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1 Internatonal Journal of Industral Engneerng & Producton Research (2010) pp March 2010, Volume 20, Number4 Internatonal Journal of Industral Engneerng & Producton Research ISSN: Journal Webste: An Adaptve Neuro Fuzzy Inference System for Supply chan Aglty Evaluaton J Jassb, SM Seyedhossen & N Plevar J Jassb, Department of Industral Management Islamc Azad Unversty, Scence and Research Branch Tehran, Iran SM Seyedhossen, Department of Industral Engneerng, Iran Unversty of Scence and Technology, Tehran, Iran N Plevar, Department of Industral Management Islamc Azad Unversty, Scence and Research Branch Tehran, Iran KEYWORDS Aglty; Agle supply chan; Adaptve Neuro Fuzzy Inference System (ANFIS); Supply chan; Supply chan management ABSTRACT Nowadays, n turbulent and volate global markets, aglty has been consdered as a fundamental characterstc of a supply chan needed for survval To acheve the compettve edge, companes must algn wth supplers and customers to streamlne operatons, as well as aglty beyond ndvdual companes Consequently Agle Supply Chan (ASC) s consdered as a domnant compettve advantage However, so far a lttle effort has been made for desgnng, operatng and evaluatng agle supply chan n recent years Therefore, n ths study a new approach has been developed based on Adaptve Neuro Fuzzy Inference System (ANFIS) for evaluatng aglty n supply chan consderng aglty capabltes such as Flexblty, Competency, Cost, Responsveness and Quckness Ths evaluaton helps managers to perform gap analyss between exstent aglty level and the desred one and also provdes more nformatve and relable nformaton for decson makng Fnally the proposed model has been appled to a leadng car manufacturng company n Iran to prove the applcablty of the model 2010 IUST Publcaton, All rghts reserved Vol 20, No4 1 Introducton Wth an ncreasng global competton, at the begnnng of the 21st century, companes have wtnessed sgnfcant changes n the market, such as hgh degree of market volatlty, shortened lfecycles, uncertan demand and unrelable supply Mass markets are contnung to fragment as customers' demands and expectatons rse These developments have caused a major revson of busness prortes and strategc vson [23] The need to respond of volatle envronment has been addressed n recent years by the Correspondng author J Jassb Emal: Jassb@srbauacr seyedhosen@yahoocom n_plevar@yahoocom Paper frst receved July ,and n revsed form Nov 01, 2009 concept of aglty Companes have recognzed that aglty s crucal for ther survval and compettveness Aglty s defned as" the ablty to cope wth unexpected challenges as opportuntes" [23] Other related defntons of aglty have been proposed snce the construct s stll n ts ntal stage of applcaton to organzatonal phenomenon For nstance, [21] has defned aglty as" the ablty to detect opportuntes for nnovaton and seze those compettve market opportuntes by assemblng requste assets, knowledge and relatonshps wth speed and surprse" Researchers studyng aglty have emphaszed that frm's ablty to respond s a key measure of aglty [7] Whle aglty s accepted as a wnng strategy for growth, even a bass for survval n certan busness envronments, the dea of creatng agle supply chan has become a logcal step for

2 188 J Jassb, SM Seyedhossen & N Plevar An Adaptve Neuro Fuzzy Inference System for Supply chan companes [11] Aglty n supply chan, accordng to [11], s the ablty of supply chan as a whole and ts members to rapdly algn the network and ts operatons to dynamc and turbulent requrements of the customers [24] has defned aglty as the ablty of a supply chan to rapdly respond to changes n market and customers' demands The combnaton of Supply Chan Management (SCM) and aglty s a sgnfcant source of compettveness whch has come to be named Agle Supply Chan (ASC) The lack of systematc approach to aglty does not allow companes to develop the necessary profcency n change, a prerequste for aglty [14] ASC has been advocated at the 21st century supply chan paradgm, and s seen as a wnnng strategy for companes wshng to become natonal and nternatonal leader [35] However, the ablty to buld agle relatonshps has developed more slowly than antcpated [24] and also lttle effort has been made to buld ASC assessment methodology n recent years After embracng ASC an mportant queston must be asked: How companes can evaluate aglty n supply chans? Ths evaluaton s essental for managers as t asssts n achevng aglty effectvely by performng gap analyss between exstent aglty level and the desred one and also provdes more nformatve and relable nformaton for decson makng Therefore, ths study attempts to answer ths queston wth a partcular focus on measurng aglty Lack of effcent measurng tool for aglty of supply chan system made us to develop a procedure wth aforementoned functonalty The