Partner Selection Factors for Successful CPFR Implementation Using Fuzzy DEMATEL

Transcription

1 Partner Selecton Factors for Successful CPFR Implementaton Usng Fuzzy DEMATEL Farhad Panahfar, Cathal Heavey, P J Byrne, and M Asf Salam Abstract In compettve global ndustres, the relatonshp between frms has changed from rvalry to collaboraton usng developed collaboratve schemes lke Collaboratve Plannng, Forecastng and Replenshment CPFR Through partnershps, both partes should be mutually benefted The key factor that mantans such relatonshps les n addressng the queston on how to select the rght partner In other words, lack of compatblty of partners abltes and mproper selecton of partners wll result n the falure of collaboraton Whle CPFR s a way that manufacturers and retalers mostly collaborate, there exsts an mportant challenge for manufacturers to select proper retalers The purpose of ths study s to ntroduce and explore the key factors consdered by manufacturers n retaler selecton and the relatonshps between these factors Based on a comprehensve lterature revew and applyng experts vews the most mportant retaler selecton factors are ntroduced Fuzzy DEMATEL s appled to further analyse ther nterrelatonshps Based on the emprcal results, conclusons and suggestons are proposed as a reference for manufacturers and retaler Index Terms CPFR mplementaton, partnershp, retaler selecton factors, fuzzy DEMATEL I INTRODUCTION CPFR s known as a most effectve approach between collaboratve nnovatons durng the last decade A broad mpressve and promsng result of CPFR mplementaton has been publshed n prevous studes (Increase revenues and earnngs, Reduce producton and nventory costs [1]; Improvement of forecastng accuracy [2] However, there exst dfferent challenges and obstacles for successful CPFR mplementaton whch lead to slower than expected mplementaton rate of CPFR [3] Research conducted by [3] has comprehensvely nvestgated CPFR mplementaton barrers n hgh-tech ndustres The fndngs of ths research whch s the key motvaton of ths paper concludes that lack of compatblty of partners abltes s one of the most mportant obstacles, whch s caused by mproper selecton of partners In other words, to successfully mplement CPFR, there must be a certan degree of compatbly n abltes of supply chan tradng partners [4] Tradng partners who wsh to mplement CPFR need to assess the potental relatonshp accordng to targeted and realstc objectves of CPFR mplementaton For successful mplementaton of CPFR, frms requre some ndcatons from ther tradng partners to ensure that the potental capablty exsts to run a CPFR project Whle CPFR s a means by whch manufacturers and Manuscrpt receved October 5, 2014; revsed December 31, 2014 Farhad Panahfar s wth the Unversty of Lmerck, Ireland (e-mal: farhadpanahfar@ule) retalers collaborate, there exsts an mportant challenge for manufacturers to select retalers for partcpaton n CPFR Once a manufacturer dentfes several potental retalers to mplement CPFR, they need to compare and contrast each partner aganst relevant selecton crtera Ths study selects hgh-tech ndustres as research subjects to explore the key retaler selecton factors and relatonshps among these factors Hgh-tech ndustres such as semconductor, computer and perpheral equpment, telecommuncatons, pharmaceutcal and medcal devces face constant dffcultes wth demand forecastng for hgh-tech products especally when ntroducng new products To address ths problem n hgh tech ndustres, [5] proposed two major solutons: ) excess capacty to buffer aganst demand varablty and ) develop hgh levels of collaboraton wth tradng partners When a new hgh tech product s ntroduced to the markets, dfferent retalers/dstrbutors are competng to sell ths product Ths gves power to manufacturer to select approprate retalers to run CPFR On the other hand, t s argued that retalers have better and accurate nformaton about consumer demand and market than manufacturers [6] Hence, manufacturers face a hgher demand uncertanty than retalers [7] Ths demand uncertanty has motvated manufacturers to nnovate dfferent uncertanty reducton strateges such as CPFR wth retalers Followng a comprehensve lterature revew, the sgnfcant factors