Expert Systems wth Applcatons 38 (2011) 2741 2751 Contents lsts avalable at ScenceDrect Expert Systems wth Applcatons journal homepage: www.elsever.com/locate/eswa A combned methodology for suppler selecton and performance evaluaton Mthat Zeydan a,, Cüneyt Çolpan b, Cemal Çobanoğlu c a Department of Industral Engneerng, Engneerng Faculty, Ercyes Unversty, 38039 Kayser, Turkey b Department of Qualty Assurance, 2nd Turksh Ar Supply and Mantenance Center Command, Kayser, Turkey c Department of Procurement, Hyunda Assan Automotve Factory, _ Izmt/Kocael, Turkey artcle nfo abstract Keywords: Suppler evaluaton Mult-crtera decson makng technques Performance mprovement Today, organzatons that wsh to carry on the sustanable growng need a robust strategc performance measurement and evaluaton system because of changng demands of consumers, reduced product lfe cycle, compettve and globalsed markets. In ths study, a new methodology s ntroduced and proposed for ncreasng the suppler selecton and evaluaton qualty. The new approach consders both qualtatve and quanttatve varables n evaluatng performance for selecton of supplers based on effcency and effectveness n one of the bggest car manufacturng factory n Turkey. Ths new methodology s realzed n two steps. In the frst stage, qualtatve performance evaluaton s performed by usng fuzzy AHP (Analytcal Herarchcal Process) n fndng crtera weghts and then fuzzy TOPSIS (Technque for Order Preference by Smlarty to Ideal Soluton) s utlzed n fndng the rankng of supplers. So, qualtatve varables are transformed nto a quanttatve varable for usng n DEA (Data Envelopment Analyss) methodology as an output called qualty management system audt. In the second stage, DEA s performed wth one dummy nput and four output varables, namely, qualty management system audt, warranty cost rato, defect rato, qualty management. As a result, comparng wth the present system appled by the car factory, the new method seems to be some advantages and superortes for makng the decson n buyng the qualty car luggage sde part (panel) by selectng the sutable suppler(s) n an automotve factory of Turkey. Ó 2010 Elsever Ltd. All rghts reserved. 1. Introducton To choose the rght suppler deals, wth an mportant evaluaton, and selecton problems n the purchasng functon of a busness. A good suppler selecton makes a sgnfcant dfference to an organzaton s future to reduce operatonal costs and mprove the qualty of ts end products. There have been a lot of factors n today s global market n whch that nfluence companes to search for a compettve advantage by focusng on purchasng raw materals and component parts represents the largest percentage of the total product cost. For nstance, hgh technology products such as motor vehcles, ralroad&transport equpment, machnery&equpment components, purchased materals and servces account for up to 80% of the total product cost. Therefore, selectng the rght supplers s a key to the procurement process and represents a major opportunty for companes to reduce costs. On the other hand, selectng the wrong supplers can cause operatonal and fnancal problems (Weber, Current, & Benton, 1991). The tradtonal approach to suppler selecton has been to select supplers solely on the bass of prce for many years. However, as companes have learned that prce as a sngle crteron for suppler selecton s Correspondng author. Tel.: +90 352 4374901/32454; fax: +90 352 4375784. E-mal address: mzeydan@ercyes.edu.tr (M. Zeydan). nsuffcent, they have turned nto more comprehensve mult-crtera decson makng technques. Recently, these crtera have become ncreasngly complex as envronmental, socal, poltcal, and customer satsfacton concerns have been added to the tradtonal factors of qualty, delvery, cost, and servce. Apart from cost reducton, companes contnuously work wth supplers to reman compettve by reducng product development tme, mprovng product qualty, and reducng lead tmes. For nstance, a qualfed base of supplers helps a company acheve greater nnovaton through mproved product desgn and ncreased flexblty. Some authors have dentfed several crtera for suppler selecton, such as the net prce, qualty, delvery, hstorcal suppler performance, capacty, communcaton systems, servce, and geographc locaton, among others (Dempsey, 1978). These evaluaton crtera nvolve tradeoffs and are a key ssue n the suppler assessment process snce t measures the performance of supplers. For example, one vendor may offer nexpensve parts of slghtly below average qualty, whle another vendor may offer hgher qualty parts, wth uncertan delvery thus settng up trade-offs. In addton, the mportance of each crteron, vares from one purchase to the next and s complcated further by the fact that some crtera are quanttatve (prce, qualty, etc.), whle others are qualtatve (servce, flexblty, etc.). Thus, a technque s needed that can adjust for the decson maker s atttude toward the mportance of each crteron 0957-4174/$ - see front matter Ó 2010 Elsever Ltd. All rghts reserved. do:10.1016/j.eswa.2010.08.064
2742 M. Zeydan et al. / Expert Systems wth Applcatons 38 (2011) 2741 2751 and ncorporates both qualtatve and quanttatve factors (Bhutta & Huq, 2002). The overall objectve of the suppler evaluaton process s to reduce rsk and maxmze overall value to the purchaser. An effectve suppler survey should have certan characterstcs such as comprehensveness, objectveness, relablty, flexblty and fnally, t has to be mathematcally straghtforward. It can be concluded that mportant savngs can be realzed through effectve purchasng strateges. Ths study helps decson makers reduce a base of potental supplers to a manageable number and make the suppler selecton by means of mult-crtera technques. Ths new methodology was appled to a car manufacturng faclty n Turkey. 