PrS2) for
improvement, which was required by three top-ranked CHCs. This was consistent
with the survey finding that the respondents cited “BIM vision and leadership from
the management” (D01) as their top CDC. It was advocated that BIM implementation
starts with a well-articulated vision that is supported by the management staff
(Autodesk, 2012; Miettinen and Paavola, 2014). Thus, these projects should
incorporate such a vision into their project goals and individual corporate goals as
well, which echoes with the local government’s vision, mission, and support to drive
the local industry to implement BIM or IDD in the CTIM (BCA, 2017a). Besides,
time and efforts dedicated to BIM implementation would give the teams and firms a
competitive edge in the future market. These can be meaningful for them to start to
embark on BIM implementation.
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Table 9.8 Key areas of improvement in the organizational change framework
Component Factor Change attribute BIMIR
S1
BIMIR
S2
BIMIR
S3
People Inter-enterprise
structure
Contractual relationship √ √
Leadership
Reward arrangement √ √
Involvement √ √
Risk allocation √ √
Conflict management
Corporate
culture
Sharing √ √
Willingness to change √√√ √
Commitment on new ways √ √
Trust and transparency
Individuals and
roles
Mindset and attitude √√ √√
Knowledge, skills and
experience
√√
Training and education √
Process Management
processes
Communication √
Controlling and decision-
making
√
Corporate
strategy
Goals and requirements setting √
Vision and mission √√√ √√ √√
Top management support √ √
Processes alignment √
Task Coordination and simulation √
Documentation √
Production √
Model management √
Technology Infrastructure Hardware and software
solutions
Data exchange Interoperability √√√
Construction
method
Prefabrication
External
environment
Socioeconomic
environment
Policy
Changing market √ √
Technological
environment
New technological solutions
√ indicates the area required by one CHC to be improved for changing to a high
BIMIR status.
√√ indicates the area required by two CHCs to be improved for changing to a high
BIMIR status.
√√√ indicates the area required by three CHCs to be improved for changing to a high
BIMIR status.
The building projects of BIMIR S2 (lonely BIM implementation) should firstly target
“willingness to change” (PeC2) and “interoperability” (TD) for improvements, which
were required by three top-ranked CHCs, followed by “mindset and attitude” (PeI1)
and “vision and mission” (PrS2). For example, some stakeholders in the projects that
301
implemented lonely BIM might take advantage of information asymmetry at the
expense of others and see collaborating toward a “win-win situation” as preventing
them from optimizing the amount of benefits they could have won (Forsythe et al.,
2015). As a result, project-wide transparency and collaboration could not be built in
the project teams. The construction and operations expertise could not be
substantially input into the upfront design modeling and multidisciplinary
coordination, and potential liability issues would inevitably be raised in the dynamic
project management in different phases. Therefore, the awareness of being
collaborative and open data exchange were essential to shifting from lonely BIM
implementation to collaborative or full BIM implementation.
The building projects of BIMIR S3 (collaborative BIM implementation) should
emphasize “mindset and attitude” (PeI1), “knowledge, skills and experience” (PeI2),
and “vision and mission” (PrS2) for improvements, which were all required by two
top-ranked CHCs. The results indicated that to change from collaborative BIM
implementation towards full BIM implementation, the BIM vision from the top
management, more positive attitudes toward changing, and sufficient knowledge and
experience among the project organizations are crucial.
Meanwhile, “vision and mission” (PrS2) and “mindset and attitude” (PeI1) were two
key organizational change attributes that were at least highlighted twice in two or
three BIMIR status groups of projects. These were also the areas that need to be
constantly improved for changing towards full BIM implementation.
With the CHC-change attribute–CDC–managerial strategy paths presented in Table
9.7, the managerial strategies for the surveyed projects of the three BIMIR statuses
could be determined. Nevertheless, the interaction between the CHCs and the CDCs
should be taken into consideration when a project organization decides to implement
302
the strategies. This study formulated four priority rules that were coded in If-Then
conditional statements for the project team to prioritize the implementation of the
proposed managerial strategies and the allocation of resources as well (see Table 9.9).
The priority rules include:
(1) Rule A (Priority one). Among the overall influence paths in Table 9.7, if a path
beginning with a top-ranked CHC (among top 10) does not involve top-ranked
CDCs (among top 10), then the CHC must be overcome and its associated
organizational change attribute(s) must be improved with respective managerial
strategies indicated on this path;
(2) Rule B (Priority two). If a path beginning with a top-ranked CHC (among top 10)
involves top-ranked CDCs (among top 10), then the CHC should be further
overcome and its associated organizational change attribute(s) should be further
improved with respective managerial strategies indicated on this path, despite
already having some motivations to improve this area in the project organization;
(3) Rule C (priority three). If a path beginning with a non-top-ranked CHC (not
among top 10) does not involve top-ranked CDCs (among top 10), then the CHC
may need to be further overcome and its associated organizational change
attribute(s) may need to be further improved with respective managerial strategies
indicated on this path, and resources should be assigned to implement these
managerial strategies; and
(4) Rule D (priority four). If a path beginning with a non-top-ranked CHC (not
among top 10) involves top-ranked CDCs (among top 10), then the CHC might
be further overcome and its associated organizational change attribute(s) might be
further improved with respective managerial strategies indicated on this path
given that the project organization has relatively sufficient resources. It should be
noted that the change may be not necessarily from BIMIR Si to BIMIR Si+1, but
from the current BIMIR status to full BIM implementation (BIMIR S4).
303
Table 9.9 Priority rules of implementing strategies for changing from a lower BIMIR
status to higher BIMIR statuses
Rule If (CHC scenario) Then (decision) Priority
A Top 10 CHCs, without corresponding
top 10 CDCs (CHCs-CHCs∩CDCs)
Must improve/overcome 1 (top)
B Top 10 CHCs, with corresponding top
10 CDCs (CHCs∩CDCs)
Should further
improve/overcome, despite
already having some
motivation
2
C Non-top 10 CHCs, without
corresponding top 10 CDCs
May need some improvement
(assigning resources)
3
D Non-top 10 CHCs, with corresponding
top 10 CDCs
Might further improve (if
having sufficient resources)
4
(bottom)
The BIM Project Execution Planning Guide defines the resources of each party as
personnel (BIM team), tools and their training, and IT support (Anumba et al., 2010).
In addition, capital investment should also be included in the resources (Zhao et al.,
2014a). Insufficiency of any kind of the resources would hinder BIM implementation.
This tallies with the post-survey interviews that the local practitioners need training
and technical support for BIM implementation in practice as they are not
knowledgeable and experienced enough about higher levels of BIM implementation.
In most cases, the senior management can determine the allocation of the resources.
While the biggest firms are able to ride on the BIM wave, a huge number of SMEs
face adoption challenges such as lacking the capital investment for BIM tools and
trainings to build up BIM competencies (Forsythe et al., 2015).
Following these priority rules, the key areas of organizational change attributes and
managerial strategies on people, process, technology, and external environment
aspects for the surveyed building projects of BIMIR S1, S2, and S3 groups could be
identified, as shown in Table 9.10, Table 9.11, and Table 9.12, respectively.
Managerial strategies of higher priorities were discussed below, and those of lower
priorities may also be implemented, depending on actual situations facing projects.
304
Table 9.10 Prioritized change areas and managerial strategies for a project organization to move from BIMIR S1
Change
attribute
People Process Technology External
MS1 MS2 MS3 MS4 MS5 MS6 MS7 MS8 MS1 MS2 MS3 MS4 MS5 MS6 MS7 MS8 MS9 MS10 MS1 MS2 MS3 MS4 MS5 MS1 MS2
PeS1 B
PeS2 D
PeS3 A
PeS4 D D
PeS5 A
PeS6 C
PeC1 C C C
PeC2 D D
PeC3 D D D
PeC4 D D
PeI1 D D
PeI2 D D
PeI3 D
PrM1 C C
PrM2 A C
PrS1 D
PrS2 B
PrS3 D
PrS4 A
PrT1 A A A
PrT2 D D D
PrT3 B B B
PrT4 A A
TI C C C
TD C C C
TC C C
ES1 C
ES2 A A
ET D D
Note: MS=managerial strategy;
A The project organization must improve in this area with respective managerial strategy;
B The project organization should further improve in this area with respective managerial strategy, despite already having some motivation in this area;
C The project organization may need to improve in this area with respective managerial strategy;
D The project organization might further improve in this area with respective managerial strategy if having sufficient resources.
305
Table 9.11 Prioritized change areas and managerial strategies for a project organization to move from BIMIR S2
Change
attribute
People Process Technology External
MS1 MS2 MS3 MS4 MS5 MS6 MS7 MS8 MS1 MS2 MS3 MS4 MS5 MS6 MS7 MS8 MS9 MS10 MS1 MS2 MS3 MS4 MS5 MS1 MS2
PeS1 A
PeS2 D
PeS3 A
PeS4 B B
PeS5 C
PeS6 C
PeC1 A A C
PeC2 B B
PeC3 B A A
PeC4 D D
PeI1 A A
PeI2 D D
PeI3 B
PrM1 B B
PrM2 D D
PrS1 D
PrS2 B
PrS3 B
PrS4 C
PrT1 C C C
PrT2 A A A
PrT3 D D C
PrT4 C C
TI D D D
TD B B B
TC C C
ES1 C
ES2 C C
ET C C
Note: MS=managerial strategy;
A The project organization must improve in this area with respective managerial strategy;
B The project organization should further improve in this area with respective managerial strategy, despite already having some motivation in this area;
C The project organization may need to improve in this area with respective managerial strategy;
D The project organization might further improve in this area with respective managerial strategy if having sufficient resources.
306
Table 9.12 Prioritized change areas and managerial strategies for a project organization to move from BIMIR S3
Change
attribute
People Process Technology External
MS1 MS2 MS3 MS4 MS5 MS6 MS7 MS8 MS1 MS2 MS3 MS4 MS5 MS6 MS7 MS8 MS9 MS10 MS1 MS2 MS3 MS4 MS5 MS1 MS2
PeS1 D
PeS2 C
PeS3 D
PeS4 A A
PeS5 B
PeS6 D
PeC1 A A D
PeC2 B B
PeC3 A B A
PeC4 C C
PeI1 B B
PeI2 A A
PeI3 C
PrM1 D D
PrM2 C C
PrS1 B
PrS2 A
PrS3 A
PrS4 C
PrT1 D D D
PrT2 D D D
PrT3 D D C
PrT4 D D
TI D D D
TD D D D
TC C C
ES1 D
ES2 A A
ET C C
Note: MS=managerial strategy;
A The project organization must improve in this area with respective managerial strategy;
B The project organization should further improve in this area with respective managerial strategy, despite already having some motivation in this area;
C The project organization may need to improve in this area with respective managerial strategy;
D The project organization might further improve in this area with respective managerial strategy if having sufficient resources.
307
The analysis results in Table 9.10 indicated that the key areas that urgently needed to
be changed for the surveyed projects of BIMIR S1 (no BIM implementation) were
related to the following organizational change factors: task, corporate strategy, inter-
enterprise structure, and external socioeconomic environment, which were consistent
with the results in Table 9.8. To improve such areas, the managerial strategies,
including “lifecycle value proposition” (PrMS4), “multi-party agreement” (PrMS5),
“moving towards IFC” (PrMS7), “cultivating subcontractors” (PrMS8), “building a
multiuser access data platform” (PrMS9), “design for fabrication” (PrMS10),
“sharing interests and risks” (PeMS4), “government effort” (EMS1), and “continuous
learning and training” (EMS2) needed to be implemented with priorities. Since the
Singapore government has made plenty of efforts in driving the local firms to
implement BIM, such as the mandatory BIM e-submissions and new BIM funds
(Cheng and Lu, 2015; BCA, 2016), the local construction market had been changed
under the first CPR. The availability of some BIM infrastructure and experts was
ensured. Thus, self-motivation (vision and mission) and planning of the project teams
to adopt new technologies, such as BIM and OSM, or technological processes would
be of the greatest importance to improve the efficiency of carrying out various tasks.
