PRIORITIZATION OF GREEN MANUFACTURING DRIVERS IN INDIAN SMEs THROUGH IF-TOPSIS APPROACH

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1 U.P.B. Sc. Bull., Seres D, Vol. 80, Iss. 2, 2018 ISSN PRIORITIZATION OF GREEN MANUFACTURING DRIVERS IN INDIAN SMEs THROUGH IF-TOPSIS APPROACH Pyush JAISWAL 1, Amaresh KUMAR 2, Sumt GUPTA 3 The ssues of global warmng, landfll problems, clmate change and depletng natural resources have ganed sgnfcant attenton because of massve envronmental polluton by manufacturng ndustres. Green manufacturng (GM) has been proposed as a soluton to overcome these ssues. It s mportant to dentfy essental drvers that nfluence adopton of GM n SMEs. Thus, ths study nvestgates the common drvers and ther prortzaton by employng ntutonstc fuzzy technque for order of preference by smlarty to deal soluton approach. Study suggests that fnancal benefts s the most mportant and soco-cultural responsblty s the least mportant drver for GM mplementaton n Indan SMEs. Keywords: Green manufacturng, Drvers, IF-TOPSIS, Inda, SMEs, Manufacturng 1. Introducton Varous envronmental ssues have arsen n developng countres owng to the recent growth n the manufacturng sectors. Ths has led to a huge pressure on the manufacturng companes to mprove ther envronmental performance [1]. The manufacturng ndustres are well known for fulfllng the requrements of socety by producng products as per the customers need. Moreover, they play a sgnfcant role n enhancng the lfestyle of the people and socety as a whole. The developng countres are contnuously workng on meetng the demands and uplftng the lvng style of ther rsng populaton that has an adverse effect on the envronment. In order to reduce the negatve mpact on the envronment, there s an urgent need to mprove manufacturng processes that can reduce the waste generated by ndustres. In Inda, large scale ndustres cause less ndustral polluton as compared to small and medum enterprses (SMEs) as they have adopted new technologes whch have mnmal mpact on the envronment. Ther economc performance has also mproved as a result of employng new ecofrendly technologes. SMEs have emerged out as hghly promnent sector of the 1 Reseach Scholar, Department of Manufacturng Engneerng, Natonal Insttute of Technology Jamshedpur, Inda, emal: pyushaswal1992@gmal.com 2 Assocate Professor, Department of Manufacturng Engneerng, Natonal Insttute of Technology Jamshedpur, Inda 3 Asstant Professor, Department of Mechancal Engneerng, Amty School of Engneerng & Technology Noda, Inda

2 278 Pyush Jaswal, Amaresh Kumar, Sumt Gupta Indan economy over the last decade [2]. SMEs are also supportng ndustralzaton of backward and rural areas to reduce the regonal dsparty and to generate ample of employment opportunty for the youth of the naton [3]. In Inda, more than 48 mllon SMEs generate 40% employment of the total Indan workforce and contrbute 17% to the total gross domestc product (GDP) of the country [4]. These benefts from SMEs drve t to an mportant poston n one hand but on the other hand they are creatng more polluton as compared to large scale ndustres. The reason behnd s lack of adopton of new eco-frendly manufacturng system lke green manufacturng (GM). Accordng to Dornfeld Davd [5] Green manufacturng s a process or system whch has a mnmal, nonexstent, or negatve mpact on the envronment. In order to sustan and grow n the compettve global market, SMEs need to resolve these crtcal ssues by adoptng nnovatve approaches such as GM, n ther operatons and supply chan. The sustanablty elements of GM process assst the management to effectvely address the manufacturng cost, power