International Journal of Supply and Operations Management

Transcription

1 Internatonal Journal of Supply and Operatons Management IJSOM May 2015, Volume 2, Issue 1, pp ISSN-Prnt: ISSN-Onlne: A fuzzy AHP-TOPSIS framework for the rsk assessment of green supply chan mplementaton n the textle ndustry Muhammad Nazam a*, Jupng Xu b, Zhmao Tao b, Jaml Ahmad b and Muhammad Hashm c a Insttute of Busness Management Scences, Unversty of Agrculture, Fasalabad, Pakstan b Uncertanty Decson-Makng Laboratory, Busness school Schuan Unversty, Chengdu, Chna c Department of Busness Admnstraton, Natonal Textle Unversty, Fasalabad, Pakstan Abstract In the emergng supply chan envronment, green supply chan rsk management plays a sgnfcant role more than ever. Rsk s an nherent uncertanty and has a tendency to dsrupt the typcal green supply chan management (GSCM) operatons and eventually reduce the success rate of ndustres. In order to mtgate the consequences, a fuzzy mult-crtera group decson makng modelng (FMCGDM) whch could evaluate the potental rsks n the context of (GSCM) s needed from the ndustral pont of vew. Therefore, ths research proposes a combned fuzzy analytcal herarchy process (AHP) to calculate the weght of each rsk crteron and sub-crteron and technque for order performance by smlarty to deal soluton (TOPSIS) methodology to rank and assess the rsks assocated wth mplementaton of (GSCM) practces under the fuzzy envronment. The proposed fuzzy rsk-orented evaluaton model s appled to a practcal case of textle manufacturng ndustry. Fnally, the proposed model helps the researchers and practtoners to understand the mportance of conductng approprate rsk assessment when mplementng green supply chan ntatves. Keywords: Fuzzy AHP; Fuzzy TOPSIS; Rsk assessment; Green ntatve; Textle sector * Correspondng author emal address: [email protected] 548

2 Int J Supply Oper Manage (IJSOM) 1. Introducton In recent years, green supply chan management (GSCM) has emerged as an organzatonal phlosophy. GSCM helps organzatons and ther busness to mprove compettve advantages and profts n the hgh-rsk supply chan envronment. Wth the rapd changes and pressure of global trend of ncreased collaboraton wth nternatonal supply partners and expanded supply networks, mplementng greener practces could also ntensfy the probable chances of experencng dsruptve events n supply chans that substantally threaten normal routne operatons of the ndustres n the supply chan system. These obstacles and ssues could nclude maxmzaton of total costs and goodwll rsk from falures along the supply chan. An extensve dscusson about these ssues tendences can be found n Slbermayr et al. (2014) and Azevedo et al. (2011). The mplementaton of green ntatves can generate hgher revenues n the textle ndustry as retalers green credentals are becomng an mportant dfferentator that enables frms to secure greater customer satsfacton and loyalty. Accordng to the proposed methodology of Mangla et al. (2014), the aspect of envronmental consderaton or gong green needs to be consdered n the varous actvtes ncludng supply and operatons of organzatons. Kannan et al. (2014) proposed the fuzzy mult-attrbute group decson makng approach based on (GSCM) practces from the hgh rsk supply chan perspectves. Accordng to Gao et al. (2010), a green product desgnng may mprove the brand mage and power to stmulate demand from green consumers. In order to accomplsh ths transton, t mght be requred to use advanced technologes n the producton-dstrbuton and supply processes, as well as the rapd change of R&D and new qualty systems. Procurement-wse, t mght be concentrated on purchasng of nnovatve raw materals and the procedure for optmal suppler selecton. Logstcs-wse, t mght requre new external and nternal logstcs along wth new packagng. Meanwhle, there s no guarantee of marketablty, retal ablty salablty and future growth of the products n the compettve global market. Accordngly, t s sgnfcant to use an ntegrated mult-crtera approach and assess the rsk nvolved n a supply chan context, thus enablng decson makers to grasp the capabltes and resources that need to be deployed so as to successfully mplement a green supply chan n the textle ndustry. Accordng to Zhu et al. (2008) and Srvastava (2007), the mplementaton of green ntatves n the ndustres can ncrease the rate of cost savngs by reducng energy consumpton and packagng waste management n tmes of rsng nput costs, wth rsng commodtes and energy costs beng a partcular concern. Deployng a proper green ntatves or polces could create a compettve advantage for a frm. Nazam et al. (2015) proposed a new model for rsk evaluaton of warehouse operatons by usng FMEA and combned AHP-TOPSIS approaches under fuzzy envronment. Samved et al. (2013) quantfed rsks n a supply chan through ntegraton of fuzzy AHP and fuzzy TOPSIS n Indan frms. Wang et al. (2012) proposed a two-stage Fuzzy-AHP model for rsk assessment of mplementng green ntatves n the fashon supply chan. It s therefore clear that just lke any strategc polcy change, mplementng green ntatves conssts of a certan degree of rsk, and hence a proper rsk assessment tool s needed n that context. Whle more pressures are emergng from stakeholders, nvestors, government bodes to prompt companes and the entre textle supply chan to adopt green supply chan practces. To the best of our knowledge, a lttle effort has been pad on assessng the rsk nvolved n mplementng varous green ntatves for manageral assessment purposes. Nowadays, there s an ncreased wakefulness and concern about mplementng the envronment frendly aspect n varous facets by varous stakeholders of the manufacturng frms. The members 549