Table 9.11 suggested that there was an urgent need for the surveyed projects of
BIMIR S2 (lonely BIM implementation) to change their practices in the following
organizational change factors: corporate culture, individuals and roles, inter-
enterprise structure, and task. Based on the priority rules, five strategies on people
aspect, including “standard contract” (PeMS2), “early involvement of major
participants” (PeMS3), “sharing interests and risks” (PeMS4), “removing inertia of
the management and employees” (PeMS5), and “highlighting short-term wins”
(PeMS7), were selected for the urgent need for building trust-based collaboration in
the project teams. Meanwhile, “lifecycle value proposition” (PrMS4), “multi-party
agreement” (PrMS5), and “cultivating subcontractors” (PrMS8) could be used to
308
more efficiently perform project delivery tasks. In addition, Forsythe et al. (2015)
advocated that open data sharing should be ensured to create “win-win” situations in
the project organizations, rather than fragmented information flow.
The analysis results in Table 9.12 implied that the areas related to corporate culture,
individuals and roles, inter-enterprise structure, corporate strategy, and external
socioeconomic environment in the organizational change framework were deemed
key to the transformation of the surveyed projects of BIMIR S3 (collaborative BIM
implementation). The following managerial strategies, namely “standard contract”
(PeMS2), “early involvement of major participants” (PeMS3), “providing project-
wide and in-house training” (PeMS6), “highlighting short-term wins” (PeMS7),
“cultivating trade contractors” (PeMS8), “long-term vision and support” (PrMS3),
“government effort” (EMS1), and “continuous learning and training” (EMS2) were
prioritized for improvements. The major stakeholders in the building projects of
BIMIR S3 had already work relatively collaboratively with each other. The main
form of contract (PeMS2) could be more integrated, to which some agreements could
be attached, such as multi-party collaboration agreements to incentivize all the major
stakeholders to participate and share insights upfront, including specialty consultants
and contractors that were usually not early involved. Meanwhile, the strategy
“sharing interests and risks” (PeMS4) was also prioritized. Once people are on the
same boat, they can perform in a best-for-project manner. Nevertheless, such efforts
also need the government’s legal support (Eastman et al., 2011).
Although the sample size of 33 met the basic requirement for generalizing the main
findings of this study, the sizes in each BIMIR status group were not large. Thus, the
analysis results in Tables 9.10, 9.11, and 9.12 were considered more as snapshots for
illustrating the BIMIR movement model, than as the generalization of the main
findings in this study.
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9.3 A BBPT Model
To generalize the findings from the surveyed building projects and the case study in
the Singapore construction industry, the BBPT model was developed for building
projects that plan to implement BIM, as shown in Figure 9.1. The model consists of
six fundamental stages that are included in two parts of the model: BIMIR status
evaluation and BIMIR status movement. The deconstruction of the model was also
demonstrated in Figure 1.4. The basic purpose of developing this model was to help
project management teams improve their BIMIR statuses and transform their project
delivery approaches for productivity gains.
The first step in this model is to obtain and compile corporate goals of primary
project participants and their typical work practices (such as BIM uses, daily work
processes, information exchange procedures, data management, and communication
patterns). After starting a building project, the owner engages its team members in
stages to build a specific project organization as the project proceeds. Normally, all
the key parties have documented their standard corporate practices of providing
specific AEC services in the building projects that they have been working on in
Singapore. The key activities related to BIM implementation in the Singapore
construction industry may echo sentiments in Table 3.5. They tend not to be
productive enough due to the partial implementation of BIM. Although the local
government has been driving the local firms to implement BIM, the state of BIM
adoption is uneven in the market. The largest firms, such as some principal design
consultants and general contractors, tend to be very advanced to adopt BIM and thus
reap the benefits more fully, whereas the others may be in the beginning phase. The
beginners account for a large proportion of the design consultants and trade contractors.
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Are there significantNVA activities?
1. obtain and compile typical work practices being carried out by the major stakeholders in different
phases of their building projects in Singapore
2. compare these work practices with the 38 critical NVA activities (Table 7.2)
No
Build a project team at the beginningof a new building project
Yes
BIMIR S4: Full BIM implementation
A proposed FSE model (Equations 4.7 to 4.12 and Table 4.5)
BIMIR S2: lonely BIM
Leading causes to the critical NVA activities in each BIMIR
status(Table 7.14)
BIMIR S3: collaborative BIM
Key parties for BIMIR S1
BIMIR S1: no BIM
Project team input: implementation level of the NVA activities
A proposed organizational change framework for building projects that implement BIM (Table 5.3)
Project team input:significance of CHCs (Table 7.19)
Project team input: significance of CDCs (Table 7.21)
5. determine priorities of strategies to be implemented using the rule: f (top 10 CHCs, top 10 CDCs) (Table 9.9)
Managerial strategies:§ People§ Process§ Technology§ External environment
Change attributes on people
Change attributes on process
Change attributes on technology
Change attributes on external environment
4. identify critical factors affecting change towards full BIM implementation
Overall paths: CHCs-change attributes-CDCs-managerial strategies (Table 9.7)
6. implement managerial strategies vis-a-vis key change areas for transforming from BIMIR S1 (e.g. Table 9.10 for the surveyed projects)
Implement managerial strategies vis-a-vis key change areas for transforming from BIMIR S2 (e.g. Table 9.11 for the surveyed projects)
Implement managerial strategies vis-a-vis key change areas for transforming from BIMIR S3 (e.g. Table 9.12 for the surveyed projects)
Weighting of project phases and the 38
NVA activities (Table 7.4)
Key parties for BIMIR S2
Key parties for BIMIR S3
Part II:
BIMIR
movement
Part I:
BIMIR
evaluation
Amongtop 10?
CHCs
Yes
Amongtop 10?
CDCs
No
Yes
Priority one Priority three Priority four
Amongtop 10?
Yes
No
Priority two
NoCDCs
3. evaluate the BIMIR status of this project
Figure 9.1 BIM-based process transformation model for building projects
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In addition, facility managers tend to operate building facilities in a conventional
manner; BIM is usually not used in the operations and maintenance stage. Although
the BIM Particular Conditions was released, the main form of contract currently
adopted in Singapore is still based on the traditional adversarial system (BCA, 2015).
Overall, the Singapore construction industry is not fully BIM-ready for project-wide
collaboration at different phases of the building project (Lam, 2014).
The second step is to compare these typical work practices with their counterparts
among the 38 critical NVA activities in the project lifecycle, as shown in Table 7.2. If
no NVA activities are found in the typical work practices that are carried out by the
major stakeholders in this project, then this project can be judged as BIMIR S4 and is
planning on full BIM implementation. Nevertheless, the post-survey interviewees
observed that currently very few building projects were adopting a full BIM
implementation approach in the Singapore construction industry. Only a few firms
had been trying to deliver their projects using the principles of IPD, and such a
delivery approach was IPD-ish, rather than full BIM implementation at all.
The next step in the BBPT model is to evaluate the BIMIR status of this project using
the proposed fuzzy BIMIR evaluation model (see equations 4.7 to 4.12 and Table
4.5). The weighting of the seven project phases and 38 critical NVA activities were
presented in Table 7.4. The project organization can input the implementation level
(frequency of occurrence) of the 38 critical NVA activities, according to their
compiled typical work practices (obtained in the first step), using the five-point scale
(1 = very low, 2 = low, 3 = medium, 4 = high, and 5 = very high; or alternatively, 1 =
never, 2 = rarely, 3 = sometimes, 4 = often, and 5 = always). Following the
calculation process that can be found in Appendix 4, the BIMIR status can be
obtained. Given the specific BIMIR status of this project, the leading causes to the
critical NVA activities can be referred to in Table 7.14, which also indicates the
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responsible team members that mostly contributed to the critical NVA activities and
would take the lead in implementing managerial strategies which would be
determined later.
Step four is to identify the factors that significantly drive this project to be at the
current BIMIR status (calculated in the third step) and prevent the project being at a
higher status. Specifically, the project leadership team needs to input the significance
level of the 44 CHCs (see Table 7.19) and 31 CDCs (see Table 7.21) in terms of
affecting their BIM implementation activities in the planning stage of this project.
Two methods can be used: (1) collective rating. The team provides its inputs after
open discussions collectively according to the actual situation of this project; and (2)
multiple team members participate and provide their ratings independently. To avoid
confusion, such as many factors sharing the same rakings, a Likert scale with a wide
range or a percentile ranking should be used in the first case. In the second case, the
data related to the significance level provided by all the team members should be
averaged. A five-point scale can be used in the rating, namely 1 = very insignificant,
2 = insignificant, 3 = neutral, 4 = significant, and 5 = very significant. Based on the
significance rating, the CHCs and CDCs could be ranked. For example, the rankings
of the CHCs and CDCs for the BIMIR S1, S2, and S3 groups of surveyed projects
can be found in Table 9.3 and Table 9.5, respectively. Since the numbers of the
surveyed projects for BIMIR S1 (8) and S3 (5) were relatively small, the ranking
results in the two tables were regarded as an example. To increase the prediction
accuracy and reflect the contemporary situation of this project, the project leadership
team may re-assign the significance level of the CHCs and CDCs according to their
actual circumstances and particular project characteristics, using the rating scale
mentioned above.
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Using the proposed framework in Table 5.3, the CHCs and CDCs can be interpreted
from the organizational change perspective, as indicated in Figures 7.1 to 7.4. The
generic overall paths that linked the CHCs and CDCs to the organizational change
attributes and managerial strategies in terms of people, process, technology, and
external environment are presented in detail in Table 9.7.
Step five is to determine the priorities of the proposed managerial strategies to be
implemented in this particular project for prediction. Based on the top 10 rankings of
the CHCs and CDCs obtained in Step four, the key areas of organizational change can
be ascertained and the managerial strategies on people, process, technology, and
external environment aspects are therefore selected as well. Following the rules (see
Table 9.9) that consider the interaction function between the top-ranked CHCs and
CDCs, the four-level priorities of implementing the selected managerial strategies in
this particular building project organization can be determined. The highest
management priority is coded as priority one (rule A).
Last but not least, the project leadership team can develop its resources allocation and
leveling plan to implement the managerial strategies with different priorities.
Considering the affordability of necessary resources for BIM implementation (such as
BIM experts, software and their training, hardware, IT, and capital investment) and
the If-Then conditional statements in Table 9.9, the managerial strategies can be
prioritized, subject to the sponsorship from the senior management of the project
organization and of the team members. For example, the four-level priorities of the
managerial strategies for the surveyed projects of BIMIR S1, S2, and S3 groups can
be referred to in Table 9.10, Table 9.11, and Table 9.12, respectively. With the
prioritized strategies well planned in the project beginning stage and implemented in
the design, construction, and operations stages of this particular building project, it is
expected that the BIMIR status would be improved.
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Although the BCA has issued the CITM in October 2017, the BBPT model developed
in this study still contributes to scholarship and practice. The implementation of the
CITM is based on widespread BIM implementation in the local construction industry,
which is the main focus of this study.
9.4 Validation of the BBPT Model
To prove that the BBPT model was effective for building projects in Singapore, a
total of six professionals were interviewed to solicit their comments on the quality,
degree of accuracy, and observed robustness of the model, as indicated in Section
6.3.1. These experts participating in the validation were coded as VE1, VE2, VE3,
VE4, VE5, and VE6 (see Table 9.13).