consumpton, waste management, ecofrendlness, operatonal safety and worker personnel health n organzaton. Indan SMEs fears to adopt new technology as t s expensve and an endless process wth a hgh obsolescence rate. The management of SMEs tends to vew these new technologes as an expense rather than as a strategc nvestment. Hence, mplementaton of any new manufacturng strategy partcularly n SMEs needs a strong motvatonal factor that can nfluence the management to adopt t. Thus, a sutable methodology s requred to facltate the SMEs to dentfy ther key drvers of GM mplementaton. Ths study employs ntegrated ntutonstc fuzzy technque for order performance by smlarty to deal soluton (IF-TOPSIS) approach to prortze the common drvers. The ntutonstc fuzzy based TOPSIS provde comprehensve method for effectvely handle the vagueness and uncertanty n complex envronments through measurng the nherent ambguty of decson makers (DMs) udgment n mult crtera decson makng (MCDM) area. Based on the lterature revew and experts opnon, eghteen drvers n the context of Indan SMEs are extracted that nfluence adopton of GM. The novelty of ths research study les n the fact that t presents a systematc approach to prortze the common drvers on four dfferent perspectves vz. Industry, Academc, Government and Consumer that nfluence the mplementaton of GM n manufacturng SMEs at Indan scenaro wth the help of ntutonstc fuzzy based TOPSIS approach. 2. Lterature revew In the past, plethora of research work has already been conducted on methodology, case study, framework, tools/technques and benefts of GM. Despte ths, there are a lmted number of studes that analyzed the drvers of GM

3 Prortzaton of green manufacturng drvers n Indan SMEs through f-topsis approach 279 n manufacturng SMEs at Indan scenaro. Agan et al. [6] averred that rule and regulatons, nternal motvaton, customer demands and frm performance are the key drvers of envronmental process. Law and Gunasekaran [7] recognzed prme motvatng factors n mplementaton of sustanable strateges n Hong Kong. Sngh et al. [8] conducted a survey n Indan ndustry and dentfed fourteen drvers that motvate GM practces. Dabat, et al. [9] developed a structural model of drvers whle usng ISM technques that affects the mplementaton of GSCM. Massoud et al. [10] dentfed the nfluencng factors n mplementaton of EMS n the Lebanese food ndustry. Zhang et al. [11] examned thrteen drvers that nfluence enterprses to mplement envronmental management practces n Chna. Gabzdylova et al. [12] examned the drvers, stakeholders and practces for the wne ndustry n New Zealand. Yuksel [13] examned the drvers to adopt cleaner producton technques from the survey questonnare of 105 large scale ndustres n Turkey. Pun et al. [14] dentfed the success factors whch nfluence the mplementaton of EMS and found that compettve pressure, customer requrement and resource conservaton are the prme drvers. Hu et al. [15] conducted a survey to nvestgate nfluencng factors n adopton of GM n Hong Kong. It s evdent from the revew of past lteratures that the quantum of research work to dentfy and prortze the drvers to mplement GM n small and medum manufacturng sector at Indan scenaro s not n proporton wth the ncredble growth n the ndustral actvtes. Therefore, the present study attempts to brdge that gap by dentfy more nfluental drvers of GM and subsequently evaluatng them to obtan a rankng preorders whch shows the most mportant drver. The lst of eghteen common drvers of GM adopton s provded n Table 1. The key hghlghts of ths study are as follows: Identfy the common drvers of GM through an extensve lterature revew and experts suggestons. Proposed a framework to prortze GM drvers n manufacturng SMEs usng IF-TOPSIS approach. Valdate the obtaned results wth exstng lterature and feedback from government, ndustry and academc experts. 3. The framework of the study The framework of the study to analyze eghteen drvers to mplement GM n Indan SMEs s demonstrated n Fgure 1, whch broadly conssts of three man stages. The prmary stage encompasses the dentfcaton of dvers based on past lterature and through experts suggeston. In the subsequent stage, IF-TOPSIS