mprecse nature of attrbutes for assocated concepts persuade us to apply fuzzy concepts and aggregate ths powerful tool wth Artfcal Neural Network concepts n favor of ganng ANFIS as an effcent tool for development and surveyng of our novel procedure Due to our best knowledge ths combnaton has never been reported n lterature before Ths paper s organzed as follows Secton 2 revews the lterature on Agle Supply Chan (ASC) and crtczes t (paper revew); Secton 3 represents the conceptual model usng agle supply chan's capabltes such as Flexblty, Competency, Cost, Responsveness and Quckness, and also contans an adaptve neuro fuzzy nference system (ANFIS) model whch s proposed to evaluate aglty n supply chans; Secton four descrbes case study; In secton 5 the applcablty of the proposed model has been tested by usng a leadng car manufacturng company n Iran Fnally, n secton 6 the man concluson of ths study s dscussed 2 Paper Prevew In the 1990s, the research nterest was focused on fndng systematc ways for manufacturer to approach aglty n ther supply chans To help managers to attan a sustanable compettve advantage, numerous studes have attempted dscuss aglty n organzatons Table 1 provdes varous ways n whch aglty has been defned However, the defnton of aglty s stll fuzzy, manly because t largely deals wth thngs already beng addressed by ndustry and whch are covered by exstng research projects and programs Snce the ntroducton of aglty paradgm, the potental benefts of mplementng t n companes were soon wdely recognzed by researchers and ndustry (Sun et al 2005) The paradgm, n ts varous forms now s recognzed as a wnnng compettve advantage [ ] Parallel developments n areas of aglty and Supply Chan Management (SCM) led to ntroducton of an agle supply chan [5] Whle aglty s accepted as a wnnng strategy for growth, even a bass for survval n certan busness envronments, the dea of creatng agle supply chans has become a logcal step for companes [11] Accordng to Ismal aglty n supply chan s the ablty of supply chan as a whole and ts members rapdly algn the network and ts operaton to dynamcs and turbulent requrements of the customers In 2000, Chrstopher has dentfed that Agle Supply Chan (ASC) requres varous dstngushng capabltes to respond changng envronments These capabltes nclude four man elements [24]: Responsveness, whch s the ablty to dentfy changes and respond to them quckly, reactvely or proactvely, and also to recover from them Competency, whch s the ablty to effcently and effectvely realze enterprse objectves Flexblty, whch s the ablty to mplement dfferent process and apply dfferent facltes to acheve the same goal Quckness whch s the ablty to complete an actvty as quckly as possble In the lterature, frameworks based on other characterstcs of supply chan aglty have also been suggested The researches can be categorzed n three man categores: Conceptual model Emprcal Expert judgment Many researchers provde conceptual over vews, dfferent reference and mature models of aglty For nstance [14] presented that to become a truly agle supply chan key enablers are classfed nto four categores: Collaboratve relatonshp as the supply chan strategy, Process ntegraton as the foundaton of supply chan, Informaton ntegraton as the nfra structure of supply chan and Customer /marketng senstvty as the mechansm of supply chan Table 2 provdes some conceptual models that have been used n ths study and ther man ponts

3 J Jassb, SM Seyedhossen & N Plevar An Adaptve Neuro Fuzzy Inference System for Supply chan 189 No Authors Kdd (1994) Goldman, Negal, (1995) Gunasekaran (1998) Bullnger (1999) Yusuf et al(1999) Naylor et al(1999) Chrstopher (2000) Van Hoek (2001) Stratton& Warburton (2003) Harrson & Van Hoak (2005) Tab 1 Defnton of Aglty Defnton of aglty Aglty s syntheszed use of developed and wellknown technologes and methods of manufacturng That s, t s mutualty compatble wth lean manufacturng, Computer Integrated Manufacturng, Total Qualty Management, Materal Requrement Plannng, Busness Process Reengneerng, employee empowerment Aglty means delverng value to customers, beng ready for change, valung human knowledge and sklls and formng vrtual partnershp Aglty s the capablty of reachng unpredctable market changes n a cost-effectve way, smultaneously prosperng from the uncertanty Aglty means moblty n an organzaton's behavor towards the envronment and can, therefore, understood an extensve answer to contnually changng markets Agle companes are n a process of constant re-determnaton, or self-organzaton, self-confguraton, and self-teamng Aglty s successful exploraton of competence bases (speed, flexblty, nnovaton, proactvely, qualty and proftablty) through the ntegraton of reconfgurable resources and best practces n a knowledge-rch envronment to provde customerdrven products and servces n a fast-changng market