ASSESSMENT OF DRIVERS TO IMPLEMENT LEAN MANUFACTURING IN INDIAN SMES USING INTUITIONISTIC FUZZY BASED TOPSIS APPROACH

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1 Internatonal Journal of Modern Manufacturng Technologes ISSN , Vol. IX, No. 2 / 2017 ASSESSMENT OF DRIVERS TO IMPLEMENT LEAN MANUFACTURING IN INDIAN SMES USING INTUITIONISTIC FUZZY BASED TOPSIS APPROACH Pyush Jaswal 1, Amaresh Kumar 2, Kana Prasad 3 1,2,3 Department of Manufacturng Engneerng, Natonal Insttute of Technology Jamshedpur, Jharhand, Inda Correspondng author: Pyush Jaswal, pyush.rsp002@ntsr.ac.n Abstract: Small and medum enterprses (SMEs) n the manufacturng sector face many problems related to qualty, cost, flexblty and delvery tme owng to ncreased competton n the current globalzed maret economy. Lean manufacturng (LM) has been proposed as a soluton to some of these ssues to mprove the overall performance. There are multple factors that act as motvaton to mplement LM. Ths study consders 18 drvers n the adopton of LM, whch have been dentfed on the bass of extensve lterature revews and opnon of the experts. The ranng of drvers n the context of Indan SMEs has been accomplshed by utlzng IF-TOPSIS approach. The results reveal that reduce cost, mprove qualty, mprove labor productvty, reduce nventory and shortened producton lead tme are the most sgnfcant drvers. Ths study provdes mportant nsght to ndustry and government personnel s to focus on essental drvers to facltate proper and effectve executon of LM. Key words: Lean manufacturng, drvers, IF-TOPSIS, SMEs, Inda. 1. INTRODUCTION Small and medum enterprses (SMEs) play an mportant role n the manufacturng sector globally n terms of employment generaton and volume producton. In the contemporary dynamc global maretplace, SMEs face the challenge of rapdly growng competton, emergng technologes and ever-changng customer behavor. Faster adopton of new emergng manufacturng technology over the outdated or exstng manufacturng practce s one of the maor challenges faced by SMEs. The other s fast changng customer behavor whereby they demand more nnovatve and better qualty products at a lesser prce wth very short perod of tme. In order to survve n the face of these challenges posed by global compettors n the fast changng maretplace, the organzatons need to swtch to lean manufacturng (LM) strategy over tradtonal manufacturng (Sahoo et al.,2008). The term lean producton was frst used n 1988 by John Krafc n hs artcle Trumph of the lean producton system Krafc (1988). Later the term was popularzed by Womac and Jones n ther boo The Machne That Changed the World (Womac et al., 1990). LM s a new manufacturng paradgm whch 30 focuses on contnuous elmnaton of nne deadly wastes wthn the organzaton and across the supply chan (Tce et al., 2005). The obectve of LM s to provde a proper channel of producng hgh qualty products wth lmted resources such as equpment, manpower, space and tme to meet the customer demand. Accordng to Khadem et al., (2008) Lean producton s an ntegrated approach to manufacturng of products (servce) wth the purpose of achevng superor qualty, tmely delvery and compettve cost leadng to customer satsfacton. SMEs n Inda have emerged as a vbrant and dynamc sector of the Indan economy over the last few decades. They play a crucal role n provdng large employment opportuntes at a comparatvely lower captal cost and can be set up even n rural areas. The 48 mllon SMEs n Inda s growng at an annualzed rate of 4.5% over the last fve years and provde 40% employment of the total worforce and contrbute 45% of manufacturng sector output whch amounts to only 17% of