2. Lterature revew Suppler evaluaton s a mult-objectve and crtera decson makng problem contanng many quanttatve and qualtatve factors because there are typcally more than one crteron (atttude) needed to be taken nto consderaton n evaluatng a supply source. All of supply sources are focused on ther performance such as delvery, qualty, servce and prce as the man factors that all frms use for evaluatng sources of supply (Ha & Krshnan, 2008). Many frms and researchers have been workng on the suppler evaluaton problem over the past decade to develop decson makng models whch can effectvely deal wth ths problem. Accordng to Ghodsypour and O Bren (1998), optmzaton models for suppler evaluaton can be classfed nto two groups: sngle objectve models whch are used to consder one crteron as the objectve functon and other crtera as constrants. The sngle objectve models have two dsadvantages: all crtera are equally weghted, whch rarely happens n practce, and they have sgnfcant dffcultes n consderng qualtatve factors. In contrast, the multple objectve models have been appled to a suppler evaluaton problem. Relyng on a sngle crteron makes the suppler selecton process rsky. Therefore, a mult-crtera approach s recommended. A poneerng work n suppler selecton crtera was that of Dckson n 1966. Despte the multple crtera nature of the problem, very lttle work has been devoted to the study of the suppler selecton problem by usng mult-crtera technques such as goal programmng, mult-objectve programmng, or other smlar approaches. Kahraman, Cebec, and Ulukan (2003) used fuzzy AHP to select the best suppler for a manufacturer frm establshed n Turkey. Bevlacqua and Petron (2002) developed a system for suppler selecton usng fuzzy logc. Some authors as used n ths paper have combned decson models n the suppler selecton process, for example, Weber, Current, and Desa (1998) combned DEA and mathematcal programmng models. Ths combnaton provded decson makers wth a tool for negotatng wth supplers. Dckson (1966) developed a model combnng mathematcal programmng model and TCO (total cost of ownershp). They derved the nventory management polcy usng actvty-based costng nformaton. Ghodsypour and O Bren (1998) used AHP and mathematcal programmng to determne the best order quantty allocaton whle consderng qualtatve crtera nto the analyss. Xa and Wu (2007) presented an ntegrated approach of AHP mproved by rough sets theory and mult-objectve mxed nteger programmng. Dulmn and Mnnno (2003) appled a model to a md-szed Italan frm operatng n the feld of publc road and ral transportaton by applyng a mult-crtera decson makng technque (promethee/ gaa) to suppler selecton problem. The suppler selecton problem s complcated and rsky, owng to a varety of qualtatve and quanttatve factors affectng the decson makng process. There have been several suppler selecton methods avalable n the lterature. Some authors propose lnear weghtng models n whch supplers are rated on several crtera and n whch these ratngs are combned nto a sngle score. These models nclude the categorcal method whch reles heavly on the experence and ablty of the ndvdual buyer, the weghted pont (Tmmerman, 1986) and the analytcal herarchcal process (Nydck & Hll, 1992). Total cost approaches attempt to quantfy all costs related to the selecton of a vendor n monetary unts, ths approach ncludes cost rato (Tmmerman, 1986) and total cost of ownershp (Dulmn & Mnnno, 2003). Mathematcal programmng models often consder only the more quanttatve crtera; ths approach ncludes the prncpal component analyss (Petron & Bragla, 2000) and neural network (Lovell & Pastor, 1999). The neural network for suppler selecton s another method that has been developed to help selectng the best suppler. Comparng to conventonal models for decson support system, neural networks save a lot of tme and money of system development. The suppler-selectng system ncludes two functons: one s the functon measurng and evaluatng performance of purchasng (qualty, quantty, tmng, prce and costs) and storng the evaluaton n a database to provde data sources to neural network. The other s the functon usng neural network to select supplers. ANN was also appled to the suppler evaluaton problem by mtatng the decson process of a buyer for suppler selecton (Lovell & Pastor, 1999). Nevertheless, these models are stll lackng of the capablty to deal wth uncertanty whch s usually present n the suppler selecton problem. Carrera and Mayorga (2008) proposed a Fuzzy Inference System (FIS) approach n suppler selecton for new product development. Experts agree that no best way exsts to evaluate and select supplers (Bello, 2003), and thus organzatons use a varety of approaches and mplements the one that suts best dependng on the company s partcular requrements. Many prevous researches, n vendor evaluaton, emphaszes conceptual and emprcal decson support models that may suffer from certan shortcomngs, such as beng mathematcally too complex or too subjectve. Practcal apprecaton needs a methodology that s smple to use and understand, but yet t shall produce reasonably accurate results. There have been a lot of hybrd methods employed n the last 10 years at the lterature n terms of suppler evaluaton and selecton methods (Morlacch, 1999; Smpson, Sguaw, & Whte, 2003; Weber, Current, & Desa, 2000; Wang, Huang, & Dsmukes, 2004; Bello, 2003). Fuzzy AHP, fuzzy TOPSIS and DEA are commonly used n the lterature separately or sometmes ther combnatons can be used at the same tme. There has not been any study n the lterature about a hybrd fuzzy AHP/fuzzy TOPSIS/DEA approach before. When the lterature s wdely looked through, MCDM (Mult-Crtera Decson Makng) technques generally used are focused on TOPSIS (or fuzzy TOPSIS), AHP (or fuzzy AHP) and DEA. There have been some advantages and dsadvantages when compared wth each other, n terms of AHP and TOPSIS (Zeydan & Çolpan, 2009). Also, fuzzy AHP and fuzzy TOPSIS are combned n ths study. But, t s the frst tme n the lterature that weghts are used by transformng qualtatve varables nto only one quanttatve varable n fuzzy TOPSIS and found as trangular fuzzy numbers wth fuzzy AHP. The hybrd method n the frst step uses fuzzy AHP to assgn crtera weght and then ranks all supplers for the qualtatve selecton by usng fuzzy TOP- SIS. The result obtaned from fuzzy TOPSIS s used n DEA as an output varable called qualty management system audt (QMSA). In the second step, t uses DEA methodology n order to choose effcent vendors n the fnal selecton process. 3. Proposed hybrd method for suppler selecton and evaluaton We used three mult-crtera decson makng method to fnd effcent and neffcent supplers senstvely. In the frst step, these are fuzzy AHP for the determnaton of crtera weghts and fuzzy
M. Zeydan et al. / Expert Systems wth Applcatons 38 (2011) 2741 2751 2743 TOPSIS to transform the qualtatve varables nto only one quanttatve varable. In the second step, DEA s used for the rankng of effcent and neffcent supplers. The man am of usng fuzzy logc s that the real world s far from certan. In our daly lves, t s rare to encounter facts that are absolutely true or false. Very often we have to deal wth ncomplete or uncertan nformaton n solvng real world problems, but ths uncertan nformaton may contrbute towards bad decsons. In order to make the correct decsons n any busness, certan and complete data s requred. Humans do not need exact nformaton to communcate wth each other. We use communcaton language n terms of the vague lngustc because of ts smplcty and accuracy. Real world problems are assocated wth a matter of degree. Because of ts ablty to deal wth the mprecson, vagueness, and outrght lack of nformaton n real world problems, fuzzy sets and fuzzy logc have been extensvely studed and employed n many dfferent areas such as decson makng, control systems, mathematcs, and transportaton models (Gomes, Souza, & Vvald, 2008). There are three man steps n the proposed hybrd method such as follows: 1. To determne the crtera weghts wth fuzzy AHP. 2. To use fuzzy TOPSIS to transform qualtatve varables nto quanttatve data. 3. To fnd the rankng of effcent and neffcent supplers. All steps are explaned step by step n Fg. 1. 