Table 9.13 Profile of the validation experts
Interviewee Experience Designation Firm
VE1 17 years Senior design manager Developer
VE2 16 years Project manager General construction firm
VE3 11 years Project manager General construction firm
VE4 11 years Senior engineer MEP consultancy firm
VE5 12 years Contracts manager Construction and development firm
VE6 6 years Senior quantity surveyor General construction firm
The number of experts was considered adequate for validating a project model, with
references to previous construction management studies. Specifically, Liu and Ling
(2005) used one expert with three cases to verify a fuzzy system for mark-up
estimations. Arain and Low (2006) used four professionals with one case to validate a
knowledge-based decision support system for managing variation orders. Imriyas
(2009) used five experts with one case to validate an expert system for insurance
premium rating.
Previous construction management studies (Liu and Ling, 2005; Lim et al., 2012;
Ling et al., 2012; Zhao et al., 2016b) have examined the validity of their models or
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systems by calculating percentage error (PE), mean PE (MPE), and mean absolute PE
(MAPE). This approach was also adopted to determine the validity of the BBPT
model in this study in terms of NVAI scores. As indicated in Section 6.3.1, the NVAI
scores estimated by the professionals regarding their building projects were coded as
𝑁𝑉𝐴𝐼𝐸, and the NVAI scores calculated from the BBPT model were coded as 𝑁𝑉𝐴𝐼𝑀.
In this study, the PE, MPE, and MAPE could be calculated using the following
equations:
𝑃𝐸 =(𝑁𝑉𝐴𝐼𝐸 − 𝑁𝑉𝐴𝐼𝑀)
𝑁𝑉𝐴𝐼𝐸⁄ × 100% (9.1)
𝑀𝑃𝐸 =∑ 𝑃𝐸𝑎
𝑛 (9.2)
𝑀𝐴𝑃𝐸 =∑|𝑃𝐸𝑎|
𝑛 (9.3)
where:
𝑎 represents validation expert 𝑎;
𝑛 represents the number of the valuation experts (𝑛 = 6).
The MPE was used to check whether the results calculated from the BBPT model
tended to be over (positive signs) or below (negative signs) the experts’ judgment,
and the MAPE reported the magnitude of the model’s errors (Liu and Ling, 2005). A
lower MAPE indicated a lower magnitude of the errors and a higher accuracy of the
model.
The validation results are presented in Table 9.14. The PE values ranged from -
31.32% to 30.84%, and the MPE values ranged from -11.59% to 12.29%. The MPE
signs implied that the model was likely to underestimate the frequency of occurrence
of the critical NVA activities in project beginning and completion, and overestimate it
from the design development phase to the construction phase as well as project NVAI
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score. Besides, only two phases received MPE values over 10%, suggesting that the
results of the BIMIR evaluation model were consistent with the experts’ estimations.
Table 9.14 Validation results of the BIMIR evaluation model
NVAI
score
PE (%) MPE
(%)
MAPE
(%) VE1 VE2 VE3 VE4 VE5 VE6
P1 4.30 4.68 6.27 13.52 13.52 2.63 7.49 7.49
P2 0.66 -14.57 -13.60 22.29 10.40 12.96 3.02 12.41
P3 -4.27 -12.70 -27.85 -16.47 8.77 7.90 -7.44 12.99
P4 -21.12 1.44 -14.99 -27.34 2.33 0.25 -9.90 11.25
P5 -25.00 -14.87 26.06 -2.38 23.08 -15.38 -1.42 17.79
P6 -30.46 22.93 -25.44 -31.32 -0.84 -4.44 -11.59 19.24
P7 -15.38 30.84 16.67 9.77 21.00 10.86 12.29 17.42
Project -27.25 0.30 -8.92 1.14 7.28 3.38 -4.01 8.04
BIMIR 0 0 0 0 0 0 0 0
In addition, the MAPE values ranged from 7.49% to 19.24%, indicating that the
BIMIR evaluation model had an assessment accuracy range of 80.76% to 92.51%. In
particular, the MAPE value of the projects’ NVAI scores was 8.04%. Moreover, the
BIMIR statuses of the projects that VE2 and VE3 participated in were estimated to be
BIMIR S3, and those of the other projects were BIMIR S2. These estimations were in
line with the adjusted translation of calculated NVAI scores to BIMIR statuses (see
Table 4.5). Thus, the model can be used to evaluate NVAI scores or BIMIR statuses
of project phases and projects.
Previous studies (Fayek and Sun, 2001; Fayek and Oduba, 2005) stated that a fuzzy
expert system would be reliable if the discrepancy between the defuzzified value and
the actual value was no more than 33%. Lee (2007b) reported a fuzzy operator that
showed the prediction accuracy range of 66.50% to 84.68%. Ling et al. (2012)
developed mathematical models to predict corporate competitiveness, with the MAPE
values of 14.40% and 22.20%. Zhao et al. (2016b) developed a knowledge-based
decision support system which had the accuracy ranging from 83.70% to 92.90% in
assessing 16 enterprise risk management maturity criteria as well as overall enterprise
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risk management maturity of Chinese construction firms. Compared with the previous
studies, the fuzzy BIMIR evaluation model in the BBPT model was deemed robust
and valid.
Moreover, the usefulness of the managerial strategies provided by the BBPT model
and their priorities were commented by the experts. All the experts believed that the
prioritized strategies were helpful to their project teams that planned to transform
towards higher BIMIR statuses. Specifically, VE2, VE4, and VE6 opined that these
managerial strategies provided a comprehensive solution that a building project
needed to enhance its BIM implementation from the organizational change
perspective. VE2 stated that a project organization, especially when BIM was first
implemented in the project, would easily refer to the strategies for help in the
decision-making process. VE4 reported that the strategies and their priorities
highlighted for the projects in each BIMIR status were reasonable, and that these
strategies were general and may not be applicable to every project due to the nature of
the project; therefore, the project should appropriately expand the strategies to figure
out an optimal solution according to the actual circumstances of this project.
However, VE3 revealed that large developers may emphasize more on marketing
than technical innovation, so the project organization would be fully ready to adopt
the BIM implementation strategies only when all the project stakeholders could
benefit. Also, VE1 pointed out that small projects would not need to change in every
aspect to adopt a number of strategies because BIM implementation would be more
helpful in huge projects. Nevertheless, VE1 admitted that the identification of the
systematically prioritized strategies catered to the Singapore context that even smaller
projects tended to be mandated to implement BIM. VE5 thought that a clear
understanding of the strategies at the project beginning was beneficial, which would
save much time and manpower in the project delivery. Therefore, the usefulness of
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the managerial strategies and the appropriateness of determining their priorities to the
project organization’s decision-making could be seen as valid.
Furthermore, the user-friendliness and functionality of the BBPT model were also
discussed. All the experts agreed that the BBPT model (see Figure 9.1) was user-
friendly and could function well in their projects. Specifically, VE1 reported that the
model seemed a little complicated. However, VE6 reported that since this model
required a project leadership team to input its project data (such as BIM
implementation activities and relevant factors), the team could get reasonable
exclusive strategies with different priorities suggested. Also, VE4 thought that overall
the model could close the gap between project team members for enhancing BIM
implementation, and that this model was theoretical, practical, and systematical. VE5
expressed that the model provided an intuitional view for the leadership team.
Nevertheless, VE6 suggested that it would be better to provide foreign versions of the
BBPT model as a large proportion of the construction workforce in Singapore were
foreigners.
9.5 Summary
The chapter presented the development of the BBPT model for building projects that
plan to implement BIM in the Singapore construction industry. Illustrated using the
33 surveyed building projects, the BBPT model can evaluate the BIMIR status of a
particular building project in the project beginning, and provide managerial strategies
from the perspectives of people, process, technology, and external environment. This
model consists of a BIMIR evaluation part-model and a BIMIR improvement part-
model. The overall influence paths that link the CHCs and CDCs with the
organizational change attributes and management strategies were figured out. Based
on the proposed four-level priority rules, appropriate managerial strategies can be
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prioritized to optimize the consumption of relevant resources. With such strategies
being well planned in the project beginning and implementation activities well
implemented at the later stages of this particular project, the BIMIR status is expected
to be improved. In addition, the BBPT model was validated by six practitioners in
Singapore, and recognized as an useful tool for enhancing BIM implementation in the
local construction industry.
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Chapter 10: Conclusions and Recommendations
10.1 Summary of Research Findings
10.1.1 Critical NVA industry practices and resulting wastes in the
Singapore construction industry
The first objective of this study was to identify critical NVA activities in current
project delivery in the Singapore construction industry, assess their influence on
productivity, and examine leading causes to these activities. As Table 4.1 indicates,
plenty of NVA industry practices were contributed by major stakeholders in different
project phases. These practices were compiled into 44 common NVA activities and of
which, 38 were deemed critical by the local practitioners participating in Survey I.
The six NVA activities that obtained either mean scores below 3.00 or p-values above
0.05 were excluded. The exclusion was supported by the comments from four post-
survey interviews. “Lack of involvement by general contractor and key trade
contractors to contribute site knowledge (not appointed)” (N3.5), “coordination of
building systems is deferred until construction phase due to unavailable trade
contractor input until then” (N3.4), “lack of involvement by manufacturer/supplier
(not appointed) to contribute knowledge of material selection, transportation, site
erection, and so on” (N3.7), “prefabrication of some systems cannot start as design is
not fixed”(N4.6), “owner and designers enable changes during construction” (N6.1),
“lack of involvement by general contractor and key trade contractors to contribute
site knowledge (not appointed)” (N2.4), “lack of involvement by
manufacturer/supplier (not appointed) to contribute fabrication knowledge” (N2.5),
“lack of involvement by facility manager (not appointed) to contribute operations and
maintenance knowledge” (N2.6), “lack of involvement by facility manager (not
appointed) to contribute knowledge of operations and maintenance” (N3.8), “architect
and engineers only pass 2D drawings or incomplete 3D BIM models to contractors
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and manufacturer/supplier” (N5.1) were the top 10 critical NVA activities. Thus,
Hypothesis 1, stating that “the construction industry agrees upon frequent NVA
activities in the current project delivery in the Singapore context”, was supported.
A total of 13 types of wastes were identified from the literature review to quantify the
impact of the critical NVA activities on productivity in the current project delivery.
Among these wastes, defects, waiting/idle time, overproduction,
transporting/handling materials, unnecessary inventory, excess processing beyond
standard, and unnecessary movement of people and equipment are major wastes in
the Toyota production system, while RFIs, reworks/abortive works, change orders,
activity delays, design deficiencies (errors, omissions, additions), and safety issues
(injuries) were raised by previous construction management studies.
A total of 53 causes to the critical NVA activities were identified. Among which,
“design models/drawings fit for mandatory BIM submissions, but not fit for intended
downstream use” (C3.04), “does not permit design changes as these are expensive
once fabrication has commenced” (C5.01), “general contractor has to make extra
efforts to reconfigure or reformat data” (C4.03), “architect and engineers do not
understand field operations enough and lack construction input in design” (C3.05),
“lack of skilled BIM experts to engage” (C3.06), “lack of skilled BIM experts to
engage to help construction manager and unable to see how BIM benefit them”
(C4.07), “reluctant and inexperienced to use BIM and happy to continue using
traditional CAD” (C4.09), “trade contractors have to make extra efforts to reconfigure
or reformat data ” (C4.21), “training cost and high learning curve (initial productivity
loss) to use BIM” (C4.08), “trade contractors only have 2D drawings or incomplete
3D model shared from designers or general contractor” (C4.20), and “trade
contractors use CAD and cannot integrate BIM models from general contractor into
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their site models” (C4.22) were the top 10 leading causes. Thus, the first research
objective was fulfilled.