n the retaler selecton process are reported Because of the gap of retaler selecton factors n the relevant lterature, experts vews are also used to dentfy other potental factors to provde a comprehensve lst of factors, not ncorporated wthn the prevous lterature Fuzzy DEMATEL s then used to explore the key factors consdered by manufacturers n retaler selecton and develop the contextual relatonshps between those dentfed The study presented here has been carred out n the hgh tech sector, where the mportance of collectve practces wth tradng partners, and n turn CPFR s hghlghted [3], [8] II AN OVERVIEW OF RELATED LITERATURE In recent years, the hgh-tech ndustres are one of the most compettve n the world [9] The term hgh-tech used wdely from the early 1970s to denote hgh technology Hgh-Tech was often assocated wth the utlzaton of advanced technology Snce 1970 dfferent classfcaton and categorsng of hgh-tech ndustres have been presented In the 1990s, hgh-tech ndustres were defned to nclude 10 major areas: communcaton; nformaton; consumer electroncs; sem-conductor; precson machnery and automaton; avaton; hgh-grade materal; specal chemcal DOI: /JOEBM2015V

2 and pharmaceutcal; medcal care; and polluton preventon [9] Hgh-tech ndustres are characterzed by rapd changes of technology, hgh nnovaton, ntense competton, long producton lead tme, short product lfecycles and a hghly uncertan envronment and market [3], [10] The rapd technologcal change n hgh-tech ndustres can affect both nter and ntra-company collaboratons [10] The dynamcs and unpredctablty of the hgh-tech busness and market make optmzng performance on the supply chan dffcult The frms n ths envronment are strugglng to fnd effectve strateges to face such challenges One well-known strategy s collaboraton wth other members of a supply chan by applyng ntatves lke CPFR CPFR s known as a most effectve approach between collaboratve nnovatons durng the last decade wth a broad mpressve and promsng result of CPFR mplementaton publshed n prevous studes [1], [2], [11] However, there exst dfferent challenges and obstacles for successful mplementng CPFR whch lead to slower than expected mplementaton rate of CPFR [3], [12] Lack of compatblty of partners abltes and the lack of trust between partners are the most mportant obstacles that cause mproper selecton of partners In other words, for successful mplementaton of CPFR, there must be a certan degree of compatbly n abltes and an approprate level of trust between tradng partners [4], [11] As frms need to develop trust when adoptng CPFR, they pay greater attenton to partner selecton Partner selecton s a complex and tme consumng task n CPFR to ascertan whether a potental canddate has the abltes and resources n order to fulfl the agreed tasks Durng the last years, several technques and methods have been proposed to solve the partner selecton problems [13], [9], [14]-[16] Reference [13] usng survey methodology have emphaszed the mportance of qualty performance, delvery relablty, cost effcency, quantty flexblty and delvery speed as the most mportant factors n the selecton of supply chan partners Reference [14] proposed an ntegrated suppler selecton and mult-echelon dstrbuton nventory model (MEDIM) for an orgnal equpment manufacturng company usng fuzzy analytcal herarchy process (FAHP) and a genetc algorthm Reference [15] tred to understand the role of Ft, Trust and Strategc Expedency for partner selecton They summarzed that strategc expedency and trust have a crtcal role n a partner selecton process, and hghlghtng these roles can help decson makers to mprove partner selecton decsons Reference [9] n ther research focused on hgh-tech ndustres n Tawan to understand a manufacturer s behavours and then defned the relatonshps between the factors usng structural equaton modellng They proposed eght suppler selecton factors, comprsng prce response capablty, qualty management capablty, technologcal capablty, delvery capablty, flexble capablty, management capablty, commercal mage, and fnancal capablty Reference [16] contrbuted an alternatve quanttatve method to the partner selecton lterature by applyng a mathematcal method of formal concept analyss The authors proposed a method to analyse avalable data regardng canddates, evaluatng and comparng dfferent canddates followng several crtera when frms are nvolved n the partner selecton process for horzontal collaboraton Identfcaton of the crtera nfluencng the selecton actvty s the most mportant step n partner evaluaton and selecton process Most of the prevous researches found n the lterature have concentrated on studyng ths step To our knowledge, many of the researches conducted n the suppler selecton area have used multple crtera decson makng (MCDM) technques However, there are stll gaps n the exstng knowledge on partner selecton for collaboraton especally when a hgh-tech manufacturer selects a retaler or a set of retalers n order to launch a sustanable CPFR scheme Although, the lterature on partner selecton topc s qute rch, only a very small porton of prevous papers have studed the dstrbutor or retaler selecton Ths paper tres to narrow ths gap and ntends to explore retaler selecton problem n the context of collaboratve plans III METHODOLOGY There s an ncreasng use of MCDM methods especally n the fuzzy area Decson-makng s the process of defnng the decson objectves, gatherng relevant nformaton, and selectng the optmal alternatves [17] MCDM methods are wdely used n partner selecton studes as partner selecton s a mult-crtera decson problem [18] DEMATEL whch stands for Decson-makng tral and evaluaton laboratory method orgnally developed by the Scence and Human Affars Program of the Battelle Memoral Insttute of Geneva between 1972 and 1976 The DEMATEL method s a potent method that helps n gatherng group knowledge for formng a structural model, as well as n vsualzng the causal relatonshp of subsystems through a causal dagram The DEMATEL model, was ntended to study and resolve the complcated and ntertwned problem group [19], has been successfully appled n many felds and areas, such as outsourcng provders, project management, marketng strateges, IC desgn servce, green supply chan management practces [17], [20]-[25] Fuzzy DEMATEL has been recently utlsed n partner selecton and evaluaton studes [20], [26], [27] Prevous research appled DEMATEL because of ts ablty to confrm nterdependence among consdered factors, and also ts capablty n showng the nterrelatonshps among factors [28] Accordng to [20] DEMATEL can be appled n suppler selecton studes as t can fnd key crtera to mprove performance and provde decson-makng nformaton and t does not need a large amount of data The methodologcal framework n ths study s based largely on expert opnon and percepton and ncludes three supportng technques: lterature revew, structural ntervews and fuzzy technques IV IDENTIFYING CRITERIA FOR RETAILER SELECTION EVALUATION Partner selecton s a very prudent and challengng task When companes plan for a long-term collaboraton, partner selecton needs ntense precson As ponted out earler, there s an mportant gap n the lterature of manufacturers 1139

3 retaler selecton and tll the tme of ths paper there s no paper n the area of manufacturers selecton of retaler for adoptng CPFR There are only a small number of papers n the area of dstrbutor selecton whch have been partally used n ths paper An earler paper n the topc of selectng dstrbutors was conducted by [29] They ntroduced a computer-aded decson support tool for qualfyng potental dstrbutors The authors appled n-depth ntervews wth experenced nternatonal busness executves to propose fve man dmensons affectng dstrbutor selecton These elements consst of fnancal and company strengths, product factors, marketng sklls, commtment and facltatng factors Another paper orgnatng from ths area s [30] They revewed determnants of manufacturers selecton of dstrbutors and put forward four major dmensons to cover 16 proposed factors nfluencng the selecton of dstrbutors Four key constructs n ther research, were derved from marketng, supply chan, and the logstcs lterature to nvestgate ther nfluences on dstrbutor selecton, whch are frm nfrastructure, marketng capabltes, relatonshp ntensty, and logstcs capabltes TABLE I: RETAILER SELECTION CRITERIA Dmenson Crtera Tag Organzatonal and Fnancal strengths Management abltes and sklls C 1 Hgh nternal algnment C 2 Flexble organzaton C 3 Organzatonal sze C 4 Workforce sklls and tranng C 5 Fnancal strength C 6 Physcal facltes C 7 Reputaton C 8 Marketng abltes Sales strength C 9 Market coverage C 10 Famlarty wth the product(s) C 11 Customer servce orentaton and C 12 capablty Product compatblty C 13 Footfall C 14 Relatonshp strength Logstcs capabltes Technologcal capabltes Retalers' commtment to agreed order C 15 Wllngness to share nformaton C 16 Retalers ntatve to buld trust C 17 Enthusasm to collaboraton C 18 Manufacturer s famlarty wth the C 19 retaler Inventory management C 20 On-tme delveres C 21 Informaton securty system C 22 Strong IT nfrastructure C 23 Technology Compatblty C 24 Due to the lack of exstng scales for retaler evaluaton or selecton, the general structure for the man dmensons proposed by these two papers after ncorporatng the vews of three members of an expert group are adopted n ths research These three experts were fully aware of the CPFR mplementaton n hgh-tech ndustry wth more than 20 years of experences n nternatonal marketplaces Usng a comprehensve lterature revew on CPFR mplementatons and ncorporatng hgh-tech experts vews and perceptons, the approprate factors were ntally dentfed to be 28 factors whch after further dscusson were reduced to an agreed total of 24 factors It s argued that CPFR should not be seen as an approach to create a good relatonshp wth new players; rather, t can help to enhance a good relatonshp wth an exstng tradng partner [31] Therefore, sgnfcant attenton of the authors and the experts group nvolved has been to dentfy and ntroduce the factors whch mght play mportant roles n CPFR mplementaton rather than ntroducng retaler selecton factors for a general collaboraton To better categorze the factors, the dmensons proposed n the aforementoned papers have been examned n detal Fnally, three of the fve dmensons have been adopted from these two papers ncludng organzatonal and fnancal strengths [29] marketng abltes and logstcs capabltes [30] To comprehensvely classfy the dentfed factors, ths study adds two more dmensons ncludng relatonshp strength and technologcal capabltes based on dscussons wth the group of experts Table I shows all dmensons and factors n more detal Ths study then surveys 12 experts wth extensve knowledge and experences of CPFR mplementaton to dentfy the most sgnfcant and effectve retaler selecton factors All experts are ether ndustral practtoners wth an average of 16 years of experences n nternatonal hgh tech ndustres or academc scholars wth research concentraton n supply chan management area V FUZZY DEMATEL Ths study employs Fuzzy DEMATEL for achevng the objectves of the research whch s to construct nterrelatons between crtera consstng of CPFR partner selecton factors The steps of Fuzzy DEMATEL are explaned n detals n the followng paragraphs Step 1: Defnng the evaluaton crtera and desgn the fuzzy lngustc scale To deal wth the ambgutes of human assessments, the research wll apply the fuzzy lngustc scale used n the group decson makng proposed by [32] whch expressed the dfferent degrees of nfluence wth fve lngustc terms as No nfluence, Very low nfluence, Low nfluence, Hgh nfluence, Very hgh nfluence and ther correspondng postve trangular fuzzy numbers are shown n Table II TABLE II: THE LINGUISTIC VARIABLES AND THEIR CORRESPONDING FUZZY NUMBERS Lngustc scale Influence Trangular fuzzy scale score No nfluence (No) 0 (0, 0, 025) Very low nfluence (VL) 1 (0, 025, 050) Low nfluence (L) 2 (025, 050, 075) Hgh nfluence (H) 3 (050, 075, 100) Very hgh nfluence (VH) 4 (075, 100, 100) Step 2: Establshng the drected-relaton matrx To measure the relatonshp between crterac C 1,2,, n a decson group of p experts were asked to make sets of par-wse comparsons n terms of 1 2 p lngustc terms Hence, p fuzzy matrces Z, Z, Z each correspondng to an expert and wth trangular fuzzy numbers as ts elements, were obtaned Fuzzy matrx Z s called the ntal drect-relaton fuzzy matrx where: 1140

4 and Z j l j,m j, r j C 0 Z 1 12 Z 1n C 2Z 21 0 Z Z 2n CnZ n1 Z n2 0 are trangular fuzzy numbers where the followng formulas are applcable to fuzzy numbers: k Z ( kl, km, kr) N N ( l l, m m, r r ) (1) (2) (3) ( Z Z Z ) Z (4) P Step 3: Establshng and analysng the structural model Equaton (5) s used to transform the crtera scales nto comparable scales and = z l, m, r n n n n j j1 j1 j1 j1 P n u max r j (5) 1 n 1 The normalzed ntal drecton-relaton fuzzy matrx s: x x x x21 x22 x X x x x n 2n m1 m2 mn z where j lj mj rj xj,, u u u u Step 4: The total-relaton matrx To calculate the total-relaton matrx ( k lm x 0 k (6) ) we assume k To calculate x we use the equaton 7 n order to multple two fuzzy numbers 0 l 21 X l l n1 n2 N N l l, m m, r r l X l, m, r 12 1n 0 l j j j j l l 2n 0 X m 0 m m 21 m n1 n2 12 1n 0 m m m 23 0 X r 0 r 21 r r n1 n2 12 1n 0 r r r 2n 0 Then the total-relaton matrx s defned by the equaton 8 as outlned here: T X X X X I X (7) 1 2 k 1 ( ) (8) 2 k lm Then: tj l j, m j, r j T X X X k and t11 t12 t1 n t21 t22 t2n T t31 tn2 t nn Accordng the assumpton n step 4 we have: k lm X [0] and nn l j Xl I Xl k m j X m (I X m) r j X r (I X r ) 1 1 (9) (10) (11) Step 5: The sum of rows and columns As we have calculated (T ) n the last step, we can now calculate the relevant amounts of D R and D R whereas D s the row sum and R s the sum of columns n (T ) and T t j,, j 1,2,, n R n D d tj j1 ; n 1 n r t 1 n 1 1 j j n n1 (12) There are three methods to determne the best nonfuzzy performance (BNP) value n the lterature of the multple crtera decson makng (MCDM) methods whch ncludng: (a) mean of maxmal (MOM), (b) center of area (COA), and (c) -cut [33] Utlzng the COA method to determne the BNP s smple and practcal, and there s no need to ntroduce the preferences of any evaluators [34] Thus, to defuzfy the fuzzy weghts n order to compare the alternatves n a nonfuzzy rankng method, the (COA) s appled n ths paper usng the followng equaton: BNP l r l m l 3 (13) The values of def D R and def D R wll be calculated usng Equaton 13 Step 6: The cause and effect dagram A causal and effect graph can be acheved by mappng the 1141

5 dataset of (D+R, D-R) The mportance of each crteron s obtaned by addng D to R whch named Promnence whch forms the horzontal axs vector Smlarly, the vertcal axs (D-R) named Relaton s made by subtractng D from R, whch may group crtera nto a cause group Or, f the (D-R) s negatve, the crteron s grouped nto the effect group VI APPLICATION OF PROPOSED METHOD The Fuzzy DEMATEL method s mplemented wth the followng steps Frst, a fuzzy lngustc scale (Table II) s used for makng assessments In step 2, the relatons of nterdependences among key factors n retaler selecton are quanttatvely analyzed through nvestgatons of experts perceptons Through twelve questonnares, ths study obtans ther perceptons of varous retaler selecton crtera n CPFR mplementaton projects Then, the ntal drect-relaton matrx was produced by Equaton 4 whch called Fuzzy matrx Z In step 3, based on the ntal drect-relaton matrx, the normalzed drect-relaton matrx was obtaned by equaton 6 To produce the total-relaton matrx, three matrces are constructed whch are labeled X l, X and m X r The values of l, j m and j r wll be then calculated wth j the help of Equatons 9-11, respectvely In the next step, the total-relaton matrx (Table III) was acqured In step 5, the values of R, D, D R and D Rare obtaned as shown n the Table IV To complete Table IV, all calculated D R and D R are defuzfed through a COA (center of area) defuzfcaton method (Equaton 13) In the fnal step, the cause and effect dagram are developed usng Table IV The results are the values of def D R whch shows the mportance of all factors and def D R whch assgn selecton factors nto cause and effect groups Usng ths nformaton obtaned n Table IV, the cause and effect dagram (see Fg 1) s acheved n step 6 The values of X, and X m X have been calculated usng r the matrx X as shown n the followng matrces, respectvely The next matrx shows the values of matrx l j calculated usng these three matrces The matrces rwll be calculated n a smlar method j VII FINDINGS AND DISCUSSION whch s m j and In ths paper, a fuzzy DEMATEL model s presented to deal wth the nfluental relatonshp between the evaluaton crtera n selectng retalers to CPFR mplementaton and to dentfy ther casual relatonshps Some of the major fndngs of ths study are hghlghted n the followng paragraphs: In Table IV, the values of show how sgnfcant a crteron s and the values of dvde the crtera nto cause and effect groups If the value of s postve, the crteron belongs to the cause group and f ts value s negatve, the crteron s a member of the effect group It s also shown n the causal dagram that the retaler selecton factors for CPFR mplementaton extracted by usng explanatory factor analyss were dvded nto the cause group ncludng C 1, C 3, C 5, C 12, C 13, C 16, C 19, C 21, C 22, C 23 and C 24 The effect group was composed of C 2, C 4, C 6, C 7, C 8, C 9, C 10, C 11, C 14, C 15, C 17, C 18 and C 20 So, management abltes and sklls, flexble organzaton, workforce sklls and tranng, customer servce orentaton and capablty, product compatblty, wllngness to share nformaton, manufacturer s famlarty wth the retaler, on-tme delveres, nformaton securty system, strong IT nfrastructure, technology compatblty are cause factors whereas hgh nternal algnment, organzatonal sze, fnancal strength, physcal facltes, reputaton, sales strength, market coverage, famlarty wth the product(s), footfall, retalers' commtment to agreed order, retalers ntatve to buld trust, enthusasm to collaboraton and nventory management are dentfed as effect factors Generally, the factors n the effect group tend to be easly mpacted by others, whch make effect factors unsutable to be a crtcal success factor n a partner selecton process The results show that C12 (customer servce orentaton and capablty) wth the greatest value of s dentfed as the most sgnfcant factor whereas C7 (physcal facltes) s the least mportant crteron wth the value of 3793 for retaler selecton to successful mplement CPFR C12 has also receved a hgh Rank of Impact (ROI) whch shows ths element s a crtcal factor n retaler selecton actvtes On the other hand, physcal facltes (C7) has receved also the greatest negatve score of wth the value of whch shows ths factor s not defntely a crtcal factor n selectng retalers from the vewpont of manufacturers Accordng to the results, the second greatest value of the ndex of sgnfcance belongs to C 1 (Management abltes and sklls) wth the value of 6067 On the other hand, ths factor s classfed n the cause group wth a relatvely large value of 0253 Therefore, t s ntroduced as a crtcal factor for the objectve of partner selecton Ths ndcates that for the purpose of supportng collaboratve plannng ntatves, lke CPFR, management abltes and sklls of the retalers have a strong nfluence on the frm and the whole chan Moreover, the results revealed that C 19 (manufacturer s famlarty wth the retaler) and C 5 (workforce sklls and tranng) wth the greatest value of def (0342 and 0311, D R respectvely) are confrmed to have a more nfluental effect on the other factors and wll thus generate the compettve advantages Workforce sklls and tranng has been ranked also as the thrd greatest sgnfcant factor wth the value of 5412 Ths factor s thus ntroduced as a crtcal factor n the retaler selecton process because t dfferentates a frm from ts compettors The results of ths study ndcate that all the three factors from technologcal dmenson are dentfed as cause factors Ths hghlghts that retaler technologcal capabltes can smplfy and enhance the speed and flexblty of CPFR However, t s well documented n the CPFR lterature that technology can play a role as key enabler, s no longer seen as a major barrer to success [3]-[31] 1142

6 C1 C2 C3 TABLE III: THE TOTAL RELATION FUZZY MATRIXT C1 (019,068, 024) (047, 105, 277) (039, 094, 276) C22 C23 C24 (041, 091, 251) (042, 094, 260) (039, 079, 247) C2 (043,090, 025) (012, 048, 198) (043, 087, 243) (029, 069, 214) (029, 070, 221) (028, 065, 211) C3 (034, 084, 026) (041, 090, 258) (012, 048, 211) (025, 065, 221) (024, 064, 227) (020, 057, 213) C4 (023, 060, 215) (033, 068, 215) (024, 058, 207) (015, 046, 180) (017, 050, 189) (013, 041, 176) C5 (048, 106, 283) (046, 102, 271) (046, 10, 271) (032, 076, 238) (029, 077, 246) (022, 063, 226) C6 (031, 079, 251) (023, 070, 237) (023, 067, 236) (030, 071, 220) (030, 073, 229) (022, 061, 214) C7 (011, 042, 177) (009, 037, 165) (010, 038, 169) (007, 032, 150) (009, 034, 157) (004, 027, 145) C8 (035, 085, 271) (035, 085, 260) (032, 075, 258) (026, 067, 225) (026, 069, 234) (025, 061, 224) C9 (041, 095, 265) (032, 084, 249) (032, 081, 247) (017, 058, 212) (020, 062, 227) (018, 057, 212) C10 (025, 071, 248) (025, 069, 235) (025, 066, 235) (026, 068, 219) (030, 072, 231) (025, 060, 216) C19 (032, 082, 251) (028, 077, 238) (028, 071, 235) (027, 068, 214) (017, 061, 218) (019, 059, 210) C20 (034, 083, 246) (033, 078, 235) (026, 071, 234) (027, 066, 208) (024, 065, 218) (017, 053, 201) C21 (042, 095, 264) (040, 091, 252) (034, 079, 249) (022, 066, 217) (026, 069, 228) (020, 061, 216) C22 (034, 082, 257) (025, 068, 233) (034, 079, 246) (009, 040, 181) (036, 078, 230) (024, 063, 213) C23 (034, 085, 245) (034, 081, 233) (027, 070, 229) (027, 066, 205) (008, 040, 179) (022, 060, 203) C24 (023, 067, 227) (027, 069, 221) (021, 059, 215) (020, 058, 198) (030, 069, 212) (006, 030, 160) TABLE IV: THE VALUES OF R, D, D + R, D - R, D def R, def D R R D def D R def D R ROS ROI C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 (0784, 1952, 5985) (0866, 2229, 6385) (0726, 1856, 5685) (0591, 1626, 5386) (0685, 1740, 5667) (0639, 1744, 5761) (0139, 1437, 4989) (0418, 1249, 4687) (0676, 1798, 5645) (0805, 2099, 6148) (0205, 1905, 5930) (0568, 1635, 5528) (0434, 1260, 4578) (0241, 0925, 3942) (0740, 1920, 5879) (0695, 1859, 5967) (0771, 1966, 5979) (0729, 1936, 5779) (0735, 1898, 5769) (0664, 1789, 5668) (0671, 1748, 5562) (0582, 1649, 5313) (0806, 2025, 6001) (0852, 2220, 6485) (0526, 1466, 4972) (0518, 1591, 5369) (0553, 1547, 5177) (0469, 1462, 5042) (0705, 1821, 5726) (0528, 1591, 5382) (0682, 1776, 5661) (0704, 1911, 5767) (0682, 1804, 5650) (0642, 1722, 5321) (0688, 1794, 5617) (0639, 1742, 5502) (0545, 1488, 5041) (0665, 1838, 5597) (0640, 1701, 5619) (0516, 1577, 5320) (0656, 1734, 5585) (0720, 1900, 5742) (0533, 1475, 5015) (0607, 1709, 5584) C23 (0567, 1547, 5247) (0534, 1617, 5262) C24 (0486, 1358, 4949) (0445, 1395, 4991) Note: ROS=Rank of sgnfcance; ROI=Rank of mpact 1143

7 Fg 1 The cause and effect dagram VIII CONCLUDING REMARKS Fndng rght partner wth rght capabltes s known to be the most strategc needs of any collaboraton Companes need to ensure the tradng partner s potental to commt resources To ths end, t s mperatve to companes have a clear understandng of effectve partner selecton factors n creatng a successful collaboraton Whle CPFR s a way that manufacturers and retalers mostly collaborate, there arses an mportant challenge for manufacturers to select proper retalers Hence, ths study uses a lterature revew, expert vews and the fuzzy DEMATEL technque to comprehensvely fnd out, develop and analyze possble retaler selecton factors for successful CPFR mplementaton A new selecton model s then formulated The results of ths study can help enterprses precsely recognze whch retalers are sutable to run CPFR by focusng on crucal factors dentfed n ths study Dscusson wth experts helped us to classfy the varous crtera of decson-makng nto fve dmensons: organzatonal and fnancal strengths, marketng abltes, relatonshp strength, logstcs capabltes and technologcal capabltes In other words, we suggest that the retaler selecton problem to mplement CPFR may be nfluenced by these fve dmensons The proposed framework brngs several contrbutons to a manufacturer s retaler selecton to mplement CPFR Frst, a novel model for selectng retaler wth emphass on CPFR mplementaton has been developed Second, wth the proposed methodology, the complex nteractons between retaler selecton crtera can be transformed nto a vsble structural model, enablng frms to capture the most mportant anddomnant factors Thrd, the results show that the manufacturer should note that retaler s customer servce orentaton and capablty (C 12 ) s the most mportant factor for CPFR mplementaton and ts mprovement can lead to the ameloraton of the whole system Customer servce capabltes lke delvery and on-shelf avalablty generate a better servce to end users allowng manufacturer and retaler to enhance nformaton vsblty of the whole supply chan ACKNOWLEDGEMENT Ths research has been co-fnanced by the European Unon (European Socal Fund ESF) and Greek natonal funds through the Operatonal Program "Educaton and Lfelong Learnng" of