A Fuzzy-Network Analysis Approach for Modeling and ... · Unified Method (OUM) for Oracle ERPs, and 3) Consultant specific in which implementation methodologies combine of vendor-specific

Proceedings of the International Conference on Industrial Engineering and Operations Management Pilsen, Czech Republic, July 23-26, 2019

© IEOM Society International

A Fuzzy-Network Analysis Approach for Modeling and Analyzing the Critical Success Factors for the ERP

Implementation Projects

Shaikha Binkhatim and Hamdi Bashir Department of Industrial Engineering and Engineering Management

Sustainable Engineering Asset Management (SEAM) Research Group University of Sharjah

Sharjah, UAE [email protected], [email protected]

Abstract

The application of enterprise resource planning (ERP) is among the most complex transformational projects for any organization and is associated with high risks and cost. This study proposes a fuzzy-social network analysis approach for modeling and analyzing the crucial success factors of ERP implementation tasks. One benefit of the proposed tactic is that it permits ERP projects managers and implementors to identity the most crucial success factor for ERP implementation based on a classification system that classifies Critical Success Factors under four categories: autonomous, dependents, linkage and independent. The proposed approach is demonstrated using a case study from industry.

Keywords Enterprise Resource Planning (ERP), Critical Success Factors (CSFs), Fuzzy set theory, Social Network Analysis

1. Introduction

The implementation of Enterprise Resource Planning (ERP) system undertakings is important in situations in which businesses have utilized them to standardize processes, improve performance, and undertake better decision making. With the emergence of Enterprise Resource Planning (ERP) systems since the 1990s, it has become common for many organizations to rely on ERP systems to remain competitive within their markets and industries (Sayegh, 2010). According to Holland & Light (1999), “ERP implementation can secure enormous benefits for successful companies, or it can be disastrous for organizations that fail to manage the implementation process.” Enterprise resource planning systems or ERP can be defined as “a set of applications that automate finance and human resource departments and help manufacturers handle jobs such as order processing and production scheduling” (Gupta, 2000). Nah & Lau (2001) defined ERP as “a packaged business software system that enables a company to manage the efficient and effective use of resources (materials, human resources, finance, etc.) by providing a total integrated solution for the organization’s information-processing needs.” In the 1960s, most organizations implemented centralized computing systems to automate their systems for inventory control utilizing inventory control packages (IC). Subsequently, material requirements planning (MRP) systems were introduced in the 1970s, which entailed planning product requirements that were referred to a master production schedule. After that, in the 1980s, new software systems entitled manufacturing resources planning (MRP II) emerged to optimize manufacturing processes by linking materials with production requirements. Enterprise resource planning systems evolved from the MRPII system. ERP systems were introduced in the late 1980s, and by the 1990s, the system was known for its enterprise-wide inter-functional integration (Rashid, Hossain, & Patrick, 2002).

ERP implementation has several strategic implementation approaches, one which is the implementation of a standard package with few departures from the standard settings. Another approach is to customize a system to match business requirements (Holland & Light, 1999). In multi-cites companies, ERP can be implemented simultaneously at all facilities, or it can be implemented as phases. The advantages of the simultaneous implementations that they require less time for the integration of the entire system. Moreover, sequential implementation can create positive feedback

710

mailto:[email protected]

mailto:[email protected]



from early successes will permit the lessons learned in early implementations to be applied to the next implementations (Umble & Umble, 2002).

According to Nagpal, Khatri, & Kumar (2015), one traditional way to categorize ERP implementation depends on if all organizational functions must be implemented at one time (Big Bang approach) or through a phased approach in which ERP is implemented in phases. Moreover, the authors classified ERP implementation approaches into 3 main categorizes: 1) Custom-Made in which the implementers choose their own project activities that are unique to the implementation of an ERP, 2) Vendor-Specific in which the majority of the ERP vendors propose their specific product implementation methodologies such as ASAP methodology & SAP Activate for implementing SAP ERPs and Oracle Unified Method (OUM) for Oracle ERPs, and 3) Consultant specific in which implementation methodologies combine of vendor-specific implementation with best practices.

