A green supplier selection model for high-tech industry

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A green supplier selection model for high-tech industry

Amy H.I. Lee

a

, He-Yau Kang

b

, Chang-Fu Hsu

c,d,*

, Hsiao-Chu Hung

e a

Department of Industrial Engineering and System Management, Chung Hua University, Taiwan b

Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taiwan c_{Quality Assurance Division, United Microelectronics Company, Taiwan}

d_{Department of Industrial Engineering and Management, National Chiao Tung University, 1001 Ta-Hsueh Road, HsinChu, Taiwan} e

Graduate Institute of Technology Management, Chung Hua University, Taiwan

a r t i c l e

i n f o

Keywords:

Analytic hierarchy process Environment

Fuzzy set theory FEAHP Green supplier

a b s t r a c t

With growing worldwide awareness of environmental protection, green production has become an impor-tant issue for almost every manufacturer and will determine the sustainability of a manufacturer in the long term. A performance evaluation system for green suppliers thus is necessary to determine the suit-ability of suppliers to cooperate with the ﬁrm. While the works on the evaluation and/or selection of sup-pliers are abundant, those that concern environmental issues are rather limited. Therefore, in this study, a model for evaluating green suppliers is proposed. The Delphi method is applied ﬁrst to differentiate the criteria for evaluating traditional suppliers and green suppliers. A hierarchy is constructed next to help evaluate the importance of the selected criteria and the performance of green suppliers. Since experts may not identify the importance of factors clearly, the results of questionnaires may be biased. To consider the vagueness of experts’ opinions, the fuzzy extended analytic hierarchy process is exploited. With the proposed model, manufacturers can have a better understanding of the capabilities that a green supplier must possess and can evaluate and select the most suitable green supplier for cooperation.

1. Introduction

With increasing government regulation and stronger public awareness in environmental protection, ﬁrms today simply cannot ignore environmental issues if they want to survive in the global market. In addition to complying with the environmental regula-tions for selling products in certain countries, ﬁrms need to imple-ment strategies to voluntarily reduce the environimple-mental impacts of their products. The integration of environment, economic and so-cial performances to achieve sustainable development is a major business challenge for the new century (Verghese & Lewis, 2007). Environmental management is becoming more and more impor-tant for corporations as the emphasis on the environmental protection by organizational stakeholders, including stockholders, governments, customers, employees, competitors and communi-ties, keeps increasing. Programs such as design for the environment, life cycle analysis, total quality environmental management, green supply chain management and ISO 14000 standards are popular for environmentally conscious practices (Sarkis, 1998). Both proactive and reactive methods have been implemented to protect the envi-ronment. For instance, environmentally conscious design and

man-ufacturing (ECD&M) is a proactive method that aims to reduce the resource consumption, hazardous emission and energy usage by reengineering the design and manufacturing process and selecting appropriate materials (Zhang, 2004). On the other hand, end-of-life (EoL) strategy and management is a reactive method that provides technology and methodologies to handle the wastes which are al-ready present (Zhang, 2004).

As environmental awareness increases, buyers today are learn-ing to purchase goods and services from suppliers that can provide them with low cost, high quality, short lead time, and at the same time, with environmental responsibility. Legislative and regulatory initiatives have also emerged in developed countries, especially in Europe and Japan. Some pioneer enterprises have already joined the trend of green supply chain long before the EU environmental orders were enforced. In order to have a long-term success in the global market, a firm not only should stress on financial terms in evaluating suppliers, but also should take various criteria, includ-ing pro-environmental concerns, into consideration. Therefore, green procurement approach must be compliant with customers, laws and regulations, and a green supplier evaluation system is necessary for a firm in determining the suitability of a supplier as a partner in the green supply chain.

The rest of this paper is organized as follows. Section2reviews some recent works on environmental management and green supplier evaluation. Analytic hierarchy process (AHP), fuzzy set theory and fuzzy-extended AHP (FAHP) are presented in Section

* Corresponding author. Address: Department of Industrial Engineering and Management, National Chiao Tung University, 1001 Ta-Hsueh Road, HsinChu, Taiwan. Tel.: +886 3 518 6582; fax: +886 3 518 6575.

E-mail address:hcf2330@yahoo.com.tw(C.-F. Hsu).

Contents lists available atScienceDirect

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3. Section4proposes a FAHP model applied to evaluate green sup-pliers. Some concluding remarks are made in the last section.

2. Environmental management and green supplier evaluation 2.1. Environmental management

People are increasingly aware of the strong links between the economy and the environment these days. Exploiting the synergies between the two is essential to maximize both well-being and eco-nomic growth. As a result, many countries have started to enforce environmental legislations and regulations for controlling the use of products, processes and wastes that may be detrimental to the environment. For instance, EU has set a range of environmental policies such as RoHS and WEEE. The RoHS Directive (the restric-tion of the use of certain hazardous substances in electrical and electronic equipment) bans manufacturers, sellers, distributors and recyclers of electrical and electronic equipment (EEE) the plac-ing on the EU market of new electrical and electronic equipment containing more than agreed levels of lead, cadmium, mercury, hexavalent chromium, polybrominated biphenyl (PBB) and poly-brominated diphenyl ether (PBDE) flame retardants (RoHS, 2008). The RoHS Directive came into force on 1 July 2006. The WEEE (waste electronics and electrical equipment) Directive aims to re-duce waste arising from electrical and electronic equipment (EEE), decrease the wastes of natural resources, prevent pollutions from occurring, and make manufacturers, sellers, distributors and recyclers of EEE responsible for the environmental impact of their products (Netregs, 2008). The WEEE Regulations came into force on 1 January 2007 with the main requirements and obligations on producers and distributors of EEE into force from 1 April 2007 (Netregs, 2008). WEEE is aimed at the life cycle of product, and RoHS is exploited during the design stage of products. While there are environmental regulations and mandatory programs, pressures to protect the environment also come from other exter-nal stakeholders. Thus, many firms are introducing voluntary envi-ronmental programs for gaining competitive advantages. Indeed, environmental management is becoming the focus of corporate strategy and an arena of competition, rather than simply as a com-pliance-driven function (Sarkis, 1995). Sarkis (1998)categorized environmentally conscious business practices into five major com-ponents: design for the environment, life cycle analysis, total qual-ity environmental management, green supply chain and ISO 14000 environmental management system requirements.

