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ASSESSING GREENING ACTIVITIES OF SMALL AND
MEDIUM ENTERPRISES IN THE AUTOMOBILE
INDUSTRY IN THAILAND BY DEA APPROACH
BY
NIYAT TESFAI
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF MASTER OF
ENGINEERING IN LOGISTICS AND SUPPLY CHAIN SYSTEMS
ENGINEERING
SIRINDHORN INTERNATIONAL INSTITUTE OF TECHNOLOGY
THAMMASAT UNIVERSITY
ACADEMIC YEAR 2014
ASSESSING GREENING ACTIVITIES OF SMALL AND
MEDIUM ENTERPRISES IN THE AUTOMOBILE
INDUSTRY IN THAILAND BY DEA APPROACH
BY
NIYAT TESFAI
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF MASTER OF
ENGINEERING (LOGISTICS AND SUPPLY CHAIN SYSTEMS
ENGINEERING)
SIRINDHORN INTERNATIONAL INSTITUTE OF TECHNOLOGY
THAMMASAT UNIVERSITY
ACADEMIC YEAR 2014
ii
ABSTRACT
ASSESSING GREENING ACTIVITIES OF SMALL AND MEDIUM ENTERPRISES IN THE AUTOMOBILE INDUSTRY IN THAILAND BY DEA
APPROACH
By
NIYAT TESFAI
B.Sc. (International Management), University of Applied Sciences Karlsruhe, 2014
Small and medium enterprises (SMEs) are contributing more and more to Thailand’s economy. As a result, the Thai industry is increasing in energy intensity, mainly in the manufacturing sector due to outdated machines. Furthermore, studies have revealed that non-OECD countries will continue to increase its energy consumption, as a consequence of their growing numbers of inhabitants as well as economy. The purpose of this study is to assess greening activities and benchmark the energy consumption of SMEs in the Thai automotive sector. The study is fulfilled in corporation with GIZ Bangkok, which are implementing a Green Auto Project in which they consult SMEs regarding greening activities. With respect to the project, data has been collected for the evaluation, including the energy consumption as well as amount of goods produced. The contribution of this proposed study is a method of grouping several SMEs from the automotive sector according to production processes, so that the Data Envelopment Analysis (DEA) can be done amongst homogenous Thai SMEs in the automotive sector and benchmarking them is more representative. Furthermore, it is the first study to apply DEA to benchmark energy efficiency in the Thai automotive sector for SMEs. The DEA model is input-oriented, i.e. that given the same amount of outputs; inputs (energy consumption) shall be reduced. Efficient SMEs will be reported and results will reveal a slack analysis, indicating the amount of input that individual inefficient DMUs must reduce in order to reach the efficiency frontier.
Keywords: Data Envelopment Analysis, Energy Efficiency, Small and Medium Enterprises
iii
Table of Contents
Chapter Title Page
Signature Page i
Abstract ii
Table of Contents iii
List of Tables v
List of Figures vi
List of Abbreviations vii
1 Introduction 1
1.1 Statement of the Problem and Motivation 2
2 Literature Review 5
2.1 Environmental Issues 5
2.2 Green Manufacturing 6
2.3 Measuring Green Production 9
2.4 Measuring Performance of Small and Medium Enterprises in
Thailand 14
3 Data Envelopment Analysis (DEA) 18
3.1 Theoretical Framework 18
3.2 Benchmarking with Data Envelopment Analysis 21
3.2.1 Parametric and Non-Parametric Methods 22
3.3 Advantages and Disadvantages of Data Envelopment Analysis 24
iv
4 The German International Corporation 27
4.1 Company Profile 27
4.2 Introduction of the Green Auto Project 28
4.3 Approach for Working Packages of Project 31
5 Methodology and Procedure 37
5.1 Data Collection 37
5.2 Data Standardization Approach 38
5.3 Problem Formulation 42
5.4 Methodology and Modelling Undesirable Measures 47
6 Energy Efficiency Analysis 50
6.1 Profitable Environmental Management Approach 50
6.2 Best Available Technique Approach 55
6.3 Summary 58
6.4 Policy Recommendations 60
6.5 Conclusion 62
7 Limitations and Delimitations 64
7.1 Limitations of Study 64
7.2 Limitations of Instrument 64
References 66
Appendices 74
BAT Raw Data 74
v
List of Tables
Tables Page
Table 2.3.1: Metrics and examples of SSCM .......................................... 10
Table 2.3.2: Major themes used by identified metrics ............................. 11
Table 5.2.1: Raw data of BAT peer group 1 ............................................ 40
Table 5.2.2: Standardized BAT data ........................................................ 41
Table 5.3.1: Supply Chain Operations ..................................................... 42
Table 6.1.1: PREMA Data ....................................................................... 51
Table 6.1.2: DEA Results of PREMA ..................................................... 52
Table 6.1.3: Slacks for PREMA Data ...................................................... 54
Table 6.2.1: DEA Results of BAT ........................................................... 55
Table 6.2.2: Standardized BAT Data ....................................................... 57
Table A.1: BAT Raw Data ...................................................................... 74
vi
List of Figures
Figures Page
Figure 4.2.1: Project Structure ................................................................. 30
Figure 4.2.2: GIZ’s project target group .................................................. 30
Figure 5.2.1: Z-Standardisation ............................................................... 39
Figure 5.3.1: Efficient Frontier of 5 DMUs ............................................. 44
Figure 5.3.2: Output-oriented Efficient Frontier ..................................... 46
vii
List of Abbreviations
BAT: Best Available Technique BMZ: Bundesministerium für wirtschaftliche Zusammenarbeit und
Entwicklung BREF: Best Available Techniques Reference Documents IC: Intellectual Capital CO2: Carbon dioxide CP: Clean Production CRS: Constant Return to Scale CSR: Corporate Social Responsibility DEA: Data Envelopment Analysis DEDE: Department of Alternative Energy Development and
Efficiency DFA: Distribution Free Approach DIP: Department of Industrial Promotion DMU: Decision Making Unit e.g.: exempli gratia (for example) EE: Energy Efficiency EENet: Energy Efficiency Network ENCON: Energy Conservation ESCO: Energy Service Companies etc.: et cetera EU: European Union FDH: Free Disposal Hull GDP: Gross domestic product GIZ: Gesellschaft für internationale Zusammenarbeit GSCM: Green Supply Chain Management i.e.: id est (that is) IEO: International Energy Outlook IQA: Intelligent Quality Assurance ISO: International Organization for Standardization KPI: Key Perfomance Index LRP: Loss Reduction Process ME: Medium Enterprises MEPS: Minimum Energy Performance Standards n.d. No date NGO: Non Governmental Organisation OECD: Organization for Economic Cooperation and Development OSMEP: Office of Small and Medium Enterprises Promotion RE: Renewable Energy SCP: Sustainable and Clean Production SE: Small Medium Enterprises SEA: Southeast Asia SFA: Stochastic Frontier Approach SME: Small and Medium Enterprises
viii
SOP: Standard Offer Program SP: Sustainable Production SSCM: Sustainable Supply Chain Management TAI: Thai Automotive Institute TFA: Thick Frontier Approach VRS: Variable Return To Scale
1
Chapter 1
Introduction
The importance of green manufacturing is raising awareness in today’s global
economy. Businesses are taking sustainable production and environmental activities
as part of their corporate social responsibilities (CSR), as they have a huge impact on
environmental changes (Tikul, 2014). Not only are companies modifying single
processes with respect to their CSR activities, but are involving the whole supply
chain as well. The social and environmental aspects can be integrated into a
companies’ supply chain through inter-organizational management (Kogg & Mont,
2012).
This study focuses on small and medium enterprises (SMEs) in Thailand and aims to
support the Green Auto Project of GIZ1 (Deutsche Gesellschaft für internationale
Zusammenarbeit) in Bangkok. Thailand’s SMEs have contributed 36.6% of the
overall gross domestic product (GDP) in 2011 and thus, have a great impact on the
economy. When looking at the size of enterprises it should be indicated that small
enterprises (SE) account more to the countries GDP than medium enterprises (ME).
While the value of SEs was 24.5% of Thailand’s total GDP in 2011, MEs only
contributed 12.1%. Another important finding from the Office of Small and Medium
Enterprises Promotion (OSMEP) in Thailand is that when considering the GDP
structure, the agricultural sector contributed 13.3% while non-agricultural sector
constituted 86.7% of Thailand’s total GDP in 2011. When breaking down the non-
agricultural sector the manufacturing sector stands out with the most influential
contribution to the economy with 34% of Thailand’s total GDP (OSMEP, 2011). The
economy in Thailand and Asia in general is growing continuously. The end of 2014
predicts that Thailand’s automotive production capacity reaches three million
vehicles. The automotive sector in Thailand has a large impact on the economy. In
fact domestic and exported sales accounted 10% of the GDP in 2012. Looking at the
largest automobile manufacturers of the world, Thailand ranked 15th largest in 2011
1 Engl: German International Corporation
2
producing 1.5 million vehicles, wherein the total world production accounted 80
million (Thailand Automotive Institute, 2012).
As a result, GIZ in Thailand started a project named Greening Supply Chains in
the Thai Auto and Automotive Parts Industries – in short, Green Auto Project - which
aims to improve the evaluation and optimization of outdated processes of SMEs
concerning a better environmental performance. To be specific, the project helps
SMEs to improve productivity and increase the quality of products, reduce waste and
decrease energy consumption. This can be done by using improved technologies,
sharing relevant information and developing policies (GIZ, n.d.). With respect to the
high influential number of SMEs in Thailand and GIZ’s project, this research focuses
on the energy consumption of SMEs in Thailand, which belong to GIZ’s target group.
1.1 Statement of the Problem and Motivation
SMEs are gaining a lot of thoughtfulness in CSR literature. CSR activities
include social, environmental and sustainable aspects. The rising relevance of SMEs
can be explained through the big contribution they make to the economy and their
uniqueness compared to large companies. The main characteristic is that families or
owners conduct SMEs, which are obliged to follow policies of business partners and
the community. Furthermore, SMEs have a shortage of resources and support that will
help them to execute greening activities. Hence, the characteristics of SMEs bring
obstacles along that hinder the implementation of CSR in terms of greening activities.
It should be noted that the community and non-governmental organizations (NGOs)
are demanding SMEs to perform more sustainable activities along their supply chain.
The goal is to influence business partners, so that a basis of pro-environmental and
sustainable behaviors can be developed (Ciliberti et al., 2008).
As mentioned above, the NGO GIZ in Thailand is currently implementing a
Green Auto Project targeting 250 out of 500 manufacturing SMEs in Thailand.
Activities that are done with respect to the project are advising SMEs on investing in
new technologies, so that waste and energy consumption can be reduced. In order to
be able to invest, GIZ supports in gaining access to green processing, i.e. the capacity
of the bank, giving trainings to bank officers about the importance sustainability,
3
sharing information between banks and the target groups as well as guide the SME
bank in Thailand to develop a concept of green loan. Furthermore, giving seminars
about green concepts strengthens existing clubs. At last but not least, policy
recommendation on green production and sustainability are given through marketing
consultants so that more companies can be attracted (see chapter 4).
The target groups, in this case the SMEs that have actually implemented the
concept tend to have different results. Some companies may have invested in new
technologies, however, were not able to achieve the same amount of savings as other
SMEs. Also the diversity of SMEs leads to different results. Some organizations are
smaller, others bigger and some may have enough resources while others don’t. The
complexity lies in comparing the SMEs and finding commonalities, so that they can
be benchmarked to one another. This research will focus on energy efficiency that the
SMEs were able to accomplish through the participation of the project and benchmark
the enterprises respectively. The overall goal is to identify a best practice enterprise,
whose strategic approach can be implemented by those who did not do well. It aims to
motivate SMEs to participate in the Green Auto Project and inform them about the
energy savings they can possibly achieve.
The Thai consumption of electricity constitutes 169.4 billion kWh (2012) and
ranks number 23 compared to the rest of the world (The World Factbook, 2012).
According to the U.S. Energy Information Administration's International Energy
Outlook 2013 (IEO2013) countries that do not belong to the Organization for
Economic Cooperation and Development (OECD), will have a continuous increase in
energy consumption due to the growing economies and number of inhabitants. In fact,
the energy consumption share in non-OECD countries is said to increase from 54% of
the overall world energy consumption in 2010 to 65% in 2040 (EIA, 2013). As a
result, Thailand as one of the most influential manufacturing countries needs to adapt
new strategies and regulations, in order to be able to reduce its energy consumption.
GIZ’s Green Auto Project has strongly contributed on the achievement of reducing
energy consumption for the targeted SMEs in Thailand. The indicated growing energy
consumption in non-OECD countries shows the relevance of investing in new
technologies and green processing, as environmental changes have and will affect the
whole world. Thus, the purpose of this study is to benchmark the energy efficiency for
4
the targeted SMEs in order to benchmark the enterprises using data envelopment
analysis (DEA) and identify the best-practice SME.
DEA functions as a performance tool that analyzes and compares multiple units
to one another at any given point of time. Most importantly, “DEA is a performance
assessment tool since it estimates relative efficiency of a set of decision making units
(DMUs)”. DMUs can be described as units that use multiple inputs in order to
produce multiple outputs. The overall goal of DEA is to optimize the efficiency of a
DMU by modifying input and output. Traditionally, this can be done in two ways.
One may either maintain the number of output while decreasing the number of input
or increase output while maintaining input (Azadi et al., 2014). Chapter 3 will give
further details on DEA.
Upon completion of the thesis, the following questions shall be answered:
a) What are the reasons of different achieved results for the SMEs?
b) Which SMEs have reached the efficient frontier?
c) What can be done to make the energy consumption more efficient?
After results have been obtained, the above-mentioned questions will be answered as
a summary.
5
Chapter 2
Literature Review
2.1 Environmental Issues
The importance and awareness of green manufacturing is rising from year to year
and has reached a point where companies are considering strategies to implement it.
Within the last decades, manufacturing companies have caused tremendous harm to
the environment, as through production a lot of energy and water is consumed as well
as waste generated, which has lead to environmental changes that will affect all living
things. However, although manufacturing firms are aware of the harms caused, a lot
of firms do not consider to change their manufacturing process into a sustainable one,
as it is perceived that the benefits do not outweigh the risks involved (Lee et al.,
2014). Deif (2011) claims that because of the rising global awareness of
environmental changes and risks, customer preferences have changed and are in favor
of sustainable manufacturing. Tseng et al. (2013) highlight the importance of green
production by stressing out that the current used technologies are outdated, which is a
big factor adding pollution and harm to the environment. As a result, several changes
must be done in terms of policies, education and processes to eventuate in greater
awareness of environmental changes, especially in Asia. For Severo et al. (2014),
companies can reduce their contribution to environmental harm by adapting
environmental-friendly practices for e.g. cleaner production approaches.
