A novel model to measure supplier performance in the supplier selection process

Transcription

1 Unversty of Wollongong Research Onlne Faculty of Engneerng - Papers (Archve) Faculty of Engneerng an Informaton Scences 2012 A novel moel to measure suppler performance n the suppler selecton process Alptekn ULUTAS au683@uowmal.eu.au Senev Krena Unversty of Wollongong, skren@uow.eu.au Peter Gbson Unversty of Wollongong, peterg@uow.eu.au Publcaton Detals Ulutas, A., Krena, S. Gbson, P. (2012). A novel moel to measure suppler performance n the suppler selecton process. 7th Internatonal Congress on Logstcs an SCM Systems (pp. 1-8). Korea: Research Onlne s the open access nsttutonal repostory for the Unversty of Wollongong. For further nformaton contact the UOW Lbrary: research-pubs@uow.eu.au

2 A Novel Moel to Measure Suppler Performance n the Suppler Selecton Process Alptekn Ulutas 1, Senev Krena 2 an Peter Gbson 3 1,2,3 Faculty of Engneerng, Unversty of Wollongong, Australa, au683@uowmal.eu.au,skren@uow.eu.au,peterg@uow.eu.au Abstract Suppler evaluaton has become a sgnfcant topc over the past ecaes, as companes have starte to become more outsource orente. However, prevous research on ths topc has not pa aequate attenton to the lmtatons assocate wth avalablty of accurate an relable ata relatng to the performance of potental supplers. In an attempt to aress ths ssue, ths paper proposes a novel suppler evaluaton moel that can hanle mprecse quanttatve an qualtatve ata. Atonally, Decson Maker s opnons regarng both qualtatve an quanttatve crtera are ncorporate nto ths moel so that a more comprehensve an realstc assessment of suppler performance can be acheve. The moel combnes fve separate methos that have specfc capabltes to hanle multple lmtatons n the exstng methos: Fuzzy Analytcal Herarchy Process an Fuzzy TOPSIS metho are use to analyse qualtatve crtera/ata; Analytcal Herarchy Process an Axomatc Desgn are use to analyse quanttatve crtera/ata, wth a partcular focus on hanlng varablty n performance ata; an Data Envelopment Analyss s use to ntegrate the results of the two approaches above so as to comparatve assessment of suppler performance. Ths moel s verfe usng a numercal example. Key Wors: Suppler Selecton, Analytcal Herarchy Process, Fuzzy TOPSIS, Axomatc Desgn, Data Envelopment Analyss. 1. Introucton Toay s compettve busness envronment forces companes to contnuously optmse ther busness processes to mantan a strategc avantage n global markets. However, competton ncreasngly occurs at the level of supply chans rather than at the frm level. Therefore, companes must cooperate an collaborate wth ther supply chan partners towars enhancng the performance of the overall supply chan. In ths regar, suppler selecton has an mportant role to play as the performance of nvual supplers rectly affects the performance of the whole supply chan. There are many aspects of suppler performance that nee to be consere n suppler selecton an these can be broaly ve nto qualtatve an quanttatve crtera. Both qualtatve an quanttatve crtera are mportant measures n selectng supplers as the effects of factors are often complementary [11]. Despte these complementartes, there seem to have been a strong sparty n the way researchers have use such crtera, especally, between those who have fferent scplnary backgrouns. For example, researchers wth an operatons research backgroun have tratonally focuse on quanttatve crtera n ther solutons whle those wth busness management backgroun have emphasse the sgnfcance of qualtatve crtera [11]. Such sngular-perspectve treatment can lea to ncreasng potental errors n suppler selecton ecsons. Numerous methos have