In order to reap the greatest benefits from environmental man-agement, firms must integrate all members in the green supply chain. Green supply chain management has emerged as a way for firms to achieve profit and market share objectives by lowering

environmental impacts and increasing ecological efﬁciency (van

Hock & Erasmus, 2000). The definition of green supply chain man-agement ranges from simple green purchasing to an integrated supply chain flowing from supplier, manufacturer, customer, and to reverse logistics (Zhu & Sarkis, 2004). Working on reducing product life cycle impact in saving energy, saving resources and eliminating hazardous substances are important issues for all members in the supply chain. In fact, one effective way to facilitate environmental protection is to focus on waste prevention and con-trol at the source through green purchasing (Min & Galle, 1997). That is, firms must include suppliers in environmentally-friendly practices for purchasing and materials management, starting even from suppliers’ design for environment (DfE). Green purchasing, or green procurement, is linked to the product and process aspects of the supplier, including ‘‘eco-labels, the avoidance of environmen-tally relevant substances, energy use, use of recycled materials, product mass, re-usability of some parts, recyclability, the use of

environmental management systems and the application of DfE or life cycle assessment (LCA)” (Nagel, 2003). A green supplier is expected not only to achieve environmental compliance but also to undertake efﬁcient, green product design and life cycle analysis activities. Thus, in a green supply chain, companies need to have extensive supplier selection and performance evaluation processes (Kainuma & Tawara, 2006).

Manufacturers and exporters these days need to overcome the

green obstacle to increase competition power (Deng & Wang,

1998). For instance, EU forces importers to follow the environmen-tal policies, change their working processes, and purchase more environmental-friendly equipment and costly green materials. With the enforcement of environmental regulations and arising eco-awareness, manufacturers need to ﬁnd substitutes to replace the detrimental substances if they want to export their products to environmental-conscious countries. Since many Taiwanese busi-nesses are OEM (original equipment manufacturing) and ODM (own design manufacturing), in order to export their products overseas, the ﬁrms not only need to comply with the environmental policies, but also need to have their own corporate environmental policies.

The LCD industry in Taiwan has expanded tremendously in the past ten years. Taiwan is currently the world’s largest supplier of TFT–LCDs, and produces more than 40% of the world’s supply (Hung, 2006). By 2005, there were 123 companies in Taiwan’s ﬂat-panel display industry, creating a value of US$15.49 billion,

of which TFT–LCDs accounted for around 66% (Government

Infor-mation Ofﬁce (GIO). Taiwan yearbook, 2005). However, in order to maintain the competitiveness, manufacturers in the TFT–LCD supply chain not only need to adapt to the increasing demands, scale of economies and lower price, they also need to comply with the environmental regulations of the countries they export the products to. On top of that, a higher green standard than the base-line of the regulations may even need to be met by the manufac-turers in order to maintain a good relationship with existing customers and to attract new international customers.

2.2. Green supplier evaluation

In the current business environment, purchasing has become critical in establishing value-added contents of products and a vital determinant to ensure the proﬁtability and survival of a company. The research on supplier selection is abundant. First publications

can be traced back to the 1960s, andWeber, Current, and Benton

(1991)andGhodsypour and O’Brien (1998)did a comprehensive review on the past research. Some popular methods include the categorical method, the weighted-point method, the matrix meth-od, the vendor proﬁle analysis, and the ANP approach, to name a

few (Noci, 1997). Recent works were reviewed in Kahraman,

Cebeci, and Ulukan (2003), Lin and Chen (2004), Bayazit (2006), Talluri, Narasimhan, and Nair (2006), andLee (2009). While litera-ture related to supplier evaluation is plentiful, the works on green supplier evaluation or supplier evaluation that consider environ-mental factors are rather limited (Handﬁeld, Steven, Srouft, & Mel-nyk, 2002; Humphreys, McIvor, & Chan, 2003b; Humphreys, Wong, & Chan, 2003a; Noci, 1997).

The purchasing process becomes more complicated when envi-ronmental issues are considered. This is because green purchasing must consider the supplier’s environmental responsibility, in addi-tional to the tradiaddi-tional factors such as the supplier’s costs, quality, lead-time and flexibility. The management of suppliers based on strict environmental compliance is not sufficient, and a more proac-tive or strategic approach is required.Noci (1997)designed a green vendor rating system for the assessment of a supplier’s environ-mental performance based on four environenviron-mental categories, namely, ‘green’ competencies, current environmental efficiency, suppliers’ ‘green’ image and net life cycle cost, by applying AHP.

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Walton, Handﬁeld, and Melnyk (1998)designed a simple ﬂowchart for determining appropriate methods and criteria for supplier

eval-uation and selection in environmental management. Enarsson

(1998)used an Ishikawa ﬁshbone diagram for evaluating suppliers from an environmental viewpoint by adopting a quality

improve-ment prospective.Zhu and Geng (2001)studied large and

med-ium-sized state-owned enterprises (LMSOEs) in China and examined their environmental developments such as green pur-chasing in their business practices. Among the supplier selection models being used, environmentally preferable bidding and life-cy-cle assessment (LCA), which assesses green purchasing impacts and their ﬁnancial consequences through the entire product life-cycle, are the most popular in these enterprises.Handﬁeld et al. (2002) used AHP to evaluate the relative importance of various environ-mental traits and to assess the relative performance of several