One must bear in mind the increasing energy intensity of the Thai industry
mainly caused by the rising manufacturing sector, which are still using inefficient
industrial plants. The increasing energy consumption has led to environmental
changes, causing global warming. The transport sector is said to be the most energy
intensive, as Thailand has an increased level of motorization and not to forget the high
dependence on the road transport. The Thai Government has been trying to reduce
energy consumption by introducing laws and regulations. The under the Thai Energy
Conservation and Promotion Act 1992 developed Energy Conservation Fund
(ENCON Fund) has been a supportive financial act for developing energy efficiency
(EE) and renewable energy (RE). This aid is controlled by the baseline of the
6
governments conservation plan (Wang et al., 2013). However, although the Thai
government has established several EE and RE projects, the energy consumption has
not changed much from the year 2000 onwards. The main causes for the stagnated
development are the small enhancements in EE and the fact that the economy requires
energy-intensive industries. As a result, the Thai government has developed a 20-Year
National Energy Efficiency Development Plan that ensures to reduce energy intensity
by 25% likened with levels of 2005 by 2030. Alongside, another 15-year Renewable
Energy Development Plan has been established, which commits to increase the usage
of alternative energy by 6.4% in 2008 to 20% by 2022 (Wang et al., 2013). Some
obstacles however still hinder the implementation to enterprises. For example, the
energy managers programs and measures which help to ensure a development of EE
did not manage to reach the promised results, as managers are not motivated in
implementing EE investments due to the high transaction costs. Additionally, EE
investments are avoided because of the risks involved. Managers often doubt a
successful realization. Furthermore, financial institutions such as banks do not have
the required knowledge and lack expertise that is necessary for establishing EE
business portfolios. This is the reason why Thai banks cannot provide corresponding
EE loans, especially to SMEs and Energy Service Companies (ESCOs) (Wang et al.,
2013).
2.2 Green Manufacturing
The term green manufacturing was introduced to indicate the current
manufacturing trend that aims to be more sustainable by using green
strategies/objectives and principles as well as green technology and innovation (Deif,
2011). However, existing barriers are making the implementation of green
manufacturing difficult. According to Voon-Hsien Lee et al. (2014) the barriers that
hinder manufacturing firms from implementing a green manufacturing process can be
classified into external forces in the industry and internal forces within the company.
One aspect that stems the implementation is the fact that not only single
manufacturing processes must be modified into a sustainable one, but the whole
supply chain instead. This includes all suppliers and partners, which makes it very
7
difficult to control (Lee et al., 2014). Additionally, many enterprises are not aware of
the clear definition of “green”, which leads to confusion and restricts the modification
of green manufacturing. Not to neglect are the costs and regulations that result in
barriers too (Tseng et al., 2013). The term “green” relates to every activity that is
environmental-friendly. Thus, “green manuacturing” stands for the realisation of
production that intends to minimize the harm it causes to the environment (Yusuff and
Panjehfouladgaran, n.d.).
The number of researches concerning green manufacturing are getting more and
more, as the awareness and importance of it arises. The existing studies mainly focus
on the relevance and concept of green manufacturing as well as proposed models,
tools and practices to actually implement the green manufacturing paradigm.
Voon-Hsien Lee et al. (2014) have revealed that involving sustainability into the
whole supply chain will result in increased monitoring of environmental-friendly
activities and the implementation of the so-called “Re-“ activities, which are known
as remanufacturing, recycling, reclamation and reverse logistics. The adoption of
green supply chain management (GSCM) takes all environmental issues into account
and considers these aspects when doing strategic decisions. Tseng et al. (2013) agree
that the incorporation of partners throughout the whole supply chain will establish a
strong customer-supplier relationship, while additionally cutting costs. A good
structured GSCM requires the integration of material and information flow within the
circle of suppliers, manufacturers and customers (Tseng et al., 2013).
The main drivers that encourage manufacturing firms to implement green
production are on the one hand side ethical reasons, i.e. in terms of internal values of
the company and on the other hand side business-related reasons, as green supply
chain management (GSCM) is said to increase the competitiveness of a firm. Findings
have proofed that companies dealing with GSCM do generally add value to their
businesses. As a matter of fact, a study has found that the leading ISO14001-certified
companies in South-East Asia (SEA), which do have a GSCM have optimized their
competitiveness and their economic output (Lee et al., 2014). If companies change
their environmental-friendly activities into an internal strategy, they will be able to
enhance their overall environmental performance while at the same time earn better
economic results (Deif, 2011). For Severo et al. (2014), green manufacturing practices
8
such as the cleaner production (CP) programs must be incorporated to their
organization and continuously be managed by the leading forces. Otherwise, a green-
manufacturing program cannot secure long-term environmental process if the
implementation of such practices takes place only during the duration of the
project/program. Therefore, organizations must be persuaded and satisfied with the
improvements that come along with cleaner production projects.
Deif (2011) approves that green manufacturing will indeed increase competitiveness
as well as their productivity and efficiency. Stakeholders, public attention,
communities and the media, are all keeping companies under pressure to be more eco-
efficient. Additionally, the author states that the three motivational aspects to invest in
green manufacturing are efficiency, market share and governmental support and
regulations. A more efficient manufacturing process leads to saving energy and
materials. Hence, the benefits of green manufacturing outweigh the investment costs,
which have a positive effect on the revenue. As already mentioned, customer
preferences have changed due to the higher awareness of environmental changes.
Thus, green manufacturing helps to gain more market share. The reduction of material
waste itself for instance, will increase productivity and lower production costs. Lastly,
global governmental organizations are developing many regulations, policies,
obligations, taxes and penalties, which is yet another point that puts pressure on
companies and motivates them to be eco-efficient (Deif, 2011). Tseng et al. (2013)
too, do guarantee that green innovation, as a practice for strategic development, will
result in improved productivity and competitiveness. Similar to the Green Auto
Project by GIZ, the United Nations Development Program has established the Cleaner
Production (CP) program, which aims to prevent further harm as a basic tool and has
been implemented by several developing countries (Severo et al., 2014).
Voon-Hsien Lee et al. (2014) developed five practices that can be done in order
to be more sustainable. These are internal environmental management, eco-design,
investment recovery, green procurement as well as customer-cooperation. In order to
achieve a certain level of sustainability, the above-mentioned practices must be
incorporated to one another, implying the collaboration of cross-functions. One way
to facilitate the complexity of GSCM is green procurement. Green procurement
requires design specifications that automatically integrates environmental aspects for
9
the procurements from suppliers and supports the monitoring-process of GSCM as
well as ensures the cooperation with ISO 14001-certified suppliers (Lee et al., 2014).
According to Ahmed Deif (2011) green manufacturing requires products and
processes that consume less energy and materials. Other than that, it also involves the
substitution of input materials, such as renewable for non-renewable and the reduction
of waste as well as the transformation from output into input, also known as recycling.
The author developed a system model for green manufacturing, which aims to support
companies in green manufacturing with respect to the following:
• Acquire previous activities to comprehend the existent level of green
manufacturing
• Indicate the proposed green transformation plan and the required tools and
metrics in order to achieve the transformation
• Methods that support to sustain the accomplished development and maintain
the eco-efficient system
The proposed model by Deif (2011) is divided into two sections; first, the design and
planning process of the green manufacturing system is demonstrated and second,
control tools that measure the design and planning process at each level are suggested,
which helps to implement and evaluate green manufacturing. Tseng et al. (2013)
outlines the disassembly of products as another important green manufacturing
practice, as it contributes the highest effect on sustainability.
2.3 Measuring Green Production
As new strategies and processes are applied when doing sustainable projects,
organizations would like to measure and evaluate their progress, in order to see the
improvements achieved. In principle, “measuring performance can be described as the
process of enumerating the efficiency and effectiveness of an action quantitatively
and/or qualitatively”. The main reason to measure progress, is so that organizations
can control and assess their development, to signalize their improvements, strengthen
their knowledge on key processes, recognizing possible complexities as well as being
able to prepare cognition for future actions (Ahi and Searcy, 2014). Herani et al.
(2005) demonstrate that the level of performance measures is derived from what the
10
organization wants to achieve, i.e. their goal. For instance, the available financial
measures must be regarded by organizations, such as ROI, market share, profitability
as well as the increased revenue in terms of a competitive and strategic level. Olugu et
al. (2011) state that the green process cannot be reached within a day and highlight the
necessity of measurement by demonstrating that it discloses the scope a company has
reached through investing in its GSCM at any given point of time.
Ahi and Searcy (2014) have analyzed 445 published articles after 2012 and have
identified 2555 unique metrics to measure green and sustainable supply chains.
However, most of the identified metrics have only been used once, which shows that
there is no consistent common metric that can be used to measure green supply
chains. Nonetheless, five identified metrics have been used for more than 20 times,
which indicates that they are appropriate and might also serve as potential metrics in
the long run. The most common identified metrics with respect to the research of Ahi
and Searcy (2014) are quality (31 times), air emission (28), greenhouse gas emissions
(24), energy use (24) and energy consumption (21). Subsequently, the environmental
characteristic is the most used metric within the analyzed publications in this study.
Table 2.3.1 indicates the importance of the economic and environmental focus in
sustainable supply chain management, as the study revealed that they were the most
frequently used metrics. The numbers in parenthesis in the table imply the frequency
rates of the used examples throughout the reviewed study’s. Furthermore, Table 2.3.2
reveals an overview of the topics used by at least 10 metrics. A total of 113 metrics
concentrated on energy, wherein most of the topics focused on energy efficiency and
energy consumption.
Table 2.3.1: Metrics and examples of Sustainable Supply Chain Management (SSCM)
characteristics (Ahi and Searcy, 2014)
SSCM Characteristics
Economic focus Environmental focus No. of Metrics
Examples of Metrics No. of Metrics
Examples of Metrics
Environmental focus
168 Environmental costs (11), Buying environmental-friendly materials (7)
Social focus 70 Customers’ satisfaction (14), Customer returns
78 Environmental Social Concerns (4), Cooperation
11
Table 2.3.2: Major themes used by the identified metrics (Ahi and Searcy, 2014)
Major Themes No. of Metrics Example of metrics (frequency rates) Product(s) 261 Product characteristics (11), Product design for
remanufacturing (6) Cost(s) 176 Cost (12), Environmental cost (11) Waste 148 Solid waste (19), Reduction of solid waste (11) Recycle/Reuse 140 Recycling (19), Recycling revenues (7) Material(s) 131 Decrease of consumption for Hazardous/Harmful/Toxic
materials (8), Buying env. -friendly materials (7) Energy 113 Energy use (24), Energy consumption (21) Emission(s) 91 Air emissions (28), Greenhouse gas emissions (24)
The authors came to the conclusion that there is an urgent need for identifying
metrics that demonstrate a wider area of sustainability within a supply chain. Olugu et
(6) with customers for green packaging (2)
Flow focus 2 Cash flow (1), Cash flow provided by operating activities (1)
1 Annual mass-flow of different materials used (excluding energy carriers and water) (in tons) (1)
Coordination focus
1 Cooperation degree (1) 3 Collaborating with other companies and organizations for environmental initiatives (1), Improving opportunities for reducing waste through cooperation with other actors (1)
Relationship focus
1 Networks (2) 1 Interaction and harmony co-exist with natural systems on production and consumption systems (1)
Value focus 59 Profit (12), Market share (11), Sales (4)
3 Energy requirement per unit of net value added (1), Global warming contribution per unit of net value added (1)
Efficiency focus 5 Existing efficiency vs. cost of upgrading (2), Increased cost efficiency (1)
14 Energy efficiency (11), Recycling efficiency (3)
Performance focus
43 Cost savings (8), Operational performance (4)
39 Process optimization for waste reduction (8), Optimization of process to reduce air emissions (4)
12
al. (2011) on the other hand have developed measures of the green supply chain in the
automotive sector. The focus lies on the automotive sector as it has a very complex
supply chain - as an automobile is made of many components from different locations
- and the rising importance of measuring such complex supply chains in terms of
greening. It should be noted that the authors divided the supply chain into forward and
backward chain. Forward chain refers to ensuring green manufacturing and the
termination of the product at the customer, whereas backward chain assures the
recycling process when the product returns to the chain. Aspects observed to measure
sustainability included environmental certification of supplier, supplier’s performance
on sustainability, greening costs, customer perspective and many more. In order to
examine the key performance indicators, a survey had been carried out to the industry
in which customers’ perspectives have resulted as the most important measure with
93.4% votes from organizations and academics. Hervani et al. (2005) suggest the
usage of ISO 14031, as it contains various environmental performance indicators. An
OECD green-framework has been used by Kim et al. (2014), in order to select 12
indicators that have been developed for measuring cross-country comparison of green
growth strategies. Selected indicators were compared to the value of a given country
and evaluated on a scale from 1-10. Respectively, five categories have been observed
which were:
(a) Environmental efficiency of production and changes in production patterns,
(b) Environmental efficiency of consumption and changes in consumption patterns,
(c) Natural capital stocks and environmental quality,
(d) Objective as well as subjective environmental quality of life and
(e) Economic actor responses.
As mentioned before, this study will use Data Envelopment Analysis (DEA) as a
performance and efficiency measurement tool, which has been applied for several
studies regarding sustainability and greening before. Azadi et al. (2014) used DEA as
a target-setting model for the application of GSCM of public transportation providers.
Developing a feasible and realistic plan with the target-setting model was the main
objective of the authors. Furthermore, Dobos and Vörösmarty (2014) also applied
DEA with the Common Weights Analysis (CWA) to identify the environmental
13
factors as the influential decision factors when selecting green suppliers. However,
the applied method did not serve as an optimal solution but rather supported decision-
makers in evaluating the selection criteria’s. It ranks the suppliers according to the
most efficient DMUs (Dobos & Vörösmarty, 2014). Sueyoshi and Wang (2014) used
DEA to achieve a higher level of corporate sustainability, so that organizations can
assess their progress in terms of environmental performances. More importantly, the
proposed DEA tool provides information concerning the degree of investment for
technology innovation in order to reduce unwanted output, such as CO2. Another
research examines environmental assessment for corporate sustainability by resource
utilization and technology innovation using DEA in the Japanese industrial sector
(Sueyoshi & Goto, 2014). Houshyar et al. (2012) applied DEA for measuring the
sustainable and efficient energy of the corn production in Iran combining it with
multi-fuzzy modeling. DEA identified efficient and inefficient farmers, so that the
inefficient ones could apply some techniques from the more efficient farmers, in order
to improve their own efficiency.
