Alternative Selection for Green Supply Chain Management: A Fuzzy TOPSIS Approach

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1 Alternative Selection for Green Supply Chain Management: A Fuzzy TOPSIS Approach Mohit Tyagi #, Pradeep Kumar, and Dinesh Kumar # MIED, Indian Institute of Technology, Roorkee, Uttrakhand (UK) , India Abstract Green Supply Chain Management (GSCM) is a charming subject today in business competition among the organization s. In this competitive and globalized environment, most of the all organization s are emphasizing more efforts to improve their green supply chain practices. This paper is used the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach to select the best alternative, when seven green characteristics namely Saving energy, Design for environment, Waste minimization, Reuse of hazardous waste, Green management abilities, Use of information and communication technologies and Proper mode of transport are considered. Three mutuallyimportant alternatives namely suppliers, web based technologies and advanced manufacturing technologies have been used for the performance measurement of green supply chain management. The result shows alternative Web-based technologies ranks first among the above three alternatives and plays a very important role in improving the performance of green supply chain. Keywords: GSCM, Performance Measurement, Green criteria, fuzzy TOPSIS Introduction In the recent years, green supply chain is the challenging issue among the organizations. Environmental societies believed that many business operations like sourcing, manufacturing and logistics are responsible for most of the environmental related problems (Beamon, 1999). Various inside and outside groups such as government, workers, neighbors, not-for-profit societies and non-government organizations (NGOs) are increasing more pressure on organizations to make a green supply chain (Sarkis, 2006). Greening concept is a practice which provides an environmental friendly image of the products, processes, systems and technologies (Vachon and Klassen, 2006a,b). To provide better environmental performance, integration among the supply chain stages is required (Zhu et al., 2001). It also assumes that customers demand is fulfilled through the integration of distribution channels and different stages in a supply chain (Lambert, 2008; Chopra and Meindl, 2001). According to Beamon (1999) green supply chain is defined as addition in the traditional supply chains by including activities that aims is minimizing environmental impacts of a product throughout its complete life cycle, ranging from green design to the product recycle or reuse.gscm has emerged as an organizational philosophy which helps organizations and their partners to achieve corporate profit and market share objectives by

2 reducing environmental risk and impacts while improving ecological efficiency (Rao and Holt, 2005; Zsidisin and Siferd, 2001). During this study to evaluate the performance of green supply chain management seven criteria and three alternatives have been considered. On the basis of existing literature and by collecting the experts' views, these criteria were selected. The name of seven criteria are:saving energy, design for environment, waste minimization, reuse of hazardous waste, green management abilities, use of information and communication technologies and proper mode of transport and the three alternatives as: suppliers, web based technologies, and advanced manufacturing technologies. Hierarchical view of these criteria and alternatives is shown in figure -1. This paper proposed a fuzzy TOPSIS approach for the selection of best alternative under consideration of above mentioned criteria. Literature Review GSCM is an interested theme among researchers and practitioners in the extent of supply chain management. It integrates the environmental practices with the decision-making process to convert the resources into the usable products. Humphreys et al. (2003) discussed various integrated environmental criteria in the supplier selection process namely as follows: environmental management competencies, environmental image of suppliers, development of products with high environmental performance, environmental management system and environmental competencies. Hart (1995) stated that the concept of green supply chain management has full accountability of a firm towards environment related problems of its products from the raw materials up to final use and disposal of the products. From the existing literature it is clear that green supply chain initiatives have positive environmental outcomes. According to Frosch (1994) an inter-firm linkage can lead to improvement in environmental performance. Florida (1996) says that a closer bonding should be exist between suppliers and customers for environmentally friendly or clean production. There are various existing studies for the GSCM, some of them as follows: Lu et al. (2007) used multi objective decision analysis for green supplier evaluation.wang et al. (2009) proposed a hierarchical TOPSIS that employs rules based on Euclidean distances for supplier selection. Onut et al. (2009) uses fuzzy AHP and fuzzy TOPSIS in a telecommunications company for the selection of long term supplier. Chen et al. (2006) uses fuzzy TOPSIS by using trapezoidal fuzzy numbers for supplier selection. In this study we are using fuzzy TOPSIS with triangular fuzzy numbers to select the alternative for GSCM. Fuzzy TOPSIS Approach The fuzzy TOPSIS is the extension of TOPSIS approach, it includes the fuzzy assessments of criteria and alternatives (Hwang and Yoon, 1981). In the TOPSIS approach selection of alternative is based on the distance closest to the positive ideal solution and farthest from the negative ideal solution. A positive ideal solution is the key concern for the best performance values of each criterion where as the negative ideal solution gives worst performance values. The various steps of fuzzy TOPSIS (modified from Awasthi et al., 2010) are as follows: Step 1: Ratings assigns for the criteria and alternatives Let us assume that m criteria C i (i= 1,2,.., m), n alternatives A j (j= 1, 2,., n) and k decision makers D k (k= 1, 2,, k),then criteria weights are denoted by w i (i= 1,2,..,m). The performance