sup-pliers. Humphreys et al. (2003a) identiﬁed the environmental

criteria which influence a firm’s purchasing decision, and catego-rized the criteria into two groups: quantitative environmental cri-teria and qualitative environmental cricri-teria. A knowledge-based decision support system was developed next to integrate the envi-ronmental criteria into the supplier selection process.Humphreys et al. (2003b)proposed a similar system as inHumphreys et al. (2003a). A knowledge-based system, which employs both case-based reasoning (CBR) and decision support components including multi-attribute analysis (MAA), was constructed to integrate envi-ronmental factors into the supplier selection process.Chen (2005) divided the supplier selection into two stages: first stage, environ-mental performance as the minimum requirement; and second stage, general purchase practices such as quality, delivery, perfor-mance records, etc. Only the suppliers that have the certification of ISO 14000 can be included in the second-stage evaluation. The procedure, however, has its flaws. The implementation of ISO 14000 does not guarantee that the supplier indeed has a good envi-ronmental performance, and the envienvi-ronmental issues are not con-sidered at all in the second stage.Humphreys, McCloskey, McIvor, Maguire, and Glackin (2006)proposed a hierarchical fuzzy system with scalable fuzzy membership functions to facilitate the supplier selection process by incorporating environmental criteria.Lu, Wu, and Kuo (2007)constructed a multi-objective decision making pro-cess for green supply chain management to help managers in mea-suring and evaluating suppliers’ performance using fuzzy AHP. Among all the above studies, however, most of them only focused on an environmental viewpoint and did not consider other impor-tant non-environmental factors. In a comprehensive green supplier selection model, all conventional factors, on top of environmental issues, need to be incorporated together to find the most suitable supplier that performs well in all important perspectives.

With environmental awareness, increasing amount of works on green supplier selection has been done in the past decade. However, the existing works generally only considered environmental aspect only. For a ﬁrm to select the most appropriate supplier for cooper-ation, it needs to consider both the environmental protection issue and the traditional supplier selection factors. Therefore, a compre-hensive green supplier selection model is proposed in this paper.

3. AHP, fuzzy set theory and FEAHP 3.1. Analytic hierarchy process (AHP)

The analytic hierarchy process (AHP) was ﬁrst proposed by Saaty in 1971, and it is one of the most commonly used methods for solv-ing multiple-criteria decision-maksolv-ing (MCDM) problems in

politi-cal, economic, social and management sciences (Saaty, 1980).

Through AHP, opinions and evaluations of decision makers can be integrated, and a complex problem can be devised into a simple

hier-archy system with higher levels to lower ones. The qualitative and quantitative factors can then be evaluated in a systematic manner. The application of AHP to a complex problem involves six essential steps (Chi & Kuo, 2001; Lee, Kang, & Wang, 2006; Murtaza, 2003):

1. Deﬁne the unstructured problem and state clearly the objec-tives and outcomes.

2. Decompose the complex problem into a hierarchical structure with decision elements (criteria and alternatives).

3. Employ pairwise comparisons among decision elements and form comparison matrices.

4. Use the eigenvalue method to estimate the relative weights of decision elements.

5. Check the consistency property of matrices to ensure the judg-ments of decision makers are consistent.

6. Aggregate the relative weights of decision elements to obtain an overall rating for the alternatives.

3.2. Fuzzy set theory

The conventional AHP has some shortcomings, and one of them is that the experiences and judgments of humans are not well-de-fined; that is, they are not quantitatively digital (Cheng, 1999). To overcome the problem, fuzzy set theory can be combined with the AHP. Fuzzy set theory was introduced by Zadeh in 1965 to solve problems involving the absence of sharply defined criteria (Zadeh, 1965). If the uncertainty (fuzziness) of human decision-making is not taken into account, the results can be misleading. Since its introduction, fuzzy theory has been applied in a variety of fields.

A fuzzy number is a fuzzy subset of real numbers whose mem-bership function is uMðxÞ: R ! ð0; 1Þ. There are two most commonly used fuzzy numbers: trapezoidal fuzzy number and triangular fuz-zy number. The membership function of a triangular fuzfuz-zy number is shown inFig. 1and is deﬁned as follows (Lee et al., 2006):

uMðxÞ ¼ ðx m_{Þ=ðm m}_{Þ; m}₆_{x 6 m} ðx mþ_{Þ=ðm m}þ_{Þ; m 6 x 6 m}þ 0; otherwise 8 > < > : 9 > = > ; ð1Þ

The m_{and m}+_{represent respectively the lower bound and the} upper bound of the triangular fuzzy number of M, and m is the strongest grade of membership. Thus, the triangular fuzzy number of M is indicated by (m_{, m, m}+_).

3.3. Fuzzy extended AHP (FEAHP)

Many fuzzy AHP methods are proposed to solve various types of problems. The main theme of these methods is to use the concepts of fuzzy set theory and hierarchical structure analysis to present sys-tematic approaches in selecting or justifying alternatives (Bozbura, Beskese, & Kahraman, 2007). In this paper, FEAHP is used to solve the green supplier selection problem because the steps of this approach are relatively easier, less time taking and less computational

ex-0

m m m+

1

µ

x

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pense than many other fuzzy AHP approaches, and at the same time, it can overcome the deﬁciencies of the conventional AHP. The ap-proach not only can adequately handle the inherent uncertainty and imprecision of the human decision making process but also can provide the robustness and ﬂexibility needed for the decision maker to understand the decision problem (Chan & Kumar, 2007).

FEAHP was ﬁrst introduced byChang (1992, 1996). To

deter-mine the priorities of decision criteria, pairwise comparison of tri-angular fuzzy numbers is carried out, and the extent analysis method for the synthetic extent value of the pairwise comparison is applied. By FEAHP, the fuzziness of the data involved in deciding the preferences of different decision variables can be handled (Chan & Kumar, 2007). Bozbura et al. (2007) applies the FEAHP to prioritize human capital measurement indicators.Chan and Ku-mar (2007)adopt FEAHP to provide a framework for the organiza-tion to select the global supplier considering risk factors.

The extent analysis method (EAM) is brieﬂy introduced here. Two triangular fuzzy numbers M1(m1, m1, m1+) and M2(m2, m2, m2+) shown inFig. 2are compared. When m1Pm2, m1Pm2, m1+Pm2+, we deﬁne the degree of possibility V(M1PM2) = 1.