Also, because a single outlier can influence all the efficiency score results of a
peer group, Lu et al. (2014) have worked on establishing a peer-wise energy
benchmarking model with DEA. The objective was to develop a model that is able of
identifying significant factors, discovering the influential outliers and to segment the
efficiency scores into either self-efficiency change or peer-efficiency change. This
aims to determine the causes of efficiency changes, i.e. either due to changes of
energy efficiency within itself or the peer-group. Thus, with the removal of outliers
the efficiency scores of the models cannot be influenced. This model has been applied
to multifamily properties for the purpose of benchmarking energy efficiency.
Furthermore, a large number of researches are using multi-stage DEA models.
Multi-stage DEA models are models wherein the DEA method is applied on every
stage and the results of one stage are the basis for the next stage’s analysis (Zhu,
2009). This can be applied for multiple process problems. This approach is favorable,
because generally it is a more precise analysis of DMUs analyzing the efficiency step
by step and it has been proofed that it obtains more trustworthy results (Wu et al.,
n.d.). An example of this is a research on economic and environmental efficiency of
Danish pig farms by Asmild and Hougaard (2006). In their paper they analyse the
14
economic and environmental efficiency by using DEA with a 2-stage analysis. The
environmental efficiency concerns the reduction of undesirable outputs, i.e. nutrients,
while the economic efficiency concerns the revenue. In this case, the two efficiency
scores are computed seperately and compiled on a second step.
2.4 Measuring Performance of Small and Medium Enterprises in Thailand
The Office of SME Promotion (OSMEP) founded in 2001 aims to establish a
national SME policy strategy in coorporation with the Thai government. For this
matter, OSMEP introduces SME master plans every 5 years (OECD, 2011). Up to
now, two SME master plans have been completed. The first plan lasted from 2002-
2006 and targeted the following achievements: to increase the efficiency and capacity
of SMEs by building up an environment in which SMEs can grow, strengthen their
competitiveness and contribute into the regional economy. The second plan from
2007-2011 adressed the capability of growth as well as sustainability with respect to
skills and knowledge (Charoenrat & Harvie, 2014). The third master plan is ongoing
(2012-2016) and intends to make the SMEs the most influencial factor to the Thai
economy. This shall be achieved by increasing their capacity development and
improving the manager and sectoral standards in terms of innovation, know-how,
creativity and cultural uniqueness (Goverment Public Relations Department, 2014).
According to the OECD, the barriers apprehended by SMEs are political
instability, corruption, lack of financial assets, lack of railway transport system etc.
For this matter, it is important that barriers that hinder the development of SME
efficiency and effectiveness must be investigated, so that they can be erased. There
are several methodologies which can be applied to evaluate policies, programmes or
instruments including case studies, surveys, cost-benefit analysis as well as a number
of quantitative and qualitative approaches (OECD, 2011). Thus, the following section
aims to indicate research-based reports that have been applied to measure
performances of SMEs in Thailand.
Similar to this work, Charoenrat and Harvie (2014) have analysed the technical
efficiency of SMEs for the Thai manufacturing sector by applying the Stochastic
15
Frontier Analysis (SFA). The study solely focused on SMEs due to its growing
relevance in the country as also pointed out in chapter 1. The data obtained for the
purpose of the analysis are from the years 1997 and 2007 as the objective was to
benchmark the level of technical efficiency performance in the pre and post Asian
Fincancial Crisis period. Furthermore, the authors examined firm-specific factors that
cause technical inefficiency as well as gave policy recommendations with the
foundation of the obtained results. As for the measurement tool, SFA has been
prefered over DEA because it considers random shocks and considers that these,
especially if not regulated by the firm , can possibly have an impact on the amount of
output. The study revealed that researches for Thailand that applied SFA or DEA
mainly focus on firm size, location and governmental support. The key findings were
that many SMEs were rather inefficient during the post crisis, except for medium-
sized enterprises that showed an improvement in efficiency. Furthermore, the SMEs
in the manufacturing sector have been found to be very labor-intensive with mainly
unskilled workers. This can be explained with the lack of capital investment.
Consequently, the authors suggest that governmental policy recommendations should
adress the improvement of technical efficiency and competitiveness of SMEs. The
policies should focus on capacity and capability through enhanced labor forces and
managerial quality as well as investing in technology (Charoenrat & Harvie, 2014).
Because several Thai SMEs went bankrupt after the Thai economic crisis in 1997
the authors (n.d.) Srivihok and Intrapairote measured the status of intellectual capital
(IC) of Thai SMEs enterpreneurs in order to strengthen the managerial understanding
of IC within their organization. The findings aim to be a supportive guide for firms
that are willing to enhance their managerial skills as well as increase their IC assets.
For this matter, the authors have adapted an intellectual capital measurement model
from a Scandinavian Intellectual Model. The proposed model is made up of three
clusters, namely human capital, structural capital and relational capital. To obtain
results the authors analyzed E-commerce websites of Thai SMEs in June 2013. The
surveys were collected for a total number of 55 websites and the sectors considered
were manufacturing, food-service, software house and agriculture. The results
revealed that most of the SMEs do not invest in the development of their human
resources and are not aware of the IT relevance within their organization.
16
Furthermore, the SME websites have the intention of providing information than to
enable transaction function, which also leads to the lack of customer relationships
through their websites. At last, findings implied that SMEs rather have horizontal
relationships with partners than vertical relationships with suppliers. Subsequently,
the authors suggest the organizations should increase awareness of the importance of
intellectual capital as well as the integration of IT-based management to maximize
efficiency and competitiveness (Srivihok & Intrapairote, n.d.).
To point out the importance of applying both financial and non-financial
attributes when evaluation a companies’ performance, Sawan et al., (2007) have
published a report, in which they investigated the impact of a balanced scorecard
approach by measuring innovation effectiveness of 144 Australian and Thai SMEs.
Innovation is a major factor that improves performance of an organization. When
assessing the benefits an innovation has brought to an organization it might not be
sufficient to only focus on the financial benefits, as it does not reflect the overall
performance of a firm. Hence, a balance between financial and non-financial
measures should be applied. For this purpose, the authors quantitatively analysed the
degree of performance measure utilization of SMEs with respect to their innovation
effectiveness. The comparison of Thai and Australian SMEs is due to the
demonstration of metrics that are used across different cultures. The data was
obtained by using a questionnaire. Findings state that 79.5% Australian and 75,5%
Thai SMEs apply both financial and non-financial metrics to assess the innovation
effectiveness. Additionally, 17.7% of Australian and 12.2% of Thai SMEs reported
that they solely use financial metrics. Only a number of 2.9% Australian and 12.2%
Thai SMEs apply non-financial metrics. The most often metrics used by Australian
SMEs were; customer satisfaction (non-financial), sales and ROI (financial), while the
most often metrics used by Thai SMEs were the product/ service quality (non-
financial) as well as sales and profit margin (financial). No differences due to cultural
reasons could be found in the applied metrics by Australian and Thai SMEs. The
authors strongly recommend that both financial and non-financial metrics shall be
used for performance evaluation in terms of innovation as more positive percceptions
across several attributes could be found (Sawang et al., 2007).
17
Research-based papers on the performance of Thai SMEs by applying
methodological approach are still minor but are gaining importance within the years.
SMEs are aiming to become Thailands most influential contributor into the economy,
especially now that the Asean Economic Community has been established (OECD,
2011). The next chapter aims to give a detailed discussion on the tool that will be used
for this work, namely DEA.
18
Chapter 3
Data Envelopment Analysis (DEA)
3.1 Theoretical Framework
DEA is a data-oriented analysis tool that assesses the performance of a
particular number of peer entities, also known as Decision Making Units (DMUs),
which transfer multiple inputs into multiple outputs. The DMUs used for DEA are
diverse and flexible. Examples for DMUs are hospitals, banks, and organizations or
simply processes, regions etc. One crucial function of DEA is that it does not require
much internal information and assumptions; it simply considers internal information
as a “black box”. Solely the multiple inputs and multiple outputs of DMUs are of
relevance for the analysis tool (Zhu, 2009)
With respect to the DEA-results “full (100%) efficiency is attained by any DMU
if and only if none of its inputs or outputs can be improved without worsening some
of its other inputs or outputs”. Moreover, as the focus is on benchmarking DMUs to
one another, one has to consider the relative efficiency also. It states that “a DMU is
to be rated fully (100%) efficient on the basis of available evidence if and only if the
performances of other DMUs does not show that some of its inputs and outputs can be
improved without worsening some of its other inputs and outputs” (Cooper et al.,
2004). In other words, the efficiency of a DMU is assessed relatively to all the other
DMUs to be examined, with the only limitation that all DMUs are located on or under
an efficient frontier (Atici & Podinovski, n.d.).
Farrell first developed DEA in 1957 and has established the efficient frontier.
Subsequently, Charnes, Cooper and Rhodes enhanced this method by developing a
nonparametric analysis in using efficiency assessment in 1978. DEA functions by
using a linear programming methodology that converts inputs into outputs produced.
DEA identifies a frontier (best practice) and compares the relative performance of
organizations or units to the best practice. The range of efficiency is between 0 and 1
for the input-oriented model, in which 1 is the most efficient unit/organization and 0
the least. Furthermore, the DEA method helps to identify the reason of inefficiency
(Liu et al., n.d.). The output-oriented model on the other hand computes the target
19
value as a number, to which the output variables must be increased. The inefficient
DMUs have an efficiency score of >1, for example 1.2 indicating that the output
values must be increased to 120%. The efficient DMUs have equivalent to the input-
oriented model, an efficiency score of 1 (Bielecki, 2012).
The two major types of DEA; can be as indicated above either input- or output-
oriented. The input-oriented model refers to the minimization of the objective
function, i.e. the objective is to minimize the amount of input, while maintaining the
same amount of output. Similarly, the output-oriented model considers the
maximization of the problem. The objective is to maximize the amount of output with
the same level of input (Atici & Podinovski, n.d.). However, it should be mentioned
that output could be classified into desirable (e.g. products) and undesirable (e.g.
waste) outputs. It is often the case that one wants to maintain the amount of input,
while reducing the amount of output (undesirable), in order to improve its
performance. The norm usually assumes that input needs to be decreased and output
should be increased, however, in this case we may treat the undesirable output as an
input, as it must be decreased. However, this procedure would not represent a proper
production process. There are also cases where the amount of input must increase, in
order to improve the performance. An example for this case is when enhancing the
performance of waste treatment process; the amount of waste (undesirable input) to be
considered must be increased (Zhu, 2009). Generally, we must acknowledge the
existence of interdependencies in between desirable and undesirable outputs. This
shall be demonstrated by considering a simple production process. Given the
assumptions that producing one product simultaneously produces one waste product,
it is obvious that the more products produced the more waste is generated (Pasupathy,
2002).
The methodology of how to solve these out of norm problems with undesirable
measures will be discussed later on.
Besides the difference in input- and output-orientation, DEA can also be
differentiated according to its condition, which can be a constant or variable-return-to-
scale, CRS and VRS. The CRS model implies that the output level is proportional to
the input level, while the VRS model supposes that the output level is higher or less
20
than the increased input (Liu et al., n.d.). For example let us assume that there are 2
inputs and 2 outputs in a model. According to the CRS-Model, if both inputs are
increased by 10% then both outputs will increase by 10% accordingly. Under the
assumptions of a VRS-Model no proportionality is assumed (Atici & Podinovski,
n.d.). Lets take the CSR Model for instance and assume that Xij is the number of input
i that produces Yrj for output r. The objective is to maximize the efficiency score (Ej)
for all DMUs (j= 1,...,n). Thus,
𝑀𝑎𝑥𝑖𝑚𝑖𝑧𝑒 𝐸! =!!"!!"!
!!!
!!"!!"!!!!
(3.1.1)
𝐸! =!!"!!"!
!!!
!!"!!"!!!!
≤ 1 (3.1.2)
where Vrj, Uij > 0; for all r, i and j (3.1.3)
The problem formulation of a CSR Model is the ratio of weighted outputs to
weighted inputs. Vrj and Uij are the weights for the multiple outputs and multiple
inputs. The maximum efficiency reached is 1, thus, the DMUs should score less than
or equal to 1 (Ozcan, 2008).
To aggregate the multiple input and multiple output factors, DEA combines
the individual factors through the weights to a single virtual input and a single virtual
output. Hereby, the weights of the aggregation are unknown and are a part of the
optimization process (Bielecki, 2012).
Also, there are three efficiency types that can be evaluated, which are known
as the technical, allocative and economic efficiencies. The technical efficiency can be
defined as “the degree to which a decision making unit produces the maximum
feasible output from a given bundle of inputs, or uses the minimum feasible amount
of inputs to produce a given level of output”. More precisely, it reflects the input- or
output-oriented models. Furthermore, the allocative efficiency examines “the ability
to produce a given level of output using cost-minimizing input ratios”. If we add up
both the technical and allocative efficiency, it will make up the economic efficiency
(Atici & Podinovski, n.d.).
21
Another important criterion, which is crucial for the envelopment analysis that
needs to be considered, is the determination of the number of input and output data.
This is important because it can influence the discriminatory power of efficiency
scores. Assuming the number of DMUs is n and the amount is less than adding the
number of inputs and outputs (m + s) together, then the results will lead to a large
number of efficient DMUs. Hence, the efficiency discrimination of DMUs is rather
doubtful because of the inappropriate number of degrees of freedom. In principle, the
data set should be as few as possible. For this purpose, a rule of thumb has been
established that estimates the appropriate number of input and output:
n (Number of DMUs) ≥ max 𝑚×𝑠, 3 × (𝑚 + 𝑠)
Thus, following this rule will help to avoid the problem of loss of discriminatory
power (Cooper et al., 2006).
3.2 Benchmarking with Data Envelopment Analysis
Benchmarking can be defined as “a measurement of the quality of an
organization’s policies, products, programs, strategies, etc., and their comparison with
standard measurements, or similar measurements of its peers” (Business Dictionary,
n.d.).
Especially managers are constantly seeking to enhance the performance of the
organizations they lead. In order to enhance their performance, organizations must
undergo evaluations, so that they know where they stand in comparison to their
competitors. As a result, benchmarking tools are widely used, as they identify best
practice organizations.
There are many benchmarking methods used to evaluate performances of
DMUs. Some practitioners use performance measurement tools while others use
performance indicators. Performance indicators are often used because they evaluate a
particular aspect of performance (Zhu, 2009). According to Stroobants and Bouckaert
(2014) indicators are more informative to managers as they are expressed in absolute
numbers and thus, are useful to evaluate a DMUs performance over time and
22
benchmark them to their competitors. However, the disadvantages of performance
indicators often lead to misconceptions. On the one hand, they only have a limited
indication of performance. An example for this is that indicators only deal with
single-input and single-output data when considering efficiency performance. On the
other hand, they might as well reveal controversial results, as one DMU might be
efficient on one indicator, but inefficient on another (Stroobants & Bouckaert, 2014).
Generally, benchmarking methods can be classified as to parametric and non-
parametric methods. This shall be further discussed in the following chapter.
3.2.1 Parametric and Non-Parametric Methods
Parametric methods deal with the “description and examination of
relationships between different parameters, such as costs and schedules”
(BusinessDictionary, n.d.).
In principle, the parametric methods are classified into stochastic and deterministic
models. The deterministic approach encloses all the observations and computes the
distance amongst the maximum and observed production. Finally, it identifies the
technical inefficiency, whereas the stochastic method is capable of differentiating
between technical efficiency and statistical noise (Murillo-Zamorano & Vega-
Cervera, 2001). Three parametric methods have been widely used for frontier
efficiency approaches; these are the Stochastic Frontier Approach (SFA), the Thick
Frontier Approach (TFA) and the Distribution-Free Approach (DFA) (Paradi & Zhu,
2013).
According to Stroobants and Bouckaert (2014) “Non-parametric methods use
linear programming to construct a piecewise frontier that envelops all observations
(DMUs) of the sample used, against which each DMUs efficiency can be evaluated”.
The main difference between these methods and indicators is that unlike performance
indicators, it provides relative numbers as the DMUs are assessed relatively to one
another and thus, can be referred to benchmarking (Stroobants & Bouckaert, 2014). In
general, there are two mainly used non-parametric approaches for efficiency
evaluation known as Free Disposal Hull (FDH) and DEA.
23
FDH considers the minimum number of restrictions on data, because it
supposes free disposability of resources and strong disposability of outputs. The first
refers to the capability of an entity to consume more inputs than technically required
for a particular amount of output, while the latter refers to the ability of an entity to
produce less output than technically feasible with the same amount of input. Similar
to DEA, FDH computes efficiency scores and identifies the best practice amongst a
peer group. Furthermore, it assesses the input- and output-efficiencies, i.e. identifies
whether the entities are input- or output-oriented. The difference between DEA and
FDH is that FDH only compares existing observations within a set, while DEA also
compares virtual DMUs. The efficient-frontier of FDH is stairway-shaped (Stroobants
& Bouckaert, 2014).
The main differences amongst the parametric and non-parametric frontier
efficiency approaches are the assumptions made considering the efficient frontier, the
presence of random errors as well as the allocation of inefficiencies and random
errors. Usually, econometric approaches include a random error and an error that
shows inefficiency. However, non-parametric methods do not include random errors
and the necessity of a few assumptions only occurs when determining the efficient
frontier (Paradi & Zhu, 2013).
According to Paradi and Zhu (2013) some studies have focused on comparing
the parametric and non-parametric methods in terms of accuracy of estimations.
Ferrier (1990) for instance has compared SFA and DEA with respect to measuring the
cost efficiency of U.S. banks. The outcome revealed that both approaches had similar
results, however, differ when considering the decomposition of cost inefficiencies
amongst the technical and allocative inefficiency. Moreover, there was a poor
correlation of rank-order between SFA and DEA. Furthermore, Bauer et al. (1989)
proposed a study where they examined DEA, SFA, TFA and DFA in order to
determine which frontier efficiency measure would be most precise for regulatory
analysis. The purpose was to assess the consistency in terms of efficiency levels,
rankings and identification of best practice. As a result, the outcome revealed that no
consistency was obtained amongst parametric and non-parametric approaches (Bauer
et al., 1998). Reinhard et al. (2000) also assessed environmental efficiency by using
both SFA and DEA approaches. Their conclusion was that different results were
24
obtained, in this case mean technical efficiency scores of 89% SFA and 78% DEA
(output-oriented). The different results are caused because of the fact that with DEA
regularity is assumed, i.e. the environmental efficiency scores can be computed for all
specifications. SFA includes hypothesis testing and the rejection of particular
specifications such as bad inputs that do not satisfy the theoretical restrictions and
thus, cannot be estimated (Reinhard et al., 2000).
Consequently, DEA is a suitable benchmarking tool, because it involves
multiple performance measures. It involves mathematical programming techniques as
well as models, to assess the performance of peer units as mentioned above. In
particular, DEA monitors the existing resources to each unit and examines the
transformation of the inputs into outputs.
If we for instance consider a buyer-seller supply chain, then it would be of interest for
the buyer to benchmark the performance of the seller with regards to response time,
costs, flexibility, customer service, quality and customization. The poor operations
will be revealed and can thus be improved in efficiency. This will lead to decreased
costs of inputs as well as increased productivity. Benchmarking encourages
organizations or DMUs in general to get better, so that they can subsist in the global
business environment and stay competitive (Zhu, 2009).
3.3 Advantages and Disadvantages of Data Envelopment Analysis One of the main advantages of DEA is that it does not need any assumptions
in order to form the production function. Also, unlike other tools it has the ability of
determining best practices, which would be too difficult to identify via observations
(Bayraktar et al., 2012). Apart from that, one must emphasize on its ability of
handling multiple inputs and outputs that are expressed in different units as well as the
determination of the efficienct frontier, that identifies DMUs as best-practice and
leads to a comparison of DMUs to one another (Hababou, n.d.). In order to be able to
make efficiency ratios sensitive to the mixture of input and output, it is necessary to
weight the inputs and outputs according to their relative value. If the weights are not
considered, the ratio analysis is rather marginally helpful and may lead to a
misconception when analysing multiple-input and –output models. One factor of DEA
25
is that it has the ability of determining relative weights that is crucial for identifying
the value of inputs and outputs. This especially enables managers to comprehend the
insights by using operating ratios, which is valuable information when improving
performance. DEA has the power of assessing relative performance even though such
weights are often unknown. DEA computes every possbile combination of weights in
order to make the DMU most efficient. This feature, i.e. the capability of assimilating
multiple-inputs and –outputs in their original value without the availability of relative
units makes DEA an appropriate tool for assessing organizations (Sherman & Zhu,
2006).
DEA also brings its limitations along. Because DEA generates individual
linear programs for every DMU, it may become difficult to compute large problems.
Furtermore, other disadvantages are the difficulties that occur when aggregating
several aspects of efficiency. This is often the case when DMUs perform several
activities. Another problem is the insensitivity towards intangible and categorical
components, such as the service quality in specific branches. This is also related to the
problem when analysing DMUs that operate on different dimensions. If for example
the analysis is based on a DMU performing in two functions, then it may result in
being efficient on one while being inefficient on the other. To give an example while
focusing on the bank branch sector, a branch might be efficient in sales but inefficient
in service. For this matter, it might be advisable to run two DEA models, however,
one must deal with comprising both models afterwards which might be difficult
(Hababou, n.d.). Another drawback of DEA that needs to be considered is that every
DMU that deviates from the efficient frontier is identified as inefficient. The existence
of random values, for example through measurement errors is not considered. This is
a problem for measurement errors that occur within efficient DMUs, as this has an
impact on the efficiency score of the other DMUs and possibly distort these. Hence, in
order to obtain robust results, it is necessary that the applied data is of high quality.
Furthermore, DEA identifies many DMUs as efficient when small samples or many
data sets are used. The higher the number of inputs and outputs, the higher the number
of efficient DMUs (Bielecki, 2012).
26
At last, the lack of expressing the efficiency in absolute terms should be
mentioned. Especially for a managerial point of view, knowing the performance
compared to a theoretical maximum would be more useful, however, a frontier
expressed in absolute terms is difficult to obtain. In conclusion, it can be said that a
particular approach for testing the correctness of a given set of factors for the analysis
of an efficiency evaluation does not exist. DEA only reveals the efficiency scores of
every DMU of a given set, however, it does not give information whether the chosen
factors are the appropriate ones or not (Hababou, n.d.).
27
Chapter 4
The German International Corporation
This chapter gives a brief introduction on the organization GIZ by giving a
company profile and will then give detailed explanation on the Green Auto Project.
The explanation of the project is of relevance and aims to understand the common
theme of this work.
4.1 Company Profile
GIZ is a German non-profit organization. Worldwide, GIZ employs 16,510
employees, operates in 130 countries and generated a business volume of 1.9 billion
Euros in the fiscal year 2013. As an institution, it engages on topics concerning
politics, economics, and social changes. Its target is to gain sustainable and effective
improvements worldwide. The majority of GIZ’s commissions are delegated by the
German Federal Ministry of Economic Corporation and Development (BMZ).
However, there are other organizations, which delegate projects or commissions to
GIZ, such as other ministry’s as well as private and public bodies in Germany and
foreign countries. The European Union institutions, the United Nations and the World
Bank are part of the commissioners (GIZ, 2013).
The German international corporation in Thailand began in 1959 when the
King Mongkut Institute of Technology has been established in North-Bangkok. First,
the main focus of the institution was to improve the agricultural as well as education
sector. As Thailand has managed to become an emerging and industrial country,
GIZ’s focus nowadays shifted to industrialization, renewable energy, climate security
and state modernization. Currently, GIZ Bangkok is implementing various regional
and international projects, in which approximately 100 international development
organizations are participating. The main focus of GIZ in Thailand is sustainable
consumption and production, environmental and climate security, agriculture and
nutrition security, energy efficiency and renewable energy, municipal sustainability
and transportations as well as regional integration (GIZ, 2015).
28
4.2 Introduction of the Green Auto Project2
Project Scope and Structure
The goal of this project is to enhance sustainability in production within the Thai
automotive industry. For this project, only small and medium enterprises have been
targeted. The specific objectives and results are:
1. Enhancing productivity and environmental performance of Thai auto and
automotive parts production
- 250 SMEs have shown improvement in productivity and better product
quality, while at the same time, environmental harm per production unit
has been reduced
- More SMEs are now complying with international environmental
regulations
2. Improving networks, business and financial services for the greening of the
Thai auto and automotive parts industry
- Establishment of new financial packages for SMEs to invest in
improvement measures
- Improvement of alliances and networks for greening the supply chain of
Thai automotive industry
3. Disseminating good practices and promoting development and implementation
of related policy and economic instruments
- Promotion of good practices and related policies
In order to achieve the above-mentioned results, GIZ has established four
working packages, which was introduced to the participating SMEs and considered
project management and steering.
Working package 1 which is to “Improve productivity and environmental
performance of Thai Automotive Industry Clusters” is said to be the most extensive
and core element of the project. It addresses the objectives and results of number 1
indicated above. This working package includes the assistance in the enhancement of
2 Information from GIZ project proposal
29
technical machinery and optimization of operation procedures. This will ensure
productivity and competitiveness in the Thai automobile industry. Furthermore, the
improved technical equipment’s will lead to less energy and resource consumption as
well as reduced environmental harm. With regards to the new technical standards,
GIZ has planned to launch Best Available Techniques (BAT), which can be applied to
all targeted sectors and helps to implement the project by providing adequate
instruments and recommendations.
The objective and results of number 2 are addressed by working package 2
“Harmonize and implement SME specific financial support packages” and working
package 3 “Strengthen sustainable consumption and production (SCP) related
services”. One of the main drivers of this objective is investment. With respect to the
working packages, investments can be classified into financial support for technical
equipment and financial support for consultation and management services, audits and
evaluations. As it is difficult for SMEs to access Thai support programs such as the
Department of Alternative Energy Development and Efficiency (DEDE) or the
Department of Industrial Promotion (DIP), these packages aim to develop a concept
to increase SMEs’ access to financial support programs as well as direct support.
Finally, working package 4 “Dissemination of good-practice and policy
recommendations” addresses the objective and results of number 3. This package
aims to reach the final target groups and the dissemination of the improvements can
be used as input for the SCP Policy Support Component action in Thailand.
Moreover, working package 4 will correspond with the SWITCH-Asia3 philosophy
and will pursue to share the improvements with other countries through
communicating with the SWITCH-Asia Network Facility.
The following chart aims to illustrate the project structure described above:
3 With respect to the aspect of sustainable consumption and production (SCP) the European Commission has introduced a SWITCH-Asia program, which aims to support consumers, businesses and associations to switch to a better sustainable paradigm.
30
Furthermore, figure 4.2.2 demonstrates the significance of operations and processes as
well as the competitiveness of the targeted sector.
Figure 4.2.2: GIZ's project target group (GIZ Project Proposal, 2011)
Technical Standards &
Guidance (BAT)
Management Improvement
(PREMA)
Quality Improvement
(IQA)
Financial Support
Packages
Training and Consulting Program
Technical & Management
Support Services Financial Access Services
500 SME (Tier 2&3) group
+ individual services
Promoting Green Value, SC
& Green Procurement
Promoting SCP Service
Networks
Policy Recommendations Policy Support by Thai
Government
Policy Support by Thai
Government
Figure 4.2.1: Project Structure (GIZ Project Proposal, 2011)
31
The following remarks aim to strengthen the understanding of the core
processes and give further explanation of the above-illustrated figure.
Tier 1 implies that the automakers are being supplied from Tier 2 and 3, which mainly
consists of multinational automotive manufacturers and have plants based in
Thailand. The next level of the automotive production is Tier 2 and 3, which are here
classified into groups according to their operation. These groups are stamping,
plastics, rubber, machining, casting, forging, functions, electric and trimming. The
target was to provide technical and environmental management guidelines to the 500
SMEs. Moreover, clustered networks were provided, wherein the chosen SMEs in the
network determined particular individual technical improvement measures. It should
also be noted that there were trained service providers that helped the networks to
reach the targeted improvement goals, by assisting the implementation of defined
measures. Another important point is that the financial support programmes, which
were established for SCP implementation, were harmonised and adjusted to present
programmes of diverse governmental agencies, for e.g. Energy Service Company
(ESCO). This too, was supported by trained service providers so that improvement
measures could be performed. Also, it should be mentioned that all data from the
networks and particular SME measures for further implementation would be collected
and transferred into a good practice database. This will allow data evaluation and will
reveal further development of achievable and applicable standards for industry
clusters. The data evaluation can then be shown to SME sectors, stakeholders and
administrators. At last, the determined good practices will be given as policy
recommendations for e.g. to the Thai Automotive Industry Master Plan.
4.3 Approach for Working Packages of Project
The project fulfills its objective by implementing 4 working packages that
include actions that support the advancement of greening the supply chains of Thai
SMEs in the automotive sector. This chapter aims to illustrate the actions taken within
the project and gives insights on each working package.
32
Working Package 1 – Improve productivity and environmental performance of
Thai Automotive Industry Clusters
In order to be able to improve productivity and environmental performance in
the Thai automotive industry, three tools based on existing Sustainable Production
(SP)/ Cleaner Production-Technology (CT) have been established by the Thai
Automotive Institute (TAI). These are (1) Profitable Environmental Management
(PREMA), (2) Best Available Techniques Reference Documents (BREFs or BAT), as
well as initially (3) the Intelligent Quality Assurance (IQA), which has later been
replaced by the Loss Reduction Process (LRP) due to partner requests.
Profitable Environmental Management (PREMA) Approach
This tool has been established particularly for SMEs by GIZ, created to
identify, generate and implement measures, which aim to reduce production costs,
enhance environmental performance and optimize organizational competencies. Not
only has PREMA been used before in other countries, it has also been carried out in
other sectors. Previous experience and good practice recommendations will be given
to the training programs of the Green Auto Project. In particular, PREMA connects
technical and non-technical issues during workshops and advisory programs for
SMEs. It helps managers and operators in comprehending the importance of the
efficient consumption of raw materials and energy, as well with the connection
between product design, work planning and organizations and not to forget costs.
Hence, PREMA results in an optimized production process, internal organization and
product design, while continuously involving environmental aspects throughout the
production cycle. Furthermore, SMEs are obliged to reduce their undesirable output,
which are outputs that don’t end up being a part of the end product such as material
(waste), energy and water.
PREMA is applied by giving training systems to the SMEs and involves Resource
Management, Environment-oriented Cost Management, Chemical Management and
Good Housekeeping. The PREMA-trainers offer feasible tools and methods in order
to develop innovative solutions and enhance the potential of human resources. It is
based on international certifications such as quality standards (ISO 9001) and
environmental performance standards (ISO 14001).
33
Best Available Technique (BAT) Approach
Initially developed by the European IPPC Bureau in Seville, Spain, this tool
involves detailed information on the best technical solutions. It gives recommendation
on the application of the identified solutions while considering its geographical
location, the technical characteristics of the installation as well as local
environmental/ economical conditions, costs and at last but not least, the general
availability of the technique.
In fact, horizontal BATs that can be applied to all industries have been developed.
These consist of BAT on Energy Efficiency, the BAT on Economics and Cross Media
Effects and the BAT on General Principles of Monitoring. As the Thai automotive
sector is very diverse, the Green Auto Project will use all horizontal BATs with the
main focus on energy efficiency. The focus on energy is mainly because of the high
costs and environmental harm it causes and a more efficient energy consumption will
lead to a higher profitability as well as improved competitiveness. Consequently,
SMEs learn about the BAT on Energy Efficiency in their sub-groups in workshops.
Managers might be more willing to implement this, as it has a positive environmental
and economical outcome. Energy efficiency is the best and most cost-effective
opportunity to reduce greenhouse gas emissions and comes along with the advantage
of saving costs. The EU plays a role model in this matter, as they were able to reduce
20% of its energy consumption solely through energy efficiency.
Intelligent Quality Assurance (IQA) Approach
The third tool known as the Intelligent Quality Assurance (IQA) is unlike the
other tools, an IT-based monitoring system, which was specified for the Thai
Automotive Sector. This approach involves (a) Hardware, (b) Software as well as (c)
Human Resources (professional staff). With the observation of input and output data,
machinery and equipment capacity the system evaluates the production and process
quality. However, in order to apply IQA the product quality, technology and the
production process must be computer-controlled.
The application of IQA with regard to the Green Auto Project has led to an
increased quality of data collection, which is very important for the evaluation of
processes. It also enabled SMEs to enhance their production efficiency in terms of
34
decreasing input. However, within the years the IQA tool has been replaced by a tool
known as Loss Reduction Process (LRP). The latter approach identifies and
eliminates or replaces loss-causing processes.
The automotive sector in Thailand is very heterogeneous and consists of more
than 160 different fields of production and assembly. As mentioned before, tier 1
suppliers are mainly multinational manufacturing companies with their plants based in
Thailand. Companies worldwide are raising concern on environmental-friendly
production, as global and local communities are pushing them towards cleaner
production. Second and third tier suppliers are rather small and medium enterprises
and are highly diversified. As a result, SMEs are classified into clusters. The clusters
are formed according to the commonality on fields of operation and organizational
set-up size consisting of 15-20 SMEs. This way it makes it easier to apply particular
tools to each cluster accordingly. For example the cluster of the category rubber
involves SMEs that produce hoses, bushings, engine mounts, mudguards etc. In
addition to that, SMEs are classified into sub-groups under the clusters, as many of
the firms produce a large set of automotive parts. This enables better cross-linking of
SMEs that have similar production fields and require the same technical guidance and
support.
Working Package 2 – Harmonize and implement SME specific financial support
packages
With respect to this working package, the SME Bank aims to establish new
financial packages for Sustainable and Clean Production (SCP) improvement
measures. The establishment of the financial package is based on previous experience
and feedback from the training and consultancy sessions of working package 1.
Furthermore, other existing financial instruments will be taken under account such as
the Thai ESCO. The existing financial instruments in Thailand are difficult to access
for SMEs, as the financing sources are diversified and come from different
organizations, which use varied promotion agencies targeting different goals.
Financing support packages are relevant, as they provide SMEs a lot; starting from
debts financing to financing projects or provide loans, in order to go through training
35
programs or consultancy that enriches the know-how and skills to reach green
production.
SMEs have to be advised on all possible financial schemes they could engage with, as
searching for the right one is considered costly. Through workshops and consultancy
the providers of financial packages promote and educate SMEs on their offer, so
SMEs can evaluate and make their decision accordingly. The new financial support
packages will be introduced to each cluster in a workshop. It should be noted that the
packages can be classified into funding for direct investments and improvement
measures, i.e. investing in and upgrading machines and tools and funding
improvement measures indirectly, for example through training programs and
management instruments. However, there is a possibility in participating in incentive
programs that will grant an excellence award and substitutes financial packages.
Furthermore, customized training programs for financial managers or providers will
be given, in order to increase awareness and enhance the comprehension of financial
officers who are responsible for approving loans to SMEs for fields related to green
production. This aims to ensure a better access to loans for SMEs required for SCP
measures.
Working Package 3 – Strengthen Sustainable and Clean Production (SCP)
related services and networking
The Green Auto project provides a number of 30 PREMA, 15 BAT and 15
LRP experts that will support 500 SMEs to implement the project for a duration of
three years. These experts are chosen by the project partners’ network, for example
Energy Efficiency Network (EENet), Technical and Engineering SP network or other
databases such as ESCO databases.
This working package is of relevance as the quality of service providers concerning
environmental auditing and technical or management consultancy in Thailand is said
to be insufficient. As a result, SMEs wish to have more insights in accessing
qualitative services and are willing to pay for it when proven quality can be expected.
The harmonization amongst the network of automotive parts manufacturers, service
providers, government agencies and financial institutes originated are based on good
practices of the EENet development. The EENet development consists of three
36
platforms; (1) information exchange among key government agencies responsible for
improving energy efficiency in the industrial sector, (2) information platform and (3)
information exchange project implementation. The resulted successes, difficulties and
obstacles will then be transferred back to platform one, i.e. information exchange.
In addition to that, support packages and workshops will be provided with
regard to financial aspects such as investment options, turnover calculation and cost
analysis (e.g. waste and energy costs) under the consideration of working package
two. So that SMEs continue to use the service of service provided, the trustworthiness
must be given. Thus, service providers are required to update their certificates and
proof that they are undertaking additional training on a regular basis. Moreover, it is
anticipated that a database containing the qualification and experience of diverse
service providers will be established.
Working package 4 – Dissemination of good-practice examples and policy
recommendations
The goal of this working package is to collect all good practice examples,
which refer to the implementation of the project in which improvement has emerged.
This will then be made available to all other SME project participants, so they can
make use of it too. Useful policy recommendations extracted from the good-practices
will be submitted to the Thai Government.
The good practice examples can be classified into; (1) specific technical solutions for
individual SMEs and (2) cross-cutting technologies, instruments and procedures
applicable to all SMEs. The good practices that ended up having high results will be
forwarded to SMEs and stakeholders as well as be published on a website for
reference. Data will be collected through audits and evaluated for further analysis of
measures. The derived results will then be recommended as technical and operational
standards for particular production processes alongside the SMEs and TAI clusters.
The collected and analyzed data will reveal information on enhancement in green
production, reduction in greenhouse emissions as well as improved energy efficiency,
environmental performance and productivity. The noticeable potential can be
aggregated and determine the definition of emission limit values for specific
industries.
37
Chapter 5
Methodology and Procedure
After chapter 3 focused on the detailed explanation on the methodology
applied for this work, namely Data Envelopment Analysis this chapter will discuss
how the author proceeded in order to obtain results. First, the data collection process
will be explained, and then the approach of standardizing the obtained data will be
elaborated, making the data ready for application. After that a mathematical
formulation of the DEA will be given, followed by a introduction of a methodology
that solves the problem with undesirable measures, which is the case for the analysis
obtained from the PREMA data.
5.1 Data Collection
As this study is written in corporation with GIZ Bangkok, available data of the
250 SMEs that participated in the Green Auto Project will be analyzed and evaluated.
GIZ Bangkok itself collected the data from the enterprises, in order to see the progress
that they are making. Data from the PREMA and BAT approach were made available
for the purpose of this study. GIZ has created data templates for the BAT tool, which
had to be filled out by the companies. Furthermore, consultants went to the companies
and have implemented audits. The collected data has then be sorted out according to
completeness and appropriateness for the application of DEA. The obtained data was
received in Thai language and has then been translated into English. The available
inputs, involve the consumption of gross energy and electrical energy. The output data
consists of the number of end products.
For the PREMA approach company reports have been collected, including flow
charts of the respective input and output data per process. Similar to the BAT data, the
input data for PREMA was the energy consumption. The output data mainly
represents scrap per process or gross energy consumption (heat), which are also
known as undesirable or non-product outputs. The selection of particular SMEs or
processes for the evaluation was decided upon the availability of data. As common
data and units of inputs and outputs are required for the application of DEA, the
author grouped the peer groups according to the commonality of data in order to make
38
the evaluation feasible. Thus, the processes that were selected for the assessment of
the PREMA data were welding, piercing and cutting. As mentioned above, the BAT
data was collected with the help of a template and thus commonality of data was
already given. The only difference was that some products were expressed in
kilograms, others in tons or pieces. Hence, the author grouped the peer groups
according to the units of end products. However, because the SMEs in a BAT peer
group produce various and different types of automotive parts, one product might
require more energy than another, thus the data is rather heterogeneous. This is a
problem because DEA assumes homogeneity of data. As a result, the data must be
standardized for the purpose of applying DEA. The standardization of data will be
explained in the next chapter.
5.2 Data Standardization Approach
The availability of data for different SMEs within one sector, in this case the
automotive sector, does not imply equivalent interpretation of data. SMEs in the
automotive sector vary and have different backgrounds that cannot be considered by
simply observing data. One might wants to know whether the indicated value within a
data set is relatively high or low compared to the values in its distribution. This can be
reflected when using the standard deviation of a data set as an interpretation indicator.
It implies how many standard deviation units an indicated value is above or below the
mean of its distribution. Furthermore, if two indicated values of two samples
respectively should be compared to one another, then the deviation from the mean
should rather be considered for each sample. This transformation of data is known as
z-standardization, the transformed data are known as z-scores (Uni Hamburg, n.d.).
Thus, before applying the data for the DEA analysis, the data must first be
standardized for the purpose of comparison. Prior to the explanation of the
standardization process, the following variables shall be defined (Powers &
McMullen, 2000):
𝑋!": 𝑡ℎ𝑒 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑎𝑛 𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒 𝑗 𝑓𝑜𝑟 𝐷𝑀𝑈 𝑖
𝑋!: 𝑡ℎ𝑒 𝑚𝑒𝑎𝑛 𝑓𝑜𝑟 𝑎𝑛 𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒 𝑗 𝑎𝑐𝑟𝑜𝑠𝑠 𝑎𝑙𝑙 𝐷𝑀𝑈𝑠
𝜎!: 𝑡ℎ𝑒 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑜𝑓𝑎𝑛 𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑒𝑑 𝑣𝑎𝑙𝑢𝑒 𝑓𝑜𝑟 𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒 𝑗
𝑍!": 𝑡ℎ𝑒 𝑧 − 𝑠𝑐𝑜𝑟𝑒𝑠
39
The calculation of the mean and standard deviation is regular. The z-scores can be
computed by applying the following formula:
𝑍!" =(!!"!!!)
!! (5.2.1)
A z-score is a standardized value and indicates how many standard deviation
units and in which direction (sign) a value deviates from the mean of its sample.
Through the transformation the values of a distribution with different means and
variations are made comparable with regard to its relative deviation from its mean.
Note that the z-standardization does not change the form of a distribution. The sum of
all z-scores of an attribute j equals 0, implying that there are negative z-scores as well.
The standard deviation of a sample is always 1 and the mean 0 (Uni Hamburg, n.d.).
The following graph indicates a normal distribution and illustrates a z-standardization:
Figure 5.2.1: Z-Standardization (Lucerne University of Applied Sciences and Arts, n.d.)
40
The graph illustrates that the mean is 0 and the samples have been transformed
to a normal distribution. The distances on the x-axis represent the standard deviations,
while the y-axis indicates the amount or rate of an attribute. Through this
transformation the data is made comparable and can be applied for DEA.
However, one must bear in mind that DEA is not capable of assessing negative
data. Thus, the application of negative z-scores is not feasible. To circumvent this
problem, a random variable R will be added to the z-scores of an attribute j, so that all
the z-scores become positive. This procedure is based on the study of Powers and
McMullen (2002), where the absolute value of the minimum z-score is added to each
z-score of an attribute. The mean of an attribute is then relocated from 0 to the
random variable R. Graphically explained, the distribution is moved to the right side
until the mean reaches the random variable R. However, the standard deviation
remains 1.
The following indicates how this statistical approach has been applied to the
BAT data. A detailed example will be given by using the data of BAT peer group 1.
The following table illustrates the raw data of peer group 1, as well as the mean, the
variance and the standard deviation that are required to calculate the z-scores.