envronment Aglty means usng market knowledge and a vrtual corporaton to explot proftable opportuntes n a volate marketplace Aglty s defned as the ablty of an organzaton to respond rapdly to changes n demand both n terms of volume and varety Aglty s all about customer responsveness and market turbulence and requres specfc capabltes that can be acheved usng "lean thnkng" Innovatve products and unstable demand typfy agle supply drvers Aglty s a supply chan wde capablty that algns organzatonal structures, nformaton systems, logstcs processes and, n partcular, mndsets Table 3 llustrates the comparson between emprcal and expert judgment consderng some man attrbutes such as Smplcty, Generalzaton, Model senstvty to samplng, the type of data (fuzzy-crsp),the type of varables, calculaton speed Emprcal studes Tab 3 Comparson of Emprcal and Expert judgment based studes Author Damen JPower 2001 Margaret Weber 2002 YYusuf 2003 Descrpton Crtcal success factors n agle supply chan usng regresson analyss Aglty assessment n vrtual organzaton Study the relaton between Capabltes and objectves n ASC Usng structural Luca equaton to test Avella 2007 conceptual model Hessam 2008 Pedue Ihu 2002 Identfyng effectve factors on Agle supply chan Evaluaton model of ASC's performance Smplcty Generalzaton Model senstvty to samplng Data B B B Crsp Varable Calculaton speed contnuum Crsp contnuu m low Crsp contnuum Crsp contnuu m Medum Crsp contnuum Medum Fuzzy Lngustc Term Medum Tab 2 Conceptual model based studes Conceptual Model Authors Year Man ponts Gunasekaran key dmensons: strateges, Tec, people and system 2000 ASC's enablers Martn Chrstopher Van Hoak 2001 Aglty audt n supply chans Dens Towll 2001 Integrated model for enablng ASC: prncples, programs, actons YYusuf 2003 Agle supply chan capabltes Chng Torang 2005 A conceptual model for assessng aglty n Ln supply chan Ashs Agarval 2006 Identfyng aglty ndex n supply chan Sowford 2006 A process approach to ASC Danel Vazquez 2007 Conceptual model for assessng ASC, Aglty drvers, enablers and outcomes Gunasekaran 2008 Modelng and control of supply chan Expert Judgment based studes Assessng aglty Chng usng experts Torng 2005 judgments Ashsh Agrawal 2005 Vpul Jan 2007 Modelng the metrcs of lean, agle and leagle supply chan based on ANP model Evaluatng aglty n supply chan based on rule mnng _ Fuzzy Lngustc Term Medum Crsp contnuum fuzzy Lngustc Term

4 190 J Jassb, SM Seyedhossen & N Plevar An Adaptve Neuro Fuzzy Inference System for Supply chan The aggregaton of above approaches can be crtczed as they haven t consdered the mpact of enablers n assessng aglty n supply chans and also the scale used to aggregate the aglty capabltes has two lmtatons: (l) Such technques do not consder the ambguty and mult possblty assocated wth mappng of ndvdual judgment to a number and (2) the subjectve judgment, selecton and preference of evaluators have a sgnfcant nfluence on these methods Due to the qualtatve and ambguous attrbutes lnked to aglty assessment, most measures are descrbed subjectvely usng lngustc terms, and cannot be handled effectvely usng conventonal assessment approaches However, fuzzy logc provdes an effectve means of dealng wth problems nvolvng mprecse and vague phenomena Fuzzy concepts enable assessors to use lngustc terms to assess ndcators n natural language expressons, and each lngustc term can be assocated wth a membershp functon Furthermore, fuzzy logc has found sgnfcant applcatons n management decsons [14] Accordng to above lterature revew to assst companes n better achevng an ASC, a fuzzy nference system has been developed for mappng nput space (tangble and ntangble) to output space The proposed Fuzzy Inference System (FIS) has been based on the experences of experts to evaluate aglty of supply chans 3 Methodology To evaluate supply chan aglty two man steps should be carred out: Frstly, a conceptual model has been developed based on lterature revew to dentfy measurement crtera, n ths step capabltes of supply chan have been used to defne supply chan aglty n three basc segments: Sourcng, Manufacturng and Delvery Secondly, an ANFIS archtecture has been desgned - that can construct an nput-output mappng based on both human knowledge n the form of fuzzy f-then rules wth approprate membershp functons and stpulated nput-output data based- for dervng aglty n supply chans These two parts are nvestgated n detal n followng sectons A Model Constructon As mentoned earler, agle supply chan concerns change, uncertanty and unpredctablty wthn busness envronment and makes approprate responses to change Therefore, an agle supply chan has varous dstngushng capabltes In order to carry out the supply chan aglty assessment model, a commttee of decson-maker has been formed The member of the commttee are supply chan managers, strategc managers and fnance