When m2Pm1+, we deﬁne the degree of possibility V(M1P

M2) = 0. Otherwise, the degree of possibility V(M1PM2) is the

ordinate of the highest intersection point between

l

ðM1Þ and

l

ðM2Þ (Chang, 1996; Lee, 2009; Lee, Kang, & Chang, in press; Zhu, Jing, & Chang, 1999):

VðM2PM1Þ ¼ hgtðM1\ M2Þ ¼

l

ðdÞ ¼ m 1 mþ2 ðm2 mþ2Þ ðm1 m1Þ ð2Þ where M is a convex fuzzy set, and

a

2 ½0; 1: If x12Maand x22Ma, then

l

M (x1)P

a

and

l

M (x2)P

a

. Ma is a closed interval and

x1< x < x2, so x2Maand

l

M(x)P

a

= min(

l

M(x1),

l

M(x2)). 4. Green supplier selection model

Many works have been done on issues about supply chain and suppliers; however, limited literatures are found on green supplier and green supply chain until recent years. While some recent stud-ies have stressed on the green supplier selection problem, they considered environmental attributes solely, but not the traditional criteria. In this paper, a comprehensive green supplier selection model is proposed by considering the important criteria in various aspects for evaluating green suppliers. The steps are as follows:

(1) Deﬁne the green supplier selection problem, and identify the overall objective.

(2) Collect the evaluation criteria for green suppliers through literature review and discussion with managers in industries and eco-experts.

(3) Select the most important criteria and sub-criteria by the Delphi method. Based onSaaty (1980), if there are more than seven factors at the same level, there are too many selections

on the questionnaires, and it is tough for participants to make a choice. This problem can be overcome by the way of elim-ination or combelim-ination. The Delphi method is to reduce the number of sub-criteria while keeping real important attri-butes. The process is summarized as follows (Fowles, 1978): 3.1 Formation of a team to study the subject, and the

pan-elists are experts in the area to be investigated;

3.2 Development of the ﬁrst round Delphi questionnaire;

3.3 Transmission of the results of the ﬁrst questionnaire

to the panelists and analysis of the ﬁrst round responses;

3.4 Preparation of the second round questionnaire;

3.5 Transmission of the results of the second round

ques-tionnaire to the panelists and analysis of the second round responses (steps 3.4 and 3.5 are reiterated as long as desired or necessary to achieve stability in the results); and

3.6 Preparation of a report to present the conclusions. (4) Based on the selected criteria and sub-criteria, a hierarchy

for evaluating green suppliers is prepared.

(5) Based on the hierarchy, a pairwise comparison questionnaire is prepared. In this research, a five-point scale is used. Experts are invited to fill out the questionnaire, and the pair-wise comparison results from each expert are analyzed first to make sure that the expert’s opinion is consistent through-out the questionnaire. The consistency test (Saaty, 1980) is performed by calculating the consistency index (CI) and con-sistency ratio (CR):

CI ¼kmax n

n 1 ; and ð3Þ

CR ¼CI

RI; ð4Þ

where n is the number of items being compared in the ma-trix, and RI is random index, the average consistency index of randomly generated pairwise comparison matrix of similar size (Saaty, 1980). If CR is less than 0.1, the threshold for con-sistency, the expert’s judgment is consistent. If the consis-tency test is not passed, the expert will be asked to re-do the part of the questionnaire.

(6) From each expert’s questionnaire results, establish fuzzy pairwise comparison weights for criteria (sub-criteria or suppliers) i and j according to the membership functions deﬁned inTable 1. For expert t, the fuzzy pairwise compar-ison weight for i and j is ðpijt;qijt;rijtÞ.

(7) Calculate the fuzzy integrated pairwise comparison weights for criteria (sub-criteria and suppliers) using the geometric mean method. A triangular fuzzy number ~Dijis obtained by combining the experts’ opinions.

~ Dij¼ ðb ij;bij;b þ ijÞ ð5Þ where bij¼ Ys t¼1 pijt !1 s ;

8

t ¼ 1; 2; . . . ; s: ð6Þ bij¼ Ys t¼1 qijt !1 s ;

8

t ¼ 1; 2; . . . ; s: ð7Þ bþij¼ Ys t¼1 rijt !1 s ;

8

t ¼ 1; 2; . . . ; s: ð8Þ

and ðpijt;qijt;rijtÞ is the pairwise comparison weight of criteria (sub-criteria or suppliers) i and j from expert t.

(8) Examine the consistency of the integrated opinions of the experts. The fuzzy geometric pairwise comparison weight from step 7 is defuzziﬁed ﬁrst by (Kwong & Bai, 2003):

m2 m2 m1 d m2 m1 m1+ 1 µ x M2 M1 µ(d)

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bij¼ ðbijþ 4bijþ bþijÞ=6 ð9Þ

The consistency test as in step 5 is performed again to exam-ine the integrated opinions of the experts.

(9) Calculate the value of fuzzy synthetic extent with respect to criterion (sub-criterion or supplier) i (Chang, 1996; Lee, 2009; Lee et al., in press):

Fi¼ Xn j¼1 Bij Xn i¼1 Xn j¼1 Bij " #1 ; i ¼ 1; 2; . . . ; n and j ¼ 1; 2; . . . ; n ð10Þ whereX n j¼1 Bij¼ Xn j¼1 bij; Xn j¼1 bij; Xn j¼1 bþij ! and X n i¼1 Xn j¼1 Bij " #1 ð11Þ ¼ 1=X n i¼1 Xn j¼1 bþij;1= Xn i¼1 Xn j¼1 bij; 1= Xn i¼1 Xn j¼1 bij ! ð12Þ i=1,2,..., n and j=1,2,..., n