Table 5.2.1: Raw data of BAT peer group 1
DMUs Electrical Energy (kw/year)
Gross Energy (MJ/year)
Products in Kg
DMU1 5,207,000 18,745,200 1,272,231.92 DMU2 4,749,080 17,096,688 2,605,490.39 DMU3 42,652,553 159,727,480.8 20,841,472.38 Mean 17,536,211 65,189,790 8,239,732
Variance 3.1545E+14 4.46914E+15 7.96982E+13 Standard Deviation 17,760,920 66,851,630 8,927,385
When applying the above-mentioned formula for the z-scores, the z-score for
DMU1 in attribute 1, i.e. the electrical energy is calculated as follows:
𝑍!!:(5,207,000 − 17,536,211)
17,760,920= −1
41
As mentioned before, the value of the score Z11 = -1, reflects that the value lies
exactly one unit below the mean (which is 0 for the transformed data), meaning below
average compared to the rest of the distribution. The same calculation applies for the
rest of the z-scores. The table below illustrates the z-scores for all the peer groups. It
shall be noted that the indicated random x-values have been added to the computed z-
scores in order to overcome negativity. For example for Z11 = -1+2 (random X-value)
=1. Table 5.2.2: Standardized BAT Data
BAT Peer Group 1 DMUs Electrical Energy
(kw/year) Gross Energy
(MJ/year) Products in Kg
DMU1 1 1 0,22 DMU2 1 1 0,37 DMU3 3 3 2,41 X-Value 2 2 1
BAT Peer Group 2 DMUs Electrical Energy
(kw/year) Gross Energy
(MJ/year) Products in pieces
DMU1 3 1 2 DMU2 3 4 2 DMU3 2 2 1 DMU4 2 2 1 DMU5 1 1 5 DMU6 4 4 2 DMU7 2 2 1 DMU8 1 1 1 DMU9 1 1 2 X-Value 2 2 2
BAT Peer Group 3 DMUs Electrical Energy
(kw/year) Gross Energy
(MJ/year) Products in tons
DMU1 1.064 0.752 1.223 DMU2 0.079 0.433 3.532 DMU3 2.245 2.981 1.223 DMU4 2.612 1.834 1.222 X-Value 1.5 1.5 1.8
At this point, a standardization of data has been reached and the above-
indicated data has been applied for the DEA application of this study for the BAT
approach. Now that the data has been standardized, homogeneity of data is assumed.
The raw data of the DMUs can be found in the appendix.
42
5.3 Problem Formulation
In the following a basic DEA example by Zhu (2009) will be given, which is
input-oriented and variable-return to scale and aims to strengthen the understanding of
the analysis tool. The input-oriented envelopment VRS model will also be applied for
the analysis of this research. As the presented linear formulation is input-oriented, the
objective is to minimize input and maintain the amount of output.
Let 𝜃∗be the efficiency:
𝜃∗ = 𝑚𝑖𝑛𝜃 (5.3.1)
Subject to:
Σ!!! ! 𝜆!𝑥!" ≤ 𝜃𝑥!" i=1,2,...m; (5.3.2)
Σ!!!! 𝜆!𝑦!" ≥ 𝑦!" r=1,2,…s; (5.3.3)
Σ!!!! 𝜆! = 1 and 𝜆! ≥ 0 j=1,2,…n. (5.3.4)
Here, DMUo stands for one out of n DMUs that will be assessed and 𝑥!" and
𝑦!" represent the ith input and rth output for DMUo. If 𝜃∗, which indicates the
efficiency score equals 1, then the input can no further be reduced, as DMUo is
already on the frontier. Thus, if 𝜃∗ < 1 then the efficiency frontier is above DMUo
and DMUo can be pushed towards the frontier.
For the purpose of strengthening the understanding an example will now be given. Let
us assume there are 5 DMUs, wherein each of them has the same profit every week of
$ 2000 (output) and different supply chain costs and response time (inputs). The
following table summarizes this: scenario Table 5.3.1: Supply Chain Operations (Zhu, 2009)
DMU Cost ($ 100) Response time in days Profit ($ 1000)
1 1 5 2
2 2 2 2
3 4 1 2
4 6 1 2
5 4 4 2
43
The next step is to analyze which out of the 5 DMUs are located on the
efficient frontier. If we take the following modulation for DMU5 with respect to the
table above, then the formulation is as follows:
Objective: Min 𝜃
Subject to
1𝜆! + 2𝜆! + 4𝜆! + 6𝜆! + 4𝜆! ≤ 4𝜃 (5.3.5)
5𝜆! + 2𝜆! + 1𝜆! + 1𝜆! + 4𝜆! ≤ 4𝜃 (5.3.6)
2𝜆! + 2𝜆! + 2𝜆! + 2𝜆! + 2𝜆! ≥ 2 (5.3.7)
𝜆! + 𝜆! + 𝜆! + 𝜆! + 𝜆! = 1 (5.3.8)
𝜆!, 𝜆!, 𝜆!, 𝜆!, 𝜆! ≥ 0 (5.3.9)
Note that 𝜃 represents the efficiency and 𝜆 the weights assigned to each
DMU for the purpose of identifying the best practice. The weights (𝜆) will be
elaborated more detailed later on. The solution of the above formulated problem is
𝜃∗ = 0,5, 𝜆!∗ = 1, and 𝜆!∗ = 0 (𝑗 ≠ 2), which demonstrates that DMU5 is inefficient
and DMU2 is its benchmark. Furthermore, it reveals that DMU5 must reduce cost and
response time in order to reach the efficient frontier. The following graph indicates
the efficient frontier for the input-oriented model and shows that DMU1, 2, 3 and 4 lie
on the efficient frontier. Additionally, it shows that DMU2 is indeed the closest
benchmark for DMU5 and that it must reduce its cost by $200 and its response time
by 2 days to be exactly in DMU2’s position.
44
Figure 5.3.1: Efficient Frontier of 5 DMUs (Zhu, 2009)
The individual reduction of input or output (depending on the model
orientation) in order to reach the efficient frontier is known as DEA slack. The above
modulation for DMU5 can be done for every DMU and in this case will be 𝜃∗ = 1,
for DMU1, 2, 3 and 4 as they already reached the frontier. However, through
observation one can see that DMU4 could reduce its total supply chain costs by 2
units and would be better off it would be in the same position as DMU3. Thus, DMU4
is only weakly efficient. The characteristics of weakly efficient will be explained
more detailed.
Apart from the basic DEA envelopment model, DEA is mostly used as a 2-
stage procedure as it first analyzes the efficiency scores and the slacks later on. The
slack models can be designed on the basis or extended to the efficiency model, which
is the following in particular:
𝑠!! = 𝜃∗𝑥!" − Σ!!!! 𝜆!𝑥!" i= 1, 2,…,m (5.3.10)
𝑠!! = Σ!!!! 𝜆!𝑦!" − 𝑦!" r= 1, 2,…,s (5.3.11)
Here, 𝑠!! and 𝑠!! stand for input and output slack, as input needs to be reduced (-) and
output be increased (+) to become more efficient. The efficient DMUs will have zero
slack values (Zhu, 2009). Generally, first the efficiency scores are computed by
45
ignoring the slacks. In the second stage, the efficiency scores will be fixed and the
slacks optimized by applying the following linear programming problem (Sherman &
Zhu, 2006):
𝑚𝑎𝑥Σ!!!! 𝑠!! + Σ!!!! 𝑠!! (5.3.12)
Subject to:
Σ!!!! 𝜆!𝑥!" + 𝑠!! = 𝜃∗𝑥!" i= 1, 2,…,m (5.3.13)
Σ!!!! 𝜆!𝑦!" − 𝑠!! = 𝑦!" r= 1, 2,…,s (5.3.14)
Σ!!!! 𝜆! = 1 and 𝜆! ≥ 0 j= 1, 2,…, n (5.3.15)
It should be noted that a DMU is fully efficient if and only if 𝜃∗ = 1 𝑎𝑛𝑑 𝑠!! = 𝑠!! =
0. Should the efficiency score be 1 and the slacks ≠ 0, the DMUs are considered as
weakly efficient. Weakly efficient units will still be considered as efficient, however,
when calculating the slacks, i.e. in stage two, weakly DMUs will not be included in
the reference set as a benchmarking unit to other DMUs (Sherman & Zhu, 2006).
As a result, the input-oriented VRS envelopment model including the slack
calculation can be expressed as follows:
𝑚𝑖𝑛𝜃 − 𝜀 Σ!!!! 𝑠!! + Σ!!!! 𝑠!! (5.3.16)
Subject to:
Σ!!!! 𝜆!𝑥!" + 𝑠!! = 𝜃𝑥!" i= 1, 2,…,m (5.3.17)
Σ!!!! 𝜆!𝑦!" − 𝑠!! = 𝑦!" r= 1, 2,…,s (5.3.18)
Σ!!!! 𝜆! = 1 and 𝜆! ≥ 0 j= 1, 2,…, n
Wherein the variable 𝜀 enables the minimization of 𝜃 in order to avoid the
optimization involving 𝑠!! 𝑎𝑛𝑑 𝑠!!. Consequently, 5.3.16 runs the model in a two-
stage process. First, a maximal reduction of inputs possible is calculated and the
optimal value of 𝜃∗ from model 5.3.1 reached. Then, the inefficient DMUs are moved
towards the efficient frontier by optimizing the slack variables (Zhu, 2009).
The output-oriented VRS envelopment model is expressed as:
46
𝑚𝑎𝑥𝜙 + 𝜀 Σ!!!! 𝑠!! + Σ!!!! 𝑠!! (5.3.19)
Subject to:
Σ!!!! 𝜆!𝑥!" + 𝑠!! = 𝑥!" i= 1, 2,…,m (5.3.20)
Σ!!!! 𝜆!𝑦!" − 𝑠!! = 𝜙𝑦!" r= 1, 2,…,s (5.3.21)
Σ!!!! 𝜆! = 1 and 𝜆! ≥ 0 j= 1, 2,…, n (5.3.22)
Similarly, this model is also calculated in a two-stage process. After 𝜙∗
indicating the efficiency has been calculated, the slacks can be computed by fixing the
optimal efficiency score. Thus, the linear programming problem for calculating the
slacks is:
𝑚𝑎𝑥Σ!!!! 𝑠!! + Σ!!!! 𝑠!! (5.3.23)
Subject to:
Σ!!!! 𝜆!𝑥!" + 𝑠!! = 𝑥!" i= 1, 2,…,m (5.3.24)
Σ!!!! 𝜆!𝑦!" − 𝑠!! = 𝜙∗𝑦!" r= 1, 2,…,s (5.3.25)
Σ!!!! 𝜆! = 1 and 𝜆! ≥ 0 j= 1, 2,…, n (5.3.26)
This makes the DMUs efficient if and only if 𝜙∗ = 1 and 𝑠!! = 𝑠!! = 0 for all
i and r. It shall be demonstrated that 𝜙∗ ≥ 1 and 𝜙∗ = 1 if and only if 𝜃 = 1. This
reflects that the models 5.3.16 and 5.3.19 determine the same frontier. Furthermore,
𝜃∗ = 1/𝜙∗ (Zhu, 2009). The following figure illustrates in contrast to figure 5.3.1 an
output-oriented frontier in which inputs are fixed and outputs must be increased to
reach the efficient frontier. It shows a total number of 4 DMUs, wherein DMU 4 is the
only inefficient one. Only two outputs are considered and DMU 4 must reach point G,
in order to reach the efficient frontier.
47
Figure 5.3.2: Output-Oriented Efficient Frontier (Zhu, 2009)
5.4 Methodology of Modelling Undesirable Measures
The objective of this thesis is to assess greening activities of SMEs in the Thai
automotive sector. As energy efficiency should be improved and for example waste
belong to undesirable outputs, this chapter aims to understand how to deal with
undesirable measures when using DEA.
There are several ways of modeling undesirable measures when using DEA.
As mentioned before, undesirable measures do occur and must be modeled
differently, as they do not correspond to the norm. For example in an output-oriented
model, the objective is to increase the amount of output while maintaining the amount
of input in order to improve the overall performance and to become more efficient.
However, if a model involves undesirable outputs then the objective is to reduce these
(see chapter 3.1).
For example, one way of dealing with undesirable outputs is to simply treat
them as inputs and compute the efficiency score with the input-oriented model.
However, this does not represent a real production process. The undesirable output
can also be ignored, but this approach will not lead to the achievement of the target,
which is to reduce the amount of undesirable output, thus, other approaches must be
considered. Another way to deal with undesirable output is to use a non-linear DEA
model, however, this procedure might be more difficult. Other possibilities are to treat
48
the undesirable outputs as a regular one and later on, limit the expansion of the
undesirable output by modifying the distance measurement. Finally, one could also
use a monotone decreasing transformation by translating the undesirable output data
and use the adapted data as regular output (Seiford M. & Zhu, 2002). The latter
approach will be demonstrated in detail, as it will be used for the analysis of this
work.
Seiford and Zhu have developed a strategy to use undesirable measurements in
the DEA models. In principle, the approach “is the use of DEA classification
invariance under which classifications of efficiencies and inefficiencies are invariant
to the data transformation” (Zhu, 2009).
While considering the efficiency invariance let us assume that input and
output have been transformed into 𝑥!" = 𝑥!" + 𝑢! and 𝑦!" = 𝑦!" + 𝑣! respectively and
𝑢 and 𝑣 are ≥ 0. With respect to the transformed data, the input and output-oriented
VRS models are as follows:
𝑚𝑖𝑛𝜃 − 𝜀(Σ!!!! 𝑠!! + Σ!!!! 𝑠!!) (5.4.1)
Subject to:
Σ!!!! 𝜆!𝑥!" + 𝑠!! = 𝜃𝑥!" i= 1,2,…, m; (5.4.2)
Σ!!!! 𝜆!𝑦!" − 𝑠!! = 𝑦!" r= 1,2,…, s; (5.4.3)
Σ!!!! 𝜆! = 1 and 𝜆! ≥ 0 j=1, 2,…, n. (5.4.4)
𝑚𝑎𝑥𝜃 − 𝜀(Σ!!!! 𝑠!! + Σ!!!! 𝑠!!) (5.4.5)
Subject to:
Σ!!!! 𝜆!𝑥!" + 𝑠!! = 𝑥!" i= 1,2,…, m; (5.4.6)
Σ!!!! 𝜆!𝑦!" − 𝑠!! = 𝜃𝑦!" r= 1,2,…, s; (5.4.7)
Σ!!!! 𝜆! = 1 and 𝜆! ≥ 0 j=1, 2,…, n. (5.4.8)
The attribute Σ!!!! 𝜆! = 1 enables to have negative values for input and output before
using the VRS-Model.
49
The following example indicates a model with undesirable output referring to
the work of Seiford and Zhu (2002).