10.1.2 A fuzzy BIMIR evaluation model for building projects
BIMIR was negatively related to a NVAI continuum, which was measured by the
frequency of occurrence of the 38 critical NVA activities in the seven project phases.
The second objective of this study was to develop a BIMIR model to evaluate the
BIMIR statuses of building projects. A building project consists of many phases, and
each of them involves a number of activities that are produced by various major
stakeholders. The phases served as the evaluation criteria, and the 38 critical NVA
activities as evaluation attributes (sub-criteria). To enable BIM implementers to easily
understand these attributes and assess their BIMIR statuses by considering their
current BIM implementation practices, the NVA activities were originally identified
from the literature review and the pilot study.
Using equations 4.3 to 4.6 and the level of agreement mean scores of the critical
NVA activities, the weights of the seven project phases and the 38 critical NVA
activities were calculated. To deal with the problems involving vague, uncertain, and
subjective judgments in the implementation of the critical NVA activities, the FSE
approach was adopted in this model. Following equations 4.7 to 4.12 for computing
the NVAI score and Table 4.5 for the translation mechanism, the BIMIR status of any
building project can be computed. Thus, a fuzzy BIMIR evaluation model has been
developed, achieving the second research objective.
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10.1.3 BIMIR statuses and productivity performance of building projects
in Singapore
The third research objective was to investigate the BIMIR statuses and productivity
performance of building projects in Singapore. The data related to the frequency of
occurrence of the 38 critical NVA activities were collected from 73 building projects.
By inputting the data into the fuzzy BIMIR evaluation model, the NVAI scores of the
projects were calculated, ranging from 0.323 to 0.905, and the BIMIR statuses of
these projects were obtained. Among the 73 surveyed projects, 15 (20.55%) were in
BIMIR S1 where no BIM implementation activities were carried out, 47 (64.38%)
were in BIMIR S2 (lonely BIM implementation), the remaining 11 (15.07%) were in
BIMIR S3 (collaborative BIM implementation), and none were regarded as BIMIR
S4 (full BIM implementation). Although some projects were assessed to be in BIMIR
S3, the average BIMIR status of the surveyed building projects in Singapore was S2
(lonely BIM implementation), implying that their overall BIMIR status was low.
Thus, Hypothesis 2 that “the BIMIR statuses of building projects in Singapore are
low” was accepted.
The WC mean scores of the 13 wastes were calculated, ranging from 2.81 to 3.60.
Among which, “reworks/abortive works” (W03), “RFIs” (W02), “design deficiencies
(errors, omissions, additions)” (W12), “defects” (W01), and “waiting/idle time”
(W04) were the top five critical wastes. The WC mean scores ranged from 2.80 to
3.89 in the BIMIR S1 group of building projects, from 2.70 to 3.69 in the BIMIR S2
group of projects, and from 2.83 to 3.52 in the S3 group of projects. These values
decreased as BIMIR status increased. Therefore, BIM implementation could reduce
wastes and enhance productivity performance in the Singapore construction industry.
In addition, the post hoc test of the one-way ANOVA revealed that the mean scores
of seven wastes (W03, W05, W06, W07, W09, W10, and W11) differed between
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different BIMIR status groups of projects. Thus, Hypothesis 3 that “the higher the
BIMIR status, the lower the criticality of the wastes and the higher the productivity
performance” was supported, and the third research objective of this study was
achieved.
10.1.4 A proposed organizational change framework
The fourth research objective was to propose an organizational change framework for
building projects that plan to implement BIM. Among theories of organizational
change, Leavitt’s diamond theory and the MIT90s framework were selected because
of their constant evolvement to design better strategies for promoting new
technologies. This echoes with the Singapore government’s encouragement to
promote BIM in the local construction industry. BIM has been emerging as a new
technology or technological process, and its implementation has been proven to be an
organizational transformation (Azhar et al., 2014). Based on Leavitt’s diamond
model, the MIT90s framework, and their derivatives, this study first proposed an
organizational change framework which would be suitable for the building project
context using BIM. The proposed organizational change framework consists of four
components (people, process, technology, and external environment), which can be
further divided into 11 factors and 29 change attributes. In each project organization,
these attributes are essential to the journey of technology adoption and process
transformation. Thus, the fourth objective was fulfilled.
10.1.5 Critical factors hindering and driving change towards full BIM
implementation
Process transformation towards higher levels of BIMIR status can be influenced by
the interactions between the hindrances for and drivers to BIM implementation. The
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fifth research objective was to examine the critical factors hindering and driving the
construction industry to change towards full BIM implementation, and to analyze
them with the proposed organizational change framework. A total of 47 hindrances
and 32 drivers were identified from the comprehensive literature review. The data
related to the significance of these factors in affecting the change towards full BIM
implementation were collected in Survey II.
The analysis results suggested that 44 out of the 47 hindrances significantly hindered
BIM implementation in building projects in Singapore. Among which, “need for all
key stakeholders to be on board to exchange information” (H12), “contractual
relationships among stakeholders and need for new frameworks” (H27), “lack of
skilled employees and need for training them on BIM and OSM” (H04), “industry’s
conservativeness, fear of the unknown, and resistance to change comfortable
routines” (H05), “entrenchment in 2D drafting and unfamiliarity to use BIM” (H07),
“costly investment in BIM hardware and software solutions” (H42), “traditional
contracts protect individualism rather than best-for-project thinking” (H28),
“executives failing to recognize the value of BIM-based processes and needing
training” (H01), “technical needs for multiuser model access in multi-discipline
integration” (H46), and “firms’ unwillingness to invest in training due to initial cost
and productivity loss” (H31) obtained the top 10 significant influence. In contrast,
“few benefits from BIM go to designers while most to contractors and owners”
(H17), “lack of legal support from authorities” (H18), and “owners’ desire for
particular structures or finishes when considering OSM” (H25) did not have
statistically significant influence on BIM implementation. The three factors tended to
be either drastic or bias the reality and were excluded in the subsequent discussion.
In terms of the drivers for BIM implementation, the analysis results implied that 31
out of the 32 factors significantly drove BIM implementation in the Singapore
326
construction industry. Particularly, “BIM vision and leadership from the
management” (D01), “design coordination between disciplines through clash
detection and resolution” (D17), “owner’s requirement and leadership to adopt BIM”
(D05), “training on new skillsets and new ways of working” (D04), “gaining
competitive advantages from full BIM use” (D07), “regulatory agencies’ early
participation to BIM use” (D06), “3D visualization enabling design communication”
(D15), “producing models and drawings for construction and fabrication” (D20),
“stakeholders seeing the value of adopting their own part of BIM” (D03), and
“government support such as subsidizing training, software, and consultancy costs”
(D09) were the top 10 influential factors. Meanwhile, “enabling subcontractors to use
lower-skilled labor on site” (D10) was not perceived as a significant driver for BIM
implementation. The exclusion of this driver was supported by the post-survey
interviewees that skilled workers were still needed in the local industry to ensure
good quality and workmanship.
The critical hindrances and drivers were compared between the upfront stakeholders
and the downstream stakeholders. The independent-samples t-test results indicated
that “field staff dislike BIM coordination meetings looking at a screen” (H15), “lack
of consultants’ feedbacks on subcontractors’ model coordination” (H16), and “lack of
effective data interoperability between project stakeholders” (H29) were significantly
different between the two groups of surveyed organizations, while none of the critical
drivers were statistically distinct between the two groups of stakeholders.
As mentioned earlier, BIM implementation could be regarded as an organizational
change in the building project context. A building project team can be representative
of a cross-enterprise environment and a large project organization in which the
project participants work collaboratively to achieve common project goals within
constraints (Verdecho et al., 2012). Hence, the 44 CHCs and 31 CDCs were
327
interpreted with the 29 attributes of the proposed organizational change framework
(see Figures 7.1 to 7.4). Thus, Hypothesis 4 that “moving towards higher levels of
BIM implementation is hindered by a set of critical hindrances which can be
interpreted from the organizational change perspective” and Hypothesis 5 that
“moving towards higher levels of BIM implementation is driven by a set of critical
drivers which can be interpreted from the organizational change perspective” were
partially supported. Furthermore, the managerial strategies in terms of people (eight),
process (10), technology (five), and external environment (two) aspects were also
tailored (see Section 7.3.5). Therefore, the fifth objective that “examine the critical
factors driving and hindering the local construction industry to change towards full
BIM implementation” was achieved.
10.1.6 A BBPT model
The last objective of this study was to develop a BBPT model for building projects.
This model can evaluate the BIMIR statuses of building projects, propose appropriate
managerial strategies to improve their BIMIR statuses, and determine the priority of
implementing these strategies. The BBPT model was developed to generalize the
above findings to any building projects in the construction industry, and consisted of
two part-models: a BIMIR evaluation model and a BIMIR movement model (see
Figure 9.1). With a project team’s ratings on the frequency of occurrence of the
critical NVA activities in the project, the evaluation model can compute its BIMIR
status and provide leading causes to these NVA activities. In addition, with the
ratings on the CHCs and CDCs in this project, the improvement model can tailor
managerial strategies on people, process, technology, and external environment
aspects for the project organization to move towards higher BIMIR statuses, and
determine the priorities of these strategies to be implemented with resources. The 33
projects that were involved in both surveys were analyzed as the example to illustrate
328
the BIMIR movement model. Finally, the validation results from six interviews
implied that the discrepancies between the experts’ estimations and the calculated
values were accepted, and that the managerial strategies and their priorities provided
in the BBPT model were useful. Hence, the sixth research objective was also fulfilled.
Based on the above summary, it could be concluded that the research aim of this
study, “develop a BBPT model to assist project teams in moving towards higher
levels of BIM implementation, reducing wastes, and thus enhancing productivity
performance in building projects in Singapore”, was achieved.
10.2 Contributions
10.2.1 Contribution to scholarship
The study significantly contributes to the global body of knowledge related to BIM
implementation. The first contribution of this study is a proposed four-stage BIMIR
status in the building project context, ranging from no BIM implementation to full
BIM implementation (see Section 4.3). The implementation readiness is described by
the psychological willingness or the state of being prepared for performing BIM
implementation activities. This classification covers the stages in the existing BIM
implementation phases or maturity classification (Lee, 2007a; Succar, 2009;
Khosrowshahi and Arayici, 2012).
The second contribution is a proposed fuzzy BIMIR evaluation model for building
projects. Different from the existing BIM readiness model that used the ANN method
and focused on the architectural consultancy firms in Taiwan (Juan et al., 2017), the
proposed model in this study adopts the FSE approach which can solve the problems
related to vague, uncertain, and subjective judgments in the implementation of the
critical NVA activities. The proposed model evaluates the BIMIR statuses of building
329
projects in which all the major stakeholders are involved. The model consists of
seven project phases and 38 critical NVA activities, which have been validated in
Survey I. These evaluation criteria and sub-criteria are more comprehensive than the
existing model in the literature. Using this proposed model, the BIMIR status of any
building project can be assessed.
Thirdly, since little research has been carried out to investigate BIM implementation
in the construction industry as an organizational evolution, this present study is the
first to adapt existing theories of organizational change into the building project
context and propose an organizational change framework for building projects that
plan to implement BIM (see Table 5.3). The four-factor structure was constructed by
adapting the main blocks of Leavitt’s diamond model and the MIT90s framework as
well as their modifications. This adaption is supported with justifications in the
existing literature (Rockart and Scott Morton, 1984; Scott Morton, 1991; Teo and
Heng, 2007; Lyytinen and Newman, 2008; Croteau and Bergeron, 2009; De Haes et
al., 2012; Verdecho et al., 2012; Dahlberg et al., 2016).