(10) Compare Fi, and calculate membership function

l

(d) (shown inFig. 2) to represent the relative importance between two criteria (sub-criteria or suppliers). The degree possibility for a convex fuzzy number to be greater than k convex fuzzy

number Fkcan be deﬁned by (Chang, 1996; Lee, 2009; Lee

et al., in press):

VðF P F1;F2; . . . ;FkÞ ¼ min VðF P FiÞ; i ¼ 1; 2; . . . ; k ð13Þ (11) Calculate the weights, w0

i, of criteria (sub-criteria and suppli-ers) using Eq.(2), and normalize w0

iinto W. Assume that: dðFiÞ ¼ min VðFiPFkÞ ¼ w0iðk ¼ 1; 2; . . . ; n and k–iÞ; ð14Þ the weights, wi’, of criteria (sub-criteria or suppliers) are: W0

¼ ðw0

1;w02; . . . ;w0nÞ T

ð15Þ

After normalization, the priority weights of criteria (sub-cri-teria and suppliers) are:

W ¼ ðw1;w2; . . . ;wnÞ T

ð16Þ (12) Aggregate the weights of sub-criteria and criteria and the performance of suppliers with respect to each sub-criterion to obtain an overall rating for suppliers.

5. Case study

Global environmental concern is a reality, and an increasing attention is focusing on the green production in various industries. Regarding the current production facilities in TFT–LCD industry, many different materials are procured and included in the prod-ucts. A TFT panel (with TFT-array substrate, liquid crystal and color filter substrate), a driving-circuit unit (with LCD driver IC (LDI) chips, multi-layer PCBs and driving circuits) and a backlight & chassis unit (with backlight, lamp, light-guide panel (LGP) and chassis) are required to assemble a TFT–LCD module. These com-ponents are produced in different kinds of production processes, and many of them are purchased from different suppliers. In order to manufacture an environmental friendly TFT–LCD module, a TFT– LCD manufacturer needs to cooperate with suppliers who can pro-vide environmental friendly components in the first place. The objective of the case study is to construct an analytic framework for the decision making in selecting the most appropriate supplier. In the first part of the study, the most important factors for eval-uating traditional suppliers and for evaleval-uating green suppliers are examined. A total of 11 criteria and 41 sub-criteria are identified after a detailed review of the literature and interviews with domain experts. Based onSaaty (1980), if there are more than seven factors in the same cluster, it is very confusing and difficult for participants to make pairwise comparisons. Therefore, the concept of the Delphi method is used to generate a consensus of opinions among experts and to extract the most important sub-criteria. A nine-point scale is used in the questionnaires to collect experts’ opinions, with prefer-ences of very unimportant, unimportant, normal, important and very important (scores of 1, 3, 5, 7 and 9, respectively), and the value of 2, 4, 6 and 8 is the mid-opinion between 1, 3, 5, 7 and 9. An excerpt of the questionnaire is as shown inTable 2. Eleven experts are asked to fill out the first questionnaire, which is composed of two parts: rating of sub-criteria for evaluating traditional suppliers and rating of sub-criteria for evaluating green suppliers. The results of the two parts of the first questionnaires are analyzed and are given to the

ex-Table 1

Characteristic function of the fuzzy numbers (Lee, 2009).

Fuzzy number Characteristic (membership) function ~ 1 (1, 1, 2) ~ x (x 1, x, x + 1) for x = 2,3,4,5,6,7,8 ~ 9 (8, 9, 9) 1/~1 (21_{, 1}1_{, 1}1₎ 1/~x ((x + 1)1_{, x}1_{, (x1)}1_{) for x = 2,3,4,5,6,7,8} 1/~9 (91 , 91 , 81 ) Table 2

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perts as a reference, and the experts are asked to fill out the second round questionnaire. The results of the second round questionnaire are used to calculate the mean score of each sub-criterion under the two parts. Under each part, the sub-criteria with higher scores are extracted. We arbitrarily set threshold at 56%, and 23 sub-criteria are selected from each part. The selected criteria and sub-criteria for evaluating traditional suppliers and green suppliers are listed inTables 3 and 4, respectively. According to the results, the most important criteria for evaluating traditional suppliers include capa-bility of handling abnormal quality, credible delivery, capacapa-bility of delivery on time, and capability of quality management. Note that some environmental sub-criteria, such as green purchase trend of customers, use of harmful materials, and environment-related cer-tificates, are with some degree of importance too. For evaluating green suppliers, the most important sub-criteria are environment-related certificates, capability of preventing pollution, and use of harmful materials. In addition to environmental-related sub-crite-ria, some sub-criteria of quality and technology capability are

in-cluded. An interesting ﬁnding is that cost is not included in the green supplier sub-criteria list. An inquiry with the experts leads to the reason behind: cost is deemed as the baseline for evaluating suppliers. That is, only suppliers that can meet the basic cost requirement will be further evaluated in all other aspects.

In the second part of the research, a green supplier selection model is constructed. By using the results from the Delphi method, a hierarchy is developed for incorporating the criteria and sub-cri-teria into the supplier evaluation process. Because some crisub-cri-teria only have one or two sub-criteria selected after the Delphi method, a combination is done to reduce the number of criteria. Cost of com-ponent disposal, the only sub-criterion under criterion total product life cycle cost, is combined into criterion green product. The two sub-criteria under green image are combined into criterion green compe-tencies. The ﬁnalized hierarchy is as shown inFig. 3, and the deﬁni-tions of the criteria and sub-criteria are listed inTable 5.

A questionnaire is constructed based on the hierarchy, and an excerpt of the questionnaire is as shown inTable 6. Eight managers

Table 3

Criteria and sub-criteria for evaluating traditional suppliers.