managers academc experts It s assumed that the group members wll carry out necessary branstormng sessons and reach to consensus In other word, rather than askng the same questons to ndvdual members separately, only one response s receved from the group and t s beleved to represent the democratc majorty pont of vew of the group A conceptual model whch has been derved from expert's knowledge and lterature s shown n fgure1 t conssts of three man segments of supply chan (sourcng, manufacturng and delvery) As Prater (2001) mentoned the supply chan may be broken down nto these three basc segments, the combnaton of these supply chan segments on the one hand and supply chan's capabltes on the other hand leads to the defnton of supply chan aglty Four man attrbutes (Table 4) and twenty four subattrbutes (Table 5) are the bass of the conceptual model Tab 4 attrbutes of the conceptual model Attrbute Flexblty Responsveness & Quckness Reference Lst Sharp et al 1999, Chrstopher 2000, Swafford et al 2006, Sharf and Zhang 1999, Ln et al2006 Sharf and Zhang 1999, Goldman et al 1994, kdd 1999, Ln et al 2006 Vckery, Calantone 1999, Handfeld & Pannes 1992, Vckery & Drog 1999, Tersne & Hmmnbrg 1995 Competency Ln et al 2006, Sharf and Zhang 1999 Cost Swafford et al 2006, Sharf and Zhang 1999, Van Hoak et al 2001 Cooper & Enllarm 1993, Goldman et al 1994, Croctto & Yusuf 2003 Tab 5 Sub- attrbutes of the conceptual model Code Sub-attrbutes Reference SF 1 numerous avalable Sharf and Zhang 1999, supplers Goldman et al 1994 flexblty n Sharf and Zhang 1999, SF 2 volume Goldman et al 1994 SF 3 flexblty n varety Swafford 2006 flexble MF 1 manufacturng system Powar & Sohal 2001 MF 2 CAM based Ismal & Sharf 2005, manufacturng Towll 2001 MF 3 varety and volume of productons Sharf and Zhang 1999 DF 1 varety of supply schedules for meetng costumers' needs Swafford 2006

5 J Jassb, SM Seyedhossen & N Plevar An Adaptve Neuro Fuzzy Inference System for Supply chan 191 Code Sub-attrbutes Reference DF 2 flexblty n volume of product Swafford 2006 SR 1 Adaptablty of delver tme by Van Hoak 2001 supplers SR 2 supplers' delvery tme Van Hoak 2001 SR 3 suppler relaton Copanco 1996, Coyle et management al1996 MR 1 Tme of establshment and changng parts Sharf and Zhang 1999 MR 2 Responsveness Swafford 2006, Goldman et level to the market al 1994 changes C 1 cooperaton and Agrawal & Shankar 2002, nternal-external Lee et al1999 balance MC 1 new product ntroduce Ismal & Sharf 2005 MC 2 qualty of products Swafford 2006, Sharf & or servces Zhang 1999 MC 3 ntegraton Crstopher & Towll 2001 MC 4 tme of new product development Goldman C 2 capabltes of Wlls 1995, Sharf & human resources Zhang SO Sourcng cost Cooper 1993, Goldman et al1994 MO 1 producton cost Swafford 2006, Goldman et al1994 MO 2 establshment cost Swafford 2006, Goldman et al1994 MO 3 The cost of Swafford 2006, Goldman et changng parts al1994 DO delvery cost Van Hoak 2001, Sharf & Zhang 1999 Fg 1 The conceptual model ntellgence) there are varous ways to represent knowledge Perhaps the most common way to represent human knowledge s to form t nto natural language expressons of the type: IF premse (antecedent), THEN concluson (consequent) The form n expresson s commonly referred to as the IF-THEN rule- based form Ths form generally s referred to as deductve form It typcally expresses an nference such that f we know a fact (premse, hypothess, antecedent), then we can nfer, derve, another fact called a concluson Ths form of knowledge representaton, Characterzed as shallow knowledge, s qute n the context of lngustcs because t expresses human emprcal and heurstc knowledge n our own language of communcaton Fuzzy nference systems are one of the most appled and popular systems developed for fuzzy reasonng whch use fuzzy logc for modelng uncertanty (or mprecson) arsng from mental phenomena, whch are nether random nor stochastc Fuzzy reasonng, also known as approxmate reasonng, s an nference procedure that derves conclusons from a set of fuzzy f-then rules and known facts The fuzzy nference system s a popular computng framework based on the concepts of fuzzy set theory, fuzzy f-then rules, and fuzzy reasonng It has been appled successfully n a wde range of scence and engneerng such as control, functon approxmaton, sgnal processng, smulaton, data clusterng and data mnng and decson support systems In lterature, we can fnd some other names such as fuzzy-rule-based system, fuzzy expert system, fuzzy model, fuzzy assocatve memory, fuzzy logc controller, and smply (and ambguously) fuzzy system In the lterature, there are several nference technques developed for fuzzy rule-based systems, such as Mamdan and Sugeno [15-28] Mamdan FIS s the frst nference methodology, n whch nputs and outputs are represented by fuzzy relatonal equatons n canoncal rule-based form In Sugeno FIS, output of the fuzzy rule s characterzed by a crsp functon Typcal representaton of a fuzzy rule n a Sugeno FIS s gven by: If (1) x s A and x s A and x s A then y f x, x,, x n n 1 2 n Where A ~ are fuzzy sets (fuzzy parttons of each nput) and y s a crsp functon To calculate the fnal output value of a Sugeno FIS, y, from j=1 to m output subsets, y j, weghted by nput fuzzy set membershp values, A, for =1 to n antecedents as : B Desgnng ANFIS Archtecture Fuzzy set theory s a perfect mean for modelng uncertanty (or mprecson) arsng from mental phenomena, whch are nether random nor stochastc In the feld of artfcal ntellgence (machne y m y n j A j 1 1 m n j 1 1 A x x (2)