GDP (Vyas et al., 2015). However, n near-term, the Indan SMEs can grow and ncrease ther contrbuton n GDP wth reforms and adopton of new technologes. In Inda, SMEs n manufacturng sector currently plagued wth problems of hgh nventory, poor houseeepng, low manpower productvty, hgh scrap, global competton, unstandardzed operaton, nsuffcent slled manpower, hgh product development tme and turbulent and uncertan maret scenaro whch restrct ther growth. To survve and floursh n the global maret, SMEs needs to address these ssues by adoptng nnovatve approaches such as LM n ther operatons and supply chan. Indan SMEs ressts change as they fear new technology adopton s an expensve and a never endng process because of fast turnover n technology. SMEs managers tend to vew new technology as an expense rather than as a strategc nvestment. Hence, mplementaton of any new manufacturng strategy, partcularly n small and medum manufacturng sector, needs strong motvatng factors whch can force the management of SMEs to adopt the new manufacturng strategy. For proper and effectve mplementaton of LM strategy, management of SMEs must understand the ey drvers that motvate the adopton of LM. So, a sutable methodology s requred

2 to facltate the ndustres to dentfy the ey drvers. Ths study used ntegrated ntutonstc fuzzy-technque for order of preference by smlarty to deal soluton (IF- TOPSIS, Technque for Order of Preference by Smlarty to Ideal Soluton) approach to ran the ey drvers. IF-TOPSIS provde complete tool to handle the vagueness and uncertanty n complex envronments by measurng the nherent ambguty of decson maer s udgment n mult crtera decson mang (MCDM) area. Based on the lterature revew and expert opnon, 18 drvers are extracted that motvate the mplementaton of LM n SMEs at Indan scenaro. The unqueness of ths research wor s that t analyzes the qualtatve data whle utlzng ntegrated IF-TOPSIS methodology to ran the drvers that nfluence mplementaton of LM n Indan SMEs. 2. LITERATURE REVIEW In the past, plenty of research wor has been conducted on methodology, case study, framewor, tools/technques and benefts of LM system. Moreover, the earler researches whch have been carred out to fnd the drvng factors that nfluence LM mplementaton n the manufacturng ndustry through survey questonnares are lmted n number. Jast et al., (2016) dentfed the drvng factors through questonnare survey, n whch 71.67% respondents were from large scale ndustry; found that mprovng customer satsfacton was the prme nfluencng factor n the mplementaton of lean phlosophy n Indan manufacturng ndustres. Ghosh (2012) examned the status of lean mplementaton and dentfed three mportant drvers.e. frst-pass correct output, reducton of lead tme and productvty mprovement whch ad n adopton of lean n Indan large scale manufacturng frms. Kumar et al., (2013) recognzed 18 varables to mplement LM n Indan automoble sectors and developed a structural model whle usng nterpretve structural model (ISM). Zhou (2012) found that cost reducton, compettveness, plant utlzaton and mprovement n proft margn were the prme reasons to adopt LM n US based SMEs. Attr et al., (2013) dentfed 10 enablers and developed a herarchcal model n mplementaton of total productve mantenance, whle employng ISM technque. Bhasn (2012) carred out a survey of 68 Brtsh manufacturng ndustres and concluded that mprove performance, compettve pressure, create team sprt and customer pressure were the top most reasons to adopt LM. Upadhye et al., (2011) dentfed the drvng forces and crtcal ssues n mplementaton of LM by usng ISM methodology. Mshrot et al., (2011) explored the drvng factors and found that equpment utlzaton and scrap reducton were the most sgnfcant drvng forces to mplement LM n small frms. Sngh et al., (2010) examned and categorzed the factors nto fve dfferent categores that force the organzaton to adopt lean system. Nordn et al., (2010) nvestgated both drvers and barrers n mplementaton of LM n Malaysan automotve organzatons. Hallgren et al., (2009) dentfed nternal and external factors that drve lean and agle manufacturng and analyzed the mpact on operatonal performance whle usng structural equaton modelng technque. It becomes qute evdent from the revew of past lteratures that the quantum of research wor to dentfy and ran the drvers from three dfferent perspectves vz. ndustry, academcs and government to mplement LM n small and medum manufacturng sector at Indan scenaro through MCDM approach s not n proporton at all wth the ncredble growth n the ndustral actvtes n ths sector. Therefore, the present study attempts to brdge the gap by dentfyng the common drvers of LM and by evaluatng ts ran. The lst of 18 selected drvers of LM adopton s provded n table 1. The some hghlghts of ths study are lsted below: -Identfy the common drvers of LM through an extensve lterature revew and experts suggestons; -Proposed a framewor to ran LM drvers n small and medum manufacturng ndustres at Indan scenaro usng IF-TOPSIS; -Valdate the obtaned results through feedbac from government, ndustry and academc experts and compare wth the exstng lterature. 3. APPLICATION OF PROPOSED FRAMEWORK The framewor of the study to ran 18 drvers of LM mplementaton n Indan SMEs s exhbted n Fgure 1. The framewor broadly conssts of three stages. The frst stage comprses of dentfcaton of drvers on the bass of publshed lterature and suggestons from the experts. In the next stage, IF-TOPSIS method s appled to ran the drvers based on the opnons of decson maers from three dfferent domans vz. ndustry, academcs and government. In the last stage the results obtaned are valdated through the past lterature and feedbac of decson maers. The lngustc scale and ntuonstc fuzzy ratng for alternatve and crtera are shown n Table2. Stage 1: Identfcaton of common drvers of lean manufacturng. In the frst stage of the proposed model s to gather varous drvers of LM through exstng lterature and suggestons from experts. A systematc lterature revew technque s followed and the tme perod of search n electronc database such as Google Scholar, Web of Scence, Scopus etc., was from 1988 to 2016 wth search terms that contaned conuncton of exact word and more characters le lean manufacturng, lean producton, lean thnng, Toyota producton system, lean-agle, 31

3 Table 1. Common drvers of Lean Manufacturng S. No. Lst of drvers Notatons References 1 Compettveness D1 Zhou (2012), (Hallgren et al., 2009), (Mchael et al., 2000) 2 Improve man and machne D2 Ghosh (2012), (Vnodh et al., 2010), (Jeyara et al., 2013), utlzatons (Mshrot et al., 2011) 3 Standardze the operatons D3 (Zutng et al., 2014), (Coo et al., 1996), (Sohal et al., 1994), 4 Improve qualty D4 (Venataraman et al., 2014), Zhou (2012), (Mshrot et al., 2011) 5 Develop team sprt between D5 Bhasn (2012) employees 6 Shortened producton lead tme D6 (Jeyara et al., 2013), (Vnodh et al., 2010) 7 Increase flexblty D7 Zhou (2012), (Jast et al., 2016), (Hallgren et al., 2009) 8 Improve labor productvty D8 (Staats et al., 2011), Ghosh (2012) 9 Employ world best practces D9 (Jast et al., 2016) 10 Increase maret share D10 Zhou (2012), (Jast et al., 2016) 11 Sll development D11 (Sngh et al., 2010), (Panzzolo et al., 2012), 12 Improve customer satsfacton D12 (Jast et