3 Nazam et al. ncluded n stakeholders are consumers, non-proftable organzatons, government regulatory bodes, compettors, nvestors, shareholders, etc. Due to globalzaton and compettve market, the expectatons of stakeholders have become a drvng force for the organzatons to consder the aspect of gong envronment frendly n several functons of the organzatons. Based on these facts, supply chan s consdered as one of the mportant areas for the adopton or mplementaton of the green aspects. Accordng to the proposed framework of Adler (2006), the aspect of envronmental consderaton needs to be consdered n the varous actvtes ncludng physcal dstrbuton and operatons of organzatons. To accomplsh the envronmental responsbltes at ndustral standpont, the percepton of greenng the supply chan or green supply chan (GSC), and green supply chan management (GSCM) has been evolved (Mn and Km, 2012). GSCM ntatves may be of great value to the frms, as well as to the external envronment, and they generate economc benefts n long run Kumar et al. (2012). Ashsh et al. (2014) developed a framework n the context of tradtonal versus green supply chan management to select the green supplers and further nvestgate how to overcome the barrers n green supply chan. Accordng to Rumn et al. (2012), every busness actvty n supply chan process conssts of varous objectve rsk factors and ssues. The occurrence of the dfferent rsks dsturbs varous operatons and processes of GSC, and declnes the overall performance of the ndustres (Qanle, 2012). Thereby, n order to effectvely manage (GSC), the background of the rsks n GSC s mportant to dscuss. Therefore, to help ndustres, t s recommended to evaluate the rsks for an effectve understandng and mplementaton of GSC busness practces. The current research context focuses on (GSCM) ntatves evaluaton by determnng ther prorty or rank, whch s a mult-crtera decson type problem. For ths, the methodologes of fuzzy AHP and fuzzy TOPSIS have been used n ths research. The AHP method s flexblty based decson tool used to analyze the mult-crtera problem (Saaty, 1980). However, the process of prortzng the ntatve s not smple as there s vagueness and uncertanty due to human percepton. To deal wth ths mprecson n the decson-makng, t s suggested to use the fuzzy set theory to handle the ambgutes and uncertantes (Tseng, Ln, and Chu, 2009). A fuzzy based analytc herarchy process (AHP) approach, therefore proposed n ths study, whch s useful n prortzng or rankng alternatves n (GSCM) under fuzzy envronment (Chang et al and Chan et al. 2008). To test the rankng obtaned through the fuzzy AHP, the methodology of technque for order performance by smlarty to deal soluton (TOPSIS) s appled. Besdes, t enables the polcy makers to understand the fuzzy logc for domnance of one crteron over the other for each parwse comparson, whch otherwse, remans opaque to the mplementer as f usng the AHP method. Although such evaluaton may possbly dffer for ndustry to ndustry, for that reason, we try to keep the proposed model as generc as possble to facltate ts utlty n real-world cases. The (GSCM) case example of a Pakstan textle manufacturng company, however, s dscussed n the research that shows the usefulness and valdty of the proposed fuzzy AHP and fuzzy TOPSIS evaluaton model. The chosen case example company seeks to prortze the (GSCM) ntatves; t also wants to understand the fuzzy logc between the crterons for each pared comparson that wll mprove ts green supply chan success rate. Makng such judgment, however, s never an easy task as there are many qualtatve factors concerned wth the decsonmakng process. In the lterature, analytc herarchy process (AHP) and the technque for order performance by smlarty to deal soluton (TOPSIS) are a wdely employed methodologes to facltate ths knd of process. The tradtonal AHP s unable to deal wth another realstc concern: 550

4 Int J Supply Oper Manage (IJSOM) uncertanty. Wthout uncertanty, one may argue that rsk assessment s not necessary. Uncertanty s a partcular ssue n the textle ndustry snce demand s hghly volatle. In vew of ths, a decson model that couples AHP wth fuzzy logc, whch s used to ncorporate uncertan varables nto the proposed model, s developed n ths paper. The most relevant study was conducted by Sarmento and Thomas (2010), who proposed an AHP framework for evaluatng dfferent green ntatves. However, the framework they proposed s hghly stylzed and wthout an llustratve example. Moreover, ther approach does not consder the peculartes of the textle ndustry or consder any uncertan parameters (t was a tradtonal AHP, not a fuzzy AHP-TOPSIS approach). Ths paper addressed ths gap n the lterature by extendng the establshed combned fuzzy AHP-TOPSIS framework to the textle ndustry. In order to extend the model to the peculartes of the textle ndustry, our proposed model ncorporates crtera suggested by Chan and Chan (2010), detals of whch are structured n the Fgure 3. The rest of the paper s organzed as follows: Secton 2 presents the problem. Secton 3 brefly explans the methodology, and Secton 4 formulates the combned fuzzy AHP-TOPSIS framework, for rsk assessment when mplementng green supply chan ntatves. Then, an llustratve example s presented n Secton 5 to demonstrate how the model works. Secton 6 concludes ths paper. 2. Problem statement In recent years, the rsk assessment of mplementng green ntatves n the textle sector has drawn ncreased attenton from both researchers and practtoners. The man reason for mplementng these (GSCM) practces s that organzatons can generate more busness opportuntes than ther compettors f they can address envronmental ssues successfully. A greener product desgn may mprove brand mage and stmulate demand from green consumers Peatte (2001). Usng envronmental frendly raw materals and green producton process address ssues such as envronmental materal substtuton, waste reducton and decreasng the consumpton of hazardous and toxc materals (Vachon, 2007; Holt and Ghobadan, 2009). Zhu et al. (2008) supposed that the fnancal performance s the man drver for organzatons whch seek to mplement green ntatves. Luthra et al. (2013) studed the relatonshp between the mplementaton of green supply chans and the economc performance and compettveness of a sample of dfferent Indan manufacturng ndustres. In ths problem, a Pakstan vertcally ntegrated textle manufacturng company s chosen for ths study. The case company has approxmately 6000 employees per shft; they manufacture garments products such as sportswear, sleepwear, underwear and trousers. The company s one of the leaders n ts product segment n Pakstan; ts man customers are major natonal and nternatonal retalers. Ths company has enacted varous changes n the structure of the fnal product n order to make t comfortable, free of harmful chemcals and toxc materals, and to lower ts prce by provdng good qualty. These changes, n turn, meet both envronmental legslaton regulatons and the demands of ther customers. The company has also dedcated tself to an analyss of the lfe cycle assessment (LCA) of the product. Lfe cycle assessment (LCA) coverng all aspects about the mplementaton of green ntatves n the textle supply chans whch conducts a return of nventory, consumpton of raw materals, and waste generaton, and ths nventory allows the company to evaluate ts own usage of such resources and to mplement reducton practces. 551