6 192 J Jassb, SM Seyedhossen & N Plevar An Adaptve Neuro Fuzzy Inference System for Supply chan In a sugeno model each rule has a crsp output, gven by a functon; because of ths the overall output s obtaned va a weghted average defuzzfcaton (Eq 2), as shown n fgure 2 Ths process avods the tme-consumng methods of defuzzfcaton necessary n the Mamdan model Fg2 The Sugeno fuzzy model Artfcal neural networks (ANN) are another effcent tool of artfcal ntellgence Fuzzy systems and ANNs have ther own advantages and drawbacks Fuzzy systems have the ablty to represent comprehensve lngustc knowledge gven for example by a human expert and perform reasonng by means of rules However, fuzzy systems do not provde a mechansm to automatcally acqure and/or tune those rules On the other hand neural-networks are adaptve systems that can be traned and tuned from a set of samples Once they are traned, neural-networks can deal wth new nput data by generalzng the acqured knowledge Nevertheless, t s very dffcult to extract and understand that knowledge In other words, fuzzy systems and neural-networks are complementary paradgms Neuro fuzzy systems have been recently proposed to combne the advantages of fuzzy systems and ANNs Neuro-fuzzy systems are fuzzy systems whch use artfcal neural networks (ANNs) theory n order to determne ther propertes (fuzzy sets and fuzzy rules) by processng data samples Neuro-fuzzy systems harness the power of the two paradgms: fuzzy logc and ANNs, by utlzng the mathematcal propertes of ANNs n tunng rule-based fuzzy systems that approxmate the way human s process nformaton Adaptve neuro fuzzy nference system (ANFIS) was proposed by Jang (1993) s one of the most popular technques has been appled frequently n recent years The most common nference system used n ANFIS s a frst order Sugeno FIS whch s n the form (1) Durng the tranng procedure, rule parameters ncludng antecedent parameters and consequent parameters wll be tuned to present more accurate outputs wth the mnmum error Jang [12] presented some tranng algorthms for tunng the ANFIS Least square estmator s used to tune the consequent parameters as back propagaton for antecedent parameters Table 6 summarzes the learnng procedure n ANFIS Tab 6 Learnng Procedure n ANFIS Forward pass Backward pass Premse parameters Fxed Gradent descent Consequent Least square Fxed parameters estmate Sgnals Node outputs Error rates Now we brefly descrbe the archtecture of ANFIS and ts layers Fg 3 Structure of ANFIS layers Layer 1: n ths layer we fuzzfy all nputs by ntroducng fuzzy parttons A wde range of fuzzy membershp functons such as trangular and bell shaped membershp functons For example, by applyng a bell shaped membershp functon we have: O X 1 x c 1 a (3) 1 A 2 b Layer 2: n the second layer, dfferentable T-Norms lke product operator are used to derve the frng level of rules as below: O w X Y 2 2 A B (4) Layer 3: frng levels of rules are normalzed as below: O w w (5) 3 w Layer 4: In ths layer, the output of each node n ths layer s smply the product of the normalzed frng strength and a frst order polynomal (for a frst order Sugeno model) Thus, the outputs of ths layer are gven by: O 4 w f (6) Layer 5: n the last layer the fnal output of system s calculated as the weghted average of prevous nodes:

7 J Jassb, SM Seyedhossen & N Plevar An Adaptve Neuro Fuzzy Inference System for Supply chan 193 O 5 w f (7) 4 Case Study Dstrbuton The proposed methodology has been appled to Iran Khodro spare parts and After- sale servce co (ISACO) ISACO s an nternatonal tradng company supplyng a wde range of auto spare parts, the company s also dstrbuter for mported brands Company's doman of actvty ncludes supply automotve parts and servces, customer servces, dealer and servce network, parts sourcng, warranty sales and etc for all automobles manufactured by Iran Khodro company, the largest automotve manufacturer n the Mddle East In order to carry out aglty assessment procedure, a commttee of experts has been formed In ths step we constructed the decson team ncludng engneerng manager, qualty control and nsurance manager, purchasng manager, and fnancal manager, then