3 rating of each decision maker for each alternative with respect to each criteria are denoted by ( ) with a membership function ( ). Step 2: Determine aggregate fuzzy ratings for the criteria and alternatives In that case when the fuzzy rating of all decision makers is termed as the triangular fuzzy number ( ) then aggregated fuzzy rating is defined by ( ) where l, m and u can be computed as follows:,, and If we assume the fuzzy rating and importance weight of the kth decision maker as ( ) and ( ), i=1,2,.,m; j= 1,2,.n, respectively, then aggregated fuzzy ratings ( ) of alternatives with respect to each criteria can be assumed by ( ), where,, and (1) The aggregated fuzzy weights ( ) of each criteria are determined as ( ) where,, and...(2) Step3: Caculate the fuzzy decision matrix The fuzzy decision matrix for alternatives is denoted by ( ), and criteria ( ) is created as follows: = [ ] i = 1,2,.m; j = 1,2,.n.(3) ( ) (4) Step4: Normalization of the fuzzy decision matrix The raw data are normalized using linear scale transformation to bring the various criteria scales into a comparable scale. The normalized fuzzy decision matrix is given by, i = 1,2,..,m; j = 1,2,.,n..(5) Where ( ), and (In case of benefit criteria) (6) ( ), and (In case of cost criteria).(7) Step5: Find out the weighted normalized matrix The weighted normalized matrix for criteria are built by multiplying the weights ( )of evaluation criteria with the normalized fuzzy decision matrix., i= 1,2,.,m; j = 1,2,.,n where (8)

4 Green supply chain management performance measuring index Saving energy (C1) Design for environment (C2) Waste minimization (C3) Reuse of hazardous waste (C4) Green management abilities (C5) Use of information and communication technologies (C6) Proper mode of transport(c7) Supplier s Web-based technologies Advanced manufacturing technologies Figure- 1 Fuzzy TOPSIS based performance model Step 6: Calculate the fuzzy ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) The FPIS and FNIS of the alternatives are computed as follows: ( ) where, i= 1,2,,m; j = 1,2,,n.(9) ( ) where, i= 1,2,,m; j = 1,2,,n.(10) Step7: Determine the distance of each alternative from FPIS and FNIS The distance () of each weighted alternative i = 1,2,.,m from FPIS and the FNIS is calculated s follows: ( ), i= 1,2,,m (11)

5 ( ), i= 1,2,,m (12) Where ( ) is the distance between two fuzzy numbers. Step8: Determine the closeness coefficient (CCi) of each alternative The closeness coefficient CCi represents the distances to the fuzzy positive ideal solution (A + ) and the fuzzy negative ideal solution (A - ) simultaneously. The closeness coefficient of each alternative is calculated as CC i =, i= 1,2,,m (13) Step9: Rank the alternatives In this step, the different alternatives are ranked according to the closeness coefficient (CC i ) in decreasing order. The best alternative is nearby to the FPIS and outermost from the FNIS. Numerical Illustration of fuzzy TOPSIS In this section we are evaluating the performance of green supply chain management by using the fuzzy TOPSIS, with the consideration of seven criteria and three alternatives as shown in figure- 1. A committee of three decision makers provides the linguistic ratings for criteria and alternatives using Table-1. Linguistic ratings for the criteria and alternatives are shown in Table- 2 and Table- 4 respectively. Now calculate the aggregate fuzzy weights of each criteria using eq- 2. For criteria C2 aggregate fuzzy weightis given as: ( ) where ( ), ( ), and ( ) = (5,8.33,9) Similarly we calculate the aggregate weights for the remaining criteria as shown in Table- 3. Table- 1 Linguistic terms for criteria and alternatives rating Linguistic term for criteria Linguistic term for alternatives Triangular fuzzy numbers Very low Very poor (1,1,3) Low Poor (1,3,5) Medium Fair (3,5,7) High Good (5,7,9) Very high Very good (7,9,9) Table- 2 Linguistic ratings for the seven criteria Criteria Decision makers D 1 D 2 D 3 C 1 H VH M C 2 VH H VH C 3 VH M H C 4 M H H C 5 M M L C 6 H VH M C 7 L H M