4 280 Pyush Jaswal, Amaresh Kumar, Sumt Gupta approach s utlzed to prortze the drvers based on the opnon of decson makers (DMs) on four dfferent perspectve vz. ndustry, academc, government and consumer. In fnal phase fndngs are valdated through the lterature and feedback of DMs. The lngustc scale and ntutonstc fuzzy (IF) ratng for alternatve, crtera and relatve weghts of experts are shown n Table 2 and Table 3. Table 1 Drvers of green manufacturng S.No Drvers References 1 Envronmental conservaton [16] 2 Improve qualty [17] 3 Envronmental awareness of consumers [18] 4 Certfcaton of ISO [9] 5 Reduce waste dsposal and landfll cost [19], [20] 6 Pressure from stakeholders [21], [22] 7 Improve workng envronment [23] 8 Improve delvery speed and performance flexblty [17] 9 Fnancal benefts [17], [16] 10 Scarcty of natural resources [20] 11 Government ncentves polcy [24] 12 Bolstered corporate mage [25], [20] 13 Legslatve and regulatory complances [26] 14 Compettveness [20] 15 Supply chan pressure [27] 16 Customer demands [28] 17 Soco-cultural responsblty [29], [30] 18 Improve envronmental performance [25] Table 2 The lngustc scale and ntutonstc fuzzy ratng for alternatve and crtera S.No. Lngustc terms Intutonstc fuzzy numbers 1 Unmportant (U) (0.10,0.90,0.00) 2 Least mportant (LI) (0.35,0.60,0.05) 3 Important (I) (0.50,0.45,0.05) 4 Very mportant (VI) (0.75,0.20,0.05) 5 Most mportant (MI) (0.90,0.10,0.00) The lngustc scale for decson makers relatve mportance weght S.No. Lngustc terms Intutonstc fuzzy numbers 1 Very low (VL) (0.10,0.90,0.00) 2 Low (L) (0.35,0.60,0.05) 3 Medum (M) (0.50,0.45,0.05) 4 Hgh (H) (0.75,0.20,0.05) 5 Very hgh (VH) (0.90,0.10,0.00) Table 3

5 Prortzaton of green manufacturng drvers n Indan SMEs through f-topsis approach 281 Lterature revew Academc expert Industral expert Stage 1 Identfy essental drvers to mplement GM Questonnare wth common drvers n Indan Decson maker (DM1) IF-weght (0.90,0.10,0.00) Decson maker (DM2) IF-weght (0.50,0.45,0.05) Decson maker (DM3) IF-weght (0.75,0.20,0.05) Decson maker (DM4) IF-weght (0.50,0.45,0.05) Stage 2 Analyss the drvers of GM based on four decson makers through IF-TOPSIS approach Results and dscusson from feedback of decson makers and comparson wth past lterature Stage 3 Exstng lterature Academc expert Industral expert Valdate the results from feedback of experts Fg. 1. The proposed framework of the study 4. Applcaton of proposed framework The stages performed n the proposed framework are descrbed below: Stage 1: Identfcaton of GM drvers In ths stage common drvers of GM are collected through exstng lterature and experts suggeston. A systematc lterature revew approach s employed. The electronc databases such as Google scholar, Web of scence, Scopus etc., s used wth search topcs that contaned combnaton of exact word lke Green manufacturng, Cleaner producton, Envronmental conscous manufacturng, Sustanable manufacturng, Drvers, SMEs and tme span of search strng was 1990 to After many rounds of dscussons and content affrmaton wth experts eghteen common drvers are consdered for the study.