Successful implementations of an ERP can lead to positive outcomes for an organization. One of the most noticeable impacts of an ERP is that it allows businesses to streamline their management structures and create flexible organizations. ERP systems also involve information control centralization and the processes standardization (Davenport, 1998). Umble & Umble (2002) stated that a successful ERP implementation could reduce production cycles, provide accurate forecasts, enhance customer service, and may decrease overall information technology costs by excluding computer systems that are redundant. ERP systems can provide several intangible benefits like flexibility, integration, and process orientation. These benefits can provide firms with added capabilities and will ultimately boost strategic planning efforts (Al-Mashari, Al-Mudimigh, & Zairi, 2003). Most previous studies that have investigated the interrelationship among ERP critical success factors have considered binary relationships only; however, the relationships among factors cannot be binary and the degree of dependencies among the crucial factors for success are often vague and imprecise. Considering the limitations of previous studies, this paper proposes the use of a fuzzy-SNA approach for modeling and analyzing the critical success factors for the ERP implementation projects. The main outcome of this study is that it provides helpful guidance to firms considering implementing or upgrading their ERP systems by highlighting the critical factors that lead to a successful implementation project. 2. Literature Review 2.1 ERP Critical Success Factors (CSF) Several investigations have been conducted in the field of ERP implementation following diverse scopes and methodologies. Among the most popular topics in the ERP literature is the identification of critical success factors (Moon, 2007). According to Afaneh, AlHadid, & AlMalahmeh (2015), critical success factors can be defined as “elements related to the adoption and installation of ERP that are thought to lead to successful implementation.” Moreover, Sowan & Tahboub (2015) noted that critical success factors are used to indicate the key issues that organizations must focus upon to ensure a successful system and to affect the implementation process. Jharkharia (2011) and Das & Kumar (2017) analysed the interrelationship between the crucial failure factors of ERP implementation using Interpretive Structural Modeling ISM and MICMAC analysis. The ISM model proposed by Jharkharia (2011) analysed the critical failure factors and found that “Poor understanding of business implications and requirements,” “Poor data quality,” and “Lack of top management support” were at the root of other critical success factors and had a big impact on all additional failure factors. While Das & Kumar (2017) found that “Wrong ERP product selection” (F1), “Ineffective implementation team” (F13) and “Inappropriate business model” (F11) were ranked as the independent variables having maximum driving powers. Gandhi (2015) applied ISM and MICMAC in analyzing the 10 CSFs of ERP implementation for the Indian Small and Medium enterprises . The study indicates that Organizational Culture and Communication and Top Management Commitment and Support have high driving power and require thoughtful attention in the ERP implementation process. Similarly, by using ISM to analyse the interrelationships between 22 CSFs of ERP, Nagpal, Kumar, & Khatri (2017) found that Clear Goals and Objectives, Project Management, Project Team Competence, Top Management Support, and the Use of a Steering Committee were the most critical success factors. 2.2 Social Network Analysis According to Mitchell (1969), a social network is defined as a “specific set of linkage among a defined set of persons, with the additional property that the characteristics of these linkages as a whole may be used to interpret the behaviour of the persons involved.” Wetherell, Plakans, & Wellm (1994) noted that “social network analysis conceptualises social structure as a network with ties connecting members and channelling resources, focuses on the characteristics of ties

711



rather than on the characteristics of the individual members, and represents communities as ‘personal communities,’ that is, as networks of individual relations that people foster, maintain, and use in the course of their daily lives.” Similarly, Tindall & Wellman (2001) described social network analysis as “the study of social structure and its effects. It considers the social structure as a social network, that is a set of actors (nodes) and a set of relationships connecting pairs of actors where actors can be groups, organizations as well as persons, and the relationships are flows of resources that reflect relations of control, dependence, and cooperation.” Moreover, network analysts study the patterns of associations connecting members of social systems and how these patterns channel resources to particular locations in social structures. Social network analysis has created a new paradigm in the behavioural and social sciences that concentrates both conceptually and methodologically on the relational characteristics of social and behavioural patterns. This paradigm distinguishes itself from traditional scientific approaches that characteristically analyse the different objects of investigation as being independent of one another (Hirschi, 2010). According to Wasserman & Faust (1994), an important characteristic of social network analysis is visualizing the relationships among the actors (the objects being investigated, such as people, organizations, factors, etc.) by constructing a network consisting of nodes and arcs. The nodes represent the actors, and the arcs represent the relationships (binary or weighted) among them. The arcs can also be either undirected or directed. In addition to visualization, SNA includes several measures to be used for the analysis of the network structure. Otte & Rousseau (2002) noted that social network analysis is a technique that can be utilized in many fields and, with mathematical graph theory as its basis, SNA has become a multidisciplinary approach with several applications in sociology, the information sciences, computer sciences, and geography, among others. The literature review of Zheng, Le, Chan, Hu, & Li (2016) on the applications of SNA in the field of project management showed that the research interests in this field focused on eight topics: 1) communication and coordination, 2) governance issues, 3) IT utilization and innovation diffusion, 4) knowledge management, 5) performance and effectiveness, 6) risk management, 7) site and resource management, and 8) strategic management. Moreover, the applications of the SNA method in these categorized topics have been proven to be highly effective in quantifying and visualizing the patterns of several interactions in various areas. Most early SNA applications considered either binary or weighted relationships among actors. Yet, recent interest has been shown in using fuzzy social network analysis to deal with inexact and vague relationships among factors in some applications (Nair & Sarasamma, 2007; Fan, Liau, & Tsau-Young, 2007;Samanta & Pal, 2014; Brunelli, Fedrizzi, & Fedrizzi, 2014; Golsefid, Zarandi, & Bastani, 2015; Hua, Li, Zhang, & Wen-Cong, 2015; Chu, Liu, & Wang, 2016; Wang, Jin, & Ren, 2018). However, no applications of fuzzy social networks in the field of EPR have been reported in the literature. Nair & Sarasamma (2007) proposed a method to consolidate the information content of the fuzzy graph. Fan, Liau, & Tsau-Young (2007) generalized the concept of regular equivalence to fuzzy social networks. Moreover, Brunelli, Fedrizzi, & Fedrizzi (2014) combined the centrality measure with the fuzzy m-ary adjacency relation method. While Golsefid, Zarandi, & Bastani (2015) proposed new network measures based on fuzzy membership values. Similarly, Hua, Li, Zhang, & Wen-Cong (2015) proposed new centrality measures based on the fuzzy set theory. Chu, Liu, & Wang (2016) developed a procedure of solving group decision making problem with fuzzy preference relations where the experts are within directed social network connections. Finally, Wang, Jin, & Ren (2018) proposed a position and role analysis method for an intuitionistic fuzzy social network. 3. The Proposed Approach As shown in Figure 1, the proposed Fuzzy Social network approach comprised the following major steps: 1) Specifying a list of CSFs, 2) Developing a Structural Self Interaction Matrix, 3) Obtaining the Binary Reachability matrix, 4) Obtaining the Fuzzy Reachability Matrix, 5) Visualizing the Network of Factors, and 6) performing quantitative analysis. The application of these steps is demonstrated using a case study from industry, an investment asset management company located in the United Arab Emirates. This company was established in 2008 and started implementing the SAP ERP project in 2017. The implementation of the ERP was done in phases. In the first phase, the 5 main modules were configured and implemented, namely, Finance, Human capital, material management, sales, and leasing modules. The first phase of the implementation took 6 months to complete, then after 6 months of implementation, the ERP project management team at the company started to establish a plan for starting the second phase of the implementation project, which was concerned with new enhancements and configurations of new modules.

712



Figure 1. Research Methodology 3.1 Specifying a list of CSFs Referring to literature review on the ERP implementation critical success factors (Holland & Light, 1999; Esteves & Pastor, 2000; Nah & Lau, 2001; Zhang, Matthew , Zhang, & Banerjee, 2002; Al-Mashari, Al-Mudimigh, & Zairi, 2003; Umble & Umble, 2002; Lin, Lee, & Tserng, 2003; Somers & Nelson, 2004; Perera & Withanage, 2008; Yousef, 2010; Sayegh, 2010; Supramaniam & Kuppusamy, 2011; Tambovcevs, 2012; Beheshti, Blaylock, Henderson, & Lollar, 2014; Shatat, 2015; Ozorhon & Cinar, 2015; Gandhi, 2015; Gianopoulos, 2015; Chatzoglou, Fragidis, Chatzoudes, & Symeonidis, 2016; Umar, Khan, Agha, & Abbas, 2016; Nagpal, Kumar, & Khatri, 2017; Baykasoglu & Gölcük, 2017), and in consultation with three experts in a brainstorming session, the following 20 critical success factors were selected for inclusion in the model:

1. Support of Top Management 2. Personnel & Teamwork 3. Effective use of Project Management techniques/ Effective project Manager 4. Effective use of Change Management techniques / effective change manager 5. Business Process Change (BPC) and Business Process Reengineering (BPR) 6. Business Plan, Vision, and Objectives 7. Client Training 8. Effective Communication 9. ERP Strategy 10. Monitoring and Feedback 11. Legacy system Integration 12. User Involvement 13. Use of Consultants 14. Project Champions 15. Data Analysis and Conversion 16. Interdepartmental Cooperation 17. Management of Expectations 18. Usage of a Steering Committee 19. Partnership with vendor/ vendor support 20. Avoid /minimum Customization

The profiles of the experts as follows:

1. Specifying a list of CSFs Based on the Literature Review

2. Creating a Structural Self Interaction Matrix

3. Obtaining the Binary Reachability Matrix

4. Obtaining the Fuzzy Reachability Matrix

5. Visualizing the Network of Factors 6. Quantitative Analysis 7. Results and Findings

713



• Expert 1: ERP SAP database administrator and a consultant in the field of ERP implementation, with expertise in SAP ERP projects configuration systems, monitoring, administration and support, and change management.

• Expert 2: An ERP project manager, who was involved previously in several ERP implementation projects and currently responsible for the upgrade and enhancement of the current ERP system.

• Expert 3: SAP ERP consultant who has experience in supporting and managing the activities of a new implemented ERP project in the United Arab Emirates, currently the expert is working as the support and change control manager of the SAP ERP system in the company.

3.2 Developing Structural Self Interaction Matrix The structural self-interaction matrix adapted from the interpretive structural modeling methodology (Malone, 1975) was developed by the experts. A structural self-interaction matrix (SSIM) of CSFs of ERP implementation indicates pair-wise relationships between CSFs using the following four variables: V means the ith variable leads to the jth variable A means the jth variable leads to the ith variable X means the ith and jth variables are interdependent O ith and jth variables are not interdependent Table 1 shows the constructed SSIM for the case study.

Table 1. Structural Self-Interaction Matrix (SSIM) of The CSFs of ERP Implementation

CSF 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2

1 V V X V V 0 V V V 0 V X V V V V V V V

2 V 0 A A V V X V V 0 0 X A V V A X X

3 V V X V V V V V V V X X V V V X A

4 V X A X X V X X X V V V X A X X

5 V X A X A V X A A V 0 X A V X

6 V X A X A X V A X A V 0 0 V

7 0 0 A X A 0 A A V 0 0 A A

8 V V A V X V V V X 0 V V

9 X X A X A X A X X V 0

10 0 0 0 0 0 V 0 0 A 0

11 0 A 0 A A A 0 A 0

12 0 0 A V 0 0 A A

13 V X A X 0 V A

14 0 0 A V 0 V

15 0 A A A A

16 0 0 A V

17 V X A

18 V X

19 V

3.3 Obtaining the Binary Reachability Matrix The Final Structural Self-Interaction Matrix, SSIM was converted into binary Initial Reachability Matrix according to the following rules: • “If the (i,j) entry in the SSIM is V, the (i,j) entry in the reachability matrix becomes 1, and the (j,i) entry becomes

0;

714



• If the (i,j) entry in the SSIM is A, the (i,j) entry in the reachability matrix becomes 0, and the (j,i) entry becomes 1; • If the (i,j) entry in the SSIM is X, the (i,j) entry in the reachability matrix becomes 1 and the (j,i) entry also becomes

1; • If the (i,j) entry in the SSIM is O, the (i,j) entry in the reachability matrix becomes 0 and the (j,i) entry also becomes

0;” The obtained binary reachability matrix is demonstrated in Table 2.

Table 2. Binary Reachability Matrix

3.4 Obtaining the Fuzzy Reachability Matrix Because the adjacency matrix considers only binary relationships (0 or 1), it is therefore assumed that all the existing relationships among factors are equally important. To overcome that, the binary adjacency matrix needs to be replaced with weights representing the strength of the relationships among the factors. The fuzzy set theory that Zadeh (1965) introduced is appropriate for assigning weights to relationships by using membership functions and fuzzy sets valued in the real unit interval [0, 1]. Membership functions can be of different shapes, but triangular membership functions are used most frequently (Pedrycz, 1994). A triangular function is demonstrated by three components; a lower limit l, an upper limit r, and a value m, where l < m < r. The points l, m, and r represent the x coordinates of the three vertices of membership function “𝜇𝜇Ã(𝑥𝑥)”in a fuzzy set A, defined by equation (1).

𝝁𝝁Ã(𝒙𝒙) =

⎣⎢⎢⎢⎢⎢⎢

𝟎𝟎 𝒙𝒙 < 𝒍𝒍𝒙𝒙 − 𝒍𝒍𝒎𝒎 − 𝒍𝒍

𝒍𝒍 ≤ 𝒙𝒙 ≤ 𝒎𝒎𝒓𝒓 − 𝒙𝒙𝒓𝒓 −𝒎𝒎

𝒎𝒎 ≤ 𝒙𝒙 ≤ 𝒓𝒓

𝟎𝟎 𝒙𝒙 > 𝒓𝒓 ⎦

⎥⎥⎥⎥⎥⎥

(𝟏𝟏)

The experts in this study were requested to quantify the level of interactions among the critical success factors of the ERP using linguistic variables that were transformed into their corresponding triangular fuzzy numbers, as shown in Table 3.

Factors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1-Top Management Support 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 12-Personnel & Teamwork 0 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 0 0 0 13-Effective use of Project Management techniques/ Effective project Manager

0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

4-Effective use of Change Management techniques / effective change manager

0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1

5-Business Process Change (BPC) and Business Process Reengineering (BPR)

0 1 0 1 0 1 1 0 1 0 1 0 0 1 1 0 1 0 1 1

6-Business Plan, Vision and Objectives 0 0 0 1 1 0 1 0 1 0 1 0 1 1 1 0 1 0 0 17-Client Training 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 08- Effective Communication 0 1 0 1 1 0 1 0 1 1 0 1 1 1 1 1 1 0 1 19-ERP Strategy 1 1 1 0 1 1 1 0 0 0 1 1 1 0 1 0 1 0 1 110-Monitoring and Feedback 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 011-Legacy system Integration 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 012-User Involvement 0 0 0 1 1 1 0 1 1 1 0 0 0 0 0 0 1 0 0 013- Use of Consultants 0 0 0 1 1 1 1 0 1 0 1 1 0 0 1 0 1 0 1 114- Project Champions 0 1 0 1 1 0 1 0 1 0 0 1 1 0 1 0 1 0 0 015- Data Analysis and Conversion 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 016-Interdepartmental Cooperation 0 0 0 1 1 1 1 1 1 0 1 0 0 0 1 0 1 0 0 017- Management of Expectations 0 1 0 1 1 1 1 0 1 0 1 0 1 0 1 0 0 0 1 118- Use of Steering Committee 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 119-Partnership with vendor/ Vendor Support 0 0 0 1 1 1 0 0 1 0 1 0 1 0 1 0 1 1 0 120- Avoid/ Min Customization 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

715



Table 3. The linguistic variables and their corresponding triangular fuzzy numbers Assigning linguistic variables can be done either by consensus or each member of the group is asked to use the linguistic variables to provide his or her subjective opinion on the strengths among the CSFs. The assigned linguistic variables are then converted into their relative triangular fuzzy numbers. If the consensus approach is not adopted, then each relationship that exists between every two CSFs is assigned different “n” triangular fuzzy numbers, where n is the number of people involved. These different triangular fuzzy numbers can then be combined into one triangular fuzzy number using the average score. After obtaining the triangular fuzzy number for each relationship between all the pairs of factors, the fuzzy adjacency matrix was then obtained by defuzzification of the triangular fuzzy numbers into the best non-fuzzy performance (BNP) value which is defined by equation (2). BNPij = [(r−l)+(m−l)

3+ l (2)

—where ij indicates the crisp possible rating of the strength between factors i and j. Table 4 shows the obtained fuzzy matrix.

Table 4. Fuzzy Adjacency Matrix

3.5 Visualizing the Network of Factors By using the fuzzy adjacency matrix, the network can be easily plotted using any of the SNA software packages which visualize the relationships among the actors (the objects being investigated, such as people, organizations, factors, etc.) by formulating a network of nodes and arcs. The nodes represent the factors, and the arcs represent the

Factors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 201-Top Management Support 0.0 0.8 0.8 0.8 0.6 0.7 0.4 0.6 0.7 0.5 0.0 0.6 0.3 0.6 0.0 0.8 0.6 0.8 0.8 0.62-Personnel & Teamwork 0.0 0.0 0.4 0.8 0.0 0.4 0.5 0.0 0.3 0.0 0.0 0.6 0.4 0.6 0.4 0.8 0.0 0.0 0.0 0.23-Effective use of Project Management techniques/ Effective project Manager

0.0 0.7 0.0 0.0 0.6 0.7 0.6 0.6 0.6 0.8 0.2 0.7 0.8 0.6 0.2 0.6 0.7 0.6 0.8 0.5

4-Effective use of Change Management techniques / effective change manager

0.0 0.5 0.6 0.0 0.9 0.8 0.9 0.6 0.6 0.5 0.5 0.8 0.4 0.5 0.4 0.7 0.6 0.0 0.5 0.4

5-Business Process Change (BPC) and Business Process Reengineering (BPR)

0.0 0.4 0.0 0.6 0.0 0.7 0.5 0.0 0.8 0.0 0.6 0.0 0.0 0.4 0.7 0.0 0.6 0.0 0.6 0.6

6-Business Plan, Vision and Objectives 0.0 0.0 0.0 0.7 0.8 0.0 0.4 0.0 0.7 0.0 0.6 0.0 0.5 0.5 0.4 0.0 0.9 0.0 0.0 0.47-Client Training 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.08- Effective Communication 0.0 0.6 0.0 0.6 0.6 0.0 0.3 0.0 0.3 0.6 0.0 0.6 0.3 0.3 0.4 0.6 0.7 0.0 0.6 0.69-ERP Strategy 0.6 0.4 0.6 0.0 0.6 0.6 0.5 0.0 0.0 0.0 0.6 0.6 0.7 0.0 0.7 0.0 0.6 0.0 0.7 0.810-Monitoring and Feedback 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.011-Legacy system Integration 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.012-User Involvement 0.0 0.0 0.0 0.6 0.6 0.5 0.0 0.5 0.2 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.0 0.013- Use of Consultants 0.0 0.0 0.0 0.4 0.6 0.5 0.8 0.0 0.6 0.0 0.8 0.4 0.0 0.0 0.7 0.0 0.6 0.0 0.6 0.814- Project Champions 0.0 0.5 0.0 0.6 0.6 0.0 0.5 0.0 0.5 0.0 0.0 0.6 0.6 0.0 0.6 0.0 0.3 0.0 0.0 0.015- Data Analysis and Conversion 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.4 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.016-Interdepartmental Cooperation 0.0 0.0 0.0 0.6 0.6 0.4 0.4 0.6 0.4 0.0 0.5 0.0 0.0 0.0 0.4 0.0 0.4 0.0 0.0 0.017- Management of Expectations 0.0 0.6 0.0 0.8 0.7 0.9 0.6 0.0 0.5 0.0 0.3 0.0 0.4 0.0 0.3 0.0 0.0 0.0 0.4 0.618- Use of Steering Committee 0.8 0.5 0.4 0.6 0.5 0.6 0.4 0.5 0.7 0.0 0.0 0.5 0.4 0.5 0.2 0.5 0.6 0.0 0.5 0.419-Partnership with vendor/ Vendor Support 0.0 0.0 0.0 0.6 0.4 0.5 0.0 0.0 0.6 0.0 0.5 0.0 0.7 0.0 0.6 0.0 0.6 0.4 0.0 0.620- Avoid/ Min Customization 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Linguistic variable Triangular fuzzy number

Very low influence (VL) (0.0, 0.1, 0.3) Low influence (L) (0.1, 0.3, 0.5) Medium influence (M) (0.3, 0.5, 0.7)

High influence (H) (0.5, 0.7, 0.9) Very high influence (VH) (0.7, 0.9, 1.0)

Complete influence (C) (1.0, 1.0, 1.0)

716



relationships (binary or weighted) among the factors. These SNA software packages contain various features for the network analysis and visualization, including the ability to reflect the node size, colour, or its level by any of the social network analysis measures. For this case study, the fuzzy adjacency matrix shown in Figure 4 was used as input to the Social Network Visualizer (SocNetV 2.4) software, to construct a network consisting of 20 nodes and 193 arcs as seen in Figure 2. below. In this network, the nodes were mapped to levels to reflect their corresponding out-degree values. Consequently, as represented by their levels, Factors 1, 3, and 4 have the maximum out-degree values.

Figure 2. Network Visualization of the CSFs

3.6 Quantitative Analysis

In addition to visualizing the problem, SNA involves analyzing the structure of the network using a set of network-level measures and node-level measures. For the current case study, in-degree centrality and out-degree centrality measures were used. Table 5 provides the computed values of these measures. Based on the in-degree and out-degree centrality measures, the CSFs can be classified into four clusters, autonomous, dependent, independent, and linkage, as shown in Figure 3. This classification is adapted from Cross-impact matrix multiplication analysis (Duperrin & Godet, 1973).

• Cluster 1 (Autonomous CSFs). CSFs in this cluster have a weak out-degree and in-degree powers and are located nearest to the origin. The autonomous CSFs are relatively disconnected from the system because they have only a few weak links. Hence, they do not impact the success of ERP implementation much. The out-degree–in-degree power diagram indicates that only one autonomous CSF exists in this cluster, which is factor (CSF10) Monitoring and Feedback.

• Cluster 2 (Dependent CSFs). Cluster 2 is a dependent quadrant having weak driver powers and high dependent powers. In this current study, seven CSFs were in this category, namely, (CF7) Client Training, (CF11) Legacy System Integration, (CF12) User Involvement, (CF14) Project Champions, (CF15) Data Analysis and Conversion, (CF16) Interdepartmental Cooperation, (CF20) Avoid/Min Customization.

• Cluster 3 (Linkage CSFs). The Cluster that is located in the north-east corner of the chart is known as linkage CSFs. CSFs with high out-degree and high in-degree powers reside in this category. CSFs belonging to this group are unstable since lower level CSFs influence them and, in turn, they impact other, which might

717



affect the effective ERP implementation either in a positive or negative way. In the current study, eight CSFs are classified in the linkage category. These are: (CF2) Personnel & Teamwork, (CF4) Effective Use of Change Management Techniques / Effective Change Manager, (CF5) Business Process Change (BPC), Business Process Reengineering (BPR) (CF6) Business Plan, Vision and Objectives, (CF9) ERP Strategy, (CF13) Effective Use of Consultants, (CF17) Management of Expectations and (CF19) Partnership with Vendor (Vendor Support). The client company should take special care in considering these CSFs when they plan to start an ERP implementation project.

• Cluster 4 (Independent CSFs). The last group includes the driver/independent CSFs having strong out-degree power but a weak in-degree power. These CSF are considered as the most critical success factors. For the case study, four CSFs belong to the independent region, namely, (CF1) Top Management Support, (CF3) Effective Use of Project Management Techniques/ Effective Project Manager, (CF8) Effective Communication and (CF18) Use of Steering Committee. These CSFs can have a strong influence on other CSFs in ERP implementation in either a positive or negative way; thus, factors in this cluster need more caution and attention

Table 5. Social Network Analysis Results

Node In degree Out degree

1 1.389 10.744

2 4.978 5.356

3 3.278 10.178

4 7.667 10.278

5 8.122 6.556

6 8.133 6.011

7 6.889 1.367

8 3.4 7.089

9 8.711 7.967

10 2.8 1.067

11 5.211 0.5

12 6.244 3.467

13 5.522 6.722

14 3.967 4.789

15 6.533 1.511

16 3.978 4.411

17 8.367 6.111

18 1.822 8.589

19 5.633 5.678

20 6.433 0.689

718



Figure 3. CSFs Categorization Based on the In-Degree and Out-Degree Measures

4. Conclusions The implementation of an ERP project is a challenging and high risky project for any organization; thus, a clear understanding of the critical success factors and their interactions will help reduce the risk of failure. It is important to have an applicable way to quantify and visualize the level of interactions between these factors; which is proposed to be applicable via the usage of social network analysis software packages; however, the applications of social network analysis in the field of ERP implementation CSFs was not addressed in the literature. To overcome the highlighted limitation of the literature, this study proposed a fuzzy-social network analysis approach for modeling and analyzing the weighted level of interaction between the ERP CSFs. Based on fuzzy set theory, this approach incorporates the feedback of subjective experts about the interrelationships among factors. In addition to that, the proposed approach includes the usage of two measures, namely, in-degree and out-degree centrality that are used for classifying the critical success factors into four categories: Autonomous, Dependent, Linkage, and Independent critical success factors. Using this categorization and by modeling and visualizing the interactions of the CSFs through the proposed fuzzy-SNA approach, the ERP project manager, ERP implementation team and/or ERP consultants can distinguish among the factors and their relationships; consequently, the key factors can be easily determined. Therefore, the proposed approach can be used as guidance for an organization seeking to implement its ERP system. One advantage of this study is the usage of social network analysis software, which is a practical tool for modeling, analyzing, and visualizing the interactions among the factors. The other advantage of the suggested approach is the application of fuzzy set theory to deal with a vague and imprecise level of interactions among the various ERP CSFs. For demonstration purposes, the proposed approach was applied to a case study from industry, where the interactions between 20 factors were modelled via a network with 193 arcs. The study found that 4 factors were considered as having the greatest influence on the factors. These factors belonged to the independent CSFs and were (CF1) Top Management Support, (CF18) Use of Steering Committee, (CF3) Effective Use of Project Management Techniques/ Effective Project Manager, and (CF8) Effective Communication. These factors need high attention because these CSFs can influence other CSFs to the maximum extent in ERP implementation in either a positive or negative way. Second, notably, factors falling at the linkage cluster are being influenced by other factors and are influencing others at the same time; hence, they are linking the independent factors and dependent factors. Firms implementing ERP projects need to pay careful attention to factor (CF4) Effective Use of Change Management Techniques / Effective Change Manager because this factor has a high out-degree power and provides links between other factors. Previous knowledge of a CSF that offers greater potential to impact successful ERP implementations would aid project managers, consultants, ERP implementers, and clients in directing both their focus and resources towards those CSFs to obtain the desired ERP implementation outcomes.

719



The proposed Fuzzy-SNA approach has several limitations to be overcome in future studies. First, the proposed approach was demonstrated using a single case study. For a better generalization of the results, several case studies can be conducted. Second, this study assumes that the interaction level of the vital success factors of ERP implementation remains constant during an ERP project life cycle. Lastly, even though fuzzy sets are applicable for use and accordingly may be an ideal approach for quantifying the interaction level of the ERP CSFs; some other approaches may be explored. A future study can explore all these limitations.

References Afaneh, S., AlHadid, I., and AlMalahmeh, H., Relationship between organizational factors, technological factoers

and enterprise planning system implementation, International Journal of Managing Information Technology, vol.7, no.1, pp. 1-16, 2015.

Al-Mashari, M., Al-Mudimigh, A., and Zairi, M., Enterprise resource planning: A taxonomy of critical factors. European Journal of Operational Research, vol. 146, pp. 352-364, 2003.

Baykasoglu, A., and Gölcük, I. (2017). Development of a two-phase structural model for evaluating ERP critical success factors along with a case study. Computers & Industrial Engineering, vol. 106, pp. 256–274, 2017.

Beheshti, H. M., Blaylock, B. K., Henderson, D. A., and Lollar, J. G., Selection and critical success factors in successful ERP implementation. Competitiveness Review, vol. 24, no. 4, pp. 357-375, 2014.

Brunelli, M., Fedrizzi, M., & Fedrizzi, M., Fuzzy m-ary adjacency relations in social network analysis: Optimization and consensus evaluation. Information Fusion, vol. 17, pp. 36-45, 2014.

Chatzoglou, P., Fragidis, L., Chatzoudes, D., and Symeonidis, S., Critical success factors for ERP implementation in SMEs. In the Federated Conference on Computer Science and Information Systems, (pp. 1243-1252), 2016.

Chu, J., Liu, X., and Wang, Y., Social network analysis based approach to group decision making problem with fuzzy preference relations, Journal of Intelligent & Fuzzy Systems, vol. 31, no. 3, pp. 1271-1285, 2016.

Das, K. K., and Kumar, M. B., Interpretive Structural Modelling based analysis for critical failure factors in ERP Implementation. International Research Journal of Engineering and Technology, vol. 4, no. 7, pp. 1223-1230, 2017/

Duperrin, J., and Godet. M., Méthode de Hiérarchisation des éléments d’un système: Essai de prospectivité du système de l’énergie nucléaire dans son contexte sociétal [Hierarchy method elements of a system: Test system prospectively of nuclear energy in its societal context]. Commissariat à l’Energie Atomique [Commissioner for Atomic Energy, Report CEA-R-4541], Rapport CEA-R-4541, 1973.

Davenport, T. H., Putting the enterprise into the Enterprise System, Harvard Business Review, 1998. Esteves, J. M., and Pastor, J. A., An ERP life-cycle-based agenda. 1st International Workshop on Enterprise

Management Resource and Planning Systems EMRPS (pp. 359-371), Venice, Italy, 2000. Fan, T.-F., Liau, C.-J., and Tsau-Young, L., Positional analysis in fuzzy social networks, IEEE International

Conference on Granular Computing (GRC 2007), Fremont, California, IEEE, 2007. Gandhi, A., Critical success factors in ERP implementation and their interrelationship using TISM and MICMAC

analysis, Indian Journal of Science and Technology, vol. 8, no. S6, pp. 138–150, 2015. Gianopoulos, A., Critical success factors in ERP systems implementation: The case of medium and small sized

Enterprises, Journal of Business Management and Applied Economics, vol. 7, no. 2, pp. 1-16, 2015. Golsefid, S. M., Zarandi, M. H., and Bastani, S., General type-2 fuzzy degree of nodes in multi-central social networks

set NAFIPS co-authorship network, Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), Redmond, California, IEEE, 2015.

Gupta, A., Enterprise resource planning: The emerging organizational value systems, Industrial Management & Data Systems, vol. 100, no. 3, pp. 114-118, 2000.

Hirschi, C., Introduction: Applications of social network analysis. In 6th Conference on Applications of Social Network Analysis (pp. 2-3), Zurich: Elsevier Ltd, 2010.

Holland, C. P., and Light, B., A critical success factors model for ERP implementation, IEEE Software, pp. 30-36, 1999.

Hua, R.-J., Li, Q., Zhang, G.-Y., and Wen-Cong, M., Centrality measures in directed fuzzy social networks, Fuzzy Information and Engineering, vol. 7 , no 1, pp. 115-128, 2015.

Jharkharia, S., Interrelations of critical failure factors in ERP implementation: An ISM-based analysis, In 3rd International Conference on Advanced Management Science (pp. 170-174). Singapore: IACSIT Press, 2011.

Lin, Y.-C., Lee, M.-H., and Tserng, H. P., Construction enterprise resource planning implementation: Critical success factors – Lesson learning in Taiwan, In the 20th ISARC (pp. 623-628), Eindhoven, Holland: IAARC, 2003.

720



Mitchell, J. C. (1969), The concept and use of social networks, in J. C. Mitchell (Ed.), Social networks in urban situations (pp. 1-50), Manchester, University of Manchester Press, 1969.

Malone, D.W., An introduction to the application of interpretive structural modeling, Proceedings of the IEEE, vol. 63, no. 3, pp. 397-404, 1975.

Moon, Y. B., Enterprise Resource Planning (ERP): A review of the literature, International Journal of Management and Enterprise Development, vol 4 , no. 3, pp. 235-264, 2007.

Nagpal, S., Khatri, S. K., and Kumar, A., Comparative study of ERP implementation strategies. Systems, Applications and Technology Conference (LISAT), Long Island, New York, IEEE, 2015.

Nagpal, S., Kumar, A., and Khatri, S. K., Modeling interrelationships between CSF in ERP implementations: Total ISM and MICMAC approach, International Journal of System Assurance Engineering and Management, vol. 8, no. 4, pp. 782–798, 2017.

Nah, F. F.-H., and Lau, J. L.-S., Critical factors for successful implementation of enterprise systems, Business Process Management Journal, vol. 7, no. 3, pp. 285-296, 2001.

Nair, P. S., and Sarasamma, S., Data mining through fuzzy social network analysis, Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, San Diego, IEEE, 2007.

Otte , E., and Rousseau, R., Social network analysis: A powerful strategy, also for the information sciences, Journal of Information Science, vol. 28, no. 6, pp. 441-453, 2002.

Ozorhon, B., and Cinar, E., Critical success factors of enterprise resource planning implementation in construction: Case of Turkey. Journal of Management in Engineering, vol. 31, no. 6, 2015.

Pedrycz, W., Why triangular membership functions?, Fuzzy Sets and Systems, vol. 64, no. 1, pp. 21-30, 1994. Perera , H., and Withanage, T., Critical success factors in post ERP implementation, ENGINEER, vol. 21, no. 3, pp,

29 -36, 2008. Rashid, M. A., Hossain, L., and Patrick, J. D., The evolution of ERP systems: A historical perspective, Hershey,

Pennsylvania, Idea Group Publishing, 2002. Samanta, S., and Pal, M., A new approach to social networks based on fuzzy graphs, Turkish Journal of Fuzzy Systems,

vol.5, no.2, pp. 78-99, 2014. Sayegh, D. R., Factors affecting the implementation of ERP systems in organization in the U.A.E. (Unpublished

doctoral dissertation), The British University in Dubai, 2010. Retrieved from https://bspace.buid.ac.ae/ bitstream/1234/274/1/90038.pdf

Shatat, A., Critical success factors in Enterprise Resource Planning (ERP) system implementation: An exploratory study in Oman, The Electronic Journal of Information systems Evaluation, vol. 1, no. 1, pp. 36-45, 2015.

Somers, T. M., and Nelson, K. G., A taxonomy of players and activities accross the ERP project life cycle, Information and Management, vol. 41, no. 3, pp. 257-278, 2004.

Sowan, I. K., and Tahboub, R., Erp systems critical success factors ICT perspective, International Journal of Advanced Computer Science and Applications, vol. 6, no. 6,, pp. 191-196, 2015.

Supramaniam, M., and Kuppusamy, M., Analysis of critical success factors in implementing enterprise resource planning systems in Malaysian business firms, The Electronic Journal on Information Systems in Developing Countries, vol. 46 , no. 5, pp. 1-19, 2011.

Tambovcevs, A., ERP system implrmentation in Latvian manufacuring and construction company, Technological and Economic Development of Economy, vol. 18, no. 1, pp. 67–83, 2012.

Umar, M., Khan, N., Agha, M. H., and Abbas, M., Exploring the factors affecting ERP implementation quality, Journal of Quality and Technology Management, vol. 12, no. 1, pp. 137-155, 2016.

Umble, E. J., and Umble, M. M., Avoiding ERP implementation failure, Industrial Management, vol. 40, no.1, pp. 25-33, 2002.

Venkatesakumar, R., and Pachayappan, M., Application of social network analysis [SNA] metrics for systematic literature review [SLR], Man In India,vol. 97, pp. 233-247, 1997.

Wang, H., Jin, M., and Ren, P., Intuitionistic fuzzy social network position and role analysis, Cluster Computing, pp. 1-10, 2018.

Wasserman, S., and Faust, K., Social network analysis: Methods and applications, Cambridge, Cambridge University Press, 1994.

Wellman, B., and Tindall, D., Canada as social Structure: Social networks and Canadian sociology, Canadian Journal of Sociology, vol. 26, no. 3, pp. 265-308, 2001.

Wetherell, C., Plakans, A., and Wellm, B., Social networks, kinship, and community in Eastern Europe, The Journal of Interdisciplinary History, vol. 24 , pp. 639-663, 1994.

721



Yousef, S., Critical success factors in enterprise resource planning (ERP) system implementation (Unpublished master's thesis), Middle East University for Graduate Studies, 2010. Retrieved from http://www.meu.edu.jo/libraryTheses/58735b62a4b8c_1.pdf

Zadeh, L. A., Fuzzy sets, Information and Control, vol. 3, no. 3., pp. 338–358, 1965. Zhang, L., Matthew, K. O., Zhang, Z., and Banerjee, P., Critical success factors of enterprise resource planning

systems implementation success in China, in Proceedings of the 36th Hawaii International Conference on System Sciences, Hawaii, IEEE, 2002.

Zheng, X., Le, Y., Chan, A. P., Hu, Y., and Li, Y., Review of the application of social network analysis (SNA) in construction project management research, International Journal of Project Management, vol. 34, pp.1214-1225. 2016.

Biographies Shaikha Binkhatim is an ICT Associate Analyst and an SAP ERP project manager at a semi-government company in Sharjah. She earned both B.S. in Computer Engineering (2016) and Master’s in Engineering Management (2019) from University of Sharjah, UAE. Hamdi Bashir received his Ph.D. degree in 2000 from McGill University, Montreal, Canada. Currently, he is an Associate Professor of Industrial Engineering and Engineering Management at the University of Sharjah. Prior to joining this university, Dr. Bashir has held faculty positions at Sultan Qaboos University, University of Alberta, and Concordia University. During his academic career, Dr. Bashir has taught a wide variety of courses related to industrial engineering and engineering management at both undergraduate and postgraduate levels. Dr. Bashir’s research interests are in the areas of: Project management (portfolio management, stakeholder management, and project performance), Manufacturing systems (design of cellular manufacturing systems and applications of group technology), Quality management (total quality management (TQM) and excellence models), and Health care management (industrial Engineering applications in health care systems). Dr. Bashir is a senior member of the Institute of Industrial and Systems Engineers (IISE) and he was a registered professional engineer with Association of Professional Engineers, Geologists, and Geophysicists of Alberta (APEGGA), Canada.

722

Documents

A Fuzzy-Network Analysis Approach for Modeling and ... · Unified Method (OUM) for Oracle ERPs, and 3) Consultant specific in which implementation methodologies combine of vendor-specific