A Novel Method on Customer Requirements Preferences Based on Common Set of Weight

Transcription

1 Australan Journal of Basc and Appled Scences, 5(6): , 2011 ISSN A Novel Method on Customer Requrements Preferences Based on Common Set of eght 1 S. Rass, 2 M. Izad, 3 S. Saat. 1 Assocate Professor, School of Industral Engneerng, Islamc Azad Unversty- South Tehran Branch, Modelng and Optmzaton Research Centre n Engneerng and Scence 2 Industral Engneerng Faculty, Islamc Azad Unversty, South Tehran Branch, Tehran, Iran. 3 Assstant Professor, Dep. Of Mathematcs, Islamc Azad Unversty, Tehran-north Branch, Tehran, Iran. Abstract: Qualty functon deployment (QFD) has been wdely used as a mult-functonal desgn tool to translate lngual voce of customer requrements (CRs) to a product s techncal attrbutes n the desgn, development of products, process plannng and producton plannng strateges. Even though QFD efforts have been extensvely used, assessng nformaton from partcpant experts s stll dffcult task n QFD plannng. The proposed votng methodology uses common set of weght (CS) method as a well known technque n data envelopment analyss (DEA) to aggregate each of the requrements expressed by customers and comparsons among the product produced by own company wth compettve products. Usng such flexble method can reduce cogntve burden of desgners and engneers on the presence of lack of enough data and dfferent ponts of voters' vew. Based on the domnance concepts of DEA wth ncomplete nformaton, we developed a systematc two phase method for prortzng customers' requrements wth a numercal example. Key words: Qualty Functon Deployment (QFD), House of Qualty (HOQ), Data Envelopment Analyss (DEA), Common Set of eght (CS), Customer Preference INTRODUCTION Qualty Functon Deployment whch nowadays has been used successfully orgnally proposed by Akao n 1972 n Mtsubsh. It s a set of methods that nvolve creatng a few tables called House of Qualty (HOQ) to help the product development team systematcally relate the customer requrements (CRs) that represent the overall customer concerns to the engneerng characterstcs (ECs), whch represent the techncal performance specfcatons of a developng product. The HOQ matrx consttutes an assemblage of the results of the benchmarkng and cause & effect matrces methods together wth addtonal nformaton. Therefore, nformaton s ncluded both on what makes customers happy and on measurable quanttes relevant to engneerng and proft maxmzaton. QFD also help decson-makers better understand how ther system dffers from compettor systems, both n the eyes of ther customers and n objectve features to support what mght be called moderately formal decson-makng. Cohen (1995) beleved that QFD s a method to encourage product development members to communcate more effectvely wth each other to formulate busness problems and possble solutons. Experments states that QFD has reduced desgn tme by 40% and desgn costs by 60% (Revell & Cox, 1997). These mprovements are caused by the ncreased communcaton among functonal groups early n the product development process and by assurng that the voce of the customer s bult nto the development process (Hauser & Clausng, 1988; Urban & Hauser, 1993). Fg. 1 shows sequences of a typcal tradtonal four step QFD consst of product plannng, product desgn, process plannng and process control phases, whch CRs arranged as customer desres n rows and ECs n columns of the frst HOQ matrx. In each separate phase expert groups tres to sequentally fnd out the ways of conformng customer needs. As shown n the fgure at each stage, the ECs, may called "Hows" are carred to the next phase as CRs or "hats". Correspondng Author: S. Rass, Assocate Professor, School of Industral Engneerng, Islamc Azad Unversty- South Tehran Branch, Modelng and Optmzaton Research Centre n Engneerng and Scence Tel: +98(21) , Fax: +98(21) , E.mal: Rass@azad.ac.r 1544