Lastly, although a number of studies have investigated the CHCs and CDCs in the
process transformation, few studies have attempted to investigate these factors from
the organizational change perspective. This study interprets the 44 CHCs and 31
CDCs with the proposed organizational change framework, thus expanding the
existing literature related to organizational change and BIM implementation.
10.2.2 Contribution to practice
This study also significantly contributes to the practice. Specifically, 38 critical NVA
activities are identified, which provides a comprehensive picture of the current
industry practices, compared with the full BIM-enabled project delivery methods. To
330
estimate the impact of these NVA activities on productivity performance, 13 potential
wastes are identified. Among which, seven wastes are widely accepted and used in
the Toyota production system. The critical NVA activities and disruptive wastes help
the construction industry to rethink its current work processes and ensure that all
practitioners are aware of the opportunities, roles, and responsibilities associated with
incorporating BIM implementation into the current project delivery workflow
(Anumba et al., 2010).
Based on the interpretation of the CHCs and CDCs with the proposed organizational
change framework, a set of specific managerial strategies are identified to diminish
the negative influence of the CHCs and strengthen the positive influence of the CDCs
on people, process, technology, and external environment aspects.
The BBPT model developed in this study can help any building project evaluate its
BIMIR status in the planning stage and move towards a higher BIMIR status with
support from the proposed managerial strategies. As part of this model, the fuzzy
BIMIR evaluation model allows the project team to obtain a clear view of the status
quo in the building project. When they use the model, they need to rethink about the
typical work practices carried out by the team members. This thinking process is
likely to help determine the key parties that need to implement managerial strategies
at the later stages of this project. More importantly, with the ratings of the CHCs and
CDCs in the project, the BIMIR movement model can purposely propose appropriate
strategies for the project leadership team. The team should consider its project
context, such as project goal, the team members’ goals and collaboration skills, and
desired risk allocations (Barley, 1986; Anumba et al., 2010). Based on the proposed
four-level priorities rules (see Table 9.9), the resources allocated for implementing
these strategies can be prioritized efficiently. It is likely that the critical NVA
331
activities and wastes in the design, construction, and operations and maintenance
processes would be removed, leading to a more efficient project delivery.
The process transformation improves productivity although it is not directly measured
with exact figures. In the context of mandatory BIM submissions in Singapore, most
building projects must implement BIM. For example, if lonely BIM is implemented
in a project, BIM work processes tend not to be properly planned and implemented
due to the lack of collaboration. This may lead to disordered construction process on
site and require extra time or manpower to deal with the process, significantly
affecting productivity. In contrast, keeping this in mind, the local industry players
may start to prepare for transforming their delivery processes to reduce improperly
implemented BIM work activities. If the prioritized strategies are well planned from
project beginning and implemented in the later phases, this project’s BIMIR status
increases from BIMIR S2 to BIMIR S3 or BIMIR S4. With reference to the BBPT
model, because every strategy is collectively undertaken by the project team or
properly assigned to the stakeholder(s) who can best undertake it, the stakeholder(s)
involved in this process will no longer produce the critical NVA activities that would
inevitably be created in the lonely BIM-based delivery. The reduction of the critical
NVA activities contributes to fewer wastes and smooth construction process on site.
So, fewer man-days (fewer employees and/or less time) are needed to complete the
project. Therefore, according to the VAP method, increasing BIMIR status in the
project planning and execution stage will indeed improve productivity performance.
In addition, a case study was conducted in a large construction and development firm
based in Singapore. This reveals how changes were implemented when implementing
BIM in Project A of a higher BIMIR status, compared with its typical ongoing Project
B of a lower BIMIR status. Since Project A was selected as a sample project to
promote BIM in Singapore. The findings from the case study can be generalized. The
332
implication drawn from this case study allows the local BIM implementers to
understand the dynamics of process transformation when acquiring BIM technology
and work processes in their projects.
Finally, the governments that are still adopting a wait-and-see attitude should have
the clear understanding that without their leadership, encouragement, mandates, and
support, the construction industry may still be stuck to the unproductive way in the
changing market (Silva et al., 2016). Those having yet made efforts in driving BIM
implementation can refer to the managerial implications drawn from this study to
efficiently provide their support on purpose, such as conditionally mandating BIM
uses in their building projects, establishing national data exchange standards for
multidisciplinary model integration, providing technical support such as by defraying
a proportion of capital investments in consultancy, training, software purchase,
subscription, and updating, and promoting successful cases of BIM implementation.
10.3 Limitations
While this study has achieved the research objectives, there are limitations to the
conclusions. Firstly, the critical NVA activities and the factors hindering and driving
to change towards full BIM implementation were identified from the literature
review, which may not be exhaustive enough or continue to hold true as time passes.
Secondly, some wastes such as RFIs and workers’ waiting time may be interrelated,
and therefore it is not possible to achieve complete accuracy when estimating time
savings. Nevertheless, this study did not directly measure productivity.
Thirdly, among the 33 building projects that were involved in both surveys, the
numbers of projects distributed in BIMIR S1 (eight), BIMIR S2 (20), and BIMIR S3
333
(five) were not large. However, the situation was that most surveyed projects were
regarded to be in BIMIR S2 (see Table 7.6). Besides, this study mainly used these
projects to exemplify the BIMIR movement part of the BBPT model, rather than to
predict the managerial strategies for the whole industry. When it comes to a particular
project, the BBPT model would look into the CHCs (see Table 7.19) and CDCs (see
Table 7.21), and the four priority rules remain unchanged. Therefore, this limitation is
overcome.
Finally, the data of the two surveys and one case study were collected and analyzed in
the Singapore context, which may cause geographical limitation when interpreting
and generalizing the main findings of this study. Nonetheless, the theoretical and
practical implications drawn from this study are not limited to the building projects in
Singapore. Although the BBPT model was proposed for building projects in
Singapore in response to the mandatory BIM e-submissions policy and the local
government’s encouragement for project-wide BIM collaboration. Overseas
practitioners may also use this model. This is because: (1) like the public sector
taking the lead to adopt BIM in Singapore for enhanced productivity, BIM adoption
in publicly funded construction and building projects in the construction industry
overseas is also commonly encouraged, specified, or mandated (Smith, 2014;
McAuley et al., 2017); (2) the theoretical rational behind the CHCs and CDCs can be
used globally to interpret the hindrances to and drivers for change towards full BIM
implementation in their projects; and (3) overseas practitioners can use BIM
implementation fundamentals and follow the method used in this study, with minor
adjustments, to prepare their customized lists of key BIM work practices and critical
NVA activities, potential wastes, hindrances, drivers, and associated managerial
strategies in their projects according to their specific project characteristics and
political contexts.
334
10.4 Recommendations for Future Research
This study forms the foundation for future research on promoting BIM implantation
in the construction industry. It should be clarified that this study had already been
moved forward and substantially completed before the CITM was issued by the BCA
in October 2017. The CITM would not damage the contributions of this study. This is
because: (1) although the DfMA approach was promoted in both this study and the
CITM, the IPD and VDC approaches were exclusively highlighted in this study; (2) it
is expected that the Singapore construction industry needs to take years before the
DfMA approach or BIM at large are widely adopted in practice; and (3) BIM
implementation is the engine of the adoption of IDD in the CITM (BCA, 2017a).
Thus, it is believed that this study is novel and practically significant. Instead, the
main findings of this study can serve to support the fulfillment of the CITM in
Singapore.
Future work needs to be done in the following areas. Firstly, this study found that
executive vision and sponsorship significantly drove BIM implementation. The
management staff can only be convinced by concrete benefits. Thus, apart from the
reduced wastes and enhanced productivity, future work needs to be done to develop a
set of metrics that can measure project performance in all key aspects (time, cost,
quality, environment, client satisfaction, and so on). These metrics should be able to
demonstrate more tangible BIM implementation benefits to the management staff.
For example, SMEs and foreign firms based in Singapore may consider first costs as
the most critical and sometimes, the only factor in take-up of BIM (Kunz and Fischer,
2012). Practitioners need clear guidance for BIM implementation in practice because
they tend not to be knowledgeable and experienced about higher levels of BIM
implementation (Khosrowshahi and Arayici, 2012; Juan et al., 2017).
335
Secondly, future research should investigate interaction mechanisms among the 44
CHCs and 31 CDCs. The theoretical rational behind the mechanisms would be found
in the theories of organizational change as well. Using the structural equation
modeling technique, the cause-effect relationships among these factors would be
disclosed, which may further help project leadership teams to understand fewer and
essential factors.
Thirdly, implementing the proposed managerial strategies to move towards higher
levels of BIMIR status requires the project leadership teams to tailor clear action
plans. Thus, future work is also needed to identify specific actionable change
activities.
Last but not least, future research can develop a benchmarking system for BIMIR
status. A database would be established, which contains the BIMIR statuses of a large
number of building projects and AEC services providers participating in these
projects in Singapore. The benchmarking system would help users to make better
informed decisions, such as allowing developers or principal design consultants to
select qualified bidders to build qualified BIM teams.
336
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355
Appendices
Appendix 1: Questionnaire of Survey I
Survey on Non-Value Adding (NVA) Activities in Current Project
Delivery Process in the Singapore Construction Industry
Section I: Introduction
Building information modeling (BIM) is both an advanced technology and an intelligent 3D
model-based process. It equips project teams with insights and tools to more efficiently plan,
design, construct, and manage buildings.
The Singapore government has mandated BIM e-submissions of all building plans for new
building projects with a gross floor area of 5,000 m2 and above since July 2015. Nevertheless,
consultants tend to focus too much on BIM submissions for regulatory approvals, instead of
considering downstream uses. Thus, contractors and facility managers may lack quality BIM
models from the consultants. Some contractors deal with this situation by building their own
BIM teams and re-create the models. This is partial BIM adoption as it creates many NVA
activities, such as using poorly coordinated building systems and unclear plans on site. These
activities may result in wastes such as defects, requests for information, waiting for
instructions, and reworks, seriously affecting productivity.
The study aims to apply BIM to transform the current project delivery process into full BIM-
enabled processes in the Singapore construction industry to reduce critical NVA activities, and
thus enhance productivity. This survey seeks to identify the critical NVA activities and their
causes in the current process in Singapore construction industry, and evaluate the wastes
resulted from such activities. I assure you that the information provided by you will be kept
strictly confidential and will be used for academic purpose only. Any reports resulting from
this survey will make no identifiable reference to the specific sources of data. No individual
company or person will be identified in this study.
I will send you a summary of the results if you would like to leave your e-mail address in
General Information section.
Thank you for sparing your valuable time.
Sincerely,
LIAO Longhui, Ph.D. candidate
Department of Building, National University of Singapore
356
Section II: General Information
1. How would you classify your organization’s main business?
□ Architectural firm □ Structural engineering firm □ MEP engineering firm
□ General construction firm □ Trade construction firm □ Facility management firm
□ Others, specify_________________________________________________.