5 Nazam et al. Ths problem deals n achevng the followng hghlghted objectves: (1) To dentfy and understand the concept of rsks assocated wth the green supply chan (GSC) at ndustral context n textle frms. (2) To evaluate the dentfed crteron to prorty by determnng and confrmng of ther relatve mportance n effectve adopton and mplementaton of (GSCM). (3) To nterpret the fuzzy logc for domnance of one crtera over the other for the formulaton of each parwse comparson usng fuzzy AHP technque. In Pakstan, accordng to the 2015 Natonal Polcy on Sold Waste, all companes n the textle sector are now requred to take responsblty for ther post-consumer products take-back and envronmental mpacts. Because of the Pakstan government s mandate, companes recognze that offerng greener textle products not only meets customer demand but also requres locatng good green supplers to mprove ther supply chan management. Because of ths new context n Pakstan, the company s producton plannng and rsk managers seek a way to dentfy and to select the alternatve tme whch wll support the company s adopton of (GSCM) practces. Major supply chan actors have been dentfed as canddates for case company. Wth ths company s objectves n mnd, the authors of ths paper prepared a survey questonnare and submtted t for content analyss to three experts. Then, we asked the opnon of three experts who work wth the marketng context of (GSCM) n order to check ther preferences when usng (GSCM) practces to mplement green raw materals. Fg. 2 shows the step-wse framework of ths research and the development of soluton methodology adopted n ths work. The adopton of (GSCM) ntatves wll lead to better economc performance through enhanced envronmental performance such as less waste, enhanced energy effcency and an mproved recyclablty of the end product. At the same tme, new green ntatves mght requre organzatons to redesgn and mprove varous aspects of ther extng processes n order to adopt these nnovatons successfully. It s essental for the organzaton to dentfy those areas at both the ndvdual organzaton and supply chan levels that are least prepared to handle the green nnovaton successfully. Sarmnento and Thomas (2010) proposed a mult-ter AHP framework to assess supply chan resources and capabltes for mplementng green ntatves. Nevertheless, the herarchcal model n Sarmnento and Thomas s research only focuses on four man crtera: manufacturng, purchasng, logstcs, and marketng. In ths research, we proposed a more generc model n whch organzatons have the flexblty to ncorporate both envronmental and operatonal aspects and nclude more crtera and relevant sub-crtera referrng to ther busness concerns. Wthn each man crteron, the relevant subcrtera are dentfed. The alternatves at the bottom end of the herarchy are the tme wndows by whch an organzaton could successfully mplement the selected green ntatves n the condton of the potental lmtatons n nternal processes and resource. 3. Methodology Ths secton proposes a methodology for rsk assessment of mplementng green supply chan ntatves or polces n the textle sector. The methodology conssts of three man stages as gven n Fgure 2. The frst step requres the frm to come up wth a comprehensve herarchy of all the crtera whch may affect the frm. Ths s done by thoroughly studyng the consdered chan and 552

6 Int J Supply Oper Manage (IJSOM) dentfyng potental loopholes. These are then analyzed for overlaps and categorzed usng smlar characterstcs. Ths exercse should be repeated whenever a major change s made n the chan. The second step n the process nvolves assgnng weghts to the crtera accordng to ther mportance. Fuzzy AHP s used for ths purpose and expert vews are taken as nput. The thrd step nvolves determnng the scores of dfferent crtera by analyzng them under fve dfferent crtera; namely manufacturng, procurement, logstcs, flexblty, and retalng. In the fourth step, fuzzy TOPSIS approach s employed to evaluate the organzaton s readness of mplementng green raw materal. Fnally, comparson of results and manageral mplcatons has been dscussed. 3.1 Fuzzy AHP The fuzzy AHP methodology extends Saaty s AHP by combnng t wth fuzzy set theory. In fuzzy AHP, fuzzy rato scales are used to ndcate the relatve strength of the factors n the correspondng crtera. Therefore, a fuzzy judgment matrx can be constructed. The fnal scores of alternatves are also represented by fuzzy numbers. The optmum alternatve s obtaned by rankng the fuzzy numbers usng specal algebrac operators. In ths methodology, all elements n the judgment matrx and weght vectors are represented by trangular fuzzy numbers. Usng fuzzy numbers to ndcate the relatve mportance of one rsk type over the other, a fuzzy judgment vector s then obtaned for each crteron. These judgment vectors form part of the fuzzy parwse comparson matrx whch s then used to determne the weght of each crteron. Table 1 shows the meanng of lngustc expressons n the form of fuzzy numbers and Table 2 shows the random consstency ndex to calculate the consstency rato (CR). Fg. 1. represents the fuzzy membershp functon for lngustc expressons for crtera and sub-crtera. Experts are asked to gve ther assessment n the form of these lngustc expressons whch are then converted and analyzed to fnally get the weghts. Chang s extent analyss method has been used for determnng weghts from parwse comparsons. M (x) 1.0 Equally Moderately Strongly Very Strongly Absolutely Fgure 1. Fuzzy membershp functon for lngustc expressons for crtera and sub-crtera 553

7 Nazam et al. Table 1. Scale for relatve mportance used n the parwse comparson matrx. Intensty of Fuzzy Lngustc varables Trangular fuzzy Recprocal of TFNs mportance number numbers (TFNs) 1 1 Equally mportant (1, 1, 3) (0.33, 1.00, 1.00) 3 3 Weekly mportant (1, 3, 5) (0.20, 0.33, 1.00) 5 5 Strongly mportant (3, 5, 7) (0.14, 0.20, 0.33) 7 7 Very strongly mportant (5, 7, 9) (0.11, 0.14, 0.20) 9 9 Extremely more mportant (7, 9, 11) (0.09, 0.11, 0.14) Table 2. The random consstency ndex. Sze (n) RI Fuzzy TOPSIS Fuzzy set theory can be used to present lngustc value. For ths reason, the fuzzy TOPSIS method s very sutable for solvng real lfe applcaton problems under a fuzzy envronment. TOPSIS, one of the classcal mult-crtera decson makng methods was developed by Hwang and Yoon (1981). It s based on the concept that the chosen alternatve should have the shortest dstance from the postve deal soluton (PIS) and the farthest from the negatve deal soluton (NIS). TOPSIS also provdes an easly understandable and programmable calculaton procedure. It has the ablty of takng varous crtera wth dfferent unts nto account smultaneously (Buyukozkan and Cfc, 2012). Fuzzy TOPSIS has been ntroduced for varous mult-attrbute decson-makng problems. Yong (2006) used fuzzy TOPSIS for plant locaton selecton and Chena et al. (2006) used fuzzy TOPSIS for suppler selecton. Kahraman et al. (2007) utlzed fuzzy TOPSIS for ndustral robotc system selecton. Ekmekcoglu et al. (2010) used a modfed fuzzy TOPSIS to select muncpal sold waste dsposal method and ste. Kutlu & Ekmekcoglu (2010) used fuzzy TOPSIS ntegrated wth fuzzy AHP to propose a new FMEA falure modes & effects analyss whch overcomes the shortcomngs of tradtonal FMEA. Kaya and Kahraman (2011) proposed a modfed fuzzy TOPSIS for selecton of the best energy technology alternatve. Km, Lee, Cho, and Km (2011) used fuzzy TOPSIS for modelng consumer s product adopton process. Ertugrul, & Karakasoglu, (2008) conducted comparatve analyss by usng fuzzy AHP and TOPSIS methods for faclty locaton selecton. Step 1: Choose the lngustc ratng values for the alternatve wth respect to crtera Let us assume there are m possble alternatves called A { A1, A2... A m } whch are to be valuated aganst the crtera, C { C1, C2... C n }. The crtera weghts are denoted by wj { j 1,2,..., n}. The performance ratngs of each expert Dk { k 1, 2,... K} for each alternatve A { 1,2,..., m} wth respect to crtera Cj{ j 1,2,...,n} are denoted by membershp functon. The scale used for solutons ratng s gven n lngustc varable table. 554

8 Int J Supply Oper Manage (IJSOM) Table 3. Fuzzy evaluaton scores for alternatve. Lngustc varables Correspondng TFNs Very poor (VP) (1, 1, 3) Poor (P) (1, 3, 5) Medum (M) (3, 5, 7) Good (G) (5, 7, 9) Very good (VG) (7, 9, 11) Step 2: Calculate aggregate fuzzy ratngs for the alternatves If the fuzzy ratngs of all experts are descrbed as TFN R ( a, b, c ), k 1,2,..., K then the k k k k aggregated fuzzy ratng s gven by R ( a, b, c) k 1,2,..., K where a mn{a }, k k K 1 b bk, c K max{c } k k k 1 (1) If the fuzzy ratng of the kth decson maker are X ( a, b, c ), 1,2,...m, j 1,2,..,n then the aggregated fuzzy ratngs X j ( aj, bj, c j ), where a mn{a }, j k jk b j K k 1 jk jk jk jk X j of alternatves wth respect to each crtera are gven by K 1 b, c max{c } (2) jk k Step 3: Construct the fuzzy decson matrx The fuzzy decson matrx for the alternatves (D) s constructed as follows: jk A x x.... x n 1 x21 x x 2n A 2 D ,2,..., m; j 1,2,..., n A m xm1 xm2 xmn (3) Step 4: Construct the Normalze fuzzy decson matrx The raw data are normalzed usng lnear scale transformaton to brng the varous crtera scales nto a comparable scale. The normalzed fuzzy decson matrx R s gven by: R r j 1, 2,..., m; j 1, 2,..., n, mn, (4) 555

9 Nazam et al. Where aj bj c j * rj,, and c max (beneft crtera) * * * j c j c j c j c j (5) aj aj a j rj,, and a j mn aj (cost crtera) c j bj a j (6) Step 5: Construct the weghted normalzed matrx The weghted normalzed matrx j of evaluaton crtera wth the normalzed fuzzy decson matrx r j. w for crtera s computed by multplyng the weghts w j V v 1,2,...,m; j 1,2,...,n where v r. W Note that v j s a TFN represented by ajk, bjk, c jk j mn, j j j (7) Step 6: Determne the fuzzy deal soluton (FPIS) and fuzzy negatve deal soluton (FNIS) The FPIS and FNIS of the alternatves s computed as follows: 1, 2,..., n j j, j, j j max j A * v * v * v * where v * c * c * c * c * c 1, 2,..., n j j, j, j j mn j A v v v where v a a a a a (8) (9) = 1,2,,m; j=1,2,,n Step 7: Calculate the dstance of each alternatve from FPIS and FNIS The dstance d, d of each weghted alternatve = 1, 2,..., m from the FPIS and the FNIS s computed as follows: n * j, j j1 d dv v v, 1, 2,..., m (10) n j, j j1 d dv v v, 1,2,..., m (11) Step 8: Calculate the closeness coeffcent CC of each alternatve * The closeness coeffcent CC represents the dstances to the fuzzy postve deal soluton A and the fuzzy negatve deal soluton A smultaneously. The closeness coeffcent of each alternatve s calculated as: d CC (12) d d Step 9: Rank the alternatves 556

10 Int J Supply Oper Manage (IJSOM) In step 9, the dfferent alternatves are ranked or chosen accordng to the maxmum closeness coeffcent CC values n decreasng order. 4. Proposed hybrd fuzzy AHP-TOPSIS framework The textle ndustry s hghly dverse and heterogeneous due to varous complex processes. In these days, the textle ndustry has experenced a great deal of dynamc change wth global sourcng and rsng of prce competton. Therefore, the exceptonal features to garment products such as short product lfe cycle, hgh volatlty, less predctablty and a level of mpulse purchase add further uncertanty for those organzatons want to green ther supply chan operatons. Keepng n vew ths background, we proposed the hybrd fuzzy AHP-TOPSIS approach for the rsk assessment of green supply chan mplementaton n textles sector whch has the followng fve phases. Fgure 2. Proposed hybrd fuzzy AHP-TOPSIS framework for mplementng GSCM ntatves 557

11 Nazam et al. Phase 1: Identfcaton of rsks and mplementaton of green ntatves n supply chan In the frst phase, a decson group of expert panel whch s comprsed of plannng, producton and logstcs managers are formed for the rsk dentfcaton and evaluaton whle mplementng the green ntatves n the supply chan. Then the crteron of (GSCM) ntatve mplementaton n supply chan are determned through lterature revew and these experts opnon. Followng the determnaton of crteron, another expert panel s formed for evaluaton of solutons of (GSCM) mplementaton n supply chan. The expert panel s comprsed of rsk management and supply chan experts. Then the herarchy structure s formed such that the objectve s at the frst level, man crteron n the second level, sub crteron at thrd level, and alternatve ntatves solutons are n the fourth level. Phase 2: Calculaton of the crtera weght by usng fuzzy AHP After formng a decson herarchy, the weghts of the crtera of (GSCM) ntatve n supply chan wll be calculated by fuzzy AHP. Parwse comparson matrces of expert s evaluatons are constructed to acqure crtera weghts by usng the scale n Table 1-2. Computng arthmetc mean of the values found from ther evaluaton, the fnal evaluaton matrx wll be establshed. From ths matrx, the weght of the crteron wll be calculated as descrbed n prevous secton. Phase 3: Evaluaton of the solutons of green supply chan management ntatves The fve alternatve mplementaton tme wndows n Fg. 3 were evaluated wth respect to detaled sub-crtera n terms of the readness of mplementng green raw materal. Decson makers can provde a precse numercal value or a lngustc term to express ther opnons. The qualtatve explanaton of ratng level and ts correspondng trangular fuzzy numbers s descrbed n Table 3. The lngustcs terms were then converted nto trangular fuzzy numbers to formulate the fuzzy evaluaton matrx. Fgure 3. Decson herarchy for the mplementaton of GSCM ntatves for fve tmescales. 558

12 Int J Supply Oper Manage (IJSOM) Phase 4: Determnaton of fnal rank by fuzzy TOPSIS Rankng the solutons of (GSCM) ntatve n supply chan to overcome the rsk wll be determned by usng fuzzy TOPSIS. The ratng of solutons towards the crteron wll be done by lngustc scale, whch s shown n Table 3. Rankng of solutons wll be fnalzed accordng to CC values calculated by fuzzy TOPSIS n descendng order. Phase 5: Comparson of results and manageral mplcatons In ths secton, a detaled comparatve analyss of all alternatve ntatves wth respect to crteron and sub-crteron s conducted. In order to solve the problems of mplementng green raw materal, the experts suggest some valuable suggestons to assess the rsk of the case company n hgh-rsk supply chan envronment. At the end of ths paper, the experts suggested that (GSCM) s not lmted to the green envronment frendly techncal aspects) but also on the nonenvronmental crtera. The decson-makers can be able to capture a farly complete pcture of the context of GSCM mplementaton through the assessment process whch can pave the way to mprove the productvty and sustan the compettve advantages. 5. Applcaton of the proposed framework The proposed framework s used to rank the solutons of (GSCM) ntatves n supply chan to overcome ts rsks. The applcaton s based on fve phases provded n prevous secton and explaned wth numercal results as follows Case presentaton Nowadays, more and more Pakstan organzatons realze that rsk management plays an mportant role n busness success and that mplementng green ntatves n supply chan s becomng a core actvty. Few organzatons have mplemented green materals practces n ntegraton wth supply chan. But the success rate s very less due to rsk of mplementng green ntatve n supply chan. To mprove the success rate, t s essental to assess the rsks and solutons to overcome them. It s dffcult to mplement all ntatves at the same tme. Hence t s essental to prortze these solutons of mplementng green raw materal n supply chan, hence, Pakstan organzatons can concentrate on the hgh rank solutons and mplement them n a stepwse manner Case analyss Phase 1: Identfcaton of rsks and mplementaton of green ntatves n supply chan The decson group s composed of the 3 expert panel whch s comprsed of plannng manager, producton manager, and logstcs manager. In ths study, through the panel dscusson, the detaled sub-crtera under fve man crtera (manufacturng, procurement, logstcs, flexblty, and retalng) were dentfed. The results are llustrated n Fg. 3, n whch the herarchy s descended from the general crtera n the second level to more detaled sub-crtera. There are four levels n decson herarchy structure for ths problem. The overall goal of decson process determned as mplementng green raw materal n supply chan to assess ts rsks s n the frst level of herarchy. The man crtera are on the second level, the sub-crtera at thrd level, and alternatve wndows tme scale solutons n the fourth level of herarchy (See Fg 3). 559

13 Nazam et al. Phase 2: Calculaton of the crtera weght by usng fuzzy AHP In ths phase, the decson group s asked to make par wse (parwse) comparsons of fve man crteron and 24 sub crteron by usng lngustc varables by usng Table 1-2. The arthmetc mean of these values s computed to obtan the parwse comparson matrces of crtera and subcrtera are gven n Tables 4 9. The results obtaned from the calculatons based on parwse comparson matrces provded n Table 4 9 are presented n Table 10. CR values of all the matrces are less than 0.1, hence these matrces are consstent. Table 4. Parwse comparson matrx of the major crteron. C 1 C 2 C 3 C 4 C 5 C C C C C Table 5. Parwse comparson matrx of the sub-crtera wth respect to manufacturng crtera. M 1 M 2 M 3 M 4 M 5 M 6 M M M M M M Table 6. Parwse comparson matrx of the sub-crtera wth respect to procurement crtera. P 1 P 2 P 3 P 4 P 5 P P P P P Table 7. Parwse comparson matrx of the sub-crtera wth respect to logstcs crtera. L 1 L 2 L 3 L 4 L L L L Table 8. Parwse comparson matrx of the sub-crtera wth respect to flexblty crtera. F 1 F 2 F 3 F 4 F 5 F F F F F

14 Int J Supply Oper Manage (IJSOM) Table 9. Parwse comparson matrx of the sub-crtera wth respect to retalng crtera. R 1 R 2 R 3 R 4 R R R R Major crteron Table 10. Weghts of crtera and sub-crtera for mplementaton of GSCM ntatves. Major crteron weght Sub-crtera Consstency rato (CR) Rato weght Fnal weght Rankng Manufacturng M M M M M M Procurement P P P P P Logstcs L L L L Flexblty F F F F F Retalng R R R R Phase 3: Evaluaton of the solutons of green supply chan management ntatves (GSCM) The expert panel members were asked to construct a fuzzy evaluaton matrx by usng lngustc varables presented n Table 3. It s establshed by comparng solutons under each of the crteron separately (See Table 11). Then they converted lngustc terms nto correspondng TFN and constructed the fuzzy evaluaton matrx (See Table 12). Aggregate fuzzy weghts of the alternatves are computed usng Eq. (2) and presented n Table 13. In ths study, all the crtera are the rsks of mplementng green ntatves n supply chan, as per the goal mnmzaton of these rsks s requred. Hence, all the rsks are termed as cost crtera and normalzaton performed by Eq. (6) and for further detal (See Table14). The next step s to obtan a fuzzy weghted evaluaton matrx. Usng the crtera weght calculated by fuzzy AHP (See Table 10), the weghted evaluaton matrx s establshed usng the Eq. (7), whch s shown n Table

15 Nazam et al. Table 11. Lngustc scale evaluaton matrx for the mplementaton of GSCM ntatves. Sub-crteron M 1 M 2.. R 1 R 2 Experts E1 E2 E3 E1 E2 E3.. E1 E2 E3 E1 E2 E3 Alternatves A 1 VP P VP G P P.. M VP VG VP VP M A 2 M VP M P M VG.. G P P P P VG A 3 G VG M VP P G.. P VP M G VP M A 4 P M VP P M VP.. VP P VG P P VP A 5 M G P P VP M.. P M M VP G P Table 12. Fuzzy evaluaton matrx for the mplementaton of GSCM ntatves. M 1 M 2. R 1 R 2 E1 E2 E3 E1 E2 E3. E1 E2 E3 E1 E2 E3 A 1 (1,1,3) (1,3,5) (1,1,3) (5,7,9) (1,3,5) (1,3,5). (3,5,7) (1,1,3) (7,9,11) (1,1,3) (1,1,3) (3,5,7) A 2 (3,5,7) (1,1,3) (3,5,7) (1,3,5) (3,5,7) (7,9,11). (5,7,9) (1,3,5) (1,3,5) (1,3,5) (1,3,5) (7,9,11) A 3 (5,7,9) (7,9,11) (3,5,7) (1,1,3) (1,3,5) (5,7,9). (1,3,5) (1,1,3) (3,5,7) (5,7,9) (1,1,3) (3,5,7) A 4 (1,3,5) (3,5,7) (1,1,3) (1,3,5) (3,5,7) (1,1,3). (1,1,3) (1,3,5) (7,9,11) (1,3,5) (1,3,5) (1,1,3) A 5 (3,5,7) (5,7,9) (1,3,5) (1,3,5) (1,1,3) (3,5,7). (1,3,5) (3,5,7) (3,5,7) (1,1,3) (5,7,9) (1,3,5) Table 13. Aggregate fuzzy decson matrx for the mplementaton of GSCM ntatves. M 1 M 2.. R 1 R 2 A 1 (1.00,1.67,5.00) (1.00,4.33,9.00).. (1.00,5.00,11.0) (1.00,2.33,7.00) A 2 (1.00,3.67,7.00) (1.00,5.66,11.0).. (1.00,4.33,9.00) (1.00,5.00,11.0) A 3 (3.00,7.00,11.0) (1.00,3.66,9.00).. (1.00,3.00,7.00) (1.00,4.33,9.00) A 4 (1.00,3.00,7.00) (1.00,3.00,7.00).. (1.00,4.33,11.0) (1.00,2.33,5.00) A 5 (1.00,5.00,9.00) (1.00,3.00,7.00).. (1.00,4.33,7.00) (1.00,3.66,9.00) Table 14. Normalzed fuzzy decson matrx for the mplementaton of GSCM ntatves. M 1 M 2.. R 1 R 2 A 1 (0.20,0.60,1.00) (0.11,0.23,1.00).. (0.09,0.20,1.00) (0.14,0.42,1.00) A 2 (0.14,0.27,1.00) (0.09,0.17,1.00).. (0.11,0.23,1.00) (0.09,0.20,1.00) A 3 (0.09,0.14,0.33) (0.11,0.27,1.00).. (0.14,0.33,1.00) (0.11,0.23,1.00) A 4 (0.14,0.33,0.10) (0.14,0.33,1.00).. (0.09,0.23,1.00) (0.23,0.42,1.00) A 5 (0.11,0.20,1.00) (0.14,0.33,1.00).. (0.14,0.23,1.00) (0.11,0.27,1.00) Table 15. Weghted normalzed fuzzy decson matrx for the mplementaton of GSCM ntatves. M 1 M 2.. R 1 R 2 A 1 (0.0008,0.0023,0.0039) (0.0001,0.0003,0.0012).. (0.0009,0.0020,0.0098) (0.0091,0.0272,0.0634) A 2 (0.0006,0.0011,0.0039) (0.0001,0.0002,0.0012).. (0.0011,0.0023,0.0098) (0.0058,0.0127,0.0634) A 3 (0.0004,0.0006,0.0013) ((0.0001,0.0003,0.0012).. (0.0014,0.0033,0.0098) (0.0070,0.0146,0.0634) A 4 (0.0006,0.0013,0.0039) (0.0002,0.0004,0.0012).. (0.0009,0.0023,0.0098) (0.0127,0.0272,0.0634) A 5 (0.0004,0.0008,0.0039) (0.0002,0.0004,0.0012).. (0.0014,0.0023,0.0098) (0.0070,0.0173,0.0634) Phase 4: Determnaton of fnal rank by fuzzy TOPSIS In ths study, all the sub-crtera are the cost crtera. Hence, fuzzy postve-deal soluton (FPIS, A * ) and fuzzy negatve-deal soluton (FNIS, A * ) as v 0,0,0 and v 1,1,1 for all these * sub-crteron. Then compute the dstance d v of each alternatve form FPIS A and FNIS A 562

16 Int J Supply Oper Manage (IJSOM) * usng the Eqs. (10), (11). For example, the dstance d, v A1 A and, v 1 A and FNIS A, are calculated as follows. and sub-crtera M 1 from FPIS * d A A for alternatve A 1 1 d A A 3 * 1, d A, A * = d A A 3 1, , d A A = Smlarly, calculatons are done for other sub-crteron for solutons of alternatve A 1 and the cumulatve dstances of d and d as d and d are computed. By usng the Eq. (12), the closeness coeffcent (CC ) of alternatve A 1 s computed as follows. d CC d d The same procedure can be adopted to compute the dstances and CC values of remanng alternatves. The fnal results are summarzed n Table 16. Based on CC values rank the alternatves n descendng order. Alternatves Table 16. Fuzzy TOPSIS results and fnal rankng for the mplementaton of GSCM ntatves. d d A A A A A CC Rank Phase 5: Comparson of results and manageral mplcatons In ths secton, the results derved for the proposed hybrd AHP-TOPSIS framework show that A 3 has the hghest coeffcent closeness value, therefore mplementaton of green raw materal n 6 months among the fve alternatve tme wndows should be recommended. Therefore, based on the (CC ) values, the rankng of alternatves n descendng order are A 3, A 1, A 5, A 2 and A 4. It s very dffcult for the case company to mplement green raw materal at tme zero or just now because a lot of potental gaps exst n capablty and resources of supply chan. For nstance, marketngwse, the case company wll generate more busness opportuntes f green new materal can be mplemented at tme zero snce few compettors have already launched a smlar green ntatve. The mplementaton wll not only mprove the company s envronmental performance, but also 563