Criteria Sub-criteria Average Ranking

Quality Quality-related certiﬁcates 7.590909 8

Capability of quality management 7.909091 4

Capability of handling abnormal quality 8.045455 1

Finance Past ﬁnance performance 6.727273 23

Stability of ﬁnance 7.318182 13

Price 7.863636 5

Organization Attitudes of managers 7.272727 15

Future strategy direction 7.090909 17

Degree of strategic cooperation 7.318182 13

Technology capability Capacity 7.363636 12

Technology level 7.727273 7

Capability of R&D 7.545455 9

Capability of design 7.272727 15

Capability of preventing pollution 6.909091 20

Service Credible delivery 8.045455 2

Capability of delivery on time 8 3

Capability of technology support 7.772727 6

Flexibility 7.5 11

Total product life cycle cost Cost of supplied components 7.545455 9

Green image Green purchase trend of customers 6.954545 18

Pollution control Use of harmful materials 6.954545 18

Environment management Environment-related certiﬁcates 6.909091 20

Internal control process 6.818182 22

Table 4

Criteria and sub-criteria for evaluating green supplier.

Criteria Sub-criteria Average Ranking

Quality Quality-related certiﬁcates 8.227273 12

Capability of quality management 8.227273 12

Capability of handling abnormal quality 8.090909 16

Technology capability Technology level 8.045455 19

Capability of R&D 8.272727 10

Capability of design 8.227273 12

Capability of preventing pollution 8.545455 2

Total product life cycle cost Cost of component disposal 8.090909 16

Green image Ratio of green customers to total customers 8.090909 16

Social responsibility 8 23

Pollution control Air emissions 8.318182 7

Waste water 8.318182 7

Solid wastes 8.318182 7

Energy consumption 8.181818 15

Use of harmful materials 8.5 3

Environment management Environment-related certiﬁcates 8.727273 1

Continuous monitoring and regulatory compliance 8.363636 4

Green process planning 8.363636 4

Internal control process 8.045455 19

Green product Recycle 8.272727 10

Green packaging 8.045455 19

Green competencies Materials used in the supplied components that reduce the impact on natural resources 8.045455 19 Ability to alter process and product for reducing the impact on natural resources 8.363636 4

(7)

in an anonymous TFT–LCD manufacturer located in the Hsinchu Science-Based Industrial Park in Taiwan are invited to contribute their professional experience and ﬁll out the questionnaire. The company aims to choose the most suitable green glass supplier.

Based on the results of the questionnaires, the consistency of the pairwise comparisons of each expert is examined. For instance, the pairwise comparison matrix for the criteria of an expert is as follows:

b1¼ 1 2 3 2 1 2 1=2 1 2 2 1 2 1=3 1=2 1 1 2 2 1=2 1=2 1 1 1 1 1 1 1=2 1 1 1 1=2 1=2 1=2 1 1 1 2 6 6 6 6 6 6 6 6 4 3 7 7 7 7 7 7 7 7 5 ; and kmax¼ 6:464:

The consistency test is performed by calculating the consistency index (CI) and consistency ratio (CR):

CI ¼kmax n n 1 ¼ 6:464 6 6 1 ¼ 0:093; and CR ¼CI RI¼ 0:093 1:24 ¼ 0:075;

Since CR is less than 0.1, the expert’s judgment is consistent. If the consistency test is not passed, the expert will be asked to re-do the part of the questionnaire.

After the consistency test on the questionnaire results of all ex-perts is completed, the fuzzy importance weights for criteria (sub-criteria) for each expert are established using the membership functions deﬁned inTable 1. The above matrix from the expert is transformed into a fuzzy matrix as follows:

Green supplier performance C5: Green product C4: Environment management C3: Pollution control C2: Technology capability C1: Quality

SC11: Quality -related certificates SC12: Capability of quality management SC13: Capability of handling abnormal quality

SC23: Capability of design SC22: Capability of R&D

SC21: Technology level

SC32: Waste water SC31: Air emissions SC24: Capability of preventing pollution

SC41:Environment -related certificates SC35: Use of harmful material

SC34:Energy consumption SC33: Solid wastes

SC61: Materials used in the supplied components that reduce the impact on natural resources

SC53: Cost of component disposal SC52: Green packaging

SC51: Recycle SC44: Green process planning SC43: Internal control process SC42: Continuous monitoring and regulatory

compliance

SC62: Ability to alter process and product for reducing the impact on natural resources

SC63: Social reponsibility SC64: Ratio of green customers to total

customers Supplier A Supplier C Supplier B C6: Green competencies

Fig. 3. The hierarchy for green supplier selection.

~ b1¼ ð1; 1; 1Þ ð1; 2; 3Þ ð2; 3; 4Þ ð1; 2; 3Þ ð1; 1; 2Þ ð1; 2; 3Þ ð1=3; 1=2; 1Þ ð1; 1; 1Þ ð1; 2; 3Þ ð1; 2; 3Þ ð1; 1; 2Þ ð1; 2; 3Þ ð1=4; 1=3; 1=2Þ ð1=3; 1=2; 1Þ ð1; 1; 1Þ ð1; 1; 2Þ ð1; 2; 3Þ ð1; 2; 3Þ ð1=3; 1=2; 1Þ ð1=3; 1=2; 1Þ ð1=2; 1; 1Þ ð1; 1; 1Þ ð1; 1; 2Þ ð1; 1; 2Þ ð1=2; 1; 1Þ ð1=2; 1; 1Þ ð1=3; 1=2; 1Þ ð1=2; 1; 1Þ ð1; 1; 1Þ ð1; 1; 2Þ ð1=3; 1=2; 1Þ ð1=3; 1=2; 1Þ ð1=3; 1=2; 1Þ ð1=2; 1; 1Þ ð1=2; 1; 1Þ ð1; 1; 1Þ 2 6 6 6 6 6 6 6 6 4 3 7 7 7 7 7 7 7 7 5

(8)

A fuzzy integrated matrix is formed next by combining the data from all experts through the geometric mean method, and is as follows:

To ensure that the integrated opinions are still consistent, the integrated fuzzy matrix is deffuziﬁed using Eq. (9) ﬁrst and the consistency test is carried out again.