2. If your organization is “contractor” or “supplier”, its financial grade under BCA:
___________; otherwise, please go to Q3.
3. Your designation/position.
□ Government agent □ Owner □ Architect
□ Structural designer □ MEP designer □ General contractor
□ Trade contractor □ Manufacturer/Supplier □ Facility manager
4. Your e-mail address (if you would like to receive a summary of the results):
_________________________________________________.
5. Years of your work experience in the construction industry.
□ 5-10 □ 11-15 □ 16-20 □ 21-25 □ > 25
6. Years of implementing BIM in your organization.
□ 0 □ 1-3 □ 4-5 □ 6-10 □ > 10
Section III: Non-Value Adding (NVA) Activities
The following activities are identified from academic literature by comparing current project
delivery process (consultants tend to overemphasize mandatory BIM submissions, rather than
collaborating with downstream parties who are usually not involved upfront) with full BIM-
enabled processes (Integrated Project Delivery1, Virtual Design and Construction, and Design
for Manufacturing and Assembly) and are grouped according to project phases. Please rate the
level of agreement on these activities as NVA activities using a five-point scale (1=strongly
disagree, 2=disagree, 3=unsure, 4=agree, 5=strongly agree) and the frequency of occurrence
(1=never, 2=rarely, 3=sometimes, 4=often, and 5=always) of the activities in a building
project that you are participating (or recently participated).
1 Key features of Integrated Project Delivery: (1) owner continuously involves architect,
and/or engineers, general contractor, and/or key trade contractors from early design through
project completion; (2) they sign a single, multi-party agreement; (3) they waive any claim
amongst themselves except for in the instance of a wilful default; (4) they clearly define
achievable goals, jointly make decisions and control the project, and mutually share the
reward of achieving project targets and bear the risk of missing the targeted cost.
357
No. NVA activities
in current project delivery process
Level of
agreement
(1=strongly
disagree,
5=strongly
agree)
Frequency of
occurrence
(1=never,
5=always)
P1. Conceptualization 1 2 3 4 5 1 2 3 4 5
1.1 Lack of involvement by government agency
1.2 Inadequate project objectives and performance metrics
set by owner
1.3 Owner resists to use BIM in the whole project
1.4 No reward/risk sharing arrangements among major
stakeholders are set by owner
1.5 Lack of involvement by engineers (not appointed)
1.6 Lack of involvement by general contractor (not
appointed)
P2. Schematic design 1 2 3 4 5 1 2 3 4 5
2.1 Lack of involvement by government agency
2.2 Lack of joint control and agreement on project targets and
metrics by major stakeholders
2.3 Architect, engineers, and contractors do not work
together in design modeling
2.4 Architect does not share its complete model with
engineers
2.5 Architect and engineers do not submit their schematic
design models for regulatory approvals
2.6 Engineers not involved early in this phase to contribute in
architectural modeling
2.7 Lack of involvement by general contractor and key trade
contractors to contribute site knowledge (not appointed)
2.8 Lack of involvement by manufacturer/supplier (not
appointed) to contribute fabrication knowledge
2.9 Lack of involvement by facility manager (not appointed)
to contribute operations and maintenance knowledge
P3. Design development 1 2 3 4 5 1 2 3 4 5
3.1 Lack of involvement by government agency
3.2 Insufficient design review and feedback by owner
3.3 Architect, engineers, and contractors do not work
together in design modeling
3.4 Architect does not share its complete model with
engineers and contractors
3.5 Coordination of building systems is deferred until
construction phase due to unavailable trade contractor
input until then
3.6 Lack of involvement by general contractor and key trade
contractors to contribute site knowledge (not appointed)
3.7 Construction model is not developed due to
unwillingness of architect and engineers to share their
BIM models
3.8 Lack of involvement by manufacturer/supplier (not
appointed) to contribute knowledge of material selection,
transportation, site erection, and so on
3.9 Lack of involvement by facility manager (not appointed)
to contribute operations and maintenance knowledge
P4. Construction documentation 1 2 3 4 5 1 2 3 4 5
4.1 Not fully defined and coordinated between architectural,
structural, and MEP design models
4.2 Insufficient communication between architect and
engineers
358
4.3 Information such as bill of materials, assembly, layout,
detailed schedule, testing and commissioning procedures
is not documented after design
4.4 Long-lead items are not identified and defined during
design for early procurement
4.5 Shop drawing process is not merged into design as
contractors and manufacturer/supplier cannot document
construction intent
4.6 Prefabrication of some systems cannot start as design is
not fixed
P5. Agency permit/Bidding/Preconstruction 1 2 3 4 5 1 2 3 4 5
5.1 Architect and engineers only pass 2D drawings or
incomplete 3D BIM models to contractors and
manufacturer/supplier
5.2 General contractor has to re-build BIM model based on
insufficient documents from designers
5.3 General contractor extends 2D drawings (without BIM)
from designers to guide construction
P6. Construction (including Manufacture) 1 2 3 4 5 1 2 3 4 5
6.1 Owner and designers enable changes during construction
6.2 Architect and engineers need long time to respond to
contractors’ requests for information (RFIs) as their
design models cannot directly guide site work
6.3 Architect and engineers do not update their design
models
6.4 Contractors and manufacturer/supplier have excessive
RFIs and paperwork
6.5 General contractor communicates insufficiently with
other key stakeholders
6.6 Low proportion of building components in superstructure
and fitting out using off-site manufacture (OSM)
6.7 Congestion and many interfaces on site
6.8 Incomplete 2D drawings or 3D BIM models for trade
contractors and manufacturer/supplier
P7. Closeout/Closeout/Operations and maintenance 1 2 3 4 5 1 2 3 4 5
7.1 As-built BIM models are not handed to facility manager
who uses insufficient levels of detail 2D as-built
drawings
7.2 Many disputes/claims/litigations between general
contractor and owner and designers
7.3 Facility manager does not have sufficient BIM-based
design and construction information for operations and
maintenance
If there are other NVA activities that you deem as important and rational, please list them
below and provide your ratings:
Phase Other NVA activity Level of
agreement
Frequency of
occurrence
Section IV: Wastes
359
The following wastes are resulted from the NVA activities mentioned in Section III. Please
rate the frequency of occurrence (1=never, 2=rarely, 3=sometimes, 4=often, and 5=always)
and the impact on productivity of these wastes (1=insignificant effect, 2=minor detrimental
effect, 3=moderate detrimental effect, 4=significant detrimental effect, and 5=catastrophic
effect) in the same project mentioned in Section III.
No. Wastes
resulted from the NVA activities
Frequency of
occurrence
(1=never,
5=always)
Impact on
productivity
(1=insignificant
effect
5=catastrophic
effect)
1 2 3 4 5 1 2 3 4 5
1 Defects
2 RFIs
3 Reworks/abortive works
4 Waiting/idle time
5 Change orders
6 Activity delays
7 Overproduction/reproduction
8 Transporting/handling materials
9 Unnecessary inventory
10 Excess processing beyond standard
11 Unnecessary movement of people and equipment
12 Design deficiencies (errors, omissions, additions)
13 Safety issues (injuries)
If there are other wastes resulted from the NVA activities (mentioned in Section III) that you
deem as important and rational, please list them below and provide your ratings:
Other waste Frequency of
occurrence
Impact on
productivity
Section V: Causes
The following are possible causes to the NVA activities mentioned in Section III, and are
categorized into six groups based on project roles. Please rate the importance of these causes
(1=not important, 2=slightly important, 3=moderately important, 4=very important, and
5=extremely important) in the same project mentioned in Section III.
No. Causes
to the NVA activities Rating
importance
(1=not
important,
5=extremely
important)
R1: Government agency 1 2 3 4 5
360
1.1 Focusing on design stage by developing BIM submission templates and
guidelines
1.2 Mandating BIM submissions cannot guarantee collaboration and best-
for-project thinking
1.3 Unclear legislations and qualifications for precasters (versus concreter)
and inadequate codes for OSM varieties
R2: Owner (α = 0.870) 1 2 3 4 5
2.1 Inertia against use of BIM or off-site prefabrication
2.2 Establishing minimal apparent risk and minimum first cost as crucial
selection criteria
2.3 Unaware of the benefits of BIM and lifecycle management
2.4 Creating incentives for individual firms to protect their own interests
2.5 Awarding architectural and engineering design contracts solely based
on qualification
2.6 Setting vague goals with architect and rarely passing them on to
downstream parties
2.7 Focusing on assessing liability and risk transfers using mechanisms
such as guarantees and penalties
2.8 Perceiving design fees for OSM as more expensive than traditional
process
2.9 Desire for particular structures or traditional finishes
R3: Architect/Engineers 1 2 3 4 5
3.1 Because of potential liability, architect includes fewer details in
drawings or indicates that the drawings cannot be relied on for
dimensional accuracy
3.2 Architect does not model what contractors need for quantity take-offs
3.3 Not required by contract to share design models with contractors
3.4 Design models/drawings fit for mandatory BIM submissions, but not fit
for intended downstream use
3.5 Architect and engineers do not understand field operations enough and
lack construction input in design
3.6 Lack of skilled BIM experts to engage
3.7 No complete knowledge of their design decisions’ impact on
construction
3.8 Architect and engineers spend much time and effort locating,
recreating, or transferring fragmented information
3.9 Unless asked and encouraged, architect and engineers do not consider
lifecycle value of or incremental changes
3.10 Limited expertise of OSM and its processes in the market for architect
and engineers
3.11 Downstream designers have to make extra efforts to reconfigure or
reformat data
R4: General contractor/Key trade contractors 1 2 3 4 5
4.1 General contractor not required by owner and government to adopt BIM
4.2 General contractor only has 2D drawings or incomplete 3D model
shared from designers
4.3 General contractor has to make extra efforts to reconfigure or reformat
data
4.4 General contractor’s reluctance to adopt OSM
4.5 General contractor’s BIM team does modeling but not coordination for
trade contractors
4.6 General contractor requires but does not train trade contractors to use
BIM
4.7 Lack of skilled BIM experts to engage to help construction manager
and unable to see how BIM benefit them
4.8 Training cost and high learning curve (initial productivity loss) to use
BIM
4.9 Reluctant and inexperienced to use BIM and happy to continue using
traditional CAD
361
4.10 Having little knowledge of BIM and do not know how, when, and what
to use it
4.11 Lack of national BIM standards and guidelines for contractors
4.12 Doubt about the effectiveness of BIM because of limited evidence
4.13 Afraid of the unknown and resistant to change from comfortable daily
routine
4.14 Lack of legal support from authority
4.15 Lack of tangible benefits of BIM to warrant its use
4.16 Not thinking of changing conventional methods and no demand for
BIM use
4.17 Limited expertise of OSM and its processes in the market for
contractors
4.18 Trade contractors not required by general contractor/owner/government
to adopt BIM
4.19 High cost for trade contractors to engage BIM experts or outsource to
BIM drafters
4.20 Trade contractors only have 2D drawings or incomplete 3D model
shared from designers or general contractor
4.21 Trade contractors have to make extra efforts to reconfigure or reformat
data
4.22 Trade contractors use CAD and cannot integrate BIM models from
general contractor into their site models
R5: Manufacturer/Supplier 1 2 3 4 5
5.1 Does not permit design changes as these are expensive once fabrication
has commenced
5.2 Not required by owner/general contractor/government to adopt BIM in
manufacture
5.3 Lack of skilled BIM experts to engage and unable to see how BIM
benefit them
5.4 Only 2D drawings or incomplete 3D model shared from designers or
general contractor
5.5 Training cost and high learning curve (initial productivity loss) to use
BIM
5.6 Reluctant and inexperienced to use BIM and still happy to continue
using CAD
5.7 Market protection from traditional suppliers/manufacturers
R6: Facility manager 1 2 3 4 5
6.1 Not required by owner to use BIM and not involved in design phase to
contribute knowledge
If there are other causes to the NVA activities (mentioned in Section III) that you deem as
important and rational, please list them below and provide your rating (s):
Role Other cause Importance
Do you mind participating in next stage (Survey II) of this study?
□ Yes
□ No, your e-mail address (if NOT indicated in the “General Information” section):
___________________________________________________________________________.
362
Thank you once again.
If you have any queries about the survey, please feel free to contact LIAO Longhui.