they are requested to evaluate the aglty factors that appear n our conceptual model usng constructed questonnare n the range of 0-10 The ANFIS output n our case study s calculated 458 Next secton shows the mplementaton of the proposed methodology n our case study through three steps 5 Model Implementaton Step 1: Rule Generaton There are some ways for rule generaton n ANFIS The common way s grd parttonng whch parttons the nput space and sets membershp functons Another way s dervng rules by experts and nsertng rules to the system, f possble Ths way can ncrease the speed of tranng and can tran the FIS wth fewer numbers of observatons Clusterng the nputs s another effcent way for rule generaton Tradtonal K-means and fuzzy C-means are crtczed because we should mpose the number of clusters Mountan clusterng was developed by Yager and Flev [34] s an effcent clusterng approach whch approxmates the center of clusters by usng a densty functon called mountan functon Ths approach uses the grd ponts as alternatves of cluster centers Chu [3] used data ponts as clusters center alternatves nstead of grd ponts n mountan clusterng and called the method subtractve clusterng In ths paper, subtractve clusterng has been used to generate the FIS the range of nfluence, the squash factor, the acceptance rato, and the rejecton rato were set at o5, 125, 05, and 015 respectvely durng the process of subtractve clusterng Also, for dervng flexblty (FI), Competency (Cm), Cost (Co), Quckness and Responsveness (Qu) we have used four sub ANFIS (Fgure 4) accordng to ther sub crtera shown n conceptual model and the modfed rules of traned ANFIS have been shown n the appendx Step2: Data Generaton and Tranng the ANFIS In order to collect the knowledge from experts a questonnare was used whch was made by randomly generatng the combnatons of flexblty, competency, cost, responsveness and quckness n the range of data set were collected of whch 120 used for tranng the ANFIS and the rest for checkng and valdaton of the model Fgure 4 shows the archtecture of the man ANFIS for dervng supply chan aglty The output space (aglty level) was parttoned by fve membershp functons as shown n fgure 5 By nsertng ANFIS output to the system we can derve the aglty level Also, the trend of tranng error and checkng error has been shown n fgure 6 We contnued the tranng process to 70 epochs because the trend of checkng error started to ncrease afterward and over fttng occurred The value of checkng error by 70 epochs was 007 whch s acceptable Then we derved the value of supply chan aglty by a traned ANFIS The ANFIS output n Iran Khodro (a leadng car manufacturng company) s calculated 458 By matchng the selected membershp functon for aglty varable wth crsp output (458) the supply chan of ths company can be labeled "Medum agle" that, accordng to experts' opnons, t should be " agle" Flexblty (Fl) Quckness and Responsveness (Qu) Competenc y (Cm) Cost (Co) Fuzzy Partto Fg4 Archtecture of ANFIS for dervng supply chan aglty Fg 5 Membershp functons for output (Aglty level) R 1 R 2 R n Fg 6 Trend of errors Aglty n w * f 1

8 194 J Jassb, SM Seyedhossen & N Plevar An Adaptve Neuro Fuzzy Inference System for Supply chan Step 3: Valdaton of the Model As we mentoned n step 2, after recevng the data from experts about ther opnons of aglty level of supply chan to the set of nputs values, we dvded them nto two categores We used one data set for tranng ANFIS and the other for valdaton purpose between ANFIS output and the score whch experts have dentfed The plot of ANFIS outputs and testng data has been shown n fgure 7 In ths plot tranng data appears at crcles wth the checkng data, appearng as plus, so as t s observed, they conform to each other In order to valdate the accuracy of proposed ANFIS, we compared the model output wth experts' knowledge about aglty level whch has not been used for tranng ANFIS We used mean error and mean magntude for valdatng the proposed ANFIS The mean error between experts' knowledge and the output of model was 007 and Mean magntude of relatve error (MMRE) of 0012 was observed that are acceptable amounts We have also chosen sgn test for sgnfcant testng It s a standard test to test dfference between populaton means for two pared samples whch are equal The hypothess test s as follows: Ho: µ1=µ2 H1:µ1 µ2 Test statstc=mn (w-, w+) Alpha: Typcally set to 005 Concluson: the null hypothess wll be rejected f the test statstc s n crtcal regon After the hypothess testng (sgn test) PVALUE s calculated 081, consderng á=0 and pvalue >á Snce H0 cannot be rejected, there s no sgnfcant dfference between two pared samples It means our system behavor doesn t have sgnfcant dfference wth experts' knowledge And t should be mentoned that the current valdaton of ANFIS model s also based on experts' knowledge as the data