6 282 Pyush Jaswal, Amaresh Kumar, Sumt Gupta Stage 2: Applcaton of IF-TOPSIS approach In ths stage, frstly data collecton process began by schedulng an onste meetng wth DMs and the span of tme of ntervews wth each DMs was approxmately one to two hours and subsequently IF-TOPSIS approach s employed. TOPSIS s wdely acceptable and very useful technque to solve MCDM problems that was developed n 1981 by Hwang [31]. It worked on the scheme that optmal alternatve should have shortest dstance from postve deal soluton (PIS) and have a longest dstance from negatve deal soluton (NIS). TOPSIS method used the dstances from PIS and NIS to evaluate the preference order of the relatve closeness coeffcent. Owng to the massve ntrcacy n decson makng process, fuzzy sets are commonly used by DMs to deal wth the ambguty and uncertanty [32]. In 1986 ntutonstc fuzzy set (IFS) s proposed by Atanassov that s extensve form of classcal fuzzy set that tackle mprecson n planned manner n uncertan settngs [33]. Consder X s fnte set then IFS A n X s as follows: A = ( x, A( x), A( x) ) x X Where, ( ) : A 0.00,1.00 ( ) 0.00,1.00 A x X represents membershp functon and non-membershps functon respectvely 0 ( x A ) + A( x ) 1,x X (1) The factor A( x) s represents the hestaton level of x X to A and 0 A( x) 1, x X whch are calculated as follows: A( x) = 1 A( x) A( x) (2) If A and B s IFS of set X, then multplcaton operator s calculated as: A B = { ( x). ( x), A( x) + B( x) A( x). B( x) x X} (3) A B The IF-TOPSIS methodology requres the followng steps [34]: Step1: Assessment the ratng of drvers and perspectves Suppose that A = A1, A2, A3, A4,..., A be the set of possble alternatves m and x= x1, x 2, x 3, x 4,..., x n be the set of crtera and the weght of crtera are represented as w = w1, w2, w3, w4,..., wn. The ratngs of each DMs for each alternatve wth respect to crtera are denoted k, that s based on poston, work experence and educaton qualfcaton that s shown n Table 4. Ths study copes wth eghteen drvers and four perspectves. The lngustc assessment of drvers and perspectves by the DMs are shown n Table 5 and Table 6 respectvely.

7 Prortzaton of green manufacturng drvers n Indan SMEs through f-topsis approach 283 Table 4 The Bref profle of DMs S.No. Decson makers Desgnaton Type of organzaton Experence 1 DM 1 Deputy general manager Manufacturng 16 (Subect expert) ndustry 2 DM2 Senor manager Manufacturng 12 ndustry 3 DM3 Professor (Subect Academc nsttuton 20 expert) 4 DM4 Manager Non-governmental organzaton (CII) 08 Table 5 Lngustc assessment of drvers S.No. Drver Industry perspectve Academc perspectve Government perspectve Consumer Perspectve 1 D1 VI I VI I 2 D2 VI VI MI VI 3 D3 I VI MI I 4 D4 MI I MI I 5 D5 VI MI MI I 6 D6 VI MI MI VI 7 D7 MI VI VI VI 8 D8 I MI MI I 9 D9 MI MI VI MI 10 D10 I MI I VI 11 D11 VI MI VI I 12 D12 MI VI VI I 13 D13 MI MI MI I 14 D14 I MI VI VI 15 D15 I MI MI VI 16 D16 I VI MI VI 17 D17 I I VI I 18 D18 VI VI MI I Table 6 Lngustc assessment of the perspectves S.No. Crtera DM1 DM2 DM3 DM4 1 Industry perspectve MI MI VI MI 2 Academc perspectve VI MI I VI 3 Government perspectve VI I MI MI 4 Consumer perspectve I I I VI