4. Detaled defnton of proposed method 4.1. The use of FAHP methodology for the determnaton of crtera weghts The weghts of crtera as trangular fuzzy numbers (TFN) are found by applyng the followng steps: 1. Model the problem as a herarchy contanng the decson goal. 2. Establsh prortes among the crtera weghts of the herarchy by makng a seres of judgments based on par-wse comparsons of the crtera weghts. Data Collecton Input and Output Data Fuzzy AHPAnalyss for the Determnaton of Crtera Weghts 3. Synthesze these judgments to yeld a set of overall prortes for the herarchy. 4. Check the consstency of the judgments. If consstency rato s less than 0.1, judgment s true for crtera weghts. Afterwards, the scores of par-wse comparson matrx for crtera weghts are transformed nto lngustc varables based on Table 1 wth the followng step 5. 5. The method of Chang s extent analyss whch was orgnally ntroduced by Chang (1996) s used for fndng trangular fuzzy number weghts. Let X ={x 1, x 2, x 3,..., x n } an object set, and G ={g 1, g 2, g 3,..., g n } be a goal set M 1 g ; M2 g ;...; Mm g ; ¼ 1; 2;...; n; ð1þ where M j gðj ¼ 1; 2;...; mþ all are TFNs. The value of fuzzy synthetc extent wth respect to the th object s defned as " S ¼ Xm M j Xn X # 1 m ¼ Wf ¼½~w ; ~w ;...; ~w Š ð2þ g 1 2 n M j g To obtan P m Mj g, the fuzzy addton operaton of m extent analyss values for a partcular matrx s performed such as! X m M j ¼ Xm g l j ; Xm m j ; Xm u j ð3þ h And to obtan P n m 1, P Mj g the fuzzy addton operaton of M j g ðj ¼ 1; 2;...; mþ values s performed such as! X n X m M j ¼ Xn g l ; Xn m ; Xn u ð4þ Table 1 Lngustc varables for weght of each crteron. Extremely strong (9, 9, 9) Intermedate (7, 8, 9) Very strong (6, 7, 8) Intermedate (5, 6, 7) Strong (4, 5, 6) Intermedate (3, 4, 5) Moderately strong (2, 3, 4) Intermedate (1, 2, 3) Equally strong (1, 1, 1) and then the nverse of the vector above s computed, such as " X n X # 1 m M j ¼ 1 1 1 g P n u ; P n m ; P n l ð5þ Crtera weghts are determned at the end of ths process as fuzzy trangular number. Quanttatve Data Fuzzy TOPSIS Analyss for Qualtatve Data Evaluaton 4.2. Fuzzy TOPSIS method as qualtatve evaluaton DEA Analyss for Fnal Evaluaton Decson Transformed Qualtatve Data (Used as an Output Data n DEA) Fg. 1. Flow chart of new methodology. After the crtera weghts were found, we determne the ratng of supplers. Assume that a decson group has K persons, and then the mportance of the crtera and the ratng of alternatves wth respect to each crteron can be calculated as: ~x j ¼ 1 h K ~x1 j ðþþ~x2 j ðþþ ðþþ~xk j ð6þ ~w j ¼ 1 h K ~w1 j ðþþ ~w2 j ðþþ ðþþ ~wk j where ~x k j and ~wk j are the ratng and the mportance weght of the kth decson maker obtaned at the end of step 1 and (+) ndcates the ð7þ
2744 M. Zeydan et al. / Expert Systems wth Applcatons 38 (2011) 2741 2751 fuzzy arthmetc summaton functon. As stated prevously, a fuzzy mult-crtera group decson makng problem can be concsely expressed n matrx format as: 2 3 ~x 11 ~x 12... ~x 1n ~x 21 ~x 22... ~x 2n ed ¼...... 6 7 ðfound by Table 3Þ ð8þ 4 5 ~x m1 ~x m2... ~x mn fw ¼½~w 1 ; ~w 2 ;...; ~w n Š ðfound by FAHP crtera weghts based on Table 1Þ where ~x k j and ~wk j are lngustc varables that can be shown by trangular fuzzy numbers: ~x j ¼ða j ; b j ; c j Þ and ~w j ¼ðw j1 ; w j2 ; w j3 Þ. To avod the complcated normalzaton formula used n classcal TOPSIS, the lnear scale transformaton s used here to transform the varous crtera scales nto a comparable scale. Therefore, we can obtan the normalzed fuzzy decson matrx denoted by e R: er ¼½~r j Š mn ð9þ ð10þ where B and C are the set of beneft crtera and cost crtera, respectvely, and! ~r j ¼ a j ; b j ; c j ; j 2 B ð11þ c j c j c j ~r j ¼ a j ; a j ; a j ; j 2 C ð12þ c j b j a j c j ¼ max c j ; f j 2 B ð13þ a j ¼ mn a j ; f j 2 C ð14þ The normalzaton method mentoned above s to preserve the property that the ranges of normalzed trangular fuzzy numbers belong to [0, 1]. Consderng the dfferent mportance of each crteron, one can now construct the weghted normalzed fuzzy decson matrx as: ev ¼½~v j Š mn ; ¼ 1; 2;...; m; j ¼ 1; 2;...; n ð15þ where e V j ¼ ~r j ðþ ~w j. Accordng to the weghted normalzed fuzzy decson matrx, we know that the elements ~v j ; 8; j, are normalzed postve trangular fuzzy numbers and ther ranges belong to the closed nterval [0, 1]. Then, we can defne the fuzzy postve-deal soluton (FPIS, A*) and fuzzy negatve-deal soluton (FNIS, A ) as: A ¼ð~v 1 ; ~v 2 ;...; ~v n Þ A ¼ð~v 1 ; ~v 2 ;...; ~v n Þ where ~v j ¼ð1; 1; 1Þ and ~v j ¼ð0; 0; 0Þ; j ¼ 1; 2;...; n. The dstance of each alternatve A ( =1,2,..., m) from A* and A can be calculated as: d ¼ Xn d ¼ Xn dð~v j ; ~v j Þ; ¼ 1; 2;...; m ð16þ dð~v j ; ~v j Þ; ¼ 1; 2;...; m ð17þ where d(, ) s the dstance measurement between two fuzzy numbers. A closeness coeffcent s defned to determne the rankng order of all alternatves once the d and d of each alternatve A ( = 1, 2,..., m) has been calculated. The closeness coeffcent of each alternatve s calculated as: d CC ¼ d þ d ; ¼ 1; 2;...; m ð18þ Obvously, an alternatve A s closer to the FPIS (A*) and farther from FNIS (A )ascc approaches to 1. Therefore, accordng to the closeness coeffcent, we can determne the rankng order of all alternatves and select the best one from among a set of feasble alternatves. 4.3. DEA method as quanttatve evaluaton In ths stage, supplers performance values obtaned as a result of fuzzy TOPSIS analyss are consdered as an output varable. Suppler s effcency s calculated wth DEA mathematcal model below. In our study, results were obtaned based on the model of VRS (Varable Returns to Scale) BCC. A VRS model allows for the level of outputs grow proportonally hgher or lower than a correspondng ncrease n nputs. DEA output-orented BCC model for decson makng unts s summarzed as follows (Banker, Charnes, & Cooper, 1984): Objectve Functon Subject to: X n X n X n 0 E k ¼ Maxb þ e Xm s þ e Xp r¼1 s þ r ð19þ x j k j þ s x k ¼ 0; ¼ 1;...; m ð20þ y rj k j s þ r by rk ¼ 0; r ¼ 1;...; p ð21þ k j ¼ 1 ð22þ k j P 0; j ¼ 1;...; n ð23þ s P 0; ¼ 1;...; m; s þ r P 0; r ¼ 1;...; p ð24þ b s the effcency score; y rj s the output r for suppler j; x j s the nput for suppler j; s ; s þ r are slack and surplus correspondng to nput, and output r, respectvely; k j s the weghts attached to nputs and outputs of suppler j; x k, y rk are nputs () and outputs (j) of the partcular suppler (for k) whose effcency s beng evaluated and e s a non-archmedean small and postve number. 5. Applcaton n a car factory of proposed suppler selecton and evaluaton methodology The frm was establshed wth the partnershp share of 30% Turksh and 70% foregn nvestment n 1997 and t has 100.000 cars per year capacty, and an nternatonal car company whch uses n 90% of producton capacty. 1900 blue collar and 350 whte collar employees have worked n the organzaton and 15% of the whte collars are foregn personnel. The company has got factores wthn seven countres of the world, and producton strateges accordng to the economc structure of countres. Ths strategy ncludes the types and amounts of producton. A and B model are produced n the factory and 75% of A and 95% of B model s exported foregn countres. Accordng to the producton data of the year 2007, market share of the company s about 7% for all cars sold n the world. Components (Items) cost bought by car company per car from supplers are approxmately made up of 70% of total producton cost. The purchasng department of factory works based on the followng system. Frstly, the components that can be manufactured n Turkey are selected. Secondly, supplers whch have enough capac-
M. Zeydan et al. / Expert Systems wth Applcatons 38 (2011) 2741 2751 2745 ty to produce these components are determned. That means suppler pool s exsted. Then, basc techncal drawngs are sent to these supplers and advance proposals are collected from supplers, ths s named as suppler selecton process. After the advance proposals, the frst selecton s performed and selected the supplers. After vstng and comparng the supplers, the second selecton s performed. Negotaton as the last stage s made wth these selected supplers and detaled proposals are collected one by one. After the collected detaled proposals, the suppler(s) whch gves the best prce s selected and started the producton process. In the producton process, moulds are made or transferred (f the only one producton locaton) and afterwards, plot car producton s made. At the same tme, product report related wth the component s formed. Accordng to the product report and plot car producton, the frst component acceptance s gven by qualty department. Then, some tests were performed for components. If test results are wthn the tolerance, mass producton s made and lne feedng starts for the producton. All actons step by step are followed by procurement department untl ths stage. Then, procurement department follows up prce balance of the product. If there comes nto exstence a problem, the product s nterfered. As known, a purchasng department s responsble for ensurng that rght products and servces are purchased at the rght tme, the rght prce, the rght quantty, the rght qualty and from the rght sources. Vendor selecton crtera as qualtatve and quanttatve varables for the target company such as follows: Qualtatve varables: 1. New Project (C 1 ): the evaluaton of supplers project studes. 2. Suppler (C 2 ): the evaluaton of Ter 2 (suppler of OEM s suppler (2nd level suppler of OEM) system) supplers. 3. Qualty and Envronmental (C 3 ): the evaluaton of suppler qualty and envronmental targets and accomplshment status. 4. Producton Process (C 4 ): the evaluaton of suppler producton process and harmony wth qualty system documents. 5. Test and Inspecton (C 5 ): the evaluaton of process nspectons, perodc tests and equpment calbraton status. 6. Correctve&Preventve Actons (C 6 ): the evaluaton of suppler clams and countermeasure status. Quanttatve varables: 1. Defect Rato (PPM): the rejected part rato n one mllon. (PPM: Part Per Mllon). 2. Warranty Cost Rato (WAR): after sales warranty clam rato accordng to sales. 3. Qualty (QM): the evaluaton of suppler mentalty. Concepts of quanttatve and qualtatve varables are explaned n detaled n appendx wthn questons. Crtera weghts and decson matrx are formed accordng to purchasng and procurement department engneerng pont of vew. Steps of the new methodology are appled as follows: Step 1. The use of FAHP methodology for determnaton crtera weghts and results. The herarchcal structure of ths decson problem s shown n Fg. 2. The decson makers use the lngustc weghtng varables (shown n Table 2) to assess the mportance of the crtera and t s presented n Fg. 2 as herarchy. To buld the par-wse comparson matrxes, some academcs and professonals were worked carefully n the factory. The frst four steps of the use of FAHP methodology for the determnaton of crtera weghts are appled accordng to the AHP analyss for consstency. The most mportant stage of the ffth step whch s the par-wse comparson matrx for the crtera weghts as fuzzy trangular number s establshed n Table 2. The values of fuzzy synthetc extents wth respect to the crtera weghts are calculated as below (see Eq. (2)): S A ¼ð2:08; 2:43; 3:25Þð1=72:09; 1=55:01; 1=40:53Þ ¼ð0:029; 0:044; 0:080Þ S B ¼ð9:33; 12:50; 16:00Þð1=72:09; 1=55:01; 1=40:53Þ ¼ð0:129; 0:227; 0:395Þ S C ¼ð3:12; 4:58; 6:83Þð1=72:09; 1=55:01; 1=40:53Þ ¼ð0:043; 0:083; 0:169Þ S D ¼ð13:00; 18:00; 23:00Þð1=72:09; 1=55:01; 1=40:53Þ ¼ð0:180; 0:327; 0:568Þ S E ¼ð7:33; 9:50; 12:00Þð1=72:09; 1=55:01; 1=40:53Þ ¼ð0:102; 0:173; 0:296Þ S F ¼ð5:67; 8:00; 11:00Þð1=72:09; 1=55:01; 1=40:53Þ ¼ð0:079; 0:145; 0:271Þ Fuzzy crtera weghts are found wth the use of FAHP methodology such as follows: W A ¼ð0:029; 0:044; 0:080Þ; W C ¼ð0:043; 0:083; 0:169Þ W D ¼ð0:180; 0:327; 0:568Þ; W F ¼ð0:079; 0:145; 0:271Þ W B ¼ð0:129; 0:227; 0:395Þ; W E ¼ð0:102; 0:173; 0:296Þ; Trangular fuzzy numbers found at the end of step 1 wll be accepted as weght values of qualtatve crtera and used n fuzzy TOPSIS methodology. Step 2. The use of fuzzy TOPSIS and results. In ths step, fuzzy TOPSIS wll be used for transformng qualtatve varables nto only one quanttatve varable as an output varable called QMSA (Qualty Manufacturng System Audt) such as follows: a. The decson makers use the lngustc ratng varables (shown n Table 3) to evaluate the ratng of alternatves wth respect to each crteron and present t n Table 4. b. Convertng the lngustc evaluaton (shown n Table 4) nto trangular fuzzy numbers to construct the fuzzy decson matrx and determne the fuzzy weght obtaned wth FAHP of each crteron as Table 5. c. Constructng the normalzed fuzzy decson matrx as Table 6 (see Eq. (10)). d. Constructng the weghted normalzed fuzzy decson matrx as Table 7 (see Eq. (15)). e. Determne FPIS and FNIS as: A ¼½ð1; 1; 1Þ; ð1; 1; 1Þ; ð1; 1; 1Þ; ð1; 1; 1Þ; ð1; 1; 1ÞŠ A ¼½ð0; 0; 0Þ; ð0; 0; 0Þ; ð0; 0; 0Þ; ð0; 0; 0Þ; ð0; 0; 0ÞŠ f. Calculate the dstance of each canddate from FPIS and FNIS, respectvely, as Tables 8 and 9. g. Calculate d and d of seven possble supplers A ( =1,2,...,7)asTable 10 (see Eqs. (16) and (17)). h. Calculate the closeness coeffcent of each canddate as (see Eq. (18))