about aglty level of supply chan s currently not avalable Fg 7 Comparson of ANFIS outputs and testng data 6 Concluson Snce Agle Supply Chan (ASC) s consdered as a domnant compettve advantage n recent years, evaluatng supply chan aglty can be useful and applcable for managers to make more nformatve and relable decsons n antcpated changes of volatle markets As aglty assessment s assocated wth vagueness and complexty, crsp (conventonal) evaluaton are unsutable and neffectve, so we have developed an Adaptve Neuro Fuzzy Inference System (ANFIS) usng ASC capabltes for dervng aglty level n supply chan Fuzzy nference system can be used when there s a shallow knowledge and can be operated wth some experences about the system The objectve of usng ANFIS was to optmze the parameters of equvalent fuzzy nference system by applyng a learnng algorthm usng nput-output data sets Fuzzy theory has been used to handle the mprecson and vagueness of ASC'S attrbutes Ths study has been addressed the queston of how to measure and mprove supply chan aglty as we cannot manage what we can not measure We have mplemented the proposed methodology n a leadng car manufacturng company n Iran to prove the applcablty of the model and the supply chan of ths company s labeled "Medum agle" that, accordng to experts' opnons t should be " agle" Ths evaluaton helps managers to perform gap analyss between an exstent aglty level (Medum) and the desred one (hgh) Gap analyss asssts to dentfy obstacles wthn the organzaton that could block aglty achevement Furthermore, the proposed methodology has the followng features: It facltates a rapd decson makng for managers and can also ease a systematc qualty mprovement as t provdes the means for managers to devse an mprovement plan We used mean error and mean magntude of relatve error (MMRE) as crtera sets for valdatng approach We also analyzed the proposed model behavor by comparng wth experts' knowledge usng the sgn test and no sgnfcant dfference was found between these two pared samples Further research s necessary to compare effcency of dfferent models for measurng aglty n supply chan Although ths study has just been done n the Iranan manufacturng enterprses, the proposed methodology s applcable to other countres' manufacturng companes, too Consderng enablers n aglty evaluaton and nvestgatng the mpact of them on capabltes could be studed n further researches Also, fndng the relatons between enablers and capabltes could be the focus of future research n order to desgn a dynamc system for ASC assessment References [1] Agarwal, A, Shankar, R, Modelng Aglty of Supply Chan Internatonal Marketng Management 36: , 2006 [2] Bullnger, HJ, Turbulent Tme Requres Creatve Thnkng: New European Concepts n Producton Management Internatonal Journal of producton economcs 60(61): 9-27, 1999 [3] Chu, S, Fuzzy Model Identfcaton Based on Cluster Estmaton Journal of Intellgent and Fuzzy Systems 2(3): 1994, pp

9 J Jassb, SM Seyedhossen & N Plevar An Adaptve Neuro Fuzzy Inference System for Supply chan 195 [4] Chrstopher, M, Towll, D, Supply Chan Mgraton From Lean and Functonal to Agle and Customzed Supply chan management 5(4): 2000, pp [5] Chrstopher, M, The Agle Supply Chan: Competng n Volatle Markets Industral Marketng Management 29:37-44, 2000 [6] Cooper, MC, Ellram, LM, Characterstcs of Supply Chan Management and the Implcaton for Purchasng and Logstcs Strateges The Internatonal Journal of Logstcs Management 4(2): 1993, pp [7] Dove, R, Response ablty: the Language Structure and Culture of the Agle Enterprse John Wley & Sons, New York, 2001 [8] Goldman, Sl, Negal, RN, Press, K, Agle Compettors and Vrtual Organzatons Strategc for Enrchng the Customer Van Nostrand Renhold, 1995 [9] Gunasekaran, A, Agle Manufacturng : Enablers and Implementaton Framework Internatonal Journal of producton economcs 36(5): 1998, [10] Harrson, A, Van Hoak, R, Logstc Management and StrategyPrentce-Hall, 2005 [11] Ismal & Sharf, H, Supply Chan Desgn for Supply Chan: A Balanced Approach to Buldng Agle Supply Chan Proceedng of the Internatonal conference on Aglty, 2005 [12] Jang, R, ANFIS: Adaptve Network-based Fuzzy Inference System IEEE Transactons on systems man and Cybernetcs, 1993 [13] Kdd, PT, Agle Manufacturng Forgng new fronters Addson-Wesley London, 1994 [14] Ln C-T, Chu, H & Chu P-Y, Aglty Index n Supply Chan Internatonal Journal of producton economcs 100: 2006, pp [15] Mamdan, EH, Applcaton of Fuzzy Logc to Approxmate Reasonng Usng Lngustc Systems 26: 1997, pp [16] Naylor, J, Leaglty: Integraton the Lean and Agle Manufacturng Internatonal Journal of producton economcs 62: 1999, pp [17] On Systems Man and Cybernetcs 24(8): [18] Ovebye, E, Baharadwaj, A, Sambamurthy, V, Enterprse Aglty and the Enablng Role of Informaton Technology n Organzatons Organzaton Scence 11: 2000, pp [19] Plevar, N, Seyed