the Natonal Strategc Reference Framework (NSRF) - Research Fundng Program: Thales Investng n knowledge socety through the European Socal Fund REFERENCES [1] T M McCarthy and S L Golcc, Implementng collaboratve forecastng to mprove supply chan performance, Internatonal Journal of Physcal Dstrbuton and Logstcs Management, vol 32, no 6, pp , 2002 [2] J Småros, Collaboratve forecastng: A selecton of practcal approaches, Internatonal Journal of Logstcs Research and Applcatons: A Leadng Journal of Supply Chan Management, vol 6, no 4, pp , 2003 [3] F Panahfar, P J Byrne, and C Heavey, ISM analyss of CPFR mplementaton barrers, Internatonal Journal of Producton Research, vol 52, pp , 2014 [4] G Fledner, CPFR: An emergng supply chan tool, Industral Management and Date system, vol 101, no 1, pp 14-21, 2003 [5] X Yuan, L Shen, and J Ashayer, Dynamc smulaton assessment of collaboraton strateges to manage demand gap n hgh-tech product dffuson, Robotcs and Computer-Integrated Manufacturng, vol 26, no 6, pp , 2010 [6] H Cavusoglu, H Cavusoglu, and S Raghunathan, Value of and Interacton between producton postponement and nformaton sharng strateges for supply chan frms, Producton and Operatons Management, vol 21, no 3, pp ,

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Industral Marketng Management, vol 24, pp , 1995 [30] J S C Ln and C R Chen, Determnants of manufacturers' selecton of dstrbutors, Supply Chan Management: An Internatonal Journal, vol 13, no 5, pp , 2008 [31] E C R Europe, Kronberg Accenture GmbH, A Gude to CPFR Implementaton, ECR Europe, 2001 [32] R J L, Fuzzy method n group decson makng, Computers and Mathematcs wth Applcatons, vol 38, no 1, pp , 1999 [33] M F Chen, G H Tzeng, and C G Dng, Combnng fuzzy AHP wth MDS n dentfyng the preference smlarty of alternatves, Appled Soft Computng, vol 8, no 1, pp , 2008 [34] H K Chou and G H Tzeng, Fuzzy multple-crtera decson-makng approach for ndustral green engneerng, Envronmental Management, vol 30, no 6, pp , 2002 Farhad Panahfar s a PhD researcher and part-tme lecturer at the Enterprse Research Centre n Unversty of Lmerck Farhad has BSc and MSc degrees n Industral Engneerng wth more than ten years ndustral and academc experence Hs current research focus s n the area of supply chan management wth emphass on collaboratve strateges (eg CPFR) Hs research nterests nclude supply chan management, smulaton modellng, strategc plannng, decson makng technques, busness process reengneerng (BPR) and productvty mprovement Cathal Heavey s a professor of operatons management n the Department of Desgn and Manufacturng Technology at the Unversty of Lmerck He s an ndustral engneerng graduate of the Natonal Unversty of Ireland Unversty College Galway and holds a MEngSc and PhD from the same Unversty He has publshed n the areas of queung and smulaton modellng Hs research nterests nclude smulaton modellng of dscrete-event systems, modellng and analyss of supply chans and manufacturng systems, process modellng, component-based smulaton and decson support systems P J Byrne s a senor lecturer of operatons/supply chan management n Dubln Cty Unversty Busness School and s head of the management group n the school Pror to ths he worked as a senor research fellow n the Enterprse Research Centre n the Unversty of Lmerck P J has a BEng degree n producton engneerng, from the Manufacturng and Operatons Engneerng Department n the Unversty of Lmerck He has also receved a PhD from the same unversty n the area of supply chan smulaton Hs research nterests nclude supply chan desgn, analyss and optmzaton, cloud based smulaton, dscrete event smulaton, ndustral applcatons of smulaton modelng for the manufacturng and servces sectors and busness process optmzaton M Asf Salam s as an assocate professor of marketng and supply chan management n the Ted Rogers School of Management at the Ryerson Unversty of Toronto He s a doctorate n busness admnstraton n supply chan management from the Thammasat Unversty, Thaland Hs research nterests nclude nterdscplnary ssues n marketng and supply chan management, supply chan collaboraton, supply chan ntegraton, healthcare logstcs, lean and agle logstcs, and humantaran dsaster logstcs 1145