2746 M. Zeydan et al. / Expert Systems wth Applcatons 38 (2011) 2741 2751 Goal New Project (C 1 ) Suppler (C 2 ) Qualty and Envronmental (C 3 ) Producton Process (C 4 ) Test and Inspecton (C 5 ) Correctve& Preventve Actons (C 6 ) A 1 (Suppler 1)....................................... A 7 (Suppler 7) Fg. 2. Herarchcal structure. Table 2 Fuzzy par-wse comparson matrx. C 1 C 2 C 3 C 4 C 5 C 6 C 1 (1, 1, 1) (1/6, 1/5, 1/4) (1/3, 1/1/2, 1/1) (1/8, 1/7, 1/6) (1/5, 1/4, 1/3) (1/4, 1/3, 1/2) C 2 (4, 5, 6) (1, 1, 1) (2, 3, 4) (1/3, 1/2, 1/1) (1, 1, 1) (1, 2, 3) C 3 (1, 2, 3) (1/4, 1/3, 1/2) (1, 1, 1) (1/5, 1/4, 1/3) (1/3, 1/2, 1/1) (1/3, 1/2, 1/1) C 4 (6, 7, 8) (1, 2, 3) (3, 4, 5) (1, 1, 1) (1, 2, 3) (1, 2, 3) C 5 (3, 4, 5) (1, 1, 1) (1, 2, 3) (1/3, 1/2, 1/1) (1, 1, 1) (1, 1, 1) C 6 (2, 3, 4) (1/3, 1/2, 1/1) (1, 2, 3) (1/3, 1/2, 1/1) (1, 1, 1) (1, 1, 1) Table 3 Lngustc varables for the ratngs. Very good (VG) (9, 10, 10) Good (G) (7, 9, 10) Medum good (MG) (5, 7, 9) Far (F) (3, 5, 7) Medum poor (MP) (1, 3, 5) Poor(P) (0, 1, 3) Very poor (VP) (0, 0, 1) Table 4 The ratngs of the seven canddates by decson makers under all crtera. Supplers Crtera C 1 C 2 C 3 C 4 C 5 C 6 A 1 MG G G MG MG G A 2 MG MG MG MG MG MG A 3 MG MG MG MG MG G A 4 F F F F F MG A 5 MG MG MG MG MG G A 6 MG MG MG MG MG G A 7 MG MG F F MG MG CC 1 ¼ 0:1783; CC 2 ¼ 0:1685; CC 3 ¼ 0:1706; CC 4 ¼ 0:1328; CC 5 ¼ 0:1706; CC 6 ¼ 0:1706; CC 7 ¼ 0:1529 In Table 10, QMSA s found by acceptng that the bggest rato of CC (0.1783) s the pont 100. To fnd the other QMSA values, QMSA s compared to CC. At the end of the second step, frstly, crtera weghts based on sx qualtatve varables are calculated wth FAHP methodology as Trangular fuzzy number. Secondly, accordng to fuzzy TOPSIS method, qualtatve varables are transformed nto only one quanttatve varable as an output called qualty management system audt (QMSA) and wll be used n DEA as an output. Step 3. Fndng the rankng of effcent and neffcent supplers wth DEA and fnal results. In ths step, suppler performance values obtaned as a result of fuzzy TOPSIS analyss are consdered as the fourth output varable called QMSA. Input and output values collected from the purchasng and qualty records are gven n Table 11. Snce the factory focus on the fnal product, we gnore the company nputs. To measure the effcency of each suppler we assume untary nputs for all unts. In DEA, at least one nput (dummy) that has a value of 1 for all supplers should be used together wth outputs snce analyss cannot be made solely based on outputs properly (De Koejer, Wossnk, Struk, & Renkema, 2002; Leta, Soares de Mello, Table 5 The fuzzy decson matrx and fuzzy weghts of eght alternatves. C 1 C 2 C 3 C 4 C 5 C 6 A 1 (5, 7, 9) (7, 9, 10) (7, 9, 10) (5, 7, 9) (5, 7, 9) (7, 9, 10) A 2 (5, 7, 9) (5, 7, 9) (5, 7, 9) (5, 7, 9) (5, 7, 9) (5, 7, 9) A 3 (5, 7, 9) (5, 7, 9) (5, 7, 9) (5, 7, 9) (5, 7, 9) (7, 9, 10) A 4 (3, 5, 7) (3, 5, 7) (3, 5, 7) (3, 5, 7) (3, 5, 7) (5, 7, 9) A 5 (5, 7, 9) (5, 7, 9) (5, 7, 9) (5, 7, 9) (5, 7, 9) (7, 9, 10) A 6 (5, 7, 9) (5, 7, 9) (5, 7, 9) (5, 7, 9) (5, 7, 9) (7, 9, 10) A 7 (5, 7, 9) (5, 7, 9) (3, 5, 7) (3, 5, 7) (5, 7, 9) (5, 7, 9) Weght (0.029, 0.044, 0.080) (0.129, 0.227, 0.395) (0.043, 0.083, 0.169) (0.180, 0.327, 0.568) (0.102, 0.173, 0.296) (0.079, 0.145, 0.271)
M. Zeydan et al. / Expert Systems wth Applcatons 38 (2011) 2741 2751 2747 Table 6 The fuzzy normalzed decson matrx. C 1 C 2 C 3 C 4 C 5 C 6 A 1 (0.5, 0.7, 0.9) (0.7, 0.9, 1) (0.7, 0.9, 1) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.7, 0.9, 1) A 2 (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) A 3 (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.7, 0.9, 1) A 4 (0.3, 0.5, 0.7) (0.3, 0.5, 0.7) (0.3, 0.5, 0.7) (0.3, 0.5, 0.7) (0.3, 0.5, 0.7) (0.5, 0.7, 0.9) A 5 (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.7, 0.9, 1) A 6 (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.7, 0.9, 1) A 7 (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) (0.3, 0.5, 0.7) (0.3, 0.5, 0.7) (0.5, 0.7, 0.9) (0.5, 0.7, 0.9) Table 7 The fuzzy weghted normalzed decson matrx. C 1 C 2 C 3 C 4 C 5 C 6 A 1 (0.014, 0.031, 0.072) (0.091, 0.205, 0.395) (0.030, 0.075, 0.169) (0.090, 0.229, 0.511) (0.051, 0.121, 0.267) (0.055, 0.131, 0.271) A 2 (0.014, 0.031, 0.072) (0.065, 0.159, 0.355) (0.022, 0.058, 0.152) (0.090, 0.229, 0.511) (0.051, 0.121, 0.267) (0.039, 0.102, 0.244) A 3 (0.014, 0.031, 0.072) (0.065, 0.159, 0.355) (0.022, 0.058, 0.152) (0.090, 0.229, 0.511) (0.051, 0.121, 0.267) (0.055, 0.131, 0.271) A 4 (0.009,0.022,0.056) (0.039, 0.114, 0.276) (0.013, 0.042, 0.118) (0.054, 0.164, 0.397) (0.031, 0.086, 0.207) (0.039, 0.102, 0.244) A 5 (0.014, 0.031, 0.072) (0.065, 0.159, 0.355) (0.022, 0.058, 0.152) (0.090, 0.229, 0.511) (0.051, 0.121, 0.267) (0.055, 0.131, 0.271) A 6 (0.014, 0.031, 0.072) (0.065, 0.159, 0.355) (0.022, 0.058, 0.152) (0.090, 0.229, 0.511) (0.051, 0.121, 0.267) (0.055, 0.131, 0.271) A 7 (0.014, 0.031, 0.072) (0.065, 0.159, 0.355) (0.013, 0.042, 0.118) (0.054, 0.164, 0.397) (0.051, 0.121, 0.267) (0.039, 0.102, 0.244) Table 8 Dstances between A ( =1,2,..., 7) and A* wth respect to each crteron. C 1 C 2 C 3 C 4 C 5 C 6 d* d(a 1,A*) 0.9612 0.7802 0.9105 0.7442 0.8586 0.8355 5.0901 d(a 2,A*) 0.9612 0.8160 0.9244 0.7442 0.8586 0.8499 5.1542 d(a 3,A*) 0.9612 0.8160 0.9244 0.7442 0.8586 0.8355 5.1398 d(a 4,A*) 0.9713 0.8628 0.9435 0.8078 0.8950 0.8676 5.3479 d(a 5,A*) 0.9612 0.8160 0.9244 0.7442 0.8586 0.8355 5.1398 d(a 6,A*) 0.9612 0.8160 0.9244 0.7442 0.8586 0.8355 5.1398 d(a 7,A*) 0.9612 0.8160 0.9435 0.8078 0.8586 0.8499 5.2369 Table 11 Input and output data for supplers (the year 2007). Supplers Input Outputs QMSA PPM QM WAR A 1 1 100.00 16.15 5 19.82 A 2 1 94.54 4.24 5 8.08 A 3 1 95.67 18.43 8.5 19.93 A 4 1 74.48 18.34 3 20 A 5 1 95.67 17.96 8.8 20 A 6 1 95.67 18.91 4 20 A 7 1 85.78 19.89 4 20 Table 9 Dstances between A ( =1,2,..., 7) and A wth respect to each crteron. C 1 C 2 C 3 C 4 C 5 C 6 d d(a 1,A ) 0.0461 0.2620 0.1080 0.3274 0.1715 0.1895 1.1044 d(a 2,A ) 0.0461 0.2279 0.0947 0.3274 0.1715 0.1773 1.0448 d(a 3,A ) 0.0461 0.2279 0.0947 0.3274 0.1715 0.1895 1.0569 d(a 4,A ) 0.0352 0.1740 0.0727 0.2500 0.1308 0.1562 0.8189 d(a 5,A ) 0.0461 0.2279 0.0947 0.3274 0.1715 0.1895 1.0569 d(a 6,A ) 0.0461 0.2279 0.0947 0.3274 0.1715 0.1895 1.0569 d(a 7,A ) 0.0461 0.2279 0.0727 0.2500 0.1715 0.1773 0.9454 Table 10 The dstance measurement. Table 12 Output-orented BCC reference set and effcency of supplers. Supplers Effcency Reference frequency Reference set Rank A 1 104.53 1 2 A 2 94.83 A1 (0.93), A 5 (0.07) Ineffcent A 3 101.63 0 4 A 4 100.00 A 5 (0.47), A 6 (0.25), Ineffcent A 7 (0.28) A 5 103.53 2 3 A 6 101.47 1 5 A 7 105.19 1 1 d* d d* + d CC QMSA A 1 5.0901 1.1044 6.1945 0.1783 100.00 A 2 5.1542 