also been use to measure suppler performance, but they suffer from smlar rawbacks. For example, Data Envelopment Analyss (DEA), a wely use metho when use on ts own, heavly reles on quanttatve ata. Whle recognsng ths lmtaton, some researchers have use Imprecse Data Envelopment Analyss (IDEA) an Augmente Imprecse Data Envelopment Analyss (AIDEA), usng ornal ata whle others have combne other methos wth DEA to analyse qualtatve ata, such as Analytcal Herarchy Process (AHP), Fuzzy Analytcal Herarchy Process (FAHP) an Fuzzy TOPSIS metho [8][16][22]. However, these stues o not conser mprecse quanttatve ata comprehensvely. The am of ths paper s to present a comprehensve, yet practcally feasble suppler selecton moel capable of ealng wth mprecse qualtatve an quanttatve ata n measurng suppler performance. The propose moel consers Decson Maker s (DM) opnon for both qualtatve an quanttatve ata. The

3 paper wll begn by entfyng the applcaton, ssues an lmtatons of current methos use for measurng suppler performance n the suppler selecton process. It then proposes a moel to aress these ssues followe by a numercal example that llustrates the utlty of the moel. The paper conclues wth a bref scusson about the lmtatons of the propose moel an rectons for future research. 2. Lterature Revew Suppler selecton s a mult-crtera ecsonmakng problem, as there are many factors affectng the selecton of a suppler. These crtera can be ve nto two parts; qualtatve an quanttatve crtera [11]. Conserng only one type of crtera n the ecson-makng process ncreases the rsk of partal treatment suppler performance an may not entfy other mportant aspects that contrbute to a successful buyer-suppler relatonshp. For ths reason, a number of researchers have apple Mult Crtera Decson Makng (MCDM) methos, such as AHP an Analytcal Network Process (ANP) to solve ths problem. For example, Barbarosoglu & Yazgac (1997) apple AHP to solve the suppler selecton problem n a Turksh Electrc company. Akarte et al. (2001) propose web base AHP to analyse qualtatve an quanttatve crtera. Bayazt (2006) evelope an ANP base moel to select supplers conserng both suppler s performance an suppler s capablty. Although these methos have been wely apple to solve the suppler selecton problem, they rely too heavly on qualtatve ata an are therefore hghly subectve. Another popular qualtatve metho use to solve the suppler selecton problem s Fuzzy Set Theory (FST). In partcular, ths metho has been utlse to hanle uncertanty n the suppler selecton process. For example, Chen et al. (2006) propose a FST moel usng the concept of TOPSIS to obtan a Fuzzy Postve/Negatve Ieal Soluton to the problem of suppler selecton. Sarkar an Mohapatra (2006) evelope a FST moel to evaluate the performance an the capablty of supplers. FST, however, s also subectve because t reles on fuzzy numbers, whch are not selecte base on a commonly agree bass. Some authors have ntegrate FST an AHP to aress some of these ssues. Kahraman et al. (2003) propose Fuzzy AHP to select a sutable suppler for a Turksh whte goos manufacturng company. Chan an Kumar (2007) also utlse Fuzzy AHP to eal wth the suppler selecton ssue for global supply rsks. Even though these stues can be useful n measurng suppler performance, the maor rawback s that they o not conser quanttatve ata. There are many methos to hanle quanttatve ata n selectng supplers. One of these methos, s Axomatc Desgn. Ths metho s useful to analyse mprecse quanttatve ata an to obtan ecson maker s opnon. You (2011) apple Axomatc Desgn to solve suppler selecton ssue. Another metho, whch has been wely use to measure suppler performance n the lterature of suppler selecton, s