Tel: (65) 9628 8127;
Email: [email protected]; [email protected]
363
Appendix 2: Questionnaire of Survey II
Survey on Factors Driving and Hindering Process Transformation
towards Full BIM-Enabled Project Delivery Processes in the Singapore
Construction Industry
Section I: Introduction
Building information modeling (BIM) is both an advanced technology and an intelligent 3D
model-based process. It equips project teams with insights and tools to more efficiently plan,
design, construct, and manage buildings.
The Singapore government has mandated BIM e-submissions of all building plans for new
building projects with a gross floor area of 5,000 m2 and above since July 2015. Nevertheless,
consultants tend to focus too much on BIM submissions for regulatory approvals, instead of
considering downstream uses. Thus, contractors and facility managers may lack quality BIM
models from the consultants. Some contractors deal with this situation by building their own
BIM teams and re-create the models. This is partial BIM adoption as it creates many NVA
activities, such as using poorly coordinated building systems and unclear plans on site. These
activities may result in wastes such as defects, requests for information, waiting for
instructions, and reworks, seriously affecting productivity.
The study aims to apply BIM to transform the current project delivery process into full BIM-
enabled processes (Integrated Project Delivery1, Virtual Design and Construction, and Design
for Manufacturing and Assembly) in the Singapore construction industry to reduce critical non-
value adding (NVA) activities, and thus enhance productivity. This survey seeks to identify
the critical factors hindering and driving the change towards full BIM implementation. I
assure you that the information provided by you will be kept strictly confidential and will be
used for academic purpose only. Any reports resulting from this survey will make no
identifiable reference to the specific sources of data. No individual company or person will be
identified in this study.
I will send you a summary of the results if you would like to leave your e-mail address in
General Information section.
Thank you for sparing your valuable time.
1 Key features of Integrated Project Delivery: (1) owner continuously involves architect,
and/or engineers, general contractor, and/or key trade contractors from early design through
project completion; (2) they sign a single, multi-party agreement; (3) they waive any claim
amongst themselves except for in the instance of a wilful default; (4) they clearly define
achievable goals, jointly make decisions and control the project, and mutually share the
reward of achieving project targets and bear the risk of missing the targeted cost.
364
Sincerely,
LIAO Longhui, Ph.D. candidate
Department of Building, National University of Singapore
Section II: General Information
1. How would you classify your organization’s main business?
□ Architectural firm □ Structural engineering firm □ MEP engineering
firm
□ General construction firm □ Trade construction firm □ Facility
management firm
□ Others, specify_____________________________.
2. If your organization is “contractor” or “supplier”, its financial grade under BCA:______;
otherwise, please go to Q3.
3. Your designation/position.
□ Government agent □ Owner □ Architect
□ Structural designer □ MEP designer □ General contractor
□ Trade contractor □ Manufacturer/Supplier □ Facility manager
4. Your e-mail address (if you would like to receive a summary of the results):
________________________________________________________________.
5. Years of your work experience in the construction industry:
□ 5-10 □ 11-15 □ 16-20 □ 21-25 □ > 25
6. Years of implementing BIM in your organization:
□ 0 □ 1-3 □ 4-5 □ 6-10 □ > 10
Section III: Hindrances to Change towards Full BIM Implementation
The following are hindrances to change from current project delivery process (consultants
tend to overemphasize mandatory BIM submissions, rather than collaborating with
downstream parties who are usually not involved upfront) towards full BIM-enabled
processes (Integrated Project Delivery, Virtual Design and Construction, and Design for
Manufacturing and Assembly). Please rate the significance of these hindrances (1=very
insignificant, 2=insignificant, 3=neutral, 4=significant, and 5=very significant) in a building
project that you are participating (or recently participated). If you have previously participated
in Another Survey of this study, please provide your ratings according to the same building
project you referred to in Another Survey.
365
No. Hindrances to change
towards full BIM-enabled delivery processes Significance
(1=very
insignificant,
5=very
significant)
1 2 3 4 5
H01 Executives failing to recognize the value of BIM-based processes and
needing training
H02 Concerns over or uninterested in sharing liabilities and financial
rewards
H03 Construction lawyers and insurers lacking understanding of
roles/responsibilities in new process
H04 Lack of skilled employees and need for training them on BIM and
off-site manufacture (OSM)
H05 Industry’s conservativeness, fear of the unknown, and resistance to
change comfortable routines
H06 Employees still being reluctant to use new technology after being
pushed to training programs
H07 Entrenchment in 2D drafting and unfamiliarity to use BIM
H08 Financial benefits cannot outweigh implementation and maintenance
costs
H09 Lack of sufficient evidence to warrant BIM use
H10 Liability of BIM such as the liability for common data for
subcontractors
H11 Resistance to changes in corporate culture and structure
H12 Need for all key stakeholders to be on board to exchange information
H13 Lack of trust/transparency/communication/partnership and
collaboration skills
H14 BIM operators lacking field knowledge
H15 Field staff dislike BIM coordination meetings looking at a screen
H16 Lack of consultants’ feedbacks on subcontractors’ model coordination
H17 Few benefits from BIM go to designers while most to contractors and
owners
H18 Lack of legal support from authorities
H19 Lack of owner request or initiative to adopt BIM
H20 Decision-making depending on relationships between project
stakeholders
H21 Owners set minimal risk and minimum first cost as crucial selection
criteria
H22 Poor knowledge of using OSM and assessing its benefits
H23 Requiring higher onsite skills to deal with low tolerance OSM
interfaces
H24 OSM relies on suppliers to train contractors to install correctly
H25 Owners’ desire for particular structures or finishes when considering
OSM
H26 Market protection from traditional suppliers/manufacturers and
limited OSM expertise
H27 Contractual relationships among stakeholders and need for new
frameworks
H28 Traditional contracts protect individualism rather than best-for-
project thinking
H29 Lack of effective data interoperability between project stakeholders
H30 Owners cannot receive low-price bids if requiring 3D models
H31 Firms’ unwillingness to invest in training due to initial cost and
productivity loss
H32 Assignment of responsibility/risk to constant updating for broadly
accessible BIM information
H33 Lack of standard contracts to deal with responsibility/risk assignment
and BIM ownership
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H34 BIM model issues (e.g., ownership and management)
H35 Poor understanding of OSM process and its associated costs
H36 OSM requires design to be fixed early using BIM
H37 Seeing design fees of OSM as more expensive than traditional process
H38 Difficulty in logistics and stock management of OSM
H39 Unclear legislations and qualifications for precasters and inadequate
codes for OSM varieties
H40 Interpretations resulted from unclear contract documents
H41 Using monetary incentive for team collaboration results in blaming
rather than resolving issues
H42 Costly investment in BIM hardware and software solutions
H43 Interoperability issues such as software selection and insufficient
standards
H44 Need for increasingly specialized software for specialized functions
H45 Difficulty in multi-discipline and construction-level integration
H46 Technical needs for multiuser model access in multi-discipline
integration
H47 Firms cannot make most use of Industry Foundation Classes and use
proprietary formats
If there are other hindrances that you deem as important and rational, please list them below
and provide your ratings:
Other hindrance Significance
Section IV: Drivers for Change towards Full BIM Implementation
The following are drivers for change from current project delivery process (consultants tend
to overemphasize mandatory BIM submissions, rather than collaborating with downstream
parties who are usually not involved upfront) towards full BIM-enabled processes
(Integrated Project Delivery, Virtual Design and Construction, and Design for Manufacturing
and Assembly). Please rate the significance of these drivers (1=very insignificant,
2=insignificant, 3=neutral, 4=significant, and 5=very significant) in the same project
mentioned in Section III. If you have previously participated in Another Survey of this study,
please provide your ratings according to the same building project you referred to in Another
Survey.
No. Drivers for change
towards full BIM-enabled delivery processes Significance
(1=very
insignificant,
5=very
significant)
1 2 3 4 5
D01 BIM vision and leadership from the management
D02 Changes in organizational structure and culture
D03 Stakeholders seeing the value of adopting their own part of BIM
D04 Training on new skillsets and new ways of working
D05 Owner’s requirement and leadership to adopt BIM
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D06 Regulatory agencies’ early participation to BIM use
D07 Gaining competitive advantages from full BIM use
D08 All disciplines sharing models in a ‘Big Room’
D09 Government support such as subsidizing training, software, and
consultancy costs
D10 Enabling subcontractors to use lower-skilled labor on site
D11 OSM lowering safety risks by controlling work in factory
D12 Alignment of the interests of all stakeholders
D13 Governance of BIM-related policies and standards
D14 Data sharing and access on BIM platforms
D15 3D visualization enabling design communication
D16 4D simulation before construction
D17 Design coordination between disciplines through clash detection and
resolution
D18 Complex design analysis in sustainability, material selection, and
constructability
D19 Project lifecycle costing
D20 Producing models and drawings for construction and fabrication
D21 High accuracy of model-based documentation
D22 More off-site fabrication and assembly of standard elements
D23 Automatic model updating and drawing production to deal with
design changes and their implications
D24 Lifecycle information management improving operations and
maintenance
D25 Increasing use of design-build and fast-track approach
D26 On-site work proceeds in parallel with off-site production
D27 OSM standardizes design and manufacturing processes, simplifying
construction and testing and commissioning processes
D28 OSM produces building elements with better quality and consistency
D29 OSM reduces building wastes, especially on-site wastes
D30 Integrating model management tools with enterprise systems to
exchange data
D31 Increasing complexity in buildings, project delivery, and marketplace
D32 New technologies such as Computer Numerically Controlled machines
If there are other drivers that you deem as important and rational, please list them below and
provide your ratings:
Other driver Significance
Would you like to be interviewed in next stage of this study?
□ Yes, your e-mail address (if NOT indicated in the “General Information” section):
__________________________________________________________________________.
□ No
Thank you once again.
If you have any queries about the survey, please feel free to contact LIAO Longhui.
Tel: (65) 9628 8127;
Email: [email protected]; [email protected].
368
Appendix 3: Questionnaire for the Validation of the BBPT model
Validation of the BIM-Based Process Transformation Model for
Enhancing BIM Implementation and Improving Productivity in Building
Projects in Singapore
Section I: Introduction
You are invited to assess the BIM-based process transformation (BBPT) model for enhancing
BIM implementation and improving productivity performance in building projects in
Singapore. The BBPT model serves as an internal evaluation tool for project leadership teams
in the project planning stage. The objectives of the BBPT model are to: evaluate BIM
implementation readiness (BIMIR) status of a building project in the project planning stage,
and provide management strategies with different priorities for the project team to change
towards a higher BIMIR status. After the transformation, the productivity of this project is
expected to be improved.
The information that you provide will be kept strictly confidential and be used solely for
academic purposes. Your name and your project’s name will not appear in the study.
Thank you for your kind assistance.
Sincerely,
LIAO Longhui, Ph.D. candidate
Department of Building, National University of Singapore
Section II: General Information
1. Your designation: _________________________.
2. Your work experience in the construction industry: ________years.
3. Your e-mail address: __________________________________________.
4. Years of adopting BIM in your firm: ________years.
5. BCA financial grade of your firm: □ ________ □ not applicable.
6. Name of the building project that you are participating (or recently participated): ________.
7. Type of this project: □ public project □ private project.
8. Role of your firm in this project: _________________________________.
Section III: BIM Implementation Readiness (BIMIR) Status Evaluation
369
Implementation readiness of a project team that plans to implement BIM is defined as “the
psychological willingness or the state of being prepared for performing BIM implementation
activities”. BIMIR describes the condition or situation of the team in the project planning
stage. In this section, please provide information based on the project you indicated in Section
II, Question 6.