1.0448 6.1991 0.1685 94.54 A 3 5.1398 1.0569 6.1968 0.1706 95.67 A 4 5.3479 0.8189 6.1668 0.1328 74.48 A 5 5.1398 1.0569 6.1968 0.1706 95.67 A 6 5.1398 1.0569 6.1968 0.1706 95.67 A 7 5.2369 0.9454 6.1824 0.1529 85.78 Gomes, & Meza, 2005; Ramanathan, 2006; We, Zhang, & L, 1997; Zmmermann, 1985). The model we consder here s BCC wth output orentaton. Reference set and the effcency of all supplers obtaned are shown n Table 12 after DEA analyss of ths system s performed n the EMS (Effcency Measurement System) verson 1.3 software package accordng to super effcency model. Snce the DEA-CCR (Charnes Cooper Rhodes) and DEA-BCC models are weak n dscrmnatng between effcent supplers (Andersen & Petersen, 1993), analysng the hybrd fuzzy AHP/fuzzy TOPSIS-DEA model (for qualtatve and quanttatve values) s used the super effcency model for comparng the effcent supplers wth each other as shown n Table 12. As an example, Table 13 provdes detaled calculatons of suppler A 2 s composte suppler from the reference set of supplers for the year 2007. The composte of suppler A 2 n 2007 s formed from the weghted average of best-practce suppler n the effcency fronter of suppler A 2.e. suppler A 1 (0.93 A 1 ), suppler A 5 (0.07 A 5 ). suppler A 2 s comparatve effcency ratng of 94.83% ndcates the extent to whch the effcency of suppler A 2 s lackng n comparson to the effcency of ts reference set of supplers.
2748 M. Zeydan et al. / Expert Systems wth Applcatons 38 (2011) 2741 2751 Table 13 Computaton of the composte reference set for suppler A 2. Real value Reference set Target value Potental mprovement A 2 k 1 A 1 k 5 A 5 A 2 ¼ k 1 A 1 þ k 5 A 5 ða 2 A 2Þ=A 2 (%) Input 1 1 1 1 0.00 QMSA 94.54 100 95.67 99.70 5.45 PPM 4.24 0.93 16.15 0.07 17.96 16.28 283.88 QM 5 5 8.8 5.27 5.32 WAR 8.08 19.82 20 19.83 145.45 Table 14 Potental Improvement of neffcent supplers. Supplers Input QMSA PPM QM WAR Actual Target Actual Target Actual Target Actual Target Actual Target A 2 1 1 94.54 99.70 4.24 16.28 5 5.27 8.08 19.83 A 4 1 1 74.48 92.90 18.34 18.74 3 6.26 20 20 Suppler A 2 s 94.83% as effcent as ts reference set of supplers (A 1,A 5 ). Ths effcency reference set of supplers represent the bass vector n the lnear program soluton for suppler A 2. That s, a convex combnaton of the actual outputs and nputs of the reference subset of supplers results n a composte suppler that produces as much or more outputs as suppler A 2, but uses as much or less nputs than suppler A 2. In order to be able to ncrease the effcency of A 2, target values and potental mprovements are calculated and shown n Table 14 whch documents the values of defcent nputs and excess outputs that exsted wthn suppler A 2 n the year 2007. 6. Dscusson and concluson In ths study, a new methodology s ntroduced and was appled n a car manufacturng factory for the selecton and evaluaton of qualty suppler(s). Accordng to the soluton of aforementoned analyss, two supplers (A 2,A 4 ) are defned as non-effcent vendors n manufacturng the luggage sde part (panel). Hence, other fve supplers (A 1,A 3,A 5,A 6,A 7 ) are the canddates whch wll be chosen for buyng these components and wll be able to jon the bddng system for manufacturng luggage sde panel. At the end of ths evaluaton process, the suppler whch wll be able to gve the best prce wll be selected for luggage sde panel producton after the bddng and negotaton meetng. The OEM (Orgnal Equpment Manufacturng) has ts own suppler evaluaton system as other OEMs have. Accordng to the runnng system n the factory, after the yearly evaluaton, supplers should ncrease ther performance and prove them wth evaluaton tems. The most mportant evaluaton tem for the OEM s Qualty System Audt whch s qualtatve, but ths system audt s transformed nto a quanttatve result by a specfc evaluaton sheet on the bass of tem ponts as shown n appendx (each suppler s audted once n a year). After performng an audt, mprovement ponts are lsted wth a pctured audt report whch s based on Before After system. Suppler should reply the audt report wthn 15 days and put the pctures for closed actons. If an acton closng requres more tme, suppler needs a target date for unclosed actons and should close t on tme. If not, the related audt tem, Correctve&Preventve Actons, score wll be zero and other related tems, e.g. Qualty and Envronmental, wll be low scored accordng to current audt stuaton. Hence, t wll reduce next audt score and suppler wll have dsadvantages for the new projects. In general, the car company wll ask for quotatons from all seven supplers and wll have negotaton meetngs wth all of them. One suppler wll be elmnated n each step and fnally one suppler wll be selected, but ths wll take almost two months whch s very short duraton for seven dfferent quotaton evaluatons. Hence, suppler selecton wll be made n rush and some mportant cost and prce tems wll be mssed due to job stress caused by short suppler selecton perod. On the other hand, the car factory can prolong ths perod, but t wll cause project to be delayed. At ths pont, our study wll be a helpful sample for the other mplementng busnesses snce neffectve supplers (A 2,A 4 ) wll be drectly removed from part producer pool and quotaton wll not be requested from them. So, fve supplers wll present ther quotatons and jon the negotaton meetngs. Consequently, two months quotaton evaluaton and suppler selecton perod wll be used more effectve. In the current runnng system, suppler qualty level s used for suppler selecton as qualtatve when two supplers have very close, smlar quotaton. For ths reason, supplers are nformed and nvted to present ther fnal quotaton. If there s not a reasonable dfference between ther prces gven, suppler whose qualty level s hgh s selected as part producer. Otherwse, suppler whch gves the best prce for the part s selected as suppler. In our applcaton, current suppler for car luggage sde part (panel) s suppler A 7 for A type car. Ths stuaton shows that suppler A 7 gave the best prce for the part and got the project, but none of qualty evaluaton was done before project was gven to the suppler A 7 except pre-mass producton. In the hstory, we see that supplers A 4,A 6, and A 7 presented ther quotatons. The prce sequence s (from lower to hgher) occurred lke supplers A 7,A 6, and A 4. Accordng to our study, suppler A 4 would drectly be elmnated from the lst before company requested part quotatons and supplers A 7 and A 6 would be focused. Consequently; Suppler A 7 was chosen as the suppler for company. Suppler A 6 would be chosen as suppler accordng to our study. Qualty performance of both supplers n the year 2008 s shown n Table 15. Suppler A 6 s better than suppler A 7 accordng to qualty performance ndcator of the year 2008. If we thnk that the rejected parts are scrapped, ths s a very mportant result because the loss of suppler A 6 caused by rejected parts s reasonable to thnk provded that the producton of luggage sde part s feasble. As a mat-
M. Zeydan et al. / Expert Systems wth Applcatons 38 (2011) 2741 2751 2749 Table 15 Qualty performance ndcator (PPM) of the year 2008. Supplers Part reject status (2008) Accept Reject PPM A 6 463,148 262 566 A 7 260,633 348 1335 ter of fact, part rejects are the bggest porton of problems as suppler looses money and t requests cost ncrease to be able to compansate ts loss. Ths can take the company to make source change whch s transferrng the part to another suppler because OEMs do not want to ncrease part prces unless any unexpected thngs happen (e.g. raw materal ncrease). Suppler assures part qualty before startng the project and can not request any extra money (cost) about part defects and repars. Ths analyss also shows us qualtatve and quanttatve outputs are not the exact decson makng tools alone. Accordng to the qualtatve evaluaton, company should choose A 1 as suppler, but ths tme t should be ready to face wth potental part problems such as PPM. On the other hand, accordng to quanttatve evaluaton, company should choose suppler A 7, but that tme t should be careful about the qualty system of suppler and be ready to face wth part problems, because PPM and warranty clam problems have drect connecton wth poor process development. Process development s placed n suppler qualty system. If potental problems can not be caught durng project development stage, t wll brng lots of problems and concerns. Hence, before the suppler selecton and evaluaton, both qualtatve and quanttatve ndcators should be consdered together and combned. So, rsks wll be not only mnmzed and but also be analyzed effcently and effectvely. Acknowledgements The authors would lke to express ther specal thanks to the nternatonal car manufacturng frm management and ther staff because of ther valuable contrbutons n collectng all data and nformaton whch was requred. Appendx A. Suppler evaluaton crtera and ponts of each crtera and tem (C 1 ). New Project (100) 1. Procedure control for the advanced qualty plannng for new project parts. (50) * Advanced qualty plannng ncludng customer and sub-vendor development schedule s nsuffcent. (10) * Advanced qualty plannng ncludng customer and sub-vendor development schedule has been establshed, requrement for plan and actual are nsuffcent. (20) * Advanced qualty plannng ncludng customer and sub-vendor development schedule has been establshed progress revew by plan and actual status s nsuffcent. (30) * Advanced qualty plannng ncludng customer and sub-vendor development schedule has been establshed work focused on desgn/development has been progressed. (40) * CFT approach for new part development system by new car qualty team, has been organzed and performed well. Top management report and support have been performed frequently. (50) 2. Verfcaton and detaled revew of product/process. (50) * Advanced qualty plannng and procedure reflectng project phases s nsuffcent. (10) * Old model hstory master lst and mprovement status, FMEA, verfcaton of detal requrement s nsuffcent. (20) * Advance plannng phases, requrements, revew of product/process has been performed well, but qualty records are nsuffcent. (30) * Advance plannng phase requrements, revew of product/process has been performed well, but total actvty result nsuffcent practce result s nsuffcent. (40) * New parts qualty assurance before mass producton s performed, after mass producton the system of parts development hstory s satsfed. (50) (C 2 ). Suppler (150) 1. Controllng PPAP and PPAP process wth the supplers. (50) * Some tems AOI not approved. (10) * All AOI s avalable but not approved. (20) * All AOI s approved but PPAP (Producton Part Approval Process) not properly done. (30) * All AOI s/nspecton standard and PPAP done wthn customer development schedule. (40) * All AOIs / qualty agreement approved by Research&Development wthn customer development schedule. (50) 2. Controllng ncomng nspecton procedure preparaton and mplementaton. (50) * Incomng nspecton procedure not prepared and mplemented. (10) * Incomng nspecton procedure not prepared and mplemented for some products. (20) * Incomng nspecton not mplemented for some products. (30) * All ncomng nspecton done (nspecton and non-nspecton tem, gage nspecton). (40) * All ncomng nspecton done excellent and the Statstcal Process Control of man parts controlled. (50) 3. Controllng sub-vendor (suppler) evaluaton system. (50) * Sub-vendor perodcal evaluaton plan has not been establshed. (10) * Perodcal evaluaton plan has been establshed but selecton/new regstraton evaluatons are nsuffcent. (20) * Sub-vendor perodcal evaluaton plan has been establshed and performed, but dstrbuton of audt fndng lst, counter measures are nsuffcent. (30) * Counter measure of perodcal evaluaton has been regstered and counter measure has been collected. New suppler selecton/new regstraton evaluaton has been performed n procedure. (40) * Evaluaton system contnuous mprovement and qualty mprovement have been collected, qualty level has been ncreased. (50) (C 3 ). Qualty and Envronmental (150) 1. Qualty/envronment target and achevement control. (50) * Qualty/envronment target has been acheved, but establshed reasons are nsuffcent. (10) * Qualty/envronment target lst are very dfferent, and revson of change has not controlled. (20) * Detals of acton plan for qualty/envronment target have been establshed, but they are not performng. (30) * Qualty/envronment target has been performed accordng to detals of acton plan, and regular achevement to the target has been montored/revewed and controlled. (40) * Counter measure for shortage to the target has been establshed and achevement ndcator has been mproved. (50)