6 Table-3 Aggregate fuzzy criteria weights Criteria D 1 D 2 D 3 Aggregate fuzzy weights C 1 (5,7,9) (7,9,9) (3,5,7) (3,7,9) C 2 (7,9,9) (5,7,9) (7,9,9) (5,8.33,9) C 3 (7,9,9) (3,5,7) (5,7,9) (3,7,9) C 4 (3,5,7) (5,7,9) (5,7,9) (3,6.33,9) C 5 (3,5,7) (3,5,7) (1,3,5) (1,4.33,7) C 6 (5,7,9) (7,9,9) (3,5,7) (3,7,9) C 7 (1,3,5) (5,7,9) (3,5,7) (1,5,9) Now by using the eq- 1, calculate the aggregate fuzzy weights for alternatives. for example aggregate rating of alternative A 2 for criteria C 2 by considering the rating given by three decision makers is calculated as below: ( ) where ( ), ( ), and ( ) ( ) Similarly, calculate the aggregate rating for remaining alternatives with respect to corresponding criteria. Aggregate fuzzy ratings for alternatives are shown in Table- 4. Table- 4 Aggregate fuzzy ratings for alternatives Criteria Alternatives A 1 A 2 A 3 C 1 (3,5.67,9) (1,7,9) (3,7,9) C 2 (1,5.67,9) (3,7.67,9) (1,5,9) C 3 (3,7,9) (1,5,9) (5,7.67,9) C 4 (1,5,9) (3.5.67,9) (1,4.34,7) C 5 (1,3.67,7) (5,7.67,9) (3,5.67,9) C 6 (1,5,9) (5,8.34,9) (1,4.34,7) C 7 (3,7,9) (3,7,9) (1,5,9) After that, we prepared a normalized fuzzy decision matrix for alternatives by using the equations mentioned in step-4. Normalized fuzzy decision matrix for alternatives is shown in in Table- 5. Table- 5 Normalized fuzzy decision matrix for alternatives Criteria Alternatives A 1 A 2 A 3 C 1 (0.33,0.63,1) (0.11,0.77,1) (0.33,0.77,1) C 2 (0.11,0.63,1) (0.33,0.85,1) (0.11,0.55,1) C 3 (0.33,0.77,1) (0.1,0.55,1) (0.55,0.85,1) C 4 (0.11,0.55,1) (0.33,0.63,1) (0.11,0.48,0.77) C 5 (0.11,0.40,0.77) (0.55,0.85,1) (0.33,0.63,1) C 6 (0.11,0.55,1) (0.55,0.92,1) (0.11,0.48,0.77) C 7 (0.33,0.77,1) (0.33,0.77,1) (0.11,0.55,1)

7 In the next step, construct a normalized fuzzy weights decision matrix for alternatives by using an eq- 8. For example, normalized fuzzy weights for alternative A2 with respect to criteria C2 is given as follows: = (0.33,0.85,1)* (5,8.33,9) = (1.65,7.08,9) Similarly, the fuzzy weights for remaining alternatives are calculated as shown in Table- 6. Table- 6 Normalized fuzzy weights for alternatives, FPIS, FNIS Criteria Alternatives FNIS (A - ) FPIS (A + ) A 1 A 2 A 3 C 1 (0.99,4.41,9) (0.33,5.39,9) (0.99,5.39,9) C 2 (0.55,5.25,9) (1.65,7.08,9) (0.55,4.58,9) C 3 (0.99,5.39,9) (0.33,3.85,9) (3.85,5.95,9) C 4 (0.33,3.48,9) (0.99,3.98,9) (0.33,3.03,6.93) C 5 (0.11,1.73,5.39) (0.55,3.68,7) (0.33,2.72,7) C 6 (0.33,3.85,9) (1.65,6.44,9) (0.33,3.36,6.93) C 7 (0.33,3.85,9) (0.33,3.85,9) (0.11,2.75,9) In Table- 6, we also calculated the fuzzy negative ideal solution (FNIS) and fuzzy positive ideal solution (FPIS) shown in the last two columns by using the eq- 9 and 10. Now determine the distance of each alternative from the FPIS and FNIS by using a formula stated as below: If we assume two triangular fuzzy numbers ( ) and ( ) then by using the vertex method distance between them is given as: ( ) ( ) ( ) ( ) For example, the distances ( ) and ( ) for alternative A2 with respect to criteria C2 are calculated as follows: ( ) ( ) ( ) ( ) = 4.38 ( ) ( ) ( ) ( ) = 6.19 Similarly, we calculated the distances for remaining alternatives. The distances for each alternative from FPIS and FNIS are shown in Table- 7. Table- 7 Distance d v (A i, A - ) and d v (A i, A + ) Criteria d v (A i, A - ) d v (A i, A + ) A 1 A 2 A 3 A 1 A 2 A 3 C C C C C C C