al., 2016), (Sngh et al., 2010), (Nordn et al., 2010), (Mshrot et al., 2011) 13 Establsh better vendor D13 (Mshrot et al., 2011), (Zhou 2012), relatonshp 14 Development of ey performance D14 (Jast et al., 2016), (Khadem et al., 2008) ndcators 15 Reduce cost D15 (Mshrot et al., 2011), (Zhou 2012) 16 Reduce new product development D16 (Shepherd et al., 2000), (Schmdt et al., 2009) tme 17 Reduce nventory D17 Zhou (2012), (Sngh et al., 2010), (Sngh et al., 2011) 18 Create safe and better worng envronment D18 Bhasn (2012), Goldsten (2001) Lterature revew Experts opnon Stage 1 Identfcaton of drvers to mplement LM n Indan SMEs Questonnare wth common drvers crculate to varous experts Decson maer 1 Decson maer 2 Decson maer 3 Stage 2 Analyss of drvers based on three decson maers by usng IF- TOPSIS approach Feedbac Result and dscusson from feedbac of experts and comparson wth exstng lterature Feeddbac Exstng lterature Experts opnon Stage 3 Conclusons Fg. 1. Framewor of the study 32

4 1988 to 2016 wth search terms that contaned conuncton of exact word and more characters le drvers, crtcal success factors, SMEs and so on. After several rounds of dscussons and content verfcaton wth experts, 18 common drvers are selected. Table 2. Lngustc scale and ntuonstc fuzzy ratng for alternatve and crtera Lngustc terms Intutonstc Fuzzy numbers Unmportant (U) (0.10,0.90,0.00) Least mportant (LI) (0.35,0.60,0.05) Important (I) (0.50,0.45,0.05) Very mportant (VI) (0.75,0.20,0.05) Most mportant (MI) (0.90,0.10,0.00) Stage 2: Applcaton of IF-TOPSIS Approach In ths stage, common drvers of LM are analyzed by usng IF-TOPSIS approach, whch are dentfed n the prevous stage. TOPSIS s a smple and very useful technque to solve MCDM problems, whch was developed by Hwang n the year 1981 (Hwang et al., 1981). It s based on dea that optmal alternatve should have the shortest dstance from postve deal soluton (PIS) and have a longest dstance from negatve deal soluton (NIS). TOPSIS method utlzes both dstances from PIS and NIS smultaneous to ran the order of preference accordng to descendng order of the relatve closeness coeffcent. Due to the ncreased complexty n decson mang process, fuzzy sets are generally used by decson maers to embody the uncertanty and ambguty. An ntutonstc fuzzy set (IFS) proposed by Atanassov n 1986 s an extended verson of classcal fuzzy set, whch handles vagueness very well manner n uncertan envronments (Atanassov et al., 1986). They have been successfully appled n dfferent feld such as decson mang, medcal dagnoss, pattern recognton etc. Consder X s a fnte set. Then IFS A n X s wrtten as: A, A, A x X Where, A : X 0,1 and A : X 0,1 are membershp functon non-membershps functon, such that A 0 A A 1, x X (1) The thrd parameter A x s nown as the ntutonstc fuzzy ndex and show the hestaton level of x X to A and 0 A x 1, x X whch are determne as follows: A 1 A (2) A If the s small, nowledge about x s more certan. If A x s large, nowledge about x s more uncertan. If A and B s IFSs of the set x, then the multplcaton operator s defned as: A B { x. B x, A x B x A x. B x x X} A (3) The IF-TOPSIS approach nvolves the followng steps (Boran et al., 2009): A x Step 1: Assgnment of ratng to the alternatves (Drvers) and crtera (Perspectve) Let us assume that A A1, A2, A3,... A be a set of possble m alternatves, X X1, X 2, X 3,... X be a set of evaluaton n crtera and the weght of crtera are denoted w w, w, w... w. The performance ratngs of each by 1 2 3, n decson maer for each alternatve wth respect to crtera are denoted, whch s based on poston, worng experence and educaton qualfcaton. The present study