2 Fg. 1: The four phases tradtonal QFD (Cohen, 1995). From the pont of vew of procedure, QFD uses a seres of HOQ matrces. The phlosophy governng how QFD s to be appled s placed on both what needs to be done and how t s to be done. Franceschn [2002] beleved that the prncpal stages necessary for the constructon of the frst HOQ matrx ncludes: 1) Identfyng customer requrements (CRs) and ther perceptons, 2) Identfyng product and engneerng desgn requrements (ECs), 3)Drawng up a relatonshp matrx, 4)Plannng and deployng expected qualty (by lstng CRs n order of mportance and benchmarkng compettve products), 5)Comparng ECs through a techncal mportance rankng, 6) Analyzng the correlatons exstng between the varous characterstcs (correlaton matrx) and 7)Prortzng ECs. Fgure 2 llustrates the functonal bonds lnkng operatve phases and approprate HOQ zones. Carnevall and Mguel, 2008 presented a revew, analyss, classfcaton and codfcaton of the lterature on QFD produced between 2002 and They concluded that the majorty of the publshed artcles were case studes. In most of the cases, the work goals were about adaptng QFD for a specfc applcaton. There are also several studes of ntended mprovements to the method that ntroduce quanttatve tools and technques. Among them fuzzy logc, Analytc Herarchy Process (AHP), Analytc Network Process (ANP) and Artfcal Neural Nets (ANN) appled more frequently n prortzng of CRs and ECs. In most cases, the use of these technques ams at reducng the subjectvty of the analyss performed on the qualty matrx, that s, to mprove operatons that use QFD. Fg. 2: Man components of the house of qualty (HOQ). AHP method, wth or wthout fuzzy logc helped to defne the degree of mportance of the demanded qualty (Armacost et al.,1994, ang et al., 1998, Mynt, 2003, Bhattacharya et al. 2005, Ln & ang, 2008) and to help n the correlatons between the data n the matrxes (Partov, 2001, 2006). On the presence of vagueness or mprecseness of data, the fuzzy concept was also appled to such cases (see Kwong and Ba, 1545

3 2002, Tang 2008). Chen and Fung, 2006 proposed usng fuzzy weghted average method n the fuzzy expected value operator n order to rank techncal attrbutes n fuzzy QFD. Analytc network process (ANP) also used for rankng characterstc n QFD process (see Karsak et al, 2003; Partov, 2006; Rdder et al,2008). The neural network (NN) technque was also used n the QFD for consderng the uncertanty of the avalable human experts. Mynt 2003, La, Ho, and Chang, 1998 appled n the decson-makng process combnng votng and lnear programmng technques to aggregate ndvdual preference nto group consensus. Ho, La, and Chang,1999 also proposed an ntegrated group decson-makng method to aggregate team members opnons and mnmze nconsstency over group and ndvdual preferences for determnng the mportance weghts of CRs. Kwong and Ba, 2002, 2003 employed group decson-makng method and AHP ncorporated wth fuzzy set theory to determne the mportance weghts of CRs. Chn-Hung Lu, 2009 proposed a group decsonmakng method wth fuzzy set theory and genetc algorthms n qualty functon deployment. In the recent paper data envelopment analyss used for prortzng n QFD process. Ramanathan et al., 2009 proposed QFD DEA methodology to obtan the relatve mportance of ECs when several factors have to be consdered smultaneously. Also Kamvys et al., 2010 used combnaton of QFD wth AHP-ANP and Data Envelopment Analyss (DEA) wth AHP and ANP called DEAHP-DEANP methodologes to prortze selecton crtera n a servce context. Han et al. suggested a lnear partal orderng approach for assessng the knowledge from partcpants and prortzng ECs. Km et al. developed two robustness ndces and proposed the noton of robust prortzaton that ensures the ECs prortzaton to be robust aganst the uncertanty. Commonly, the QFD process nvolves varous nputs n the form of