2750 M. Zeydan et al. / Expert Systems wth Applcatons 38 (2011) 2741 2751 2. Control of safety and 5S ssues. (50) ** 0 pont for dssatsfed tems. * 5S actvty beng done and evaluated on a regular bass, contnuos mprovement beng done. (5) * 5S of equpment and workplace have been performed, and cleanness has been mantaned. (5) * Good lghtng n shop floor/no nose/smell/dust n process are not dffcult to work. (5) * Tool/mould/de are well arranged, properly dentfed and stored wth dentfcaton tag. (5) * Suppler parts storage 5S, FIFO well mantaned. (5) * The safety rule of shop floor s dsplayed and followed. (5) * Reducng polluton substance plan and producton control system has been establshed. (10) * Separate collecton, place of waste materal dentfcaton and control s good. (10) 3. Control of products (sub-vendor parts/wip/fnshed) about preventng damage, FIFO and lot traceablty. (50) ** 0 pont for dssatsfed tems. * No damage and deformaton/storage area fxed and managed well perodcal evaluaton of storage for damages/has been performed. (10) * Cleanness of pallet/box, truck and damage, deformaton, nterference, over loadng have been prevented. (10) * Durng movng between process, route card and delvery card, label has been used. (10) * Products (ncomng/process/fnshed) FIFO system has been establshed and FIFO has been performed. (10) * Fnshed products ncludng major sub-vendor parts have been controlled by lot control and follow-up control, qualty problem solutons have been performed. (10) (C 4 ). Producton Process (300) 1. Qualty document control (process FMEA, control plan, work standard). (60) * Only part of process FMEA has been wrtten and qualty documents has not been lnked wth other documents. (12) * All of process FMEA have been performed but control of the latest qualty documents has not been lnked. (24) * Among qualty documents, lnkng s done, but dentfcaton and establshment of control lsts and crtera are nsuffcent. (36) * Establshment, reflecton of counter measure n accordance wth rpn and dentfcaton of control lst and control crtera are approprate. (48) * Varous actvtes for reducng rpn has been performed contnuously, and revson control of qualty documents, lnkng of documents are good. (60) 2. SPC and specal characterstc s control. (60) * Specal characterstc about products and process and key control parameters are not dentfed. (12) * Specal characterstc and key control parameter have been dentfed but there s no control crtera. (24) * Control crtera ncludng SPC have been establshed and performed. (36) * Control of specal characterstc and key control parameter has been satsfed n control range. (48) * SPC tool used for process control/analyss tools used for solvng problems/problems not repeated. (60) 3. Workng condtons, tool change, parameter set up condton. (60) * Work condton control crtera has not been establshed and not meetng the standards. (12) * Some processes do not meet work condton and nspecton result of work condton s nsuffcent. (24) * Work condton confrms to standards and nspecton result has been performed, but when work change, verfcaton or try-out s nsuffcent. (36) * When work change, as verfcaton or try-out has been performed, work condton has met the standards and optmzed work condton has been establshed. (48) * Perodcal verfcaton of equpment relablty has been performed and t synchronzed wth present work condton. (60) 4. Equpment Mantenance system (60) * Daly equpment check has not been performed and establshng equpment mantenance plan accordng to procedure s nsuffcent. (12) * Daly check s performed and check sheet not recorded. (24) * Vsual check ponts, numercal values are recorded for daly check and equpment mantenance plan has been establshed, but mplementaton s nsuffcent. (36) * Mantenance actvty n accordance wth plan has been performed and equpment record lke breakdown/reparng tme has been controlled. (48) * Contnuous mprovement for ncreasng producton ablty and operaton rate has been performed. (60) 5. M Change Hstory (60) * Raw materals and suppler change wthout report and 4 m change approval (0) * Documentaton of procedure for 4 M change s nsuffcent/ control of 4 M record has not been performed. (12) * 4 M change has followed the procedure, but t s perfunctory. (24) * 4 M change followed, but suppler 4 M change s nsuffcent. (36) * 4 M change has been revewed and approved, but ts record s nsuffcent. (48) * 4 M change has been revewed and approved, also ts record has been controlled/revson of other qualty documents has been controlled. (60) (C 5 ). Test and Inspecton (240) 1. In-process nspecton system. (60) * Process nspecton (frequency/patrol) system has been establshed but t has not been performed. (12) * Some of process nspectons have been omtted or are perfunctory/nonconformng products have not been dentfed and solated. (24) * Process nspecton has been performed n accordance wth crtera, ts record also has been establshed, and nonconformng products have been dentfed and solated. (36) * Requre measurement for assurance of process nspecton relablty has been held and dentfed when rework, renspecton s performed by nspector. (48) * Effcency of process nspecton for nspecton procedure (lst, method, perod) mprovement has been performed and there are reasons of mprovement performance. (60) 2. Fnal Product Control. (60) * Fnshed product nspecton system has been establshed, but t has not been performed. (12) * Fnshed product nspecton has been performed perfunctorly and dentfyng and solatng nonconformng products are nsuffcent. (24) * Fnshed product nspecton to some mportant nspecton lst has been performed and ts record has been wrtten, nonconformng products have been dentfed and solated. (36) * Inspecton lst agreed wth customers has been performed 100%, methods and equpments for nspecton detect mprovement have been appled. (48)
M. Zeydan et al. / Expert Systems wth Applcatons 38 (2011) 2741 2751 2751 * Effcency of process nspecton for nspecton procedure (lst, method, and perod) mprovement has been evaluated and there are mprovement reasons. (60) 3. Regular Test Plan. (60) * Regular nspecton plan has not been establshed. (12) * Regular nspecton plan has been establshed, but performance s nsuffcent and lmt durablty test has not been performed. (24) * Regular nspecton has been performed accordng to nspecton agreement wthout omsson. (36) * Regular nspecton s fne, cause analyss to rejecton and lmt durablty test has been performed ts result has been controlled. (48) * Lst 4 s satsfed, detals of regular nspecton for relablty have been revewed and feasblty studes have been controlled contnuous. (60) 4. Calbraton&Valdaton System. (60) * Inspecton/measurng tools, nspecton/experment tools haven t been provded and calbraton plan has not been establshed. (12) * Inspecton/measurng tools, nspecton/experment tools have been provded and calbraton plan has been establshed. (24) * Calbraton has been performed n accordance wth plan, but mantenance and management are nsuffcent. (36) * Calbraton has been performed wthout omsson, keepng and management condton s fne. (48) * Provdence of necessty, calbraton record has been performed, and perodcal expermentablty has been grasped and controlled. (60) (C 6 ). Correctve&Preventve Actons (60) Problems and preventve actons stuaton * Qualty problems states by dvsons have been grasped and regstered. (12) * Cause analyss of qualty problems, counter measure have been establshed, but detals are nsuffcent and perfunctory. (24) * Correcton ncludng 3D (Dmenson) counter measures has been establshed by dvson and controlled horzontal unfoldng to smlar qualty problems. (36) * Effcency verfcaton polcy to correcton result has been establshed and grasped. 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