Data Envelopment Analyss (DEA) (Ho et al., 2010). Lu et al. (2000) propose DEA to select a preferre suppler wth regar to three nputs an two outputs crtera. Tallur & Sarks (2002) suggeste a DEA moel to measure performance of eghteen supplers wth regar to four outputs an two nputs. The savantage of usng DEA for suppler selecton s ts epenence on quanttatve ata. Ths metho cannot hanle qualtatve crtera. To be able to conser qualtatve crtera, some authors have combne other methos wth DEA. Ha an Krshnan (2008) propose AHP-DEA- Neural Network (NN) to aress the specfc ssues as follows. AHP was use to account for to qualtatve crtera the scores whch were obtane n AHP were transferre nto DEA an NN, an these scores an quanttatve crtera were analyse n DEA an NN. By comparson, Zeyan et al. (2011) propose a moel, whch nclue Fuzzy AHP, Fuzzy TOPSIS an DEA. Fuzzy AHP an Fuzzy TOPSIS were use to analyse qualtatve crtera the scores whch were obtane n Fuzzy AHP an Fuzzy TOPSIS were passe to DEA, an these scores an quanttatve crtera were analyse n DEA. Although these stues assst n the analyss of qualtatve crtera n the measurement of suppler performance, they o not conser mprecse quanttatve ata. As mprecse ata shows varatons n real contons, the analyss of ths type of ata s necessary to reflect those varatons. Some authors have use a mofcaton of DEA to analyse mprecse quanttatve an qualtatve ata n selectng a suppler. Saen (2007) propose IDEA to analyse mprecse quanttatve an qualtatve ata n measurng suppler performance. Wu et al. (2007) propose AIDEA to examne mprecse quanttatve an qualtatve ata to stngush between an neffcent an effcent suppler. Even though these stues analyse qualtatve an mprecse quanttatve ata, the Decson Maker s opnon was not reflecte n the analyss of quanttatve ata. Thus, these papers not enable the ecson

4 maker to conser more qualtatve an mprecse quanttatve ata. As such, ths paper ams at fllng the above gaps as follows: The Decson Maker s opnon wll be reflecte n the mprecse quanttatve ata, an the Decson Maker wll assgn a weght to qualtatve ata use n measurng suppler performance to stngush between neffcent an effcent supplers. Imprecse qualtatve an quanttatve ata wll be examne as two outputs to stngush between neffcent an effcent supplers comprehensvely. That s, ths moel enables the Decson Maker to conser more than one qualtatve an quanttatve crteron for the analyss of qualtatve an mprecse quanttatve ata. 3. Desgn of Moel To measure suppler performance n the suppler selecton process, there s a nee to structure suppler selecton crtera. Table 1 ncates the suppler selecton crtera that wll be apple n ths stuy. The crtera are ve nto types of ata: qualtatve an quanttatve, an have been comple from the lterature nformng ths research. Table 1: Suppler Selecton Crtera use n the Moel Crtera Defnton Authors Qualtatve/Quanttatve Complance wth sectoral prce behavours Reputaton Communcaton Defect Rato Complete Quantty Commt Delvery Proxmty of offerng prce to sectoral prce Image an Poston n Inustry The flow of nformaton beng aequate an effcent The rato of reecte parts n the receve orer Percentage of orers receve complete Percentage of orers receve on commt ate [2] Qualtatve [4] [15] [16] [17] [18] [4] [5] [7] [15] Qualtatve Qualtatve [22] Quanttatve [11] Quanttatve [11] Quanttatve 4. Propose Moel 4.1. Overvew of Moel As alreay suggeste, the moel propose n ths stuy combnes qualtatve an quanttatve ata. These two sets of ata wll be processe an use n the Data Envelopment Analyss. Ths moel s shown n Fgure 1, where t can be seen on the rght han se, the quanttatve ata s processe usng the AHP. AHP wll be use to compare the quanttatve crtera, whle Axomatc Desgn wll be use to analyse mprecse quanttatve ata of supplers usng the Decson Maker s requrements; therefore, both a comparson of quanttatve crtera an an analyss of mprecse quanttatve ata wll be prove. On the left han se of the moel, the qualtatve ata s shown to be treate usng FAHP to compare qualtatve crtera, an Fuzzy TOPSIS wll analyse qualtatve ata by usng weghts from Decson Maker; therefore, both a comparson of qualtatve crtera an analyss of qualtatve ata wll be prove. The qualtatve an quanttatve ata for each suppler that s obtane from ths process s then place nto Data Envelopment Analyss (DEA). In ths Output orente Data Envelopment Analyss DEA, ummy nput wll be calculate wth these two outputs. Ths calculaton wll stngush between neffcent an effcent supplers. Qualtatve Crtera Fuzzy Analytcal Herarchy Process Fuzzy TOPSIS Data Envelopment Analyss Fgure 1: Flow Chart of Moel 4.2. Analyss of Qualtatve Crtera FAHP s use to establsh prorty among qualtatve crtera. Steps of ths metho wll be explane as follows [12]: Let X = x 1, x 2,...,x n be an obect set, an U= u 1,u 2,...,u n be a goal set. 2 M 1,,..., m g M g M g, =1,2,..,n, where all the M g ( = 1,2,...,m) all are trangular fuzzy numbers. The value of the Fuzzy Synthetc Extent wth respect to the th obect s efne as n m 1 M W w w w n g 1, 2,... S m M g Quanttatve Crtera Analytcal Herarchy Process Axomatc Desgn (1)

5 To obtan m M g 1, the fuzzy aton operaton of m extent analyss values for a partcular matrx s performe such as m m m m M g l, m, u n m An to obtan 1 M 1 1 g (2), the fuzzy aton operaton of M g (=1,2..,m) values s performe such as n m m m m M g l, m, u (3) An then the nverse of the vector above s compute as, n m M g,, (4) n n n 1 1 u m l At the en of ths process, the weghts of crtera are obtane.. Fuzzy weghts n Table 2 are use n FAHP. Table 2: Fuzzy Weghts Equal Importance (EI) (1,1,1) Preferre Equal Importance (PEI) (1,2,3) A Lttle More Important (ALMI) (2,3,4) Preferre A Lttle More Important (PLMI) (3,4,5) Strongly Important (SI) (4,5,6) Preferre Strongly Important (PSI) (5,6,7) More Strongly Important (MSI) (6,7,8) Preferre More Strongly Important (PMSI) (7,8,9) Totally Important (TI) (8,9,9) After obtanng the weghts of each crteron, DM can assgn a lngustc ratng, as presente n Table 3, to each alternatve uner the fferent crtera usng Fuzzy TOPSIS. Steps of ths metho may be explane as follows [22]: Alternatve s ratngs can be expresse n matrx form as: x 11 x x 1n D x x x = n : : : (foun by Table 2) (5)... : : : x m x 1 m2... x mn W w, w,... w 1 2 n(weghts foun from FAHP) (6) where x s the lngustc varable that can be shown by Trangular Fuzzy Numbers an x a, b, c s the Fuzzy Assessment Value of each alternatve for each crteron, whch can be utlze to acqure the Postve Fuzzy Performance matrx. R r (7) mxn where B represents the set of beneft crtera an C represents the set of cost crtera, respectvely, an a b c r,,, B an a a a r c c c,,, c b a C for frst equaton c max c, f B an for secon equaton a mn a, f C Thus, the normalze matrx wll be obtane after ths an the weghte normalze fuzzy ecson matrx wll be obtane. Fuzzy numbers n ths matrx belong to [0,1]. Ths matrx s as follows: V v mxn, =1,2,..,m; =1,2..,n Where v = r (x) w (8) As mentone before, fuzzy numbers ( v ) n ths matrx belongs to [0,1]. Thus, we can efne the fuzzy postve-eal soluton (FPIS) an fuzzy negatve-eal soluton usng the followng formula: v 1, v2,....., vn A v, v2,....., vn v 1,1,1 an v 0,0,0 A (FPIS) an 1 (FNIS) where The stances ( an from an n 1 v, v, = 1,2,.,n. ) of each alternatve A can now be calculate. =1,2,..,m (9) n 1 v v,,=1,2,,n (10) A The CC (closeness of coeffcent) wll be efne to etermne the rank orer of all alternatves once the an of each alternatve are calculate. Ths step gves the smlartes to an eal soluton. The step s CC calculate usng the equaton below: CC Accorng to (11) CC, the rank orer of all alternatves can be etermne an the best one from among a set of feasble alternatves obtane. Table 3 shows lngustc varables to use for ratng supplers. Table 3: Lngustc Varables for Ratngs Very Goo (VG) (9,10,10) Goo (G) (7,9,10) Meum Goo (MG) (5,7,9) Far (F) (3,5,7) Meum Poor (MP) (1,3,5) Poor (P) (0,1,3) Very Poor (VP) (0,0,1)

6 4.3. Analyss of Quanttatve Crtera AHP wll be use to etermne weghtngs of crtera. AHP wll be calculate by Expert Choce. Steps of ths metho wll be summarse as follows: Structurng herarchy among crtera; that s, crtera are arrange. Assgnng contrbuton weghts to each crteron, an ths wll generate a matrx form. The total value of each column s obtane, an each column s total value ve by each crteron s value. The sum of each row s obtane, an these values are ve by the total number of crtera; thus, the weghts of crtera are obtane. If Consstency Inex s lower than 0.1, AHP s fnshe. After obtanng weghts from AHP, mprecse quanttatve ata wll be examne n Axomatc Desgn (AD). Steps of AD wll be shown as follows [21]: etermnng the esgn range (esgner-specfe), accorng to DM s tolerance an obectve mprecse value etermnng the system range (suppler s range), accorng to DM s tolerance an obectve mprecse value calculatng the nformaton content ( I ) for each crteron multplyng each nformaton content ( I ) an each crteron weght (w, obtane from AHP) as follows: commonrange p (12) systemrange So, the nformaton content s equal to: systemrange I log 2 (13) commonrange After analysng qualtatve an quanttatve crtera, two values wll be obtane. The results of FAHP an Fuzzy TOPSIS wll prove one value ( CC ) for each suppler an ths value s calle the Qualtatve Performance Value (QTPV). The results of AHP an AD wll also prove one value ( I tw ) for each suppler an ths value s calle the Quanttatve Performance Value (QPV). In orer to make a balance between quanttatve an qualtatve performance values, we wll assgn 100 as the hghest value for Qualtatve Performance Value (as hghest value n Fuzzy TOPSIS s the most precous value) an 100 as the smallest value n qualtatve performance value (as the smallest value n AD s the most sutable value). Then, the other values wll be calculate wth respect to those values. QPV an QTPV wll be use as output an nput(ummy) for the DEA output-orente BCC moel. Output-orente BCC wll be solve by Fronter Analyst 4 software. 5. Computatonal Results Company X, whch s a sut manufacturer, s supple wth fabrc from four supplers. The frm woul lke to reuce ts supply base. For ths reason, the company wll measure the performance of these supplers. The company wll select effcent supplers for fabrc supply. The Purchase Manager (PM) of company has assgne a value to each suppler an entfe the requrements of the company. Frstly, PM wll compare crtera to obtan weght of each crteron. Table 4 shows PM s weghts for qualtatve crtera. Table 4: PM s weghts for Qualtatve Crtera Crtera Complance Crtera Complance wth sectoral prces wth sectoral prces (1,1,1) Reputaton (2,3,4) Communcaton (4,5,6) Reputaton (1/4,1/3,1/2) (1,1,1) (2,3,4) Communcaton (1/6,1/5,1/4) (1/4,1/3,1/2) (1,1,1) These weghts were calculate n FAHP. Table 5 ncates the results. These results wll be ae n FTOPSIS. Table 5: Fuzzy Weghts of Qualtatve Crtera Crtera Fuzzy Weghts Complance wth sectoral prces Reputaton Communcaton PM assgne a weght for each suppler for each crteron. Table 6 shows weghts of supplers.