After the consistency test is passed, the value of fuzzy synthetic extent with respect to each criterion is calculated next. Based on

the integrated fuzzy matrix for criteria, the values of

Pn j¼1b ij; Pn j¼1bij; Pnj¼1b þ ij and Pn

j¼1Bijare calculated and shown in Table 7. Fuzzy synthetic extent with respect to each criterion is shown inTable 8. The priorities of the criteria are calculated in

Table 5

Deﬁnitions of criteria and sub-criteria for evaluating green supplier.

Criteria/sub-criteria Deﬁnitions

Quality (C1): The factors that can improve the quality of products from the supplier

Quality-related certiﬁcates (SC11) Whether the supplier has quality-related certiﬁcates, such as ISO 9000 and QS 9000, etc. Capability of quality management (SC12) The comprehensiveness of the supplier’s quality management system

Capability of handling abnormal quality (SC13) The capability of the supplier in handling abnormal quality problems

Technology capability (C2): The factors that can facilitate the new product/process development of the supplier and that can provide new and upgraded products to the firm Technology level (SC21) Technology development of the supplier to meet current and future demand of the firm Capability of R&D (SC22) Capability of R&D of the supplier to meet current and future demand of the firm Capability of design (SC23) Capability of new product design of the supplier to meet current and future demand of the

ﬁrm

Capability of preventing pollution (SC24) Capability of product design and manufacturing tools of the supplier to prevent pollution Pollution control (C3): The factors that show the control of supplier in producing pollution

Air emissions (SC31) The quantity control and treatment of hazardous emission, such as SO2, NH3, CO and HC1

Waste water (SC32) The quantity control and treatment of waste water

Solid wastes (SC33) The quantity control and treatment of solid waste

Energy consumption (SC34) The control of energy consumption

Use of harmful materials (SC35) The control of the use of harmful materials in the production Environment management (C4): The factors that show the effort of supplier in environment management

Environment-related certiﬁcates (SC41) Whether the supplier has environment-related certiﬁcates, such as ISO 14000

Continuous monitoring and regulatory compliance (SC42) The level of continuous monitoring and regulatory compliance of environment-related issues Internal control process (SC43) The capability of continuous checking and revising emergency response plan

Green process planning (SC44) The level of green process planning of the supplier Green product (C5): The factors that show the effort of supplier in producing green products

Recycle (SC51) The level of recycling of the products

Green packaging (SC52) The level of green materials used in packaging

Cost of component disposal (SC53) The processing cost at the end of life of the products (The cost is reduced as recycling increases)

Green competencies (C6): The factors that show the competencies of supplier in improving green production Materials used in the supplied components that reduce the impact on natural

resources (SC61)

The use of materials in the components that has a lower impact on natural resources Ability to alter process and product for reducing the impact on natural

resources (SC62)

The ability of the supplier to alter process and product design in order to reduce the impact on natural resources

Social responsibility (SC63) The autonomous social responsibility of the supplier towards environment protection Ratio of green customers to total customers (SC64) The ratio of customers that demand green products to the total customers of the supplier

~ b¼ ð1; 1; 1Þ ð2:79; 3:95; 6:16Þ ð1:23; 1:83; 3:83Þ ð0:90; 1:99; 3:49Þ ð0:60; 0:74; 1:73Þ ð0:50; 1:00; 2:24Þ ð0:16; 0:25; 0:36Þ ð1; 1; 1Þ ð1:15; 1:61; 3:79Þ ð1:15; 1:85; 4:04Þ ð0:90; 1:73; 3:27Þ ð1:15; 1:61; 3:79Þ ð0:26; 0:55; 0:81Þ ð0:26; 0:62; 0:87Þ ð1; 1; 1Þ ð0:80; 1:22; 2:70Þ ð1:56; 1:93; 4:21Þ ð0:94; 1:85; 3:75Þ ð0:29; 0:50; 1:11Þ ð0:25; 0:54; 0:87Þ ð0:37; 0:82; 1:25Þ ð1; 1; 1Þ ð0:74; 1:15; 2:51Þ ð0:67; 1:11; 2:59Þ ð0:58; 1:36; 1:68Þ ð0:31; 0:58; 1:11Þ ð0:24; 0:52; 0:64Þ ð0:40; 0:87; 1:36Þ ð1; 1; 1Þ ð1:04; 1:15; 3:01Þ ð0:45; 0:99; 1:99Þ ð0:26; 0:62; 0:87Þ ð0:26; 0:54; 1:07Þ ð0:39; 0:90; 1:50Þ ð0:33; 0:87; 0:96Þ ð1; 1; 1Þ 2 6 6 6 6 6 6 6 6 4 3 7 7 7 7 7 7 7 7 5 Table 6

An excerpt of the questionnaire for supplier selection problem.

Absolute Very strong Strong Weak Equal Weak Strong Very strong Absolute

9:1 7:1 5:1 3:1 1:1 1:3 1:5 1:7 1:9

Under criterion green product, which sub-criterion is more important?

Recycle Green packaging

Recycle Cost of component disposal

Green packaging Cost of component disposal

Under sub-criterion recycle, which supplier performs better?

Supplier A Supplier B

Supplier A Supplier C

(9)

Table 9. According to the experts’ opinions, the most important cri-terion is quality, with a priority of 0.2171. The next two important criteria are technology capability and green product, with priorities of 0.1942 and 0.1925, respectively. In fact, the four environmen-tal-related criteria, pollution control, environment management, green product and green competencies, comprise of 0.6392 of the to-tal priority.

A similar procedure is carried out to calculate the priorities of the sub-criteria and alternatives. The results are shown inTable 10. According to the final scores, supplier A is the most preferred supplier with a priority weight of 0.3709, followed by supplier C with 0.3481. Detailed information of priorities of sub-criteria can also be found in Table 10. For example, under quality, the most important sub-criterion is capability of handling abnormal quality, with a local priority of 0.3571, followed by capability of quality management and quality-related certificates, with local priorities of 0.3266 and 0.3163, respectively. A comparison of all 23 sub-criteria shows that the most important sub-criterion is capability of handling abnormal quality, with an integrated priority of 0.0775. The second to fourth sub-criteria are capability of quality manage-ment (0.0709), quality-related certificates (0.0687) and green pack-aging (0.0648), respectively. Note that even though the model is to evaluate green suppliers, many non-environmental-related sub-criteria have relatively high priorities. To be in more detail, six out of the top ten sub-criteria are non-environmental sub-cri-teria. This implies that the selection of green suppliers should not only consider environmental factors, but also the traditional factors.