Let us assume 𝑦!"! represents the good and 𝑦!"! the bad output. Thus, the objective is to
increase 𝑦!"! and decrease 𝑦!"! . To achieve the multi-objective problem, the
undesirable output must be multiplied by “-1” and additionally a proper value 𝑣! must
be found which will be added and will turn the negative bad output into positive
numbers. This can also be expressed as 𝑦!"! = −𝑦!"! + 𝑣! > 0 and 𝑣! = 𝑚𝑎𝑥! 𝑦!"! +
1. With respect to the above efficiency invariance, the objective function is:
Max h (5.4.9)
Subject to:
Σ!!!! 𝜆!𝑦!"! ≥ ℎ𝑦!"
! (5.4.10)
Σ!!!! 𝜆!𝑦!"! ≥ ℎ𝑦!"! (5.4.11)
Σ!!!! 𝜆!𝑥!" ≤ 𝑥!" (5.4.12)
Σ!!!! 𝜆! = 1 and 𝜆! ≥ 0, j= 1,…,n (5.4.13)
Note that h represents the efficiency. The model for (5.4.9) makes sure that desirable
output is increased and undesirable decreased. Furthermore, having a translation
vector 𝑣, we assume ℎ∗ is the ideal value for (5.4.9) at ℎ∗𝑦!"! ≤ 𝑣!. It provides non-
negativity for the optimized undesirable output of 𝑦!!! (= 𝑣! − ℎ∗𝑦!"! ) (Zhu, 2009).
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Chapter 6
Energy Efficiency Analysis
The following analysis refers to an input-oriented model with undesirable
input for the data obtained from the PREMA approach and a regular input-oriented
for the data obtained from the BAT approach. The first model is based on the
undesirable input-oriented methodology by Seiford and Zhu (2002) explained in
chapter 5.4. It shall be noted that because of the diversity of PREMA data, the SMEs
have been classified according to internal production processes. As for BAT, the
SMEs have been grouped according to the units of their end products, i.e. kilograms,
tons and pieces. The tool IQA will not be considered for the analysis, as it would
exceed the scope of this work.
6.1 Profitable Environmental Management Approach - Data and Analysis
Because the available data and the different measures available for this project
- or approach to be specific - are very diverse, different approaches for benchmarking
have been considered. First, the objective was to collect all the available data of
energy consumption, costs and goods produced. However, this approach did not seem
to be representative, as the companies differ in size, production of goods and variety
and number of production processes. Thus, the companies are heterogeneous even
though they all focus on the automotive sector. The complexity of this particular
model mainly lies on grouping the SMEs according to particular attributes for
benchmarking. The second approach considered focused on evaluating the
companies’ specific operating processes with the purpose of eliminating inefficiency.
For many reasons benchmarking the operating processes are more important than
benchmarking the institutions as a whole. First, information on specific production
processes helps to understand the basics of efficiency on a production level and might
reveal some measurement problems. Second, companies’ production processes are
responsible for the majority of operational expenses, which is of more interest from a
manager’s perspective. Looking into the available data and having selected processes
(i.e. cutting, piercing and welding), the next step leads to collecting data that would
51
apply to all the SMEs within the particular production processes. This is necessary for
the DEA approach, as otherwise the performance evaluation would not be feasible. As
a result, particular data that could be found for every company within the processes
were energy consumption as an input and scrap as an output, while the welding
process has heat as output data. The following table indicates the PREMA data
selected for the benchmarking analysis.
Table 6.1.1: PREMA Data
Welding Process DMUs Input (Undesirable)
Electrical Consumption (kw/year) Output Heat (kw/year)
DMU1 256,471 256,471 DMU2 10,214 36,771 DMU3 9,800 204,873
Cutting Process DMUs Input (Undesirable)
Electrical Consumption (kw/Year) Output Cutting Scrap (kg/year)
DMU1 63,180 1,428 DMU2 4,708 236.7 DMU3 18,200 657.34 DMU4 876 1270.4 DMU5 12,286 2385 DMU6 189,447 7200
Piercing Process DMUs Input (Undesirable)
Electrical Consumption (kw/year) Output Piercing Scrap (kg/year)
DMU1 15,343 17,280 DMU2 760,500 7,740 DMU3 15,731 3,500 DMU4 2,520 75,860
The presented data expresses the input in kilowatt per year and the outputs in
kilogram and kilowatt per year, respectively. Obviously, the outputs heat energy and
scraps are bad outputs and shall be reduced.
For the purpose of the applied method, translation vectors of v= 260,000 for
welding, v= 7,300 for cutting and v= 76,000 for piercing have been selected,
respectively. The undesirable input data of each process is subtracted from the
translation vector, leading it to a reduction of input, instead of an increase of input,
which is the norm in an input-oriented model. Hence, the translated data is used for
52
further evaluation. The data has been analysed by applying an input-oriented VRS
envelopment model by using Microsoft Excel Solver. The efficiency scores with the
applied method are indicated in the table below, as well as optimal benchmark
weights of the DMUs, which is also known as the reference set.
Table 6.1.2: DEA results of PREMA
Welding Process DMUs Efficiency Scores Reference Set DMU1 0.04 1 (DMU3) DMU2 1 - DMU3 1 -
Cutting Process DMUs Efficiency Scores Reference Set DMU1 0.02 1 (DMU4) DMU2 1 - DMU3 0.2 0,6 (DMU2); 0,4 (DMU4) DMU4 1 - DMU5 0.07 1 (DMU4) DMU6 0.004 1 (DMU4)
Piercing Process DMUs Efficiency Scores Reference Set DMU1 0.86 0.81 (DMU3); 0.19 (DMU4) DMU2 0.002 0.94 (DMU3); 0.06 (DMU4) DMU3 1 - DMU4 1 -
Within the peer group of the welding process DEA identified 2 efficient
DMUs amongst 3 DMUs. DMU1 has an efficiency score of <1, which implies that it
is inefficient. The scores can also be read as percentages and state that DMU2 and
DMU3 should keep 100% of its input and output; whereas DMU1 is advised to reduce
it to 4% from its currents level.
The two efficient DMUs, i.e. DMU2 and DMU3 encompass the best-practice
DMUs and are located on the efficient frontier. However, it is unknown which DMU
out of the best-practice is more or less efficient than the other one. The DMUs located
on the efficient frontier and which are amongst the best-practice DMUs are those that
are clearly not inefficient relative to the other DMUs in the evaluation. Also, it should
be demonstrated that these DMUs are fully efficient, as they have an efficiency score
of 1 and the slacks equal 0, which will be indicated later. The calculation of the slacks
was based on the formula 5.3.10 and 5.3.11.
53
Furthermore, DEA reveals the information of a subgroup of the SMEs, which
are referred as the efficiency reference set. For example, DMU1 has found to be
inefficient and has operating inefficiencies in direct comparison to DMU3. The values
in the column of the reference set indicate the relative weight allocated to each DMU
included in the reference set in order to calculate the efficiency rating. In other words,
the input and output of DMU3 are multiplied by the weights determined by DEA, in
this case by 1. Afterwards, these are added together and result in a composite DMU
that uses a more efficient set of input and output than that of DMU1. By applying a
combination of the operating techniques used by DMU3 the new hypothetical
composite DMU can decrease its input or decrease the bad output. More specifically,
the new hypothetical composite DMU can move towards the efficienct frontier by
either reducing its energy consumption and maintaing its heat energy consumption or
maintaining its energy consumption while reducing its heat energy consumption. This
information should be an adequate evidence to cause the SMEs to question why they
were not able to be as efficient as other SMEs and be the trigger of some new plans
and implementations with respect to energy efficiency. DMUs 2 and 3 are their own
benchmarks, thus, they do not need to be compared to other DMUs, which explains
why they have no weights indicated in the column of the reference set. The same
interpretation of results applies for the remaining DMUs in the peer groups for cutting
and piercing process.
Additionally, DEA computes the slacks of the inefficient DMUs advising
them what input or output to reduce, in order to become more efficient. For the
welding process, DMU1 is recommended to reduce its heat energy consumption by
51,600 kw/year towards the output level of DMU3. The slacks for the cutting and
piercing processes are represented in the table below.
54
Table 6.1.3: Slacks for PREMA data
Cutting Process
DMUs Reducing Electrical Energy (kw/year) Reducing Scrap (kg/year)
DMU1 - 157.6
DMU2 - -
DMU3 - 7.16
DMU4 - -
DMU5 - 1114.6
DMU6 - 5929.6
Piercing Process
DMUs Reducing Electrical Energy (kw/year) Reducing Scrap (kg/year)
DMU1 2122 32
DMU2 774 4240
DMU3 - -
DMU4 - -
The inefficient DMUs within the cutting process are advised to reduce their
scrap, while the inefficient DMUs within the piercing process can either reduce their
electrical energy or reduce their scrap. The efficient DMUs have zero slacks, which is
why no slack-reduction or recommendation is indicated. The implementation of the
electrical energy consumption or scrap can be done by updating outdated machinery
or revising the production processes. For more specific recommendations, further
energy saving opportunities will be discussed in chapter 6.4 as policy
recommendations.
At this point, it is also of relevance to emphasize that the efficient DMUs are
relatively efficient and not absolutely. Thus, amongst the DMUs in a peer group, for
example within the welding process no other DMU is more efficient than DMU 2 and
3. Consequently, they are the best available and not the best possible.
55
6.2 Best Available Technique Approach - Analysis
For the BAT approach and data, 16 SMEs were available. These DMUs had
common inputs and outputs, i.e. electrical energy consumption, gross energy
consumption and goods produced. The inputs are expresses in kilowatt/year and mega
joule/ year while the products had different units. Thus, the DMUs had to be classified
according to their unit of products, i.e. products in pieces, kilogram and tons. In this
case, the outputs are desirable, which should be increased if possible. Each peer group
has a number of 3, 9 and 4 DMUs to be evaluated, respectively. The DMUs use 2
inputs (electrical and gross energy) and produce one output, expressed in different
units. The statement of the problem is the very same as the one applied for the
PREMA data, the only difference is that this model has a desirable output. Hence, the
regular input-oriented VRS envelopment model will be applied. At this point it shall
be recalled that the data illustrated in table 5.2.2 has been used for the computation of
DEA results with the DEAFrontier Software in Microsoft Excel.
The following table reports the results of DEA that have been applied to the
below-indicated DMUs. Table 6.2.1: DEA Results of BAT
Peer group 1 DMUs Efficiency Score Reference Set DMU1 1 weakly efficient 1 (DMU2) DMU2 1 efficient - DMU3 1 efficient -
Peer group 2 DMUs Efficiency Score Reference Set DMU1 1 weakly efficient 1 (DMU5) DMU2 0.33 inefficient 1 (DMU5) DMU3 0.5 inefficient 1 (DMU5) DMU4 0.5 inefficient 1 (DMU5) DMU5 1 efficient - DMU6 0.25 inefficient 1 (DMU5) DMU7 0.5 inefficient 1 (DMU5) DMU8 1 weakly efficient 1 (DMU5) DMU9 1 weakly efficient 1 (DMU5)
Peer group 3 DMUs Efficiency Score Reference Set DMU1 0.57 inefficient 1 (DMU2) DMU2 1 efficient - DMU3 0.14 inefficient 1 (DMU2) DMU4 0.23 inefficient 1 (DMU2)
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Out of the 3 DMUs in peer group 1, DEA identified 2 DMUs as efficient.
DMU1 has an efficiency score of 1 and is weakly efficient. Recalling from chapter 5.3
a DMU is fully efficient, only and only if the efficiency score equals 1 and slacks are
zero. Because DMU2 uses less inputs and produces more output than DMU1, DEA
indicates that DMU1 has the potential to either decrease its input by producing the
same output or remain its input level and produce more output. In other words, DMU1
has found to be weakly efficient in comparison to DMU2, which is why it is being
referred to in the reference set by 100%. The second peer group has also 1 efficient
DMU, namely DMU5. DMU1, 8 and 9 are only weakly efficient, due to the fact that
the slacks are non-zero. The remaining DMUs are relatively inefficient and are
referred to DMU5 for improvement. As DMU5 is identified as relatively efficient, it
will not be compared to any other DMU within the peer group and is its own
reference. The computed weights in the column of the reference set indicated in the
above table are assigned to each reference set member in order to calculate the
efficiency rating. In other words, it reveals information for weakly efficient or
inefficient DMUs about how much weight of which reference set member to apply in
order to move towards the efficient frontier. In this case, only one efficient DMU has
been identified per peer group, which is why a full weight of 100% is assigned to the
best practice in the reference set. If there would be more than one efficient DMU, then
DEA would compute every possible combination of weights to make the weakly
efficient or inefficient DMU move towards the efficient frontier. Hence, the weakly
and inefficient DMU should apply 100% of the input and output combination the
best-practice DMU is using.
In regards to the slacks, DEA provides some recommendation with respect to
the computed weights in the reference set. Looking into peer group 1, the
recommendation is that DMU1 should increase its products. DMU1 consumes more
electrical and gross energy than DMU2 and produces less kg of output. Through the
data information of GIZ it is known that DMU1 and DMU2 in this particular peer
group operate in the very same field and are branched SMEs. Thus, it is meaningful
that DMU1 is benchmarked with DMU2. Given the fact that DMU1 uses more input
to produce less output, it has the potential of increasing its products by 1,333,259 kg
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to 2,605,490 kg, which is the same amount DMU2 produces. This can be achieved
without increasing energy consumption to reach the efficient frontier. But because the
focus of this study is to improve energy efficiency, it is recommended to reduce the
energy level to the same amount of DMU2. The slack recommendations for peer
group 2 and 3 are illustrated in the table below:
Table 6.2.2: Slacks for BAT data
Peer group 2
DMUs Reducing Electrical Energy (kw/year)
Reducing Gross Energy (kg/year)
Increase products in pieces
DMU1 decrease - -
DMU2 - decrease -
DMU3 - - increase
DMU4 - - increase
DMU5 Best practice
DMU6 - - increase
DMU7 - - increase
DMU8 - - increase
DMU9 - - increase
Peer group 3
DMUs Reducing Electrical Energy (kw/year)
Reducing Gross Energy (kg/year)
Increase products in tons
DMU1 decrase - increase
DMU2 Best practice
DMU3 decrease - increase
DMU4 decrease - increase
For peer group 2, a recommendation to decrease the electrical energy
consumption goes only to DMU1. DMU2 is advised to decrease its gross energy
consumption, while the remaining DMUs have the potential to increase their amount
of output while using the same amount of input. DEA computes the slack by
analysing the data sets and revealing the slack recommendation that it thinks is most
feasible to achieve. Because for peer group 2, DMU5 uses relatively low energy
consumption compared to its peer members, DEA rather recommends to increase the
58
output level by remaining the input level, due to the probability of feasibility. DMU5
is the best practice, thus, does not need to reduce input or increase output.
Within the third peer group, the DMUs are advised to either reduce their
inputs or increase their output. Unlike the previous cases, this peer group has the
option of choosing the slack-adaption, as two recommendations are provided.
However, this is an either-or option rather than achieving both ways simultaneously.
Because of the targeted energy efficiency input reduction is suggested. DMU2 has
been identified as best practice and thus, no slack recommendations are given.
Because the data for BAT has been standardized and transformed the author rather
gave a slack-suggestion for the energy saving areas, rather than giving exact numbers
to which the energy consumption should be reduced.
Note that the slack recommendation rather serves as an orientation to what
DEA thinks is feasible. It shall be emphasized that knowing which input to reduce or
which output to increase is more important, than the actual slack numbers. The SMEs
can use this advice and try to achieve the slack recommendations in every possible
way, especially the reduction of electrical and gross energy. It will lead to saving
costs and a greener way of producing the automotive parts.
6.3 Summary
For the purpose of concluding the analysis, the research questions shall be addressed.
a) What are the reasons of different achieved results for the SMEs?
Out of the large number of SMEs it was observable that the companies are very
diverse in size and internal processes. Especially when considering the BAT
approach, one can assume that the main problematic of achieving different results is
due to the fact that out-dated machines are still in use. This can be confuted by
referring to the BAT DEA results of peer group 1. It was mentioned that DMU1 and
DMU2 are branched companies. However, DMU2 is able to consume less energy
consumption and achieve a greater amount of output at the same time. This is just an
obvious example reflecting the internal processes as a problem for inefficiency.
Furthermore, aspects such as know-how or technical feasibility are possible reasons
for inefficiency.
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b) Which SME has reached the efficient frontier?
Upon analysis it was found that the efficiency score of both PREMA and BAT
data ranged from 0.2% to 100% out of which a total number of 10 SMEs were found
efficient with a score of 1.
With regard to the PREMA data, DEA identified a total number of 6 out of 13
efficient SMEs. These were DMU2 and 3 in the welding process, DMU2 and 4 in
the cutting process and DMU3 and 4 in the piercing process. The lowest score
obtained for PREMA was 0.2% ranging up to 100%. Whereas for the BAT data,
DEA identified 4 efficient SMEs out of 16, namely DMU2 and 3 for peer group 1,
DMU5 for peer group 2 and DMU2 for peer group 3. The lowest score within the
BAT data amounted 14%. Generally, when looking at the inefficient DMUs, higher
efficiency scores could be observed for BAT. Also BAT revealed 4 weakly efficient
DMUs, compared to the PREMA approach, which revealed none. The differences
within the ranges could be due to the standardization of data within the BAT peer
groups.
At this point it shall again be emphasized that the number of efficient DMUs
might also depend on the number of data sets and number of DMUs. Preferably, the
number of DMUs should be twice as much as the number of added inputs and
outputs together. Having too many inputs and outputs as well as few DMUs can lead
to a slight risk of increased loss of discriminatory power. In other words, more
DMUs are identified as efficient.
c) What can be done to make the energy consumption more efficient?
This highly depends on the internal processes of the SMEs. However, if no
investments in new technology have been done with respect to the Green Auto
Project and SMEs are found to be inefficient, then it is advised to upgrade
machinery, as it has the highest influence on green manufacturing or more
efficient energy consumption. Through this analysis, DEA has identified energy
saving opportunities by indicating which input to reduce in case of inefficiency
and measures energy consumption. More on the way to achieve green
manufacturing will be discussed in the following chapter.
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6.4 Policy Recommendations
Recommendation 1: Specific data collection
With respect to the analysis the development of a data template is
recommended to GIZ, so that they can collect common data for all SMEs and
benchmarking will be made easier, especially for the PREMA tool. The obtained data
was very valuable as such as it gives high-valued information on individual processes
of SMEs but lacked in commonality across all SMEs, which made benchmarking
difficult. As there was no template for the data collection, it was complex to group
and analyse the companies according to commonality of data and other attributes,
such as internal processes, so that the application of DEA or any other benchmarking
tool that will be applied for future projects could be made easier. The data also
included many data sets that were not energy-related. That is why the availability of
common energy-related data throughout the SMEs is the foundation of developing
policy recommendations that aim to improve energy consumption. Also, it is
advisable to continuously monitor the data and evaluate the improved results in a
given time period and consequently, develop energy efficiency reports. The
application of key performance indicators (KPI’s) is advisable as through indexes the
developments achieved can be easily illustrated. This shall support the achievement of
energy efficiency.
Recommendation 2: Promoting the Energy Efficiency Development Plan
In chapter 1 it has been mentioned that the Thai Ministry of Energy has
launched a National 20-year Energy Efficiency Development Plan (EEDP), which
targets particularly the transportation and industrial sector. The goal is to reduce
energy intensity by 25% in 2030, compared to the level of 2005. The EEDP will be
achieved by implementing rules and regulations as well as providing subsidies. The
rules and regulations involve the establishment of Minimum Energy Performance
Standards (MEPS) and the labelling’s of energy efficiency on appliances. The
subsidies include a Standard Offer Program (SOP) or the approval of fundings for the
achieved energy savings (Ministry of Energy). GIZ is already working with the
Ministry of Energy; however, it shall be emphasized that the integration of the EEDP
61
to the Green Auto Project should be expanded, as GIZ has a great impact on
promoting the 20-year Energy Efficiency Development Plan to its target group and
the Thai Ministry of Energy is willing to reach out to SMEs nationwide. Former
Energy Conservation Programs also included regulations and financial incentives. The
previous worked out strategic approaches will remain but further measures that
accelerate the implementation and lead to energy efficiency will be added. The five
strategic approaches that aim to support the realisation of EEDP are following
mandatory requirements through regulations, rising public awareness, developing the
know-how of human resources, the development of an Energy Conservation
Promotion and Support as well as increasing innovations and consequently, apply
improved technology (Ministry of Energy).
Recommendation 3: Establish an Energy Management Team and Implement
Small Energy Saving Actions
For the SMEs that have been found to have high-energy consumption relative
to the rest of the peer group, it is suggested to update their energy-intensive
appliances. This can be done by for example the elimination of inefficient lighting
products or air conditioners and their substitution by improved sustainable lighting
and cooling systems. Additionally, as machinery is known to be the most energy
consuming factor consultants could introduce and promote mandatory minimum
energy performance standards (MEPS) and labels that might be more cost-effective.
These kinds of investments have a high impact on energy efficiency. The achievement
of higher energy efficiency is guaranteed if the individual SMEs start an action plan
that involves the above-mentioned energy saving opportunities and the continuous
audits on energy consumption by engineers (International Energy Ageny, 2011). If
financial measures allow it investment in photovoltaic systems are suggested. By
developing individual action plans, SMEs are able to undertake self-assessments. In
particular, this could include the nomination of an energy manager and building-up a
team that focuses on managing energy efficiency. The team should define policy
regulations, audit the energy consumption and implement energy saving activities.
Also, the team would be responsible for following up the actual implementation of the
regulations. Measures, such as the Energy Efficiency Index (Wajer et al., 2007) or
62
other statistical approaches like DEA illustrate and document the actual improvements
that have been achieved. DEA provides slack-recommendation, so the slack reduction
for inputs are known and can be targeted.
Conclusively, recommendations to the Green Auto Project are to include
further workshops that address energy efficiency and educate SMEs on the impact of
energy efficiency and the implementation of action plans and policy recommendation
for their enterprise. Furthermore, with respect to the DEA results obtained for the
purpose of this work it is suggested to further investigate the reasons of inefficiency.
6.5 Conclusion
This paper assessed the energy efficiency of SMEs in the Thai automotive
sector. The relative performance of SMEs in terms of energy efficiency has been
analysed. For the obtained data from the PREMA tool that amounted 13 SMEs, 6
DMUs have been identified as energy efficient. Out of the 16 DMUs from the BAT
tool, a total number of 4 energy efficient DMUs have been identified. Furthermore, 4
DMUs are identified as weakly efficient and 8 as inefficient. The findings highlight
the value of benchmarking as a foundation for improvement opportunities in energy
savings and the demonstration of energy consumption of SMEs in the Thai
automotive sector. Additionally, results have indicated that some SMEs are able to
use less energy consumption to produce more goods than others. Hence, results
suggest that with respect to the GIZ project the reason of inefficiency should be
further investigated. The value added to the SMEs is the target-oriented slack
recommendation that gives flexibility on selecting actions to reduce energy
consumption. The SMEs identified as best-practice are not the absolute best SMEs,
but the relative best, meaning that they are not the best possible but rather the best
available.
The results are of high importance because Thailand as one of the
manufacturing countries in the world needs to further address the issue of energy
efficiency as it has a huge potential in improving it. It is important that the energy-
intensive sectors consider the high influence an efficient energy consumption can
63
make and conduct arrangements that lead to a productive way of managing energy
efficiency. The information obtained from the benchmarking results can be used to
improve the relative performance of individual SMEs. Energy has a great impact on
the economical performance of an organization. Improving energy efficiency reflects
a great opportunity to save economic resources, improve production processes while
also having a positive impact on the environment. Especially SMEs in Thailand that
contribute a lot into the Thai GDP as pointed out in the introduction have a huge
potential in enhancing energy efficiency, as it is often the case that out-dated
machinery is used, awareness is missing or financial measures are unavailable. For
this to be implemented, awareness of the latest technology should be demonstrated.
Other actions include the substitution of inefficient lightings and air conditioners to
more efficient ones, the application of efficient appliances such as Minimum Energy
Performance Standards or with efficiency labelling’s. Lights and equipment’s should
be turned off when not in use and if financial resources allow it, investments in
photovoltaic solar systems are recommended. The National 20-year Energy Efficiency
Development Plan (EEDP) is an important project that will boost the achievement of
energy efficiency in Thailand. The study has provided information on the
performance and identification of a particular peer group within the Thai automotive
sector that need to adapt the above-mentioned policy recommendations for efficiency
maximization.
The contribution of this study is that it is the first empirical study to apply the
Data Envelopment Analysis methodology to measure energy efficiency in the Thai
automotive sector for SMEs to measure energy efficiency. It has benchmarked the
level of energy consumption of SMEs and used the obtained results to identify energy
saving potentials and suggests policy recommendations.
64
Chapter 7
Limitations and Delimitations
As every work has some limitations that could be avoided in order to not
exceed the scope of the study or because the methodology applied restricts some
procedures, this chapter gives some limitations that appeared in this work and aims to
encourage future researchers to consider and avoid the following restrictions.
7.1 Limitation of Study
In order to narrow the scope of this study, only SMEs that belong to the
automotive sector in Thailand and only those that have participated in the Green Auto
Project by GIZ were examined. Thus, the limitation is associated with the available
data set that could be collected with respect to the Green Auto Project. Furthermore,
as this study focuses on Thailand only, one cannot compare the results to similar
SMEs which might be best practice in other countries.
When applying a data envelopment analysis, the data set for the performance
evaluation is mostly quantitative. However, many evaluation problems contain
qualitative aspects that should be taken into account when assessing the efficiency of
DMUs. For example, the manager’s competence or know-how are essential for the
efficiency of the unit to be evaluated. For the purpose of including this attribute to the
data set, a ranking of the DMUs scaled from best to worst can be established. A more
precise way of quantifying the attributes that reflects the value of the factor is not
possible. Thus, the best way of presenting the qualitative data sets is to rank them in
an ordinal scale rather than in a numerical sense. Each DMU must be assigned to only
one category, which might be high, medium or low in the case of the manager’s
competence (Zhu & Cook, 2007). Qualitative data has not been applied to this work,
therefore, future studies are encouraged to assess energy efficiency with DEA by
applying both quantitative and qualitative data.
7.2 Limitations of Instrument
Some limitations come along with the instrument used in this study. The scores
attained through DEA tend to be sensitive when specifiying input and output as well
65
as the size. If the size of the organization/unit to be examined is large, DEA will have
a greater scope of determining comparison partner and thus, the average efficiency
score will be reduced. However, if only the amount of input and outputs is increased
without adding further organization, the average efficieny score increases (Steering
Committee for the Review of Commonwealth/State Service Provision, 1997).
Because of data availability, peer group could not have a great number of DMUs in
this study which restricted the selection of best-practice DMUs as for example BAT
results peer group 2.
Additionally, a total number of 7 inefficient DMUs have been identified for
PREMA and 8 for BAT. The inefficient SMEs have been referred to the best practice
within the reference set for achieving improvement. As mentioned in the summary,
the lowest score obtained was 0.2%. It is difficult to compare a DMU that scores 0.2%
to a DMU in the reference set with a score of 100%. For feasible improvement, an
inefficient DMU with a low efficiency score should be compared to a DMU with
identical characteristics and capacity, which makes benchmarking to one another
more justifiable. This is a drawback of DEA as it does not concern the internal
processes and only requires input and output for computation. Especially when it
comes to the slacks, it might not be feasible to reach the recommended slack amount
as the best practice and the inefficient DMU have a high score deviation.
66
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Appendix
Bat Raw Data
Table A.1: BAT Raw Data
Peer group 1 DMUs Electrical Energy
(kw/year) Gross Energy
(MJ/year) Products in kg
DMU1 5,207,000 18,745,200 1,272,231.92 DMU2 4,749,080 17,096,688 2,605,490.39 DMU3 42,652,553 159,727,480.8 20,841,472.38
Peer group 2 DMUs Electrical Energy
(kw/year) Gross Energy
(MJ/year) Products in pieces
DMU1 2,094,720 2,109,072 3,220,290 DMU2 3,085,000 15,498,914.88 6,735,169 DMU3 953,620 3,433,032 334,613 DMU4 1,233,000 4,438,800 180,700 DMU5 345,244 1,242,878.4 26,843,769 DMU6 3,694,720 13,300,992 1,615,290 DMU7 910,712 3,278,563 47,733 DMU8 124,292 447,451 220,481 DMU9 595,000 2,142,000 4,533,638
Peer group 3 DMUs Electrical Energy
(kw/year) Gross Energy
(MJ/year) Products in tons
DMU1 10,160,000 40,457,774 33,018.46 DMU2 852,900 5,984,742.45 38,585,072.56 DMU3 21,318,720 281,286,259 18,402.4 DMU4 24,782,058 157,335,545.55 8,268.6