lngustc data, e.g., human percepton, judgment, evoluton on mportance of CRs or strengths of relatonshp between CRs and ECs are hghly subjectve and vague (Km, Moskowtz, Dhngra, & Evans, 2000; Chan & u, 2002; Ba & Kwong, 2003; Fung, Chen, & Tang, 2006). Due to exstence of ambgutes n the real votng cases, we focused on the way of prortzng CRs based on gathered nformaton of dfferent customer both n prmary and secondary phase. The proposed novel methodology uses the customers' votes and applyng common set of weght (CS) n DEA to evaluate the "hats" to derve prortes of CRs. DEA s a wdely used tool n effcency analyss and measurement. It s a lnear programmng based technque for measurng the relatve effcency of a set of competng decsonmakng unts (DMUs) where the presence of multple nputs and outputs makes the comparsons dffcult. The relatve effcency of the multple nputs and outputs n DMU are typcally defned as a rato (weghted sum of the DMU s outputs dvded by weghted sum of the DMU s nputs). So, f the hgher performance n the relatve effcency can be obtaned, the nput data of rato must have lower values and the output data of rato must have hgher values. Or, when the nput data are constraned to fxed values and the output data have hgher values, the relatve effcency also has a hgher performance. Imposng bounds on factor weghts, lmts the flexblty of DEA n assgnng ndvdual sets of weghts to each of the partcpatng DMUs. In the extreme case, when no flexblty s allowed, a Common Set of eghts s appled for the assessment of all DMUs. Such a common set can serve as a yardstck to whch the results of the ordnary ("flexble") DEA outcomes are compared. hen numbers of DMUs are less than sum nputs and outputs, basc DEA model cannot dentfy the effcent unt, hence CS s a. There are several method for dentfy CS( see Jahanshahloo et al., 1997; Hossenzadeh et al., 2000; Saat et al., 2005; Kuosmanen et al., 2006; Saat, 2008). In ths paper we used the last proposed two step method to apply CS n DEA. Step one s Bounds determnaton. To determne the upper bounds on output weghts, the model 1 s consdered n step 1. (1) 1546

4 In second step a CS model 2 s appled. (2) Through the followng model, the effcency of each DMU can be evaluated as follows: (3) where, are optmal values of (2). The rest of paper s organzed as follows: Secton 2 provdes the suggested method followed by a short background on common set of weght technque. To follow the proposed concept systematcally, a numercal example s gven n Secton 3. Secton 4 closes wth concluson. 2. The Proposed Methodology: In real cases many qualty engneers prefer to follow customer perceptons n lngual terms. A common way may make use of the subsequent states such as: Very hgh (VH), Hgh (H), Moderate (M), Low (L) and Very Low (VL) Usually, n ths manner experts vote on CRs' mportance va the mentoned lngual terms n two sequental phases. Hence we could count number of votes on each lngual term to applyng DEA method to fnd fnal customer percepton. As sad before, ths process should be appled n two sequental phases called herenafter as prmary and secondary phase. Prmary stage engaged of voters ponts of vew on each CR. Through applyng CS as a well-known technque n DEA the prmary weghts of the th P customer requrements, say CR s calculable. Secondary phase fnalzed the prmary mentoned weghts by affectng the customers' comparsons among own characterstcs wth those of other compettor's products (benchmarkng process). Such fnal weghts vector herenafter s showed by S CR for each customer requrements. The proposed procedure of calculatng K customer requrement weghts n two sequental phases s gven below: p Phase 1: Calculatng prmary weghts of the each customer requrement, CR. 1. Consder every CRs or "hats" are DMU and select N voters to declare ther pont of vew on CRs perceptons n lngual terms 2. Count number of votes, n j of the th DMU and the j th lngual term, where =1,2,..., K and j =1,2,...,m hence m =5 for the mentoned fve levels of lngual terms. 3. Apply CS technque to calculate effcency of the th DMU, E DMU and score of the j th lngual terms, S j, where each DMU have N output and one dummy nput. 4. Apply SA technque for calculatng the prmary weghts of the th customer requrement s 1547

