A Group Decision Making Method for Determining the Importance of Customer Needs Based on Customer- Oriented Approach

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1 Proceedngs of the 010 Internatonal Conference on Industral Engneerng and Operatons Management Dhaka, Bangladesh, January 9 10, 010 A Group Decson Makng Method for Determnng the Importance of Customer Needs Based on Customer- Orented Approach Mehd Rahmdel Meybod Department of Industral Engneerng Unversty of Payam Noor, Iran Azamdokht Saf samghabad Department of Industral Engneerng Unversty of Payam Noor, Iran Mahd Bashr Department of Industral Engneerng Shahed Unversty, Iran Abstract Nowadays one of the effectve technques for total qualty management s Qualty Functon Deployment that pays attenton to recognton of customer's needs and analyss process n all the plannng and producton stages. Ths paper presents a Group Decson Model to determne mportance amount of customers' needs for product of one organzaton. In ths model, all assessments are stated usng lngustc varables. Frstly, on the bass of presented approach and after collecton of customers' needs, raw weght of each customer's need s accounted. In the next stage, mplementaton of each demand n organzaton product and ts compettors s determned, and relatve weght of each customer's need s calculated wth respect to organzaton plan. Fnally presented model s llustrated for an appled case and fnal fndngs are analyzed. Keywords Group Decson, Qualty Functon Deployment (QFD), Rankng, fuzzy TOPSIS 1. Introducton Qualty Functon Deployment (QFD) s a useful tool for convertng customer needs nto current features of product and also t s for decson when we should take n to account collecton of subjects accompaned by ther requrements smultaneously durng decson. The bass of QFD s House of Qualty Matrx (HOQ) and ts functon s creaton of requrements assocated to customers. The matrx has two major sectons: Frst secton s allocated to customer data and second one to techncal data that every one s placed n a separate table [1]. Therefore we should be careful n accountng and achevng fnal results of ths matrx and should use sutable technques to collect, regster and process data. Wth regardng to ths subject that customers' vewponts about mportance of ther requrements generally are stated verbally, thus, usng of lngustc varables contanng fuzzy quantty related to them, n comparson wth utlzaton of numercal scale n assessments, s more sutable and also wll facltate statement of vewponts and wll has mportant role n promotng fnal results. La et.al (008) [] have presented one decson model usng fuzzy QFD to determne mportance of customers needs n a compettve envronment. Buyykozkan et.al (007) [3] have presented a Group Decson Model wth regardng to varous approaches for explanng vewponts of every one of customers (numercal, lngustc) about mportance of ther requrements. Ths artcle presents a new decson pattern to rank customer's needs. In ths new method, calculaton of raw weght of customers needs has been performed n group decson envronment wth respect to all assessments n the form of lngustc varables by usng fuzzy TOPSIS method. Ths method s easy and nterestng for decson makers whereas t has hgh accuracy and n ths method, number of necessary assessments wll ncrease unformly by ncreasng the number of customers' needs. Research method has been explaned n second secton of ths artcle. In ths secton, frstly, raw weght of each one of the customers' needs s determned by vrtue of assessment of 71

2 customers' needs by ther representatves and regardng to determned attrbutes for decson. And n turn, assessment of organzaton's product and ts compettors s determned by usng vewponts of assessor customers. Presentaton of customer's vewponts n ths secton also s performed by usng lngustc varables and regardng to customer's needs attrbute. at the end of ths secton, QFD team determne organzaton's program wth regardng to product poston n compettve market from vew of mplementaton of every one of customers needs and then, wth respect to ths program, relatve weght of each customers needs are calculated. In thrd secton, an appled example has been presented to llustrate the model calculatons and then results of ths model have been compared wth other methods. Fnally n fourth secton, results obtaned from research and also recommendatons for future researches are explaned. Research Method Presented method n ths research conssts of four stages that they as follows: collecton of customer's needs, calculaton of raw weght of customer's needs, assessment of organzaton and ts compettors from customers pont of vew, and fnally calculaton of relatve weght of customers needs that s attrbute for fnal rank of customer's needs. Now we wll explan each one of the stages:. Calculaton of raw weght of customers needs After dstngushng customers' needs, major decson attrbutes to measure mportance of every one of customer's needs and also techncal weght of each one of attrbute are determned by QFD team. Then by supply forms for takng opnons, assessor customers are asked to determne mportance amount of every one of current requrements wth regardng to major decson attrbute and to enter them n current decson matrx n forms. Assessor customers explan requested data by usng of defned lngustc varables. Fgure 1 shows an example of mentoned matrx. In ths matrx, Aj represent decson attrbute and Cn m ndcate every each customers' needs. Data Obtaned from opnons of every one of customers present one decson matrx, thus decson matrxes wll present as number as assessor customers exst. Fgure 1: Assessment of customers' needs wth regardng to major decson attrbutes In ths stage, to acheve fnal matrx of decson, average weght of all nput matrxes (that s as number as assessor customers) should be calculated regardng to prncples of Group Decson. To do ths, frstly lngustc varables should be converted nto relevant fuzzy numbers, because components of all matrces have been explaned as lngustc varables. To acheve fnal matrx of decson, average of all matrces should be calculated regardng to mportance amount of opnons of each customers and consequently fnal matrx of decson would acheve. Note: whenever, n an assessment process, one alternatve has the hghest value (defnte or fuzzy) of proft attrbute and the lowest value of cost attrbute, we would call t as excellence alternatve. To assess and compare all needs wth excellence alternatve (that s allocated the hghest score by all the customers), one fguratve row wth defned components of excellence alternatve should be created n end sde of matrx. After calculaton of fnal decson matrx, weght of each alternatve (customers' needs) s calculated by usng fuzzy TOPSIS []. To resolve such problems by takng nto account m as alternatve and n as decson attrbute, followng steps are recommended: calculaton of weghted normalzed matrx: t ~ ~ V = R D w n * n (1) 7