8 284 Pyush Jaswal, Amaresh Kumar, Sumt Gupta Step2: Calculate the weght of DMs th Suppose Dk = k, k, k be an IF number for ratng of k DMs. Then th weght of k DMs are obtaned by equaton (4) as shown n Table 7. k k + k k + k k = l k k k k = 1 + k + k (4) Where, k = 1,2,3,4,..., l Step3: Construct IF decson matrx The ntutonstc fuzzy decson matrx ( R) for the drvers can be represent as follows and that s shown n Table 8. ( x1 ), A ( x ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 1 1, A x 1 1 x2, A x 1 2, A x 1 2 xn, A x 1 n, A x 1 n ( x ), A ( x ), A ( x ) ( x ), A ( x ), A ( x ) ( x ), A ( x ), A ( x ) A 1 A 1 A 1 A A A2 n 2 n 2 n R = A ( x1 ), A ( x1 ), ( 1 ) ( 2 ), ( 2 ), ( 2 ) ( ), ( ), ( ) m m A x m A x A x m m A x m A x A m n x m n A x m n Step4: Calculate aggregate weght of the crtera (Perspectve) Suppose k k, k, k th w = be the IF number that assgned to crtera x by k DMs. Then aggregate weght of crtera s calculated by utlzng ntutonstc fuzzy weghted averagng (IFWA) operator by equaton (5) that s shown n Table 9. w Where, = 1,2,3,..., n ( 1) ( 2) ( 3) ( l) = (,,,..., ) w IFWA w w w w ( 1) ( 2) ( 3) ( l) = l l k l l k l ( k k ) ( k ) ( k ) ( k ) = 1 ( 1 ), ( ), ( 1 ) ( ) k = 1 k = 1 k = 1 k = 1 w w w w w Table 7 The mportance of decson makers and ther weghts DM1 DM2 DM3 DM3 Lngustc terms MI I VI I Intutonstc (0.90,0.10,0.00) (0.50,0.45,0.05) (0.75,0.20,0.05) (0.50,0.45,0.05) fuzzy numbers Crsp weght (5)

9 Prortzaton of green manufacturng drvers n Indan SMEs through f-topsis approach 285 Table 8 The ntutonstc fuzzy decson matrx S.No Drvers Industry perspectve Academc perspectve Government Perspectve Consumer Perspectve 1 D1 (0.75,0.20,0.05) (0.50,0.45,0.05) (0.75,0.20,0.05) (0.50,0.45,0.05) 2 D2 (0.75,0.20,0.05) (0.75,0.20,0.05) (0.90,0.10,0.00) (0.50,0.45,0.05) 3 D3 (0.50,0.45,0.05) (0.75,0.20,0.05) (0.90,0.10,0.00) (0.35,0.60,0.05) 4 D4 (0.90,0.10,0.00) (0.50,0.45,0.05) (0.90,0.10,0.00) (0.50,0.45,0.05) 5 D5 (0.75,0.20,0.05) (0.90,0.10,0.00) (0.90,0.10,0.00) (0.90,0.10,0.00) 6 D6 (0.75,0.20,0.05) (0.90,0.10,0.00) (0.90,0.10,0.00) (0.75,0.20,0.05) 7 D7 (0.90,0.10,0.00) (0.75,0.20,0.05) (0.75,0.20,0.05) (0.75,0.20,0.05) 8 D8 (0.50,0.45,0.05) (0.90,0.10,0.00) (0.90,0.10,0.00) (0.50,0.45,0.05) 9 D9 (0.90,0.10,0.00) (0.90,0.10,0.00) (0.75,0.20,0.05) (0.90,0.10,0.00) 10 D10 (0.50,0.45,0.05) (0.90,0.10,0.00) (0.50,0.45,0.05) (0.75,0.20,0.05) 11 D11 (0.75,0.20,0.05) (0.90,0.10,0.00) (0.75,0.20,0.05) (0.90,0.10,0.00) 12 D12 (0.90,0.10,0.00) (0.75,0.20,0.05) (0.75,0.20,0.05) (0.50,0.45,0.05) 13 D13 (0.90,0.10,0.00) (0.90,0.10,0.00) (0.90,0.10,0.00) (0.50,0.45,0.05) 14 D14 (0.50,0.45,0.05) (0.90,0.10,0.00) (0.75,0.20,0.05) (0.75,0.20,0.05) 15 D15 (0.50,0.45,0.05) (0.90,0.10,0.00) (0.90,0.10,0.00) (0.75,0.20,0.05) 16 D16 (0.50,0.45,0.05) (0.75,0.20,0.05) (0.90,0.10,0.00) (0.75,0.20,0.05) 17 D17 (0.50,0.45,0.05) (0.50,0.45,0.05) (0.75,0.20,0.05) (0.50,0.45,0.05) 18 D18 (0.75,0.20,0.05) (0.75,0.20,0.05) (0.90,0.10,0.00) (0.50,0.45,0.05) Table 9 The aggregate ntutonstc fuzzy weght of crtera Perspectve DM1 DM2 DM3 DM4 Aggregate weght P1 (0.90,0.10,0.00) (0.90,0.10,0.00) (0.75,0.20,0.05) (0.90,0.10,0.00) (0.8698,0.1221,0.0081) P2 (0.75,0.20,0.05) (0.90,0.10,0.00) (0.50,0.45,0.05) (0.75,0.20,0.05) (0.7440,0.2212,0.0348) P3 (0.75,0.20,0.05) (0.50,0.45,0.05) (0.90,0.10,0.00) (0.90,0.10,0.00) (0.8160,0.1676,0.0164) P4 (0.50,0.45,0.05) (0.50,0.45,0.05) (0.50,0.45,0.05) (0.75,0.20,0.05) (0.5623,0.3852,0.0525) Step5: Compute aggregated weghted IF decson matrx The aggregated weghted IF decson matrx ( R') s evaluated wth the help of equaton (6) and (7), as shown n Table 10. A.W ( x) = 1 A ( x) w ( x) A ( x) w ( x) + A ( x) w ( x) (6) R W = x, ( x). ( x), ( x) + ( x) ( x) ( x) x X (7) A w A w A w The aggregated IF weghted decson matrx ( R ') s defned as follows: AW ( x ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 1 1, AW x 1 1, AW x 1 1 AW x 1 2, AW x 1 2, AW x 1 2 AW x 1 n, AW x 1 n, AW x 1 n A ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 2W x1, A2W x1, A2W x1 A2W x2, A2W x2, A2W x2 A,, 2W xn A2W xn A2W xn R ' = A ( 1 ), A ( 1 ), ( 1 ) ( 2 ), ( 2 ), ( 2 ) ( ), ( ), ( ) mw x mw x AmW x A A mw x mw x AmW x A A mw xn mw xn AmW x n

10 286 Pyush Jaswal, Amaresh Kumar, Sumt Gupta Table 10 The aggregate weghted ntutonstc fuzzy decson matrx Drver Industry perspectve Academc perspectve Government perspectve Consumer perspectve D1 (0.6523,0.2976,0.0500) (0.3720,0.6288,0.2510) (0.6120,0.3340,0.0539) (0.2811,0.6618,0.0570) D2 (0.6523,0.2976,0.0500) (0.5580,0.3769,0.0650) (0.7344,0.2508,0.0147) (0.4217,0.5081,0.0696) D3 (0.4349,0.5171,0.0479) (0.5580,0.3769,0.0650) (0.7344,0.2508,0.0147) (0.2811,0.6618,0.0570) D4 (0.7828,0.2098,0.0073) (0.3720,0.6288,0.2510) (0.7344,0.2508,0.0147) (0.2811,0.6618,0.0570) D5 (0.6523,0.2976,0.0500) (0.6696,0.2990,0.0313) (0.7344,0.2508,0.0147) (0.2811,0.6618,0.0570) D6 (0.6523,0.2976,0.0500) (0.6696,0.2990,0.0313) (0.7344,0.2508,0.0147) (0.4217,0.5081,0.0696) D7 (0.7828,0.2098,0.0073) (0.5580,0.3769,0.0650) (0.6120,0.3340,0.0539) (0.4217,0.5081,0.0696) D8 (0.4349,0.5171,0.0479) (0.6696,0.2990,0.0313) (0.7344,0.2508,0.0147) (0.2811,0.6618,0.0570) D9 (0.7828,0.2098,0.0073) (0.6696,0.2990,0.0313) (0.6120,0.3340,0.0539) (0.5060,0.4466,0.0473) D10 (0.4349,0.5171,0.0479) (0.6696,0.2990,0.0313) (0.4080,0.5421,0.0498) (0.4217,0.5081,0.0696) D11 (0.6523,0.2976,0.0500) (0.6696,0.2990,0.0313) (0.6120,0.3340,0.0539) (0.2811,0.6618,0.0570) D12 (0.7828,0.2098,0.0073) (0.5580,0.3769,0.0650) (0.6120,0.3340,0.0539) (0.2811,0.6618,0.0570) D13 (0.7828,0.2098,0.0073) (0.6696,0.2990,0.0313) (0.7344,0.2508,0.0147) (0.2811,0.6618,0.0570) D14 (0.4349,0.5171,0.0479) (0.6696,0.2990,0.0313) (0.6120,0.3340,0.0539) (0.4217,0.5081,0.0696) D15 (0.4349,0.5171,0.0479) (0.6696,0.2990,0.0313) (0.7344,0.2508,0.0147) (0.4217,0.5081,0.0696) D16 (0.4349,0.5171,0.0479) (0.5580,0.3769,0.0650) (0.7344,0.2508,0.0147) (0.4217,0.5081,0.0696) D17 (0.4349,0.5171,0.0479) (0.3720,0.6288,0.2510) (0.6120,0.3340,0.0539) (0.2811,0.6618,0.0570) D18 (0.6523,0.2976,0.0500) (0.5580,0.3769,0.0650) (0.7344,0.2508,0.0147) (0.2811,0.6618,0.0570) IFPIS (0.7828,0.2098,0.0073) (0.6696,0.2990,0.0313) (0.7344,0.2508,0.0147) (0.5060,0.4466,0.0473) IFNIS (0.4349,0.5171,0.0479) (0.3720,0.6288,0.2510) (0.4080,0.5421,0.0498) (0.2811,0.6618,0.0570) Step6: Calculate IF postve deal soluton (IFPIS) and IF negatve deal solutons (IFNIS) wth the help of equaton (8) and (9). ( ) A = r, r, r,..., r = ( (x ), (x ), (x )), = 1,2,3,...,n (8) n A.W A.W A.W Where; ( ) A = r, r, r,..., r = ( (x ), (x ), (x )), = 1,2,3,...,n (9) * * * * * * * * n A.W A.W A.W (x ) = {max (x ) = 1,2,3,...,n} * A.W A.W (x ) = {mn (x ) = 1,2,3,...,n} * A.W A.W (x ) = {max (x ) = 1,2,3,...,n} A.W A.W (x ) = {mn (x ) = 1,2,3,...,n} A.W A.W Step7: Evaluate the dstance from the alternatves (Drvers) and IFPIS as well as IFNIS, wth the help of equaton (10) and (11). S 1 n * = ( A.W (x ) * (x )) (.W (x ) * (x )) (.W (x ) * (x )).W + A.W + A A.W 2 1 A A n = (10)

11 Prortzaton of green manufacturng drvers n Indan SMEs through f-topsis approach 287 S 1 = + + n ( A.W (x ) (x )) (.W.W (x ) (x )) (.W.W (x ) (x )) A A A.W 2 1 A A n = (11) Step7: Evaluate the relatve closeness coeffcent of alternatve wth respect to IFPIS by utlzng equaton (12). * S C = * S + S Where, 0 C * 1( = 0,1,2,3..., m) Step 8: The rank of drvers s evaluated accordng to score of relatve * closeness coeffcent C as shown n Table 11. (12) Table 11 Separaton measures and the relatve closeness coeffcent of alternatves S.No. Drvers * S S * C Rank 1 D D D D D D D D D D D D D D D D D D Stage 3: Result valdaton In the last stage, to verfy the relablty of the results, the obtaned outcomes are dscussed wth the ndustral and academc experts by makng an onste meetng and conversaton lasted more than one hours. In the dscusson, t was found that the results obtaned sgnfcantly corroborated wth that of the expert opnons. Also, support from the lterature s provded to valdate the obtaned results.