The performances of suppliers with respect to each criterion and each criterion are shown in Figs. 4 and 5, respectively. As

can be seen from Fig. 4, supplier A performs relatively better

than other two suppliers under most of the sub-criteria, and supplier C performs better than supplier B under most of the

sub-criteria too. From Fig. 5, we can see that supplier A

performs the best under all criteria, except C3, pollution control, and supplier C performs better than supplier B under all criteria. To summarize, supplier A should be selected for cooperation.

Table 9

Calculation ofl(d) and wi.

Comparison l(dF1) Comparison l(dF2) Comparison l(dF3) F1> F2 1 F2> F1 0.8948 F3> F1 0.8313 F1> F3 1 F2> F3 1 F3> F2 0.9514 F1> F4 1 F2> F4 1 F3> F4 1 F1> F5 1 F2> F5 1 F3> F5 1 F1> F6 1 F2> F6 1 F3> F6 1 w1’ 1 w2’ 0.8948 w3’ 0.8313 w1 0.2171 w2 0.1942 w3 0.1804

Comparison l(dF4) Comparison l(dF5) Comparison l(dF6) F4> F1 0.6578 F5> F1 0.6542 F6> F1 0.5688 F4> F2 0.7929 F5> F2 0.8013 F6> F2 0.7246 F4> F3 0.8503 F5> F3 0.8645 F6> F3 0.7936 F4> F5 0.9721 F5> F4 1 F6> F4 0.9796 F4> F6 1 F5> F6 1 F6> F5 0.9442 w4’ 0.6578 w5’ 0.6542 w6’ 0.5688 w4 0.1428 w5 0.1925 w6 0.1235 Table 8

Fuzzy synthetic extent with respect to criterion Fi.

Criteria Pn j¼1bij= Pn i¼1 Pn j¼1bþij Pn j¼1bij= Pn i¼1 Pn j¼1bij Pn j¼1bþij= Pn i¼1 Pn j¼1bij Quality (F1) 0.0955 0.2549 0.6852 Technology capability (F2) 0.0748 0.1952 0.6036 Pollution control (F3) 0.0656 0.1737 0.4958 Environment management (F4) 0.0450 0.1242 0.3467 Green product (F5) 0.0484 0.1327 0.3266 Green competencies (F6) 0.0367 0.1194 0.2742 Table 7

Integration of experts’ opinions on criteria.

Criteria Pnj¼1b ij Pn j¼1bij Pnj¼1b þ ij Quality 7.0231 10.5132 18.4431 Technology capability 5.503501 8.050217 16.24838 Pollution control 4.822784 7.162916 13.3447 Environment management 3.308808 5.12159 9.33316 Green product 3.562026 5.474975 8.790769 Green competencies 2.696967 4.924577 7.380746 Sum 26.91719 41.24748 73.54086 Table 10

Priorities of criteria, sub-criteria and alternatives.

Criteria Sub-criteria Local

priorities Integrated priorities Integrated ranking Priorities of alternatives

Quality (0.2171) Quality-related certiﬁcates 0.3163 0.0687 3 Supplier A: 0.3709

Capability of quality management 0.3266 0.0709 2

Capability of handling abnormal quality 0.3571 0.0775 1

Technology capability (0.1942)

Technology level 0.2450 0.0476 7

Capability of R&D 0.2604 0.0506 6

Capability of design 0.2165 0.0420 9

Capability of preventing pollution 0.2780 0.0540 5

Pollution control (0.1804) Air emissions 0.2125 0.0383 11 Supplier B: 0.2810

Waste water 0.1965 0.0355 15

Solid wastes 0.1893 0.0341 18

Energy consumption 0.1900 0.0343 17

Use of harmful materials 0.2118 0.0382 12

Environment management (0.1428)

Environment-related certiﬁcates 0.2430 0.0347 16

Continuous monitoring and regulatory compliance 0.2282 0.0326 21

Internal control process 0.2586 0.0369 13

Green process planning 0.2702 0.0386 10

Green product (0.1925) Recycle 0.3102 0.0441 8 Supplier C: 0.3481

Green packaging 0.4561 0.0648 4

Cost of component disposal 0.2337 0.0332 20

Green competencies (0.1235)

Materials used in the supplied components that reduce the impact on natural resources

0.2972 0.0367 14

Ability to alter process and product for reducing the impact on natural resources

0.2302 0.0284 22

Social responsibility 0.2757 0.0341 19

(10)

6. Conclusion

Environmental protection and sustainable development are get-ting more and more attention in industry. In order to extend the product life cycle and to pursue enterprise perpetuity, a firm needs to emphasize environment protection and green production as a critical part of its social responsibility. A good green supplier selec-tion model in a dynamic competitive and regulatory environment can help lessen the environmental and legal risks and increase the competitiveness of a firm. This paper proposes a model to select the factors for evaluating green suppliers, and to evaluate the per-formance of suppliers. The Delphi method is applied first to select the most important sub-criteria for traditional suppliers and for green suppliers. The results for green supplier are applied next to construct a hierarchy for green supplier evaluation problem. A FEA-HP model is constructed next based on the hierarchy to evaluate green suppliers for an anonymous TFT–LCD manufacturer in Tai-wan, and the most suitable supplier can be selected. The strength of the proposed model is that the vagueness of experts’ opinions is considered in the evaluation process and the model is easy to ap-ply. Manufacturers of related industries can use our proposed mod-el, or tailor the model to meet their own needs, to evaluate their green suppliers or to select the best green supplier for cooperation. Acknowledgement

This research is supported in part by National Science Council, Taiwan, under Grant NSC 96-2416-H-216-002.

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