3 Whch V ~ s weghted normalzed fuzzy decson matrx, ( R ~ D ) s normalzed matrx and w ~ s one dagonal matrx from obtaned weghts for attrbutes. Specfcaton of deal negatve and postve soluton: Calculaton of dstance sze based on Eucldean n leu of deal postve and negatve soluton and n queston + alternatve ( d ) and ( d ) as follow: d (1 σ ) + (1 ) + (1 ) + (1 ) ( ) + ( ) + ( ) + ( ), () k n ς τ υ σ ς τ υ + = + j= 1 j = k + 1 ( = 1,,..., m) k n ( σ ) + ( ς ) + ( τ ) + ( υ ) (1 σ ) + (1 ς ) + (1 τ ) + (1 υ) d = +,( = 1,,..., m) (3) j = 1 j = k + 1 Whch attrbute from 1 to K are proft (postve) types and attrbute from K+1 to m are cost (negatve) ones. Calculaton of relatve closeness of alternatves to deal soluton (C ): d c = (4) + ( d + d ) Calculaton of mportance amount of every one of alternatve wth regardng to excellence alternatve (That was defned fguratvely or t exsts between alternatves), we could obtan mportance of each alternatve (Customers needs) relatve to authorzed score lmt: W = ( C / C f )* M (5) Whch w s mportance amount (raw weght) of each alternatve, C relatve closeness of every one of the alternatves to deal soluton, C f closeness of excellence alternatve to deal soluton and M s authorzed score lmt for mportance of alternatve..3 Assessment of organzaton and ts compettors from customers pont of vew Major objectve n ths stage s to obtan mplementaton of customers' needs n current product of organzaton and ts compettors from customer's pont of vew. In ths stage, assessor customers evaluate organzaton's product and ts compettors by completng decson matrces whch ts example has been presented n fgure. In ths matrx, Cn m (s) ndcate needs of customers, and matrx alternatves are as followng: organzaton (Org) and organzaton's compettors (R k ). Fgure : Assessment of organzaton and compettors Calculaton algorthm for assessment of customers from organzaton's product and ts compettors s as follows: 1. Add one row wth specfcatons of excellence alternatve n end sde of each nput matrx.. Convert all matrces data whch have been explaned as lngustc varables nto related fuzzy quanttes. 3. Calculate average of all matrces and compute the defuzzfed value s by usng followng formula: ~ σ + ς + τ + N = ( σ, ς, τ, υ) N = υ () 4. Calculate score related to product of each organzaton by usng Eq. (5) n order to assess organzaton's product and every one of ts compettors relatve to score lmt. 73