1. Compared with full BIM-enabled delivery approaches (Integrated Project Delivery1, Virtual
Design and Construction, and Design for Manufacturing and Assembly), please estimate
which BIMIR status this building project is in, according to your experience and judgment.
□ BIMIR status one (no BIM implementation)
□ BIMIR status two (lonely BIM implementation: BIM is used in single parties, with low
level of collaboration or no collaboration among the parties)
□ BIMIR status three (collaborative BIM implementation: BIM is used in key parties, with
medium or high level of collaboration among the parties)
□ BIMIR status four (full BIM implementation)
2. Compared with full BIM-enabled delivery approaches (Integrated Project Delivery, Virtual
Design and Construction, and Design for Manufacturing and Assembly), currently there are
many activities in different project phases that do not add value to the complete delivery
process of this project and the final building. Please estimate the frequency of occurrence
(0%–100%) of non-value adding activities in each phase in this project.
Code Project phase Frequency of occurrence of non-value
adding activities in each phase in this
project (%)
P1 Conceptualization Score: _______________________%
P2 Schematic design Score: _______________________%
P3 Design development Score: _______________________%
P4 Construction documentation Score: _______________________%
P5 Agency permit/Bidding/Preconstruction Score: _______________________%
P6 Construction (including Manufacture) Score: _______________________%
P7 Handover/Closeout/Operations and
maintenance Score: _______________________%
Overall non-value adding index score Score: _______________________%
3. Please use the BBPT model to evaluate the BIMIR status of this project.
4. Do you think the managerial strategies provided by the BBPT model are useful and their
priorities are appropriate for your project to move towards a higher BIMIR status?
1 Key features of Integrated Project Delivery: (1) owner continuously involves architect,
and/or engineers, general contractor, and/or key trade contractors from early design through
project completion; (2) they sign a single, multi-party agreement; (3) they waive any claim
amongst themselves except for in the instance of a wilful default; (4) they clearly define
achievable goals, jointly make decisions and control the project, and mutually share the
reward of achieving project targets and bear the risk of missing the targeted cost.
370
_________________________________________________________________________
_________________________________________________________________________
_________________________________________________________________________.
5. What do you think of the functionality and user-friendliness of the BBPT model for
enhancing BIM implementation to reduce wastes and improve productivity in the
Singapore construction industry?
_________________________________________________________________________
_________________________________________________________________________
_________________________________________________________________________.
371
Appendix 4: A Calculation Example of the Fuzzy BIMIR Model
The fuzzy BIMIR model developed in Section 4.4.2 is adopted to evaluate the BIMIR status
of a surveyed building project in Singapore to illustrate the calculation process. The level of
agreement rating scores of the critical NVA activities were collected from Survey I. Using
equation 4.3, the mean scores of the critical NVA activities (𝑀𝑖) were calculated, as shown in
Table 7.2. Using equation 4.5 to 4.6, the weights of the critical NVA activities (𝑊𝑖 ) and
project phases (𝑊𝑝) were obtained, which are presented in Table 7.4. For example, the weight
of critical NVA activity N1.1 in the first project phase (P1, conceptualization) was calculated
as follows:
𝑊1 =𝑀1
∑ 𝑀𝑖4𝑖=1
= 3.51 (3.51 + 3.85 + 3.41 + 3.73) = 0.242⁄
The mean score of the first phase (𝑀1) was calculated as shown:
𝑀1 = ∑ 𝑀𝑖1 = 3.51 + 3.85 + 3.41 + 3.73 = 14.49
4
𝑖=1
The weight of the first phase (𝑊1) could be calculated as following:
𝑊1 =𝑀1
∑ 𝑀17𝑝=1
= 14.49 (14.49 + 22.41 + 29.64 + 21.97 + 11.47 + 28.97 + 10.22) = 0.104⁄
In this example, the frequencies of occurrence of the 38 critical NVA activities were rated
using the five-point scale (1=never, 2=rarely, 3=sometimes, 4=often, and 5=always). The
input data assigned by the corresponding respondent are shown in Table A.1. Since only one
response (𝑟 = 1) was received regarding this project, the TFN (𝐹𝑖𝑝
) of the frequency of
occurrence of the critical NVA activities in all phases were directly obtained according to
Table 4.3. The critical NVA activity N1.1 obtained the linguistic value of “always” and could
be transferred to the TFN of (0.75, 1.00, 1.00). Using the addition and multiplication
operations as well as equation 4.8, the TFN (𝐹1) of the frequency of occurrence of the first
project phase (P1) can be calculated as follows:
372
𝐹1 = (𝑓1𝑝
, 𝑓2𝑝
, 𝑓3𝑝
) = ∑(𝑊𝑖 × 𝐹𝑖𝑝
)
𝑘
𝑖=1
= (0.75 × 0.242 + 0.50 × 0.266 + 0.75 × 0.235 + 0.75 × 0.257, 1.00
× 0.242 + 0.75 × 0.266 + 1.00 × 0.235 + 1.00 × 0.257, 1.00 × 0.242
+ 1.00 × 0.266 + 1.00 × 0.235 + 1.00 × 0.257) = (0.684, 0.934, 1.000)
Then, using equation 4.9, the TFN of the 𝑁𝑉𝐴𝐼 of this project can be calculated:
𝑁𝑉𝐴𝐼 = (𝑛𝑣𝑎𝑖1, 𝑛𝑣𝑎𝑖2, 𝑛𝑣𝑎𝑖3)
= ∑(𝑊𝑝 × 𝐹𝑝) =
𝑞
𝑝=1
∑ {𝑊𝑝 × ∑(𝑊𝑖 × 𝐹𝑖𝑝
)
𝑘
𝑖=1
}
𝑞
𝑝=1
= (0.104 × (0.684, 0.934, 1.000) + 0.161 × (0.711, 0.961, 1.000)
+ 0.213 × (0.529, 0.779, 0.965) + 0.158 × (0.295, 0.545, 0.795) + 0.082× (0.502, 0.752, 0.917) + 0.208 × (0.318, 0.568, 0.784) + 0.073
× (0.250, 0.500, 0.750))
= (0.071 + 0.114 + 0.113 + 0.047 + 0.041 + 0.066 + 0.018, 0.097+ 0.155 + 0.166 + 0.086 + 0.062 + 0.118 + 0.037, 0.104 + 0.161+ 0.206 + 0.126 + 0.076 + 0.163 + 0.055) = (0.471, 0.721, 0.890)
Thus, 𝑛𝑣𝑎𝑖1 , 𝑛𝑣𝑎𝑖2, 𝑛𝑣𝑎𝑖3 are 0.471, 0.721, and 0.890, respectively. The crisp number of the
NVAI score of this project can be calculated using equation 4.12:
NVAI score=1∕3×(𝑛𝑣𝑎𝑖1 + 𝑛𝑣𝑎𝑖2 + 𝑛𝑣𝑎𝑖3)=1/3× (0.471 + 0.721 + 0.890) = 0.694
According to the adjusted translation rules presented in Table 4.5, the NVAI score can be
translated into: BIMIR S2 (lonely BIM implementation).
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Table A.1 Calculation process of the NVAI of a surveyed building project
Phase Critical NVA
activity 𝐹𝑖𝑗
𝑝 𝐹𝑖
𝑝= (𝑓𝑖1
𝑝, 𝑓𝑖2
𝑝, 𝑓𝑖3
𝑝) Weight of critical NVA
activity 𝐹𝑝 = ∑(𝑊𝑖 × 𝐹𝑖𝑝
)
𝑘
𝑖=1
Weight of
phase 𝑊𝑝 × 𝐹𝑝
P1 N1.1 (0.75, 1.00, 1.00) (0.75, 1.00, 1.00) 0.242 (0.684, 0.934, 1.000) 0.104 (0.071, 0.097, 0.104)
N1.2 (0.50, 0.75, 1.00) (0.50, 0.75, 1.00) 0.266
N1.3 (0.75, 1.00, 1.00) (0.75, 1.00, 1.00) 0.235
N1.4 (0.75, 1.00, 1.00) (0.75, 1.00, 1.00) 0.257
P2 N2.1 (0.50, 0.75, 1.00) (0.50, 0.75, 1.00) 0.156 (0.711, 0.961, 1.000) 0.161 (0.114, 0.155, 0.161)
N2.2 (0.75, 1.00, 1.00) (0.75, 1.00, 1.00) 0.163
N2.3 (0.75, 1.00, 1.00) (0.75, 1.00, 1.00) 0.156
N2.4 (0.75, 1.00, 1.00) (0.75, 1.00, 1.00) 0.175
N2.5 (0.75, 1.00, 1.00) (0.75, 1.00, 1.00) 0.175
N2.6 (0.75, 1.00, 1.00) (0.75, 1.00, 1.00) 0.175
P3 N3.1 (0.50, 0.75, 1.00) (0.50, 0.75, 1.00) 0.110 (0.529, 0.779, 0.965) 0.213 (0.113, 0.166, 0.206)
N3.2 (0.75, 1.00, 1.00) (0.75, 1.00, 1.00) 0.120
N3.3 (0.50, 0.75, 1.00) (0.50, 0.75, 1.00) 0.110
N3.4 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.138
N3.5 (0.50, 0.75, 1.00) (0.50, 0.75, 1.00) 0.142
N3.6 (0.50, 0.75, 1.00) (0.50, 0.75, 1.00) 0.111
N3.7 (0.50, 0.75, 1.00) (0.50, 0.75, 1.00) 0.136
N3.8 (0.75, 1.00, 1.00) (0.75, 1.00, 1.00) 0.132
P4 N4.1 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.171 (0.295, 0.545, 0.795) 0.158 (0.047, 0.086, 0.126)
N4.2 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.157
N4.3 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.160
N4.4 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.157
N4.5 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.174
N4.6 (0.50, 0.75, 1.00) (0.50, 0.75, 1.00) 0.181
P5 N5.1 (0.75, 1.00, 1.00) (0.75, 1.00, 1.00) 0.338 (0.502, 0.752, 0.917) 0.082 (0.041, 0.062, 0.076)
N5.2 (0.50, 0.75, 1.00) (0.50, 0.75, 1.00) 0.331
N5.3 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.331
P6 N6.1 (0.75, 1.00, 1.00) (0.75, 1.00, 1.00) 0.136 (0.318, 0.568, 0.784) 0.208 (0.066, 0.118, 0.163)
N6.2 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.128
N6.3 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.124
N6.4 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.130
N6.5 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.116
N6.6 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.117
374
N6.7 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.129
N6.8 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.121
P7 N7.1 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.345 (0.250, 0.500, 0.750) 0.073 (0.018, 0.037, 0.055)
N7.2 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.330
N7.3 (0.25, 0.50, 0.75) (0.25, 0.50, 0.75) 0.326
Sum – – 1 – 1 (0.471, 0.721, 0.890)
NVAI score – – – – – 0.694
375
Appendix 5: List of Publications from This Thesis
Liao, L., and Teo, E. A. L. (Published online, January 15, 2018). Managing critical
drivers for building information modelling implementation in the Singapore
construction industry: An organizational change perspective. International Journal of
Construction Management. DOI: 10.1080/15623599.2017.1423165.
Liao, L., and Teo, E. A. L. (2018). Organizational change perspective on people
management in BIM implementation in building projects. Journal of Management in
Engineering, 34 (3), 04018008.
Liao, L., and Teo, E. A. L. (2017). Critical success factors for enhancing the building
information modelling implementation in building projects in Singapore. Journal of
Civil Engineering and Management, 23(8), 1029–1044.
Liao, L., Teo, E. A. L., and Low, S. P. (2017). A project management framework for
enhanced productivity performance using building information modeling.
Construction Economics and Building, 17(3), 1–26.