17 CC scores by fuzzy TOPSIS Nazam et al. enhance the brand mage n the market. Logstcs-wse, t also brngs a substantal amount of uncertanty as t requres potental adjustments n nternal and external operatons whch may ncrease the rsk of experencng adverse events across the supply chan. However, manufacturng-wse, the company s less prepared n terms of manufacturng processes, producton capacty and techncal and nnovaton capabltes n mplementng green new materal at the moment. Such a movement requres alteratons n ther nternal and external operatons and, as a result, t may compromse the operatons performance. In fact, the company wll be better postoned from the manufacturng perspectve f mplemented n 12-month tme. The deal soluton s to mplement the ntatves n 6-month tme by whch the company wll stll have the marketng advantages over ts compettors whle ts operatonal resources are better prepared than now. It could be reflected n the further analyss of weghted performance ratngs of fve mplementaton tme wndows wth respect to ndvdual sub-crtera. The fnal results of all alternatves are descrbed n Fg. 4. It does not ndcate the mportant alternatve rankng for mplementng green ntatves, but also suggests the areas that the company s less prepared to handle the new requrements brought by the new (GSCM) ntatve. Therefore, prompt actons and necessary modfcatons should be deployed to address these ssues before the green ntatve can be fully mplemented. A1 A2 A3 A4 A A1 A2 A3 A4 A5 Alternatve tme scale wndows Fgure 4. Closeness coeffcent and fnal rankng of the alternatve tme wndows Based on the result analyss, the case study demonstrated that (GSCM) s not only lmted to the green techncal aspects, but also on the non-envronment crtera. In ths way, the managers and decson-makers are able to understand and capture a complete pcture of the context of (GSCM) mplementaton through the rsk assessment process. The proposed approach s useful for revewng (GSCM) development, whch can lead to mprovng productvty and sustanng the compettve advantages. The proposed hybrd fuzzy AHP-TOPSIS framework provdes a practcal decson support tool for (GSCM) mplementaton snce t seeks to take explct account of multcrtera n adng the decson makng, and compares and ranks (GSCM) alternatves n ndcator bass and as a system. The proposed model can be used for dentfyng mprovement areas when mplementng (GSCM) ntatves wthn the frm s operatonal condtons. In ths artcle, we presented the case study of a textle retal chan, the proposed approach can also be used by the 564

18 Int J Supply Oper Manage (IJSOM) frms n other ndustry sectors as t can be slghtly modfed and refned by set relevant crtera to ther organzaton n order to mplement t successfully. 6. Conclusons and future research In ths artcle, we formulated the fuzzy AHP-TOPSIS framework for the mplementaton of a new green ntatve whch could generate compettve advantages for the case company. It s also a rsky process nvolvng uncertanty and vagueness. The success rate of ntatves mplementaton n supply chan s relatvely low due to ts rsks. Therefore, n order to mnmze these rsks and uncertantes, the companes should focus to assess ther new green ntatves cautously and evaluate the mprovement areas when mplementng green ntatves. It s dffcult to mplement all the solutons at the same tme due to varous constrants, therefore rankng the solutons s essental n stepwse mplementaton of these solutons. We used fuzzy AHP to calculate the weghts of all crtera and sub-crtera, whle fuzzy TOPSIS s utlzed to rank the alternatve tme scale wndows. The weghts obtaned from fuzzy AHP are ncluded n fuzzy TOPSIS computatons and the soluton prortes are determned. The llustratve ndustral case study s presented to demonstrate the applcablty of the proposed framework. The proposed method successfully extends the TOPSIS method by applyng both lngustc varables and a fuzzy aggregaton method whch effectvely avods vague and mprecse judgments. From a practcal pont of vew, the llustratve example of the textle retal chan helps the researchers and practtoners understand the mportance of conductng approprate rsk assessment when mplementng (GSCM) ntatves. A comparatve analyss of alternatves wth respect to crteron was performed to dscuss and explan the results. The result shows that the proposed model s practcal for rankng solutons of (GSCM) ntatves mplementaton n supply chan to overcome ts rsks. Ths proposed scentfc framework gves a new vald and relable approach prortzng the solutons of green ntatves mplementaton n supply chan to assess ts rsks. It s the man contrbuton of ths study n lterature. In the future, the researchers and practtoners can compare the results of ths study wth other fuzzy mult-objectve and mult-crtera technques such as fuzzy VIKOR, fuzzy ELECTRE, and fuzzy PROMETHEE. Addtonally, the proposed hybrd fuzzy AHP and TOPSIS based evaluaton model could be extended to any other organzaton that wants to reduce dsruptons n ther green supply chan (GSC) functonng due to varous assocated rsk under fuzzy envronment. The judgment of experts, however, may vary wth regard to ndustry type, prortes, resources, etc. Acknowledgments: The authors wsh to thank the anonymous referees for ther helpful and constructve comments and suggestons. The work s supported by the Natonal Natural Scence Foundaton of Chna (Grant No , ), the Western and Fronter Regon Project of Humanty and Socal Scences Research, Mnstry of Educaton of Chna (Grant No. 13XJC630018), the Phlosophy and Socal Scences Plannng Project of Schuan provnce (Grant No. SC12BJ05). 565

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