4 .4 Calculaton of relatve weght of costumers needs Wth lookng to the House of Qualty Matrx, we fnd out that, requrements of customers have been dentfed and categorzed, also values related to columns of mportance of customers needs, assessment of organzaton and all ts compettors have been calculated. Now, organzaton plan should be determned to mplement each customers' needs. The QFD team wll determne the program wth respect to poston of organzaton and compettors. For example, followng accountngs method can be used as one rule to obtan organzaton program. If we call organzaton program as P o, value for assessment of organzaton as w org, and value for assessment of compettors as w R, then accountng rule for organzaton plan wll be as follows: P = Max W, W ) (7) o ( Org R By obtanng organzaton program; recovery rato, defnte weght and relatve weght of every one of customers' needs are calculated by followng formulas: P AW o IR =, AW IR Wcn Worg = RW = ( ) M Max* AW ) (8) Whch IR s recovery rato, AW s defnte weght, W cn s raw weght, and RW s relatve weght of (n) requrement. By calculaton relatve weght of all customers' needs, calculatons for frst secton of HOQ Matrx (customer data) wll be completed. 3. Numercal Example Suppose, we have 4 requrements cn 1, cn, cn 3 and cn 4 for an organzaton product. Selected assessor customers are 3 persons (DM 1, DM and DM3) that mportance amount of ther opnons the equal to 40%, 35% and 5% respectvely. Crtera for assessment of customers' needs are Qualty (QU), Effcency (EF) and Cost (CO) that ther mportance respectvely has been determned as 35%, 35% and 30%. Output for takng opnons of assessor customers as compared wth mportance of every one of requrements correspond to Table. Organzaton also has one compettor that assessment of organzaton's product and ts compettor from assessor customers' pont of vew correspond to Table 3. Used lngustc varables of customers are correspondng to Table 1. Table 1: specfcatons of used lngustc varables of customers Ttle Very low mportance (operaton) Low mportance (operaton) Medum mportance (operaton) Hgh mportance (operaton) Very hgh mportance (operaton) Symbol fuzzy quantty (trapezod) VL (0,1,1,) L (1,,3,4) M (4,5,5,) H (,7,8,9) VH (8,9,9,10) Table : Assessment of mportance of each one of requrements Decsonmakers DM1 DM DM3 Requrements Attrbutes Table 3: Assessment of organzaton's product and ts compettor Decson-makers Alternatve Attrbutes QU EF CO Cn1 Cn Cn3 Cn4 Cn1 H M L Cn H H VH Org M M M M DM1 Cn3 L H H R1 H L M H Cn4 M L M Org M H H H DM Cn1 H M M R1 M L M H Cn M H H Org M M M L DM3 Cn3 H M M R1 H M M M Cn4 L L L Cn1 M H M F VH VH VH VH Cn VH H VH Cn3 M H M Cn4 L M L F VH VH VL 74

5 3.1 Obtaned results by usng of proposed model In order to calculate raw and relatve weght of every one of customers' needs, frstly we convert nput lngustc varables nto related fuzzy quanttes and then wth regardng to mportance of customers opnons, fnal matrx of decson could be obtaned. Fnally, wth respect to postve (Qualty and Effcency) and negatve (cost) decson crtera, and deal soluton related to them, relatve closeness of every one of requrements to deal solutons could be calculated correspondng to Table 4, and n fnal raw weght of every one of customers needs could be calculated wth regardng to score lmt consdered for customers needs (M) whch n ths example s supposed as 5. Regardng to results of Table 4, we fnd out that raw weght of frst demand s hgher than others. Table 4: Relatve closeness and raw weght of each one of needs - d + d C rank W Cn Cn Cn Cn F In next stage, n order to assess organzaton's product and ts compettor, average weght of three nput matrxes are obtaned regardng to mportance of opnons of every one of 3 assessor customers, after when we converted lngustc varables n Table 3 nto related fuzzy quanttes (Table 5). Then the defuzzfed value s computed and calculate mplementaton of each one of needs n organzaton's product and ts compettor and wth regardng to supposed score lmt (number 5) lke as Table. Insert values related to raw weght of requrements and also assessment of organzaton's product and ts compettor n relevant sectons n HOQ Matrx wth regardng to performed calculatons, and calculate values related to organzaton program, recovery rato, defnte weght and fnally relatve weght of each one of requrements by usng stated formulae. All mentoned calculatons have been performed correspondng to Table 7 n secton of customers' data n HOQ Matrx. Wth regardng to obtan fnal results, relatve weght of frst demand s hgher than others whch ts reason s 1.33 recovery ratos for ths demand. Raw weght of ths demand s also hgher than other ones. Table 5: fnal decson matrx to assess organzaton's product and ts compettor Cn 1 Cn Cn 3 Cn 4 Org (4,5,5,) (4.7,5.7,,7) (4.7,5.7,,7) (3.9,4.9,5.5,.5) R1 (5.3,.3,.9,7.9) (1.7,.7,3.5,4.5) (4,5,5,) (5.5,.5,7.,8.) F (8,9,9,10) (8,9,9,10) (8,9,9,10) (8,9,9,10) Table : Score of organzaton's product and ts compettor 75 Attrbute Cn 1 Cn Cn 3 Cn 4 Org R Table 7: The relatve weght of every one of customers needs. Row Requrements Importance amount Assessment of compettor Assessment of compettor Organzaton program Recovery rato Defnte weght Relatve weght 1 Cn Cn Cn Cn

6 3.. Comparson of results To compare results of presented model n ths artcle wth other presented models, results of 3 other models have been consdered. Frst model s a tradtonal method n whch only raw weght of customers' needs s consderable. Thus, obtaned raw weght n ths research, whch has been calculated by fuzzy TOPSIS method, s taken nto account. Second model s Buyykozkan et.al (007) method. In ths model wth regardng to mentoned example, all assessments are taken nto account lngustc and weghted normalzed matrx s taken as nput for ths decson model. Thrd model s La et.al (008) method. In ths model, used raw weght s the same as raw weght whch has been calculated by fuzzy TOPSIS method. Fnal rank of mportance of customers' needs has come n Table 8, wth regardng to fnal results of 3 mentoned models and presented model n ths research. Table 8: Comparson of fnal rank of mportance of customers' needs by usng of varous methods Rank Ttle Tradtonal method (raw weght) Cn 1 Cn Cn 3 Cn 4 Buyykozkan et. al Cn 1,Cn ---- Cn 3,Cn La et. al Cn Cn 1 Cn 4 Cn 3 Proposed method Cn 1 Cn 4 Cn Cn 3 For assessment of fnal results of all mentoned methods, measurement attrbute (regardless of organzaton program) could be taken nto account as composed form from two vewponts as raw weght and market poston. In frst vewpont, only mportance of requrements s consderable from customers' pont of vew. Outcome of ths assessment s raw weght obtaned n secton 3-1. In second vewpont, only mplementaton of every one of customers' needs n organzaton's product and n current market s assessed. To perform ths assessment Presented results n Table 5 are nput for decson queston, wth ths change n whch requrements of customers, decson alternatve and organzaton's product and ts compettor are attrbutes for decson. To resolve the queston, we also use fuzzy TOPSIS method (by takng nto account same mportance for decson attrbute). Assessment attrbute of methods could be obtaned by havng results two above vewponts. Dagram1 show comparson of results of other methods wth attrbute of decson measurement Cn1 Cn Cn3 Cn4 Tradtonal method (raw weght) Buyykozkan et.al La et.al Proposed method composed method Fgure 1: Rankng of mportance of customers needs by usng varous methods On ths bass, proposed method has the lowest dfference wth measurement attrbute n rank of customers' needs, and frst and second prortes n rank for proposed and composed methods are same. Therefore, fnal results of proposed model have acceptable relablty as compared wth other methods. 4. Concluson Ths research presents a decson model to determne mportance of customers needs from organzaton's product or servce by usng of lngustc varables n a fuzzy envronment. By usng of proposed method of ths model, raw weght of each customer's needs could be obtaned by takng nto account performed lngustc assessments. Also by usng of ths model, relatve weght of each customer's needs could be calculated wth respect to assessment amount of customers from organzaton's product and ts compettors that has been explaned by customers as lngustc varables and regardng to organzaton's program whch presented product or servce. In decson model of research, 7

7 convenence of explanaton of assessments by customers s consderable, so that assessments could be explaned by usng of lngustc terms, and ths capablty would cause that more exact assessments are preformed by decsonmakers, and regardng to more correct nputs, fnal results of decsons would have hgher realty. Applcaton of the proposed method has other advantages whch among them, we refer to followng cases: Applcaton of the method n group decson, assessment of product n compettve envronment, rank of organzaton's product n current market and fnal acceptable results n connecton of rankng customers needs. Fnal results of proposed model (mportance amount of customers' needs) are a bass to determne rank of techncal requrements of product that wll use n the next stage of HOQ Matrx (Techncal data). To contnue nvestgatons about topc of research, we could pay attenton to effect of correlaton between customers' needs on calculatons related to mportance amount of each customer s needs. References 1. Ronald, G.D., 00, "Qualty Functon Deployment", Godarz Arezo, Kazemnejad Hajar, Dabr Gholamreza, Green Frazandsh Publcaton Insttute (n persan).. Xn, L., Mn, X., Kay-Chuan, T., and Bo, Y., 008, Rankng of customer needs n a compettve envronment, Computers and Industral Engneerng, 54, B., Gulcn, F., Orhan, and R., Da, 007, Fuzzy group decson-makng to multple preference formats n qualty functon deployment, Computers n Industry, 58, Zmmermann, H.-J., 1991, Fuzzy Set Theory and ts Applcatons, nd ed., Kluwer Academc Publshers, Dordrecht. 5. Lockett, A.G., Hetherngton, B., Yallup, P., Stratford, M., and Cox, B. 198, Modellng a research portfolo usng AHP: A group decson process. R&D Management, 1(), L, D.-F., 007, Compromse rato method for fuzzy mult-attrbute group decson makng, Appled Soft Computng, 7,