7 Table 6: PM s Weghts for Supplers Crtera Complance Supplers wth Sectoral Prces Reputaton Communcaton Suppler 1 (7,9,10) (G) (5,7,9) (MG) (5,7,9) (MG) Suppler 2 (9,10,10) (VG) (7,9,10) (G) (5,7,9) (MG) Suppler 3 (7,9,10) (G) (5,7,9) (MG) (7,9,10) (G) Suppler 4 (7,9,10) (G) (9,10,10) (VG) (7,9,10) (G) These weghts normalze n Table 7. Table 7: Normalze Matrx Crtera Complance wth Supplers Sectoral Prces Reputaton Communcaton Suppler 1 (0.7,0.9,1) (0.5,0.7,0.9) (0.5,0.7,0.9) Suppler 2 (0.9,1,1) (0.7,0.9,1) (0.5,0.7,0.9) Suppler 3 (0.7,0.9,1) (0.5,0.7,0.9) (0.7,0.9,1) Suppler 4 (0.7,0.9,1) (0.9,1,1) (0.7,0.9,1) Crtera Weghts (0.384,0.606,0.943) (0.178,0.291,0.471) (0.078,0.103,0.150) After normalsaton of weghts, weghts of crtera multple by supplers weghts. Table 8 ncates weghte normalze matrx. Crtera Supplers Table 8: Weghte Normalze Matrx Complance wth Sectoral Prces Reputaton Communcaton Suppler 1 (0.269,0.545,0.943) (0.089,0.204,0.424) (0.039,0.072,0.135) Suppler 2 (0.346,0.606,0.943) (0.125,0.262,0.471) (0.039,0.072,0.135) Suppler 3 (0.269,0.545,0.943) (0.089,0.204,0.424) (0.055,0.093,0.150) Suppler 4 (0.269,0.222,0.943) (0.160,0.291,0.471) (0.055,0.093,0.150) Closeness of coeffcent (CC) values, whch were calculate usng Eq.11, an score of each suppler are ncate n Table 9. Table 9: Results for Qualtatve Crtera Supplers CC Scores Suppler Suppler Suppler Suppler After obtanng results for qualtatve crtera, quanttatve crtera compare were usng AHP n Expert Choce Table 10 ncates weghts of quanttatve crtera. Table 10: Weghts of Quanttatve Crtera Crtera Weghts Defect Rato Complete Quantty Commt Delvery Consstency Inex=0.026 Table 11 shows PM s opnon regarng quanttatve ata an mprecse quanttatve ata for each suppler. Table 11: PM s opnon an Imprecse Quanttatve Data Alternatves Crtera Imprecse quanttatve ata analyse n Axomatc Desgn (AD) by usng PM s opnon. Table 12 ncates results of AD. Table 12: Results of Analyss of Quanttatve Data Crtera Supplers Purchase Manager Defect Rato Suppler 1 Complete Quantty Suppler 2 Commt Delvery Suppler Suppler Suppler Suppler Weghts obtane n AHP multple by results obtane n AD. Table 13 ncates weghte results, total value an scores of each suppler. Table 13: Overall Score for Quanttatve Data Scores of qualtatve an quanttatve ata were examne as two outputs n Output-orente DEA n whch ummy nput was use. Output-orente DEA was calculate by Fronter Analyst 4, whch s software for DEA. Table 14 shows effcency score, neffcent an effcent supplers. Table 14: Overall Results Supplers Effcency Score Ineffcent/Effcent Suppler Ineffcent Suppler Effcent Suppler Ineffcent Suppler Effcent Suppler 3 Suppler 4 Defect Rato %1-5 %4-6 %2-8 %3-6 %2-6 Complete Quantty % %95- %97 %94-98 %92-98 %96-99 Complete Delvery % %94-99 %95-99 %94-99 %96-98 Crtera Supplers Defect Rato Complete Quantty Commt Delvery Total Score Suppler Suppler Suppler Suppler For ths result, PM wll select Suppler 2 an Suppler 4 for supplyng fabrc.

8 6. Concluson In the context of toay s compettve envronment, companes are ncreasngly focusng on ther supply chan performance. Purchasng from sutable supplers wll ensure enhance suppler-buyer relatonshps an ths enhancement of suppler-buyer relatonshp n turn wll mprove supply chan performance. For ths reason, selectng approprate supplers s an mportant busness actvty for practtoners an acaemcans. There are many methos to select approprate suppler n lterature. Even though most of these methos are useful n evaluatng the performance of supplers, they o not focus on both qualtatve an mprecse quanttatve ata to measure suppler performance. Ths can lea to ecson makers selectng napproprate supplers. In ths paper, a suppler selecton moel comprsng technque capable of analysng mprecse qualtatve an quanttatve ata was presente an scusse. To take nto account the fferences between organsatons an the crcumstances n whch each organsaton make ther suppler selecton ecsons, qualtatve an quanttatve crtera were treate separately. Imprecse quanttatve ata was analyse by usng Decson Maker s opnon an qualtatve ata was analyse usng weghts from Decson Maker. Two values for each suppler, one qualtatve an one quanttatve, along wth ummy nputs were place n output-orente DEA. After ths process, preferre supplers were entfe. The propose moel proves a sutable soluton for Decson Makers as qualtatve an quanttatve ata are analyse base on the prortes (weghtngs) assgne by ecson makers to each crtera. The moel ealt wth mprecse quanttatve crtera usng AHP an Axomatc Desgn, thus conserng ecson maker s opnon regarng quanttatve crtera/ata. Atonally, Fuzzy AHP an Fuzzy TOPSIS were use to analyse qualtatve ata an to obtan ecson maker s opnon regarng qualtatve crtera/ata. As such, the approach propose n ths paper aresses the lmtatons of exstng approaches to suppler selecton Even though ths moel aresses the analyss of qualtatve an mprecse quanttatve ata, t oes not conser orer allocaton from effcent supplers. Orer allocaton from supplers s the last an mportant part of suppler the selecton process an ths s sgnfcantly affecte by varablty n eman. Varaton of eman also causes purchase costs an nventory costs. For ths reason, uncertan eman shoul be consere n the suppler selecton process. Atonally, supplers may not be able to meet the ncrease eman from manufacturers ue to lmtaton of ther capacty. Ths can lea to sruptons n manufacturer s proucton process. Therefore, the capacty of supplers shoul also be consere n the suppler selecton n future research. References [1] Akarte, M.M., Surenra, N.V., Rav, B., an Rangara, N., Web base castng suppler evaluaton usng analytcal herarchy process, Journal of the Operatonal Research Socety, Vol. 52, Issue 5,pp , [2] Barbarosoglu, G. an Yazgac, T., 1997, An applcaton of the Analytc Herarchy Process to Suppler Selecton Problem, Proucton an Inventory Management Journal, Issue 38, pp.14-21, [3] Bayazt, O., Use of analytc network process n venor selecton ecsons, Benchmarkng: An Internatonal Journal, Vol. 13, Issue 5,pp , [4] Ceb, F. an Bayraktar, D., An ntegrate approach for suppler selecton, Logstcs Informaton Management, Vol. 16, Issue 6, pp , [5] Chan, F.T.S. an Kumar, N., Global suppler evelopment conserng rsk factors usng fuzzy extene AHP-base approach, OMEGA Internatonal Journal of Management Scence, Vol. 35, pp , [6] Chen, C.T., Ln, C.T. an Huang, S.F., A fuzzy approach for suppler evaluaton an selecton n supply chan management, Internatonal Journal of Proucton Economcs, Vol. 102, Issue 2, pp , [7] Florez-Lopez, R., Strategc suppler selecton n the ae-value perspectve: A CI approach, Informaton Scences, Volume 177, pp , [8] Ha, H.S., an Krshnan, R., A hybr approach to suppler selecton for the mantenance of a compettve supply chan, Expert Systems wth Applcatons, Vol. 34, pp , [9] Ho W., Xu, X. an Dey, P.K., Mult-crtera ecson makng approaches for suppler evaluaton an selecton: A lterature revew, European Journal of Operatonal Research, Vol.202, Issue 1, pp , [10] Hou, J. an Su, D., EJB MVC orente suppler selecton system for mass Customzaton, Journal of Manufacturng Technology Management, Vol. 18, Issue 1, pp , 2007.

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