12 288 Pyush Jaswal, Amaresh Kumar, Sumt Gupta 5. Results and dscusson Ths study prortzed the common drvers n mplementaton of GM n Indan SMEs through IF-TOPSIS approach wth the help of four decson makers havng extensve experence and deep knowledge n the manufacturng sector. The prortzaton of common drvers of GM s based on relatve closeness coeffcent (RCC). The fndngs of the results show that Fnancal benefts s the most essental drver wth RCC score Whereas Soco-cultural responsblty s the least mportant drver to mplement GM wth the score On the bass of RCC values of common drvers, prortzaton of drvers n decreasng order s as follows:d9>d6>d7>d2>d13>d5>d12>d18>d11>d15> D16>D14>D8>D4>D3>D10>D1>D17. D9, D6, D7, D2 and D13 are the top fve most mportant drvers havng enormous nfluence on small and medum manufacturng ndustres to adopt GM. The management of SMEs recognzed varous fnancal benefts n short term and long term tenure from GM practces. In present scenaro materal, energy and water are the prme concern for the manufacturng ndustres and t s well known that GM mplementaton n ndustres can reduce the cost by smart use of materal and energy effcent equpment. Lee [23] dvulges n the study of Korean manufacturng SMEs that reducton of materal, water usage by 13% and 21% after adopton of GM. Rutherford et al. [35] also advocated that better GM practces mprove the costs as well as relatonshps wth the ultmate customers. Pressure from stakeholder (D6) s the second most mportant drvers. Stakeholder such as meda, NGO, local communty, etc., s drectly nfluencng the ndustres to adopt green related practces to reduce the envronmental mpact. Improve workng envronment (D7) and mprove qualty (D2) are the thrd and fourth essental drvers respectvely. Safe workng envronment s one of the most mportant consderatons for workers performng varous actvtes n the workplace. Better workng envronment and safety are the prme concern of large scale ndustres, but SMEs s lackng n these standard. GM practces provded better workng envronment by reducng pollutants and ar emssons on shop floor due to whch productvty of worker has drastcally mproved Lee [23]. GM system s better and more effcent than tradtonal manufacturng system and has the ggantc ablty to produce eco-frendly products wth very short perod of tme at a lesser prce wth better qualty. Pl et al. [36] appled envronmental practces n a pant shop and observed that changes n the producton process had the addtonal benefts of qualty mprovement along wth envronmental mprovements. Legslatve and regulatory complances (D13) are the ffth most mportant drvers for GM adopton n SMEs. Due to the sgnfcant envronmental mpact from ndustral waste, government legslatve organzatons ntroduce regulatons, polces and laws to control and regulate the envronmental performances. Zhu et al. [18] also

13 Prortzaton of green manufacturng drvers n Indan SMEs through f-topsis approach 289 supported envronmental regulaton s the one of maor drvers to enforce GM practces to protect envronment all over the world. Under the regulatory pressure and government efforts, manufacturng sector s drven towards green practces. Government of Inda also promotes zero effect zero defect (ZED) strateges for mcro, small and medum enterprses under the UNIDO-GEPF-MSME scheme. Due to ther pertnent benefts, other drvers are also assstng n effectve mplementaton of GM n manufacturng SMEs. 6. Conclusons GM has become the need of urgency for small and medum manufacturng sector because of the huge amount of waste generated by them. In developng countres, lke Inda penetratons of green practces are stll lackng. So, n order to ncrease the rate of mplementaton, dentfcaton of essental drvers s needed that nfluence the management of SMEs to adopt GM practces. Therefore, ths study provdes eghteen essental drvers based on extensve lterature and suggestons from experts. Subsequently, ntutonstc fuzzy-topsis approach has been utlzed to prortze the common drvers of GM wth the help of four decson makers. Fnancal benefts (D9), Pressure from stakeholders (D6), Improve workng envronment (D7), Improve qualty (D2) and Legslatve and regulatory complances (D13) are the fve most mportant drvers of GM for manufacturng SMEs. It s well known that mplementaton of GM strategy wll help the SMEs to take compettve advantage and fnancal benefts through subsdes and tax exemptons. For effectve and effcent adopton of GM practces n manufacturng frms, understandng the essental drvers are mportant. The present study asssts the management of SMEs to prortze the essental drvers for successful mplementaton of GM practces n ther ndustres. Ths study collected the data from four decson makers whch have vast knowledge and experence n manufacturng sectors. The future scope could be to analyze the essental drvers n dfferent sectors such as food ndustry, textle ndustry wth more experts opnon.

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