deals wth 18 alternatves and three crtera. The lngustc assessment of alternatves and crtera by the decson maers are shown n Table 3 and Table 4 respectvely. Table 3. Lngustc assessment of alternatves Notatons Industry Academcs Government D1 MI VI VI D2 VI I MI D3 VI I I D4 MI MI VI D5 I I LI D6 MI I MI D7 VI VI I D8 VI VI MI D9 I I VI D10 I VI MI D11 I I I D12 MI VI I D13 I VI VI D14 I LI I D15 MI VI MI D16 VI LI I D17 I MI VI D18 I LI VI Table 4. Lngustc assessment of the crtera Crtera DM1 DM2 DM3 Industry perspectve (C1) MI MI VI Academcs perspectve (C2) VI VI I Government perspectve (C3) I VI MI Step 2: Compute the weght of decson maers Let D,, be an ntutonstc fuzzy number for ratng of th decson maers. Then the weght 33

5 of decson maers s determned by equaton (4) and s shown n Table 5. th Where, l 1 1, 2,3,... l Table 5. The mportance of decson maers and ther weghts DM1 DM2 DM3 Lngustc MI I VI terms Intutonstc Fuzzy numbers (0.90,0.10,0.00) (0.50,0.45,0.05) (4) (0.75,0.20,0. 05) Crsp weghts Step 3: Compute IF decson matrx The ntutonstc fuzzy decson matrx R for the alternatves can be defned as follows, whch s shown n Table 6. 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 R A A A2 n 2 n 2 n x, A x, A x x, A x, A x x, A x, A x Am 1 m 1 m 1 Am 2 m 2 m 2 Am n m n m n Step 4: Compute aggregate weght of crtera All crtera may not be gve equal mportance. Let w,, be an IF number assgned to crtera decson maer. Then the aggregate x by the th weght of the crtera are obtaned by usng ntutonstc fuzzy weghted averagng (IFWA) operator from the equaton (5), whch s shown n Table 7. S 1 2 l,,..., w IFWA w w w l l w w w w w w l 1 1 l l,, 1 l (5) Where, 1, 2,3,..., n Step 6: Construct aggregated weghted ntutonstc fuzzy decson matrx After constructon of ntutonstc fuzzy decson matrx and calculaton of aggregate weght crtera, the aggregated weghted ntutonstc fuzzy decson matrx ( ') s obtaned by equaton (6) and (7), whch s shown n Table 8. x x x x x x x (6) R A.W 1 A w A w A w R W x, A x. w x, A x w x A x. w x x X (7) Step 7: Compute the ntutonstc fuzzy postve deal soluton (IFPIS) and ntutonstc fuzzy negatve deal solutons (IFNIS) by equaton (8) and (9). A r, r, r,..., r ( (x ), (x ), (x )), 1,2,...n (8) n A.W A.W A.W A r, r, r,..., r ( (x ), (x ), (x )), 1,2,...n (9) * * * * * * * * n A.W A.W A.W Where, (x ) {max (x ) 1,2,...,n} * A.W A.W (x ) {mn (x ) 1,2,...,n} * A.W A.W (x ) {max (x ) 1,2,...,n} A.W A.W (x ) {mn (x ) 1,2,...,n} A.W A.W Step 8: Calculate dstance between the alternatves and the IFPIS as well as the IFNIS, respectvely by equaton (10) and (11). 1 (10) n n * ( A.W (x ) * (x )) (.W (x ) * (x )) (.W (x ) * (x )).W A.W A A.W 2 1 A A S 1 n ( A.W (x ) (x )) (.W.W (x ) (x )) (.W.W (x ) (x )) A A A.W 2 1 A A (11) n Step 5: Compute the relatve closeness coeffcent of each alternatve wth respect to IFPIS by usng equaton (12). * S C * (12) S S Where, 0 C * 1( 0,1,2,..., m) Step 9: Ran of alternatves s determned accordng to descendng order of relatve closeness * coeffcent C whch s shown n Table 9. Stage 3: Results valdaton The obtaned results are dscussed wth the decson maers to chec the relablty of the outcomes. After dscusson wth experts, no sgnfcant dfferences are found and the results are greatly corroborated wth the opnon of the experts. Fnally, the results of ths study are also valdated wth exstng lterature. 34

6 Table 6. The ntutonstc fuzzy decson matrx Notatons Industry Academcs Government D1 (0.90,0.10,0.00) (0.75,0.20,0.05) (0.75,0.20,0.05) D2 (0.75,0.20,0.05) (0.75,0.20,0.05) (0.90,0.10,0.00) D3 (0.75,0.20,0.05) (0.50,0.45,0.05) (0.50,0.45,0.05) D4 (0.90,0.10,0.00) (0.90,0.10,0.00) (0.90,0.10,0.00) D5 (0.50,0.45,0.05) (0.50,0.45,0.05) (0.35,0.60,0.05) D6 (0.90,0.10,0.00) (0.75,0.20,0.05) (0.90,0.10,0.00) D7 (0.75,0.20,0.05) (0.75,0.20,0.05) (0.50,0.45,0.05) D8 (0.75,0.20,0.05) (0.75,0.20,0.05) (0.90,0.10,0.00) D9 (0.50,0.45,0.05) (0.50,0.45,0.05) (0.75,0.20,0.05) D10 (0.75,0.20,0.05) (0.75,0.20,0.05) (0.90,0.10,0.00) D11 (0.50,0.45,0.05) (0.50,0.45,0.05) (0.50,0.45,0.05) D12 (0.75,0.20,0.05) (0.75,0.20,0.05) (0.90,0.10,0.00) D13 (0.50,0.45,0.05) (0.50,0.45,0.05) (0.75,0.20,0.05) D14 (0.50,0.45,0.05) (0.35,0.60,0.05) (0.50,0.45,0.05) D15 (0.90,0.10,0.00) (0.90,0.10,0.00) (0.90,0.10,0.00) D16 (0.50,0.45,0.05) (0.35,0.60,0.05) (0.50,0.45,0.05) D17 (0.50,0.45,0.05) (0.75,0.20,0.05) (0.90,0.10,0.00) D18 (0.50,0.45,0.05) (0.35,0.60,0.05) (0.75,0.20,0.05) Table 7. The aggregate ntutonstc fuzzy weght of crtera Crtera DM1 DM2 DM3 Aggregate weght C1 (0.90,0.10,0.00) (0.90,0.10,0.00) (0.75,0.20,0.05) (0.8614,0.1279,0.0105) C2 (0.75,0.20,0.05) (0.75,0.20,0.05) (0.50,0.45,0.05) (0.6800,0.2669,0.0529) C3 (0.50,0.45,0.05) (0.75,0.20,0.05) (0.90,0.10,0.00) (0.7609,0.2170,0.0220) Table 8. The aggregate weghted ntutonstc fuzzy decson matrx S. No. Notatons Industry Expert Government 1 D1 (0.7752,0.2151,0.0096) (0.5100,0.4135,0.0764) (0.5706,0.3736,0.0557) 2 D2 (0.6460,0.3023,0.0516) (0.3400,0.5989,0.0632) (0.6848,0.2953,0.1990) 3 D3 (0.6460,0.3023,0.0516) (0.3400,0.5989,0.0632) (0.3804,0.5693,0.0502) 4 D4 (0.7752,0.2151,0.0096) (0.6120,0.3402,0.0477) (0.5706,0.3736,0.0557) 5 D5 (0.4307,0.5203,0.0489) (0.3400,0.5989,0.0632) (0.2663,0.6868,0.0468) 6 D6 (0.7752,0.2151,0.0096) (0.3400,0.5989,0.0632) (0.6848,0.2953,0.1990) 7 D7 (0.6460,0.3023,0.0516) (0.5100,0.4135,0.0764) (0.3804,0.5693,0.0502) 8 D8 (0.6460,0.3023,0.0516) (0.5100,0.4135,0.0764) (0.6848,0.2953,0.1990) 9 D9 (0.4307,0.5203,0.0489) (0.3400,0.5989,0.0632) (0.5706,0.3736,0.0557) 10 D10 (0.4307,0.5203,0.0489) (0.5100,0.4135,0.0764) (0.6848,0.2953,0.1990) 11 D11 (0.4307,0.5203,0.0489) (0.3400,0.5989,0.0632) (0.3804,0.5693,0.0502) 12 D12 (0.7752,0.2151,0.0096) (0.5100,0.4135,0.0764) (0.3804,0.5693,0.0502) 13 D13 (0.4307,0.5203,0.0489) (0.5100,0.4135,0.0764) (0.5706,0.3736,0.0557) 14 D14 (0.4307,0.5203,0.0489) (0.2380,0.7067,0.0552) (0.3804,0.5693,0.0502) 15 D15 (0.7752,0.2151,0.0096) (0.5100,0.4135,0.0764) (0.6848,0.2953,0.1990) 16 D16 (0.6460,0.3023,0.0516) (0.2380,0.7067,0.0552) (0.3804,0.5693,0.0502) 17 D17 (0.4307,0.5203,0.0489) (0.6120,0.3402,0.0477) (0.5706,0.3736,0.0557) 18 D18 (0.4307,0.5203,0.0489) (0.2380,0.7067,0.0552) (0.5706,0.3736,0.0557) IFPIS (0.7752,0.2151,0.0096) (0.6120,0.3402,0.0477) (0.6848,0.2953,0.1990) IFNIS (0.4307,0.5203,0.0489) (0.2380,0.7067,0.0552) (0.2663,0.6868,0.0468) Table 9. Separaton measures and the relatve closeness coeffcent of alternatves * Drvers S S * C Ran D D D

7 D D D D D D D D D D D D D D D RESULTS AND DISCUSSIONS The result of the present research wor shows that reduce cost (D15) s the most mportant drver wth relatve closeness coeffcent , whereas develop team sprt between employees (D5) s the least mportant drver n mplementaton of LM n Indan SMEs wth score Based on the relatve closeness coeffcent, prortzaton of drvers s as follows:d15>d4>d8>d1>d6>d2>d10>d12>d17> D13>D7>D9>D3>D18>D16>D11>D14>D5. The fve top most drvers.e. D15, D4, D8, D1 and D6 whch enormously nfluence the ndustres to mplement LM are dscussed n detal. Due to ther pertnent benefts, other drvers are also help to mplement LM n SMEs. For any maret segment, cost of the product s a crucal factor that determnes the customer s choce (Yusuf et al., 1999). The sellng prce of product sgnfcantly nfluences the revenue of any organzaton and the only way to ncrease the proft margn s by reducng the product prce. LM was developed n order to reduce cost and mprove process flexblty through the complete elmnaton of waste (Yusuf et al., 2002). The LM system consders not only the manufacturng cost, but also the costs assocated wth sales, captal nvestments and admnstraton. In the UK, department of trade and ndustry endorse to mplement LM wthn SMEs (Achanga et al., 2006). Zhou (2012) also dentfed that reduce cost s the most nfluencng drver to mplement LM n SMEs and suggested that mplementaton of LM would help n reducton of the product cost. Improve qualty (D4) s the second hghest raned drver wth score These days SMEs are facng the problem of hgh rate of reecton. Snce, the cost of raw materal and manpower s too hgh, reecton of product due to poor qualty wll tremendously mpact the organzaton. In earler studes suggest that those organzatons whch 36 mplement LM phlosophy acheve sgnfcant mprovement n terms of qualty. Thus, a qualty mprovement ntatve motvates Indan SMEs to mplement LM to mprove overall qualty and to reman n compettons n global busness (Ghosh 2012). The thrd most mportant drver s mprove labor productvty (D8) wth score SMEs are extremely affected by low manpower productvty due to whch they need to hre more worers to perform the wor, whch ultmately dmnshes the proft margn of organzaton. Seelger et al., (2005) found that the labor productvty mproves by 20% after mplementaton of LM. The lean system layout of the worplace allows mult-process holdng by mult-functonal worer due to whch productvty of labor s mproved multtudnously. Compettveness (D1) s the fourth most mportant drver wth score Due to globalzaton, SMEs face ferocous challenges from domestc as well as global frms that nfluenced many of them to mplement LM n order to enhance the compettveness (Zhou 2012). To develop global compettveness among Indan SMEs, government of Inda ntroduced lean manufacturng compettveness scheme under natonal manufacturng compettveness program. In unpredctable global maret, LM phlosophy helps the ndustry to handle the stuaton strategcally through a mult-process holdng by flexble worforce. Shortened producton lead tme (D6) s the ffth top most mportant drver wth score Nowadays, SMEs face huge pressure from maret to produce products wth shorter lead tme. Ghosh (2012) also supported that reducton n producton lead tme s a ey drver to mplement LM n manufacturng ndustry. Due to longer lead tme, nventory n terms of raw materals, wor n process and fnshed goods wll be requred n hgher volumes to cover the tme needed to meet the customers demands. In lean system, pull system and smaller batch sze concept

8 sgnfcantly contrbute to shortened lead tme due to whch on tme delvery performance s greatly mproved. Dhandapan et al., (2004) also observed that 50% reducton n lead tme after adopton of lean phlosophy n the steel ndustry. 5. CONCLUSIONS In ths mmensely compettve envronment, the role of SMEs s very mportant towards the economes of many developed and developng countres of the world. At present, SMEs of Inda are facng massve challenges from global competton n all aspects of busness. Scarcty of raw materal and hgh labor cost are two maor factors whch hghly mpact the small and medum manufacturng frms. In Inda, LM penetraton n SMEs s sgnfcantly low as compared to large scale ndustres. LM facltates the ndustry to become more reactve towards maret demand wth on tme delvery and provde better qualty products at a lesser prce than ther compettors. For successful adopton of LM emphass should be n the need of ranng the drvers that motvate ts mplementaton n SMEs. Therefore, n the present research wor 18 drvers are dentfed from an extensve lterature revew and dscusson wth experts. Subsequently, IF- TOPSIS method has been appled to ran the drvers of LM whle tang opnons of the personals from ndustry, academcs and government. Reduce cost (D15), mprove qualty (D4), mprove labor productvty (D8), compettveness (D1) and shortened producton lead tme (D6) are top fve mportant drvers of LM. Adopton of LM wll help the SMEs to tae compettve advantage and economc benefts through contnuous mprovement n ther value stream. In emergng countres le Inda, too much wor needs to be done to enable LM effectvely and properly. The dentfcaton and understandng of the factors that motvate mplementaton of LM practces are mportant. The present study asssts the shop floor managers of SMEs to dentfy the common drvers to mplement LM n ther ndustres. Ths study collected the data from a lmted number of experts at Indan scenaro. So, the further scope of the wor could be analyzng the potental drvers whle tang data from multple experts of dfferent countres n dfferent sectors. 6. REFERENCES 1. Achanga, P., Shehab, E., Roy, R., and Nelder, G. (2006). Crtcal success factors for lean mplementaton wthn SMEs. Journal of Manufacturng Technology Management, 17(4), Atanassov, K. T. (1986). Intutonstc fuzzy sets, Fuzzy Sets and Systems, 20(1), Attr, R., Grover, S., Dev, N. and Kumar, D. (2013). An ISM approach for modellng the enablers n the mplementaton of Total Productve Mantenance (TPM), Internatonal Journal of System Assurance Engneerng and Management, 4(4), Bhasn, S. (2012). An approprate change strategy for lean success, Management Decson, 50(3), Boran, F. E., Genç, S., Kurt, M., and Aay, D. (2009). A mult-crtera ntutonstc fuzzy group decson mang for suppler selecton wth TOPSIS method, Expert Systems wth Applcatons, 36(8), Coo, R. L., and Rogows, R. A. (1996). Applyng JIT prncples to contnuous process manufacturng supply chans, Producton and Inventory Management Journal, 37(1), pp Dhandapan, V., Potter, A., and Nam, M., (2004). Applyng lean thnng: a case study of an Indan steel plant, Internatonal Journal of Logstcs Research and Applcatons, 7(3), Ghosh, M. (2012). Lean manufacturng performance n Indan manufacturng plants, Journal of Manufacturng Technology Management, 24(1), Goldsten, M., (2001). Sx Sgma program success factors, Sx Sgma Forum Magazne, 1(1), Hallgren, M., and Olhager, J., (2009). Lean and agle manufacturng: external and nternal drvers and performance outcomes, Internatonal Journal of Operatons & Producton Management, 29(10), Hwang, C. L., and Yoon, K., (1981). Methods for multple attrbute decson mang, In Multple attrbute decson mang Sprnger Berln Hedelberg, Jast, N. V. K., and Kodal, R., (2016). An emprcal study for mplementaton of lean prncples n Indan manufacturng ndustry, Benchmarng: An Internatonal Journal, 23(1), Jeyara, K. L., Muraldharan, C., Mahalngam, R., and Deshmuh, S. G., (2013). Applyng value stream mappng technque for producton mprovement n a manufacturng company: a case study, Journal of the Insttuton of Engneers (Inda), Seres C, 94(1), Khadem, M., Al, S. A., and Sefoddn, H. (2008). Effcacy of lean metrcs n evaluatng the performance of manufacturng systems, Internatonal Journal of Industral Engneerng: Theory, Applcatons and Practce, 15(2), Krafc, J. F., (1988). Trumph of the lean producton system. MIT Sloan Management Revew, 30(1), pp Kumar, N., (2013). Implementng lean manufacturng system: ISM approach, Journal of

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