Hossen, SM, Jassb, J, Fuzzy Logc Supply Chan Aglty Assessment Methodology IEEM Industral Engneerng, Snapore, 2008 [20] Press, k, A System Perspectve of Lean and Agle Manufacturng Aglty and Global Competton 1(1): 1997, pp [21] Sambamurthy, V, Baharadwaj, A, Grover, V, Shapng Aglty Through Dgtal Optons MIS Quarterly 27(2): 2003, pp [22] Seyed hossen, SM, Fekr, R, Alahmad, A, Identfyng the Crtcal Success Factors of Agle NPD Process n Iranan Manufacturng Enterprses Kuwat Journal of Scence and Engneerng, 2009 [23] Sharf, H, Zhang, Z, A Methodology for Achevng Aglty n Manufacturng Organzaton: An Introducton Internatonal Journal of producton economcs 62:7-22, 1999 [24] Sharp, JM, Iran, Z, Desa, S, Workng Towards Agle Manufacturng n the UK Industry Internatonal Journal of producton economcs 62: 1999, pp [25] Sohal, AS, Power, DJ, Terzovsk, M, Integraton of Supply Chan Management From Wholesaler's Perspectve: Two Australan Case Studes Internatonal Journal of Physcal Dstrbuton & Logstc management 32(2): 2003, pp [26] Stratton, R, Warburton, RDH, The Strategc Integraton of Agle and lean Supply Internatonal Journal of producton economcs 85: 2003, pp [27] Swafford, PM, Ghosh, S, Murthy, N, A Frame Work for Assessng Value Chan Aglty Internatonal Journal of operaton & producton management 26(2): 2006, pp [28] Takag, T, Sugeno, M, Fuzzy Identfcaton of Systems and ts Applcaton to Modelng and Control IEEE Transacton on systems man and Cybernetcs 15(1): 1985, pp [29] Toloe, A, Zandehessam, H, Process Based Agle Supply Chan Model Accordng to BPR and IDEF 30 Concept Contemporary Engneerng Scences 2(3): 2009, pp [30] Van Hoak, R, Mtgatng the Mnefeld of Ptfalls n Creatng Agle Supply Chan Internatonal conference of Aglty, Otanem, Fnland, 2005 [31] Vazquez, D, Avella, L, Aglty Drvers, Enablers and Outcomes: Emprcal Test of an Integrated Agle Manufacturng Model Internatonal Journal of Operaton & Producton management 27(12): 2007, pp [32] Vpul, J, Benyoucef, L, Deshmukh, SG, A new approach for evaluatng aglty n supply chan usng Fuzzy Assocated Rules Mnng Artfcal Intellgence, 2007 [33] Whte, A, Danel, EM, Mohdzan, M, The Role of Emergent Informaton Technologes and Systems n Enablng Supply Chan Aglty Informaton Management 25: 2005, pp [34] Yager, RR, Flev, DP, Approxmate Clusterng Va the Mountan Method IEEE Transectons, 1994

10 196 J Jassb, SM Seyedhossen & N Plevar An Adaptve Neuro Fuzzy Inference System for Supply chan [35] Yusuf Y, Gunasekaran, A, Agle Manufacturng : the Drvers Concepts and Attrbutes Internatonal Journal of producton economcs 62: 1999, pp Appendx Ths Appendx shows the fuzzy rule base There are 26 fuzzy rules: 1 If (Fl s MF1) and (Qu s MF1) and (Cm s MF1) and (Co s MF1) then Ca 03938Fl Qu Cm Co If (Fl s MF2) and (Qu s MF2) and (Cm s MF2) and (Co s MF2) then Ca Fl Qu Cm Co If (Fl s MF3) and (Qu s MF3) and (Cm s MF3) and (Co s MF3) then Ca Fl Qu Cm Co If (Fl s MF4) and (Qu s MF4) and (Cm s MF4) and (Co s MF4) then Ca Fl Qu Cm Co If (Fl s MF5) and (Qu s MF5) and (Cm s MF5) and (Co s MF5) then Ca 049 Fl Qu Cm Co If (Fl s MF6) and (Qu s MF6) and (Cm s MF6) and (Co s MF6) then Ca 049 Fl Qu Cm Co If (Fl s MF7) and (Qu s MF7) and (Cm s MF7) and (Co s MF7) then Ca Fl Qu Cm Cu If (Fl s MF8) and (Qu s MF8) and (Cm s MF8) and (Co s MF8) then Ca Fl Qu Cm Co If (Fl s MF9) and (Qu s MF9) and (Cm s MF9) and (Co s MF9) then Ca Fl Qu+0146 Cm Co If (Fl s MF10) and (Qu s MF10) and (Cm s MF10) and (Co s MF10) then Ca Fl Qu Cm+0312 Co If (Fl s MF11) and (Qu s MF11) and (Cm s MF11) and (Co s MF11) then Ca Fl Qu Cm Co If (Fl s MF12) and (Qu s MF12) and (Cm s MF12) and (Co s MF12) then Ca Fl Qu Cm Co If (Fl s MF13) and (Qu s MF13) and (Cm s MF13) and (Co s MF13) then Ca Fl Qu Cm Co If (Fl s MF14) and (Qu s MF14) and (Cm s MF14) and (Co s MF14) then Ca Fl+0338 Qu Cm Co If (Fl s MF15) and (Qu s MF15) and (Cm s MF15) and (Co s MF15) then Ca 0599 Fl Qu Cm Cu If (Fl s MF16) and (Qu s MF16) and (Cm s MF16) and (Co s MF16) then Ca Fl Qu Cm Co If (Fl s MF17) and (Qu s MF17) and (Cm s MF17) and (Co s MF17) then Ca 0357 Fl Qu Cm Co If (Fl s MF18) and (Qu s MF18) and (Cm s MF18) and (Co s MF18) then Ca Fl Qu Cm Co If (Fl s MF19) and (Qu s MF19) and (Cm s MF19) and (Co s MF19) then Ca Fl Qu Cm Co If (Fl s MF20) and (Qu s MF20) and (Cm s MF20) and (Co s MF20) then Ca 046 Fl Qu Cm+039 Co If (Fl s MF21) and (Qu s MF21) and (Cm s MF21) and (Co s MF21) then Ca Fl Qu+0179 Cm Co If (Fl s MF22) and (Qu s MF22) and (Cm s MF22) and (Co s MF22) then Ca Fl Qu Cm Co If (Fl s MF23) and (Qu s MF23) and (Cm s MF23) and (Co s MF23) then Ca Fl Qu Cm Co If (Fl s MF24) and (Qu s MF24) and (Cm s MF24) and (Co s MF24) then Ca Fl Qu Cm Co If (Fl s MF25) and (Qu s MF25) and (Cm s MF25) and (Co s MF25) then Ca Fl Qu Cm+0348 Co If (Fl s MF26) and (Qu s MF26) and (Cm s MF26) and (Co s MF26) then Ca Fl Qu Cm Co+03308