8 Then we calculate the distances and by using eqs- 11 and 12. The values of and for each of the alternative are shown in Table- 8. Table- 8 Closeness coefficient (CCi) for the three alternatives Criteria Alternatives A 1 A 2 A CC i Finally, we determine the closeness coefficient of each alternative by using the corresponding values of and. For alternative A1 closeness coefficient (CCi) is calculated as: CCi = = ( ) = Similarly, calculate the value of CCi for alternative A2 and A3, values comes and respectively. Results Thus alternative A 2, Web based technologies (with a closeness coefficient value ) has been found to be considerably more desirable than the other two alternatives A 1 and A 3 (with closeness coefficient values and , respectively). Conclusion This paper presents a fuzzy TOPSIS approach to measure the performance of green supply chain management. In this paper we proposed a model to measure the performance of three alternatives namely suppliers, web based technologies and advanced manufacturing technologies on the behalf of seven criteria namely as; saving energy, design for environment, waste minimization, reuse of hazardous waste, green management abilities, use of information and communication technologies and proper mode of transport. By applying a stepwise procedure of fuzzy TOPSIS, the finding gives highest closeness coefficient for alternative A 2 as compared to the other alternatives A 1 and A 3. It means, alternative web based technologies comes out as a best alternative and played a very important role in improving the performance of GSCM. References Awasthi A., Chauhan Satyaveer S., Goyal S.K A fuzzy multicriteria approach for evaluating environmental performance of suppliers.international Journal of Production Economics 126: Beamon BM Designing the green supply chain. Logistics Information Management 12(4): Chen, Chen-Tung, Lin, Ching-Torng, Huang, Sue-Fn A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics 102 (2): Chopra, S. and Meindl, P Supply Chain Management, Strategy, Planning, and Operation. Prentice-Hall. Upper Saddle River, New Jersey. Florida R Lean and green: the move to environmentally conscious manufacturing. California Management Review39(1): Frosch R Industrial ecology: minimizing the impact of industrial waste. Physics Today 47(11): 63 Hart SL A natural-resource-based view of the firm. Academy of Management Review 20(4): Humphreys, P.K., Wong, Y.K., and Chan, F.T.S Integrating environmental criteria into the supplier selection process. Journal of Materials Processing Technology 138:

9 Lambert, D.M Supply Chain Management: Processes, Partnerships, Performance. 3rd ed., Supply Chain Management Institute, Sarasota, FL. Lu, L.Y.Y., Wu, C.H., and Kuo, T.C Environmental principles applicable to green supplier evaluation by using multi-objective decision analysis. International Journal of Production Research 45(18 19): Onut, Semih, Kara Selin, Soner, Isik, Elif Long term supplier selection using a combined fuzzy MCDM approach: a case study for a telecommunication company. Expert Systems with Applications 36 (2): Rao, P. and Holt, D Do green supply chains lead to competitiveness and economic performance?.international Journal of Operations and Production Management 25(9): Sarkis J Greening the supply chain. Springer, London. VachonS, KlassenRD. 2006a. Extending greenpractices across the supply chain: the impact of upstream and downstream integration. International Journal of Operations & Production Management26(7): Vachon S, Klassen RD. 2006b. Green project partnership in the supply chain: the case of package printing industry. Journal of Cleaner production 14(6): Wang, Jia-Wen, Cheng, Ching-Hsue, Huang, Kun-Cheng Fuzzy hierarchical TOPSIS for supplier selection. Applied Soft Computing 9(1): Zhu, Q., Sarkis, J. and Geng, Y Green supply chain management in China: Pressures, practices, and performance. International Journal of Operations & Production Management 25(5): Zsidisin. G.A., Siferd. S.P Environmental purchasing: a framework for theory development. European Journal of Purchasing & Supply Management 7(1):

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