5 m p CR Sjnj j 1 s Phase 2: Calculatng Secondary weghts of the each customer requrement, CR. 5. Follow N voters' perceptons on the Κ customer requrements. To deal correctly wth each of CRs expressed by customers to have comparson among the product produced by own company, C 1, and some compettve products, say L, belongng to the same market segment, C ; l 1,2,..., L. th 6. Consder every compettors are DMU and count number of votes, n j of the l DMU and the j th lngual term, where =1,2,...,L and j =1,2,...,m hence m=5 for the mentoned fve levels of lngual terms. 7. Apply CS technque to calculate effcency of the th DMU on th customer requrements, E DMUl and calculate the secondary score of the j th lngual terms, SS j, where each DMU have N output and one dummy nput. 8. Apply SA technque for calculatng the secondary weghts of the th customer requrement s m p CR SSjnj j 1 9. Prortze customer requrements based on ther secondary weghts. 4. Illustrate Example: In ths secton a numercal example based on the proposed method showed step by step. For ths ssues assume 1, 2, 3, 4, 5 show fve customer requrements dentfed as major "hats" and we ask over 10 voters for evaluaton of the mportance of them. Also suppose there are three companes for customer compettve evaluaton whch shows them wth phrase (C 1, C 2, C 3, C 4 ). On them C 1 s relevant to own company and C 2, C 3, C 4 for compettve. Table 1 delvers the prmary voters' perceptons on the each CR based on the mentoned lngual term abbrevatons. Table 1: The ten experts' perceptons on the relatve mportance of the 5 "hats". hats Voters' number L H M VH M H M VH H H 2 H L H H VH M H M H VH 3 VL M L M H M H VH M M 4 M H M H M VH M VL H L 5 M H VL M M L M H L L After countng of total votes of each lngustc varable table 2 could gan. Consder each "hats" act as a DMU, and voters are ther outputs. Then the present problem conssts of fve DMU wth ten outputs and one dummy nput acheved value one. After runnng CS model, effcences of each DMU and scores of the defned lngual terms may get as shown n tables 3 and 4. Table 2: Number of votes of each lngustc term on each "hats". DMUs Very low Low Moderate Hgh Very hgh l 1548

6 Table 3: Calculated effcency of decson makng unts based on the CS model. DMUs Effcency Table 4: Calculated scores of the gven lngustc terms based on the CS model. lngustc varable VL L M H VH Score By usng smple addtve weghtng method (SA) weghts of each DMUs are acqurable as: 1 = 0( )+ 1( )+ 3( )+ 4( )+ 2( )= = 0( )+ 1( )+ 2( )+ 5( )+ 2( )= = 1( )+ 1( )+ 5( )+ 2( )+ 1( )= = 1( )+ 1( )+ 4( )+ 3( )+ 1( )= = 1( )+ 3( )+ 4( )+ 2( )+ 0( )= In order to normalzng the DMUs weghts we dvded them to the largest score, hence t s 3 = Therefore the pror weghts of CRs based on the voters' ponts of vew wll be P, P, P, P, P CR CR CR CR CR 0.845, 0.845,1,1, In order to fnalze customer perceptons, comparson among the product produced by own company wth compettve products belongng to the same market segment are necessary. So a questonnare s sent to a group of 10 customers to nqure on the level of mportance of each CRs. In t ndvdual customers are requested to evaluate the degree of satsfacton obtaned wthn own company from the use of the product, as well as the degree of satsfacton obtaned from the product marketed by ther strongest compettors. Table 5 conveys level of pleasure of each voter on each "hats" through comparson between own company showed by compettor # 1 and three other compettors codfed as 2, 3 and 4. Table 6 offers total votes on each lngustc term. Consder hence C 1, C 2, C 3, C 4 act as decson makng unts and the ten voters' lngustc varables perform as ther output and assume one dummy nput wth value1. After carryng out CS model, effcences of each DMU and scores of the defned lngual terms may get as shown n tables 7 and 8. Table 5: Degree of satsfacton obtaned from the ten voters perceptons on the 5 "hats". Voter # Compettor # M H M L VH VH H M H M H M M H M L M H M M 2 L M M L H M H M L M M M M VH H M L M L M 3 H VH M M M H M M VH H M H H H L M H L M M 4 L M H M M VH H M H H L M M M H M L L M L 5 M H M L M H M L M L M M L H VH M M M L L Voter # Compettor # L H VH M VL M M L M H M M VH H M H L M H M 2 M M L M L H M M M M H L H H M M VL M L L 3 M H M L M VH L L H VH H M VL L H M M H M L 4 L M M M M M VL L H H M H VH M VH M H M H M 5 M H M M H L L M M M L VL M M L M M M H M Table 6: Number of votes of each lngustc term on each "hats" VL L M H VH VL L M H VH VL L M H VH VL L M H VH VL L M H VH C C C C

7 Table 7: Effcency of DMUs for each "hats" and compettors. DMUs C C C C Table 8: Effcency of the compettors. DMUs C 1 C 2 C 3 C 4 Effcency By usng SA technque, the fnal weghts of each CRs or "hats" are acqurable as: S, S, S, S, S CR CR CR CR CR 0.549, 0.616, 0.827, 0.705, The fnal weghts vector shows prortes proceed as CR3 CR5 CR4 CR2 CR1 5. Concluson: Dervng rankngs of CRs and ECs s a crucal step towards successful QFD when dfferent experts have dfferent ponts of vew. Rankng on the mportance of each customer requrement s essental n every QFD process and s on the deep focus of the present effort. In ths paper, we proposed a novel method for prortzng customer requrement n two sequental phase. The proposed methodology used votng process to assess percepton nformaton n QFD process, DEA model wth common weghts to determne the values of the lngustc terms, and effcency of DMUs. The man benefts of the proposed method are: 1. Customer requrements' preferences calculated quanttatvely on two sequental dependent phase called prmary and secondary. 2. The proposed method utlzes general lngual terms and votng process and s able to aggregate dfferent votes acceptably and precsely. 3. Due to the model structure, there are possblty to apply smultaneous other goals respect to restrctons. The numercal example has been nvestgated to llustrate the applcatons of the proposed novel methodology. ACKNOLEDGEMENTS The authors thank the referee and the grateful edtor n chef for ther valuable comments and suggestons. Ths research plan was defned n and supported by deputy of research n Islamc Azad Unversty, South Tehran Branch and also the Modelng & Optmzaton Research Center n Engneerng and Scence/ Industral engneerng faculty. REFERENCES Armacost, R.L., P.J. Componaton, M.A. Mullens and Swart, An AHP framework for prortzng customer requrements n QFD: an ndustralzed housng applcaton. IIE Transacton, 26(4): Ba, H., C.K. Kwong, Inexact genetc algorthm approach to target values settng of engneerng requrements n QFD. Internatonal Journal of Producton Research, 41(16): Bhattacharya, A., B. Sarkar, S.K. Mukherjee, Integratng AHP wth QFD for robot selecton under requrement perspectve. Internatonal Journal of Producton Research, 43(17): Carnevall, J.A. and C.P. Mguel, Revew, analyss and classfcaton of the lterature on QFD -Types of research, dffcultes and benefts. Internatonal Journal of Producton Economcs, 114: Chang, Hee., Jae. Han, Km. Kyeong, Cho. Sang Hyun, Prortzng engneerng characterstcs n qualty functon deployment wth ncomplete nformaton: A lnear partal orderng approach. Internatonal Journal of Producton Economcs, 91: Chan, L.K., M.L. u, A systematc approach to qualty functon deployment wth a full llustratve example. Omega: The Internatonal Journal of Management Scence, 33(2): Chan, L.K. and M.L. u, Qualty functon deployment: A lterature revew. European Journal of Operatonal Research, 143:

8 Chen, Y., R.Y.K. Fung and J. Tang, Ratng techncal attrbutes n fuzzy QFD by ntegratng fuzzy weghted average method and fuzzy. European Journal of Operatonal Research, 174(3): Cohen, L., Qualty functon deployment: how to make QFD work for you. Addson-esley Publshng Company, MA. Deok-Hwan, Km., Km. Kwang-Jae, Robustness ndces and robust prortzaton n QFD. Expert Systems wth Applcatons, 36: Forenzo, Franceschn., Advanced qualty functon deployment, ISBN , Florda: CRC Press, LLC. Fung, R.Y.K., Y. Chen, J. Tang, A qualty-engneerng-based approach for conceptual product desgn. Internatonal Journal of Advanced Manufacturng Technology, 32: Fung, R.Y.K., Y. Chen, J. Tang, Estmatng functonal relatonshps for product plannng under uncertantes. Fuzzy Sets and Systems, 157: Hossenzadeh, L., F. Jahanshahloo, G. and A. Memaran, A method for fndng common set of weghts by multple objectve programmng n data envelopment analyss. South-west Journal of Pure and Appled Mathematcs, 1: Hauser, J.R., How Purtan-Bennet used the House of Qualty. Sloan Management Revew, (sprng), Hauser, J.R. and D. Clausng, The House of Qualty. Harvard Busness Revew, 66(5/6): Ho, E.S.S.A., Y.J. La and S.I. Chang, An ntegrated group decson-makng approach to qualty functon deployment. IIE Transactons, 31: Jahanshahloo, G., M. Alrezaee, M.S. Saat and S. Mehraban, The role of bounds on multplers n DEA; wth an emprcal study. Journal of Scences, 19(22): Kwong, C.K., H. Ba, A fuzzy AHP approach to the determnaton of mportance weghts of customer requrements n qualty functon deployment. Journal of Intellgent Manufacturng, 13(5): Kwong, C.K., H. Ba, Determnng the mportance weghts for the customer requrements n QFD usng a fuzzy AHP wth an extent analyss approach. IIE Transactons, 35(7): Karsak, E.E., S. Sozer and S.E. Alptekn, Product plannng n qualty functon deployment usng a combned analytc network process and goal programmng approach, Computers and Industral Engneerng, 44(1): Km, K.J., H. Moskowtz, A. Dhngra and G. Evans, Fuzzy mult crtera models for qualty functon deployment. European Journal of Operatonal Research, 121: Kamvys, K., K. Gotzaman, C.A. Georgou and A. Andronkds, Integratng DEAHP and DEANP nto the qualty functon deployment. The TQM Journal, 22(3): Kuosmanen, T., L. Cherchye and T. Splanen, The law of one prce n data envelopment analyss: Restrctng weght flexblty across frms. European Journal of Operatonal Research, 170: Lu, C.H., A group decson-makng method wth fuzzy set theory and genetc algorthms n qualty functon deployment, Qualty and Quantty. DOI /s L, Y.L., J.F. Tang, J.M. Yao, Y. Pu and J. Xu, Mult-object decson-makng methodology for selectng engneerng characterstcs n qualty functon deployment. Computer Integrated Manufacturng Systems, CIMS, 14(7): Ln, M., C. ang, M. Chen, C. Chang, Usng AHP and TOPSIS approaches n customer-drven product desgn process. Computers n Industry, 59: La, X., M. Xe, K.C. Tan and B. Yang, Rankng of customer requrements n a compettve envronment. Computers & Industral Engneerng, 54: La, Y.J., E.S. Ho, S.A. and S.I. Chang, Identfyng customer preferences n qualty functon deployment usng group decson-makng technques. Integrated product and process development, ley: New York, Mynt, S., A framework of an ntellgent qualty functon deployment (IQFD) for dscrete assembly envronment. Computers and Industral Engneerng, 45(2): Partov, F.Y., An analytc model for locatng facltes strategcally. OMEGA: The Internatonal Journal of Management Scence, 34(1): Partov, F.Y., An analytc model to quantfy strategc servce vson. Internatonal Journal of Servce Industry Management 12(5): Revell, J.. and C.A. Cox, The QFD handbook. John ley & Sons, New York. Ramanathan, R. and J. Yunfeng, Incorporatng cost and envronmental factors n qualty functon deployment usng data envