DEVELOPMENT OF DECISION SUPPORT SYSTEM FOR SELECTING QUALITY MANAGEMENT SYSTEMS AND MANAGEMENT TOOLS

Transcription

1 Proceedng 7 th Internatonal Semnar on Industral Engneerng and Management DEVELOPMENT OF DECISION SUPPORT SYSTEM FOR SELECTING QUALITY MANAGEMENT SYSTEMS AND MANAGEMENT TOOLS Saranya Sukkarn 1, Natcha Thawesaengskultha 2 1 The Department of Industral Engneerng, Faculty of Engneerng Chulalongkorn Unversty, Bangkok, Thaland. 2 The Department of Industral Engneerng, Faculty of Engneerng Chulalongkorn Unversty, Bangkok, Thaland. 1 hopup_p@hotmal.com, 2 Natcha.t@chula.ac.th ABSTRACT The purpose of ths study s to develop a decson support system for the selecton of QMS and management tools. The combnaton of ANP and TOPSIS are used for mult-crtera decson makng technque n selectng QMS technques. ANP determnes the relatve weghts of multple evaluaton crtera whle the modfed TOPSIS approach was used to rank the alternatves. For the management tools selecton, the matrx dagram of the tools benefts and process step s employed. The paper llustrates how the proposed approach was appled to a problem. Keywords: Decson Support System, MCDM, Qualty Management Systems (QMS) And Management Tools. 1. INTRODUCTION The food ndustry s an ndustry that s mportant to the economc development of Thaland sgnfcantly. In 2011, the food ndustry was an ndustry wth the hghest producton values as shown n Fgure 1, but there are stll crtcal ssues n the ndustry development relatng to the enforcement of producton standards as well as the Non- Tarff Barrers n terms of standard of products and producton processes, the dffculty n the selecton of qualty system standards and tools used for qualty management (Deslandres and Perreval, 1997) (Fotopoulos, Psomas and Vouzas,2010), the decson crtera used n the selecton of qualty system standards and tools used for qualty management (Holleran, Bredahl and Zabet,1999) (Jn, Zhou and Ye,2008) (Arpanutud, Keeratpbul, Charoensupaya et al, 2009) (Thawesaengskultha, 2007), and the problems of human decson makng. Thus, t requres the development of decson support systems on the bass of the model to assst n makng decson and make decson more effectve. Fgure 1. Gross Domestc Product orgnatng from MANUFACTURING at Current Market Prces 2011 Buldng the decson support systems for the selecton of qualty management systems and qualty management tools n the food ndustry requres the approach called MCDM to assst n decdng to choose the best opton that meets the goals set n whch t s consdered the complex problem. There s a varety of decson crtera same as the problems of ths research. Another popular method for solvng MCDM problems s the TOPSIS (technque for order performance by smlarty to dea soluton) whch was frst developed by Hwang and Yoon. The TOPSIS bases upon the concept that the optmal alternatve should have the shortest dstance from the DSS-13

2 Proceedng 7 th Internatonal Semnar on Industral Engneerng and Management postve dea soluton (PIS) and the farthest dstance from the negatve dea soluton (NIS). Although the concept of TOPSIS s ratonal and understandable, and the computaton nvolved s uncomplcated, the nherent dffculty of assgnng relable subjectve preferences to the crtera s worth of note. The ANP s a comprehensve decsonmakng technque that captures the outcome of dependence and feedback wthn and between clusters of elements (Saaty 1996). ANP nvolves a combnaton of two parts, where the frst comprses a control herarchy or network of crtera and sub-crtera that controls the nteractons, and the second part comprses a network of nfluences among the elements and clusters. Whereas AHP represents a framework based on a undrectonal herarchcal relatonshp, ANP permts more complex nterrelatonshps among decson levels and attrbutes. Not only do the mportances of the crtera determne the mportance of the alternatves as n a herarchy, but the mportance of the alternatves may also nfluence the mportance of the crtera (Saaty 1996). Matrx evaluatons (Penwat, 2007) referred to methods for presentng nformaton to facltate the evaluaton of alternatves. It provded nothng more than smple structures to assst a facltator and mprove understandng of the problem. It dd not lmt the number of crtera or factors consdered n the analyss. It may descrbe factors and sub-factors nvolved n a problem wth ther rankng scores, or t may provde the relatve overall postons of alternatves n a multdmensonal space. Ths paper has modeled the selecton of qualty management systems and management tools n the food ndustry as an MCDM problem and proposed a four-phase. For the selecton of qualty system standards, we have appled the technque of analytc network process (ANP) and modfed TOPSIS. The ANP method was used n obtanng the relatve weghts of crtera but not the entre evaluaton process to reduce the large number of parwse comparson. As for the performance correspondng to each alternatve, the modfed TOPSIS approach usng a new defned weghted Eucldean dstance was DSS-14 conducted to rank qualty management systems. For the selecton of tools used for qualty management, we have appled the Matrx Evaluaton method n creatng the decson support systems snce the Matrx Evaluaton s capable of outlnng the scope of the problem and can be easly used. The method presented here dd not account for dervng the evaluaton crtera for qualty management systems and management tools selecton. However, the proposed model may provde organzatons a way to devse and refne adequate crtera and allevate the rsk of selectng sub-optmal solutons. The rest of ths paper s structured as follows: In the next secton, we brefly ntroduce the orgnal ANP and TOPSIS method. Secton 3 the procedure s presented and an overvew of the technques used n our model s gven. In Secton 4, we present our results and dscuss an emprcal study. In Secton 5, we conclude the results reported n ths paper. 2. THEORETICAL BACKGROUND 2.1. The ANP method The ANP, also ntroduced by Saaty, s a generalzaton of the AHP (Saaty 1996). Whereas AHP represents a framework wth a un-drectonal herarchcal AHP relatonshp, ANP allows for complex nterrelatonshps among decson levels and attrbutes. The ANP feedback approach replaces herarches wth networks n whch the relatonshps between levels are not easly represented as hgher or lower, domnant or subordnate, drect or ndrect (Meade and Sarks 1999). For nstance, not only does the mportance of the crtera deter-mne the mportance of the alternatves, as n a herarchy,but also the mportance of the alternatves may have mpact on the mportance of the crtera (Saaty 1996). Therefore, a herarchcal structure wth a lnear top-to-bottom form s not sutable for a complex system. AHP s a comprehensve framework that s desgned to cope wth the ntutve, the ratonal, and the rratonal when we make mult-objectve, mult-crteron, and multactor decsons, wth or wthout certanty for any number of alternatves. The basc assumptons of AHP are that t can be used

3 Proceedng 7 th Internatonal Semnar on Industral Engneerng and Management n functonal ndependence of an upper part or cluster of the herarchy from all ts lower parts and the crtera or tems n each level (Meade and Sarks 1999). Many decsonmakng problems cannot be structured herarchcally because they nvolve the nteracton and dependence of hgher level elements on lower level elements (Saaty and Takzawa 1986; Saaty 1996). Structurng a problem nvolvng functonal dependence allows for feedback among clusters. Ths s a network system. Saaty (1996) suggested the use of AHP to solve the problem of ndependence on alternatves or crtera, and the use of ANP to solve the problem of dependence among alternatves or crtera. The major dfference between AHP and ANP s that ANP s capable of handlng nterrelatonshps between the decson levels and attrbutes by obtanng the composte weghts through the development of a supermatrx (Shyur 2006). The supermatrx s actually a parttoned matrx, where each matrx segment represents a relatonshp between two components or clusters n a system (Saaty 1996). In addton to ths, fnal weghts can be calculated usng matrx operatons, especally where the numbers of crtera n the model are relatvely few. Matrx operatons are used n order to convey wth ease the workngs of the methodology used and how dependences are worked out. Supermatrx s wthout doubt the better choce when the number of elements ncreases (Yüksel and Da gdevren 2007). Matrx operatons of Saaty and Takzawa (1986) were used n ths study as they are easy-to-understand n the calculaton of the weghts of crtera by ANP. The process of ANP nvolves three sub steps and shown as follows (Shyur 2006): Step1: Wthout assumng the nterdependence among crtera, the decson makers are asked to evaluate all proposed crtera parwse. They responded questons such as: whch crtera should be emphaszed more n personnel, and how much more? The responses were presented numercally and scaled on the bass of Saaty s 1 9 scale. Each par of crtera s judged only once. A recprocal value wll be automatcally assgned to the reverse comparson. Once the parwse comparsons are completed, the local weght vector w 1 s computed as the unque soluton to (1) Aw w 1 max 1 where max s the largest egenvalue of parwse comparson matrx A. The obtaned vector s further normalzed by dvdng each value by ts column total to represent the normalzed local weght vector w 2. Step 2: Next to resolve the effects of the nterdependence that exsts between the evaluaton crtera. The decson makers examne the mpact of all the crtera on each other by usng parwse comparsons as well. Questons such as: whch crteron wll nfluence crteron 1 more: crteron 2 or crteron 3? And how much more? are answered. Varous parwse comparson matrces are formed for each of the crteron. These parwse comparson matrces are needed to dentfy the relatve mpacts of crtera nterdependent relatonshps. The normalzed prncpal egenvectors for these matrces are calculated and shown as column component n nterdependence weght matrx of crtera B, where zeros are assgned to the egenvector weghts of the crtera from whch a gven crteron s gven. Step 3: Now we can obtan the nterdependence weghts of the crtera by syntheszng the results from prevous two steps as follows: T B (2) w w c 2 There are many studes n the lterature usng ANP to solve decson makng problems. Meade and Sarks (1998, 1999) used ANP n two of ther studes. In the frst study, alternatve projects for agle manufacturng are evaluated va ANP and logstcs and supply chan management analyss s per-formed n the second. Also n two separate studes performed by Lee and Km (2000, 2001), ANP s used n the nterdependent nformaton system project selecton process. Besdes, Karsak et al. DSS-15

4 Proceedng 7 th Internatonal Semnar on Industral Engneerng and Management (2002) and Partov and Corredora (2002) used ANP n qualty functon deployment process, whle Meade and Presley (2002) used ANP to evaluate alternatve researchdevelopment projects. Mohanty et al. (2005), Agarwal et al. (2006) and Rav et al. (2005) employed ANP n R&D project selecton problem, modelng the metrcs of lean, agle and leagle supply chan, analyzng alternatves n reverse logstcs for end-of-lfe computers, respectvely. ANP s used by Yüksel and Da gdevren (2007) for SWOT analyss and by Da gdevren et al. (2008) to determne faulty behavor rsks n work systems The TOPSIS method The TOPSIS was frst developed by Hwang and Yoon (1981). Accordng to ths technque, the best alternatve would be the one that s nearest to the postve deal soluton and farthest from the negatve deal soluton (Ertugrul and Karakasoglu 2007). The postve deal soluton s a soluton that maxmzes the beneft crtera and mnmzes the cost crtera, whereas the negatve deal soluton maxmzes the cost crtera and mnmzes the beneft crtera (Wang and Elhag 2006). In short, the postve deal soluton s composed of all best values attanable of crtera, whereas the negatve deal soluton conssts of all worst values attanable of crtera (Wang 2007). The TOPSIS method conssts of the followng steps (Shyur and Shh 2006): Step 1: Establsh a decson matrx for the rankng. The structure of the matrx can be expressed as follows: (3) [ ] Where A denotes the alternatves, =1,2,...,m; F j represents jth attrbute or crteron, j =1,2,...,n, related to th alternatve; and f j s a crsp value ndcatng the performance ratng of each alternatve A wth respect to each crteron F j. Step 2: Calculate the normalzed decson matrxr(=[r j ]). The normalzed value r j s calculated as: fj rj, 1,,m j1,,n m fj 2 1 (4) Step 3: Calculate the weghted normalzed decson matrx by multplyng the normalzed decson matrx by ts assocated weghts. The weghted normalzed value v j s calculated as: vj wjrj, 1,,m j 1,,n (5) where w j represents the weght of the j th attrbute or crteron. Step 4: Determne the postve-deal and negatve-deal solutons. V{ v 1, v 2, vn } {( jj),( jj')}, maxv j mnv V { v1, v 2, v n } {( jj),( jj')}, mnv j maxv j (6) where J s assocated wth the beneft crtera, and J s assocated wth the cost crtera. Step 5: Calculate the separaton measures, usng the m-dmensonal Eucldean dstance. The separaton of each alternatve from the postve-deal soluton ( D ) s gven as n 2 D ( vjv j ), 1, m, j 1 j (7) Smlarly, the separaton of each alternatve from the negatve-deal soluton ( D ) s as follows: n ( ) 2 D vjv j, 1, m, j 1 (8) Step 6: Calculate the relatve closeness to the dea soluton and rank the performance order. The relatve close-ness of the alternatve A wth respect to V+ can be expressed as DSS-16

5 Proceedng 7 th Internatonal Semnar on Industral Engneerng and Management C D/ D D, 1,,m (9) where the C ndex value les between 0 and 1. The larger the ndex value means the better the performance of the alternatves. In the aggregaton process, a set of alternatve canddate s to be compared wth respect to predefned crtera (Shyur 2006): The performance ratng of each canddate for each crteron s assgned and formed as a decson matrx. In addton, the normalzaton formula as shown n Eq.4 s used to transform the varous scales nto a comparable scale. The normalzed decson matrx s weghted by multplyng each column of the matrx by ts assocated crtera weght n the above process. Then the overall performance of an alternatve canddate s then determned by ts Eucldean dstance to V + and V - (Shyur 2006). However, Shpley et al. (1991) ponts out that ths dstance s nterrelated wth the crtera weghts and should be ncorporated n the dstance measurement (Shyur 2006). Ths s because all alternatves are compared wth V + and V -, rather than drectly among themselves. Deng et al. (2000) presents the weghted Eucldean dstances to nstead of creatng a weghted decson matrx. In ths process, Shyur (2006) defned the postve-deal soluton (R + ) and the negatve-deal soluton (R - ), whch are not depended on the weghted decson matrx, as R{ r, r 1 2, rn } {( mnr j jj),( mnr j jj')}, R{ r, r, r} {( jj),( jj')}, 1 2 n mnr j mnr j (10) The weghted Eucldean dstances, between A and R +, and A and R -, are calculated, respectvely, as n 2 w ( r r), 1, j 1 m, n 2 D w j( rjrj ), 1, j 1 m, D j j j (11) where the value of w j (j =1 to n) s the element of vector w c whch s calculated by Eq.2. Then closeness coeffcent can obtaned for each alternatve based on Eq.9. Fnally, a set of alternatve canddate can be preference ranked accordng to the descendng order of closeness coeffcent. There are some studes n the lterature whch consder the TOPSIS. Deng et al. (2000) used the TOPSIS method n the comparson nter-company wth objectve weghts. Shyur (2006) developed a decson makng model for COTS evaluaton wth TOPSIS. In addton to these studes Shyur and Shh (2006), for strategc vendor selecton; Tsou (2007), Mult-objectve nventory plannng; Onüt and Soner (2007) n the transshpment ste selecton; Olcer (2008) for optmzaton problems n shp desg and shppng, used TOPSIS method. 3. RESEARCH METHOD The evaluaton procedure of ths study conssts of several steps as shown n Fgure. 2. The frst step s to dentfy the multple crtera that are consdered n the decson makng process for the decson makers to make an objectve and unbased decson. Create metrc model for the selecton of management tools from academc texts and varous research studes. Then a relatonshp between crtera that shows the degree of nterdependence relatonshp s determned by group expert dscusson n general. After constructng the relatonshp of a crtera network structure, the crtera weghts can be calculated by applyng ANP. Then, we conduct a modfed TOPSIS approach to acheve the fnal rankng results. The detaled descrptons of each step are elaborated n each of the followng subsecton. Fnally, we develop the decson support systems, and select a qualty management system and management tools n the form of Mcrosoft Excel. DSS-17

6 Proceedng 7 th Internatonal Semnar on Industral Engneerng and Management Create metrc model for the selecton of management tools Create a decson support system for the selecton of management tools. Relevant theores and research Crtera for QMS Selecton Identfy Relatonshp BetweenCrtera Weghts of Crtera Create Decson Matrx Rankng of QMS Create a decson support system for the selecton of QMS. Develop the decson support systems Fgure 2. Research methodology 4. RESULT AND DISCUSSION ANP TOPSIS To llustrate the proposed determnaton process for selectng qualty management systems (QMS), the study presents an applcaton that s based on practcal experence and that has been mplemented at a food ndustry. Durng the frst stage, revewng the lteratures and ntervewng qualty experts who work n the case study. Experts were subjected to questonng by NGT to determne the evaluaton crtera for selectng qualty management systems. Next, the screenng crtera were created to conduct a QMS ntatve lookng for sutable and narrow the feld, where screenng crtera are the mnmum requrements about QMS. Durng the second stage, two rounds of NGT were used to make sure the multdrectonal relatonshp among evaluaton crtera and sub-crtera. The decson problem faced by the experts nvolved prortzng potental alternatve for selectng QMS. The phase composes of a seres of complex revew and decson but short of an explct approach. In ths study, the proposed method was appled to solve ths problem and the computatonal procedure s summarzed as follows: Step 1: The experts were asked to assess all proposed crtera and sub-crtera n a parwse fashon whle assumng no nterdependence exsted. The normalzed egenvector was calculated as unque soluton (1) and resembles w 21, whch represents the related local prorty of the crtera and w 32 whch represents the related mportance of sub-crtera n terms of ther upper level crtera. Step 2: Addtonally the dependence among the selecton crtera and sub-crtera was consdered and analyzed. The experts separately examned the mpact of all the crtera va parwse comparson. The normalzed egenvectors for these matrces are calculated and presented as w 22 and w 33, where zeros are assgned to the egenvector weghts of the crtera and subcrtera on whch a gven crtera s based. Step 3: The relatve mportance of the crtera consderng nterdependence now can be obtaned by syntheszng the results from Steps 1 and 2. Step 4: In ths step of the decson framework, evaluators were asked to buld the decson matrx by comparng canddates under each of the sub-crtera separately. The experts were asked to provde a set of crsp values wthn the range 1 to 10 to represent the performance of each alternatve n terms of each sub-crteron. After the decson matrx was determned usng formula (3), the matrx was normalzed usng formula (4) to (5). Step 5: The fnal rankng procedure starts at the determnaton of the deal and negatvedeal solutons. The deal and negatve-deal solutons are defned by formula (9). 5. CONCLUSION Selectng qualty management systems and management tools n the food ndustry s dffcult and subjectve whch and nvolves a mult-crtera decson-makng (MCDM). The qualty management systems and management tools selecton problem should be solved on the bass of an objectve decson-makng process rather than the personal judgments of the decson-makers. In ths study, we proposed a comprehensve model for the qualty management systems and management tools selecton problem DSS-18

7 Proceedng 7 th Internatonal Semnar on Industral Engneerng and Management usng ANP and modfed TOPSIS methods and matrx dagram. The ANP method was used to obtan dependence weghts of the crtera consdered n the selecton process and the modfed TOPSIS method was adopted n rankng of the alternatves. Although the applcaton of the model proposed n ths study s specfc to a company, t can also be used wth slght modfcatons n other ndustres. To ncrease the effcency and ease-of-use of the proposed model, smple software such as MS Excel can be used. Parwse comparsons related to the crtera used n qualty management systems selecton and the dependence among these crtera s carred out once at the begnnng of the decson-makng process; so, decsonmakers can skp these steps n the future use of the model. Evaluaton of the alternatves on the bass of the crtera only wll be suffcent for the future applcatons of the model and mplementaton of ths evaluaton va smple software wll speed up the process. Besdes, some crtera could have a qualtatve structure or have an uncertan structure whch cannot be measured precsely. In such cases, fuzzy numbers can be used to obtan the evaluaton matrx and the proposed model can be enlarged by usng fuzzy numbers. 6. REFERENCES (a) Deslandres, V. and H. Perreval (1997). "Knowledge acquston ssues n the desgn of decson support systems n qualty control European Journal of Operatonal Research (b) Fotopoulos, C. V., E. L. Psomas, et al. (2010). "ISO 9001:2000 mplementaton n the Greek food sector." The TQM Journal 22(2): (c) Holleran, E., M. E. Bredahl, et al. (1999). "Prvate ncentves for adoptng food safety and qualty assuranc." Food Polcy 24: (d) Jn, S., J. Zhou, et al. (2008). "Adopton of HACCP system n the Chnese food ndustry: A comparatve analyss." Food Control 19(8): (e) Arpanutud, P., S. Keeratpbul, et al. (2009). "Factors nfluencng food safety management system adopton n Tha food-manufacturng frms: Model development and testng." Brtsh Food Journal 111(4): (f) Thawesaengskultha, N. (2007). Selectng Qualty Management and Improvement Intatves: Case studes of ndustres n Thaland Industral engneerng and Operatons Management, Unversty of Nottngham. Doctoral dssertaton. (g) Penwat, K. (2007). "Crtera for evaluatng group decson-makng methods." Mathematcal and Computer Modelng 46(7-8): (h) Ln, C.-T. and M.-C. Tsa (2008). "Locaton choce for drect foregn nvestment n new hosptals n Chna by usng ANP and TOPSIS." Qualty & Quantty 44(2): () Shyur, H.-J. (2006). "COTS evaluaton usng modfed TOPSIS and ANP." Appled Mathematcs and Computaton 177(1): AUTHOR BIOGRAPHIES Saranya Sukkarn receved the Bachelor of Scence n Industral Engneerng n 2011 from Prnce of Songkla Unversty, Songkhla, Thaland. Currently, I'm studyng Master of Industral Engneerng Chulalongkorn Unversty, Bangkok, Thaland. Emal address s <hopup_p@hotmal.com> Dr. Natcha Thawesaengskultha s an assstant professor at Industral Engneerng, Chulalongkorn Unversty, Thaland. Dr.Natcha s the author of more than forty publcatons n the felds of qualty management, nnovaton management, qualty engneerng technques, and mprovement ntatves. Emal address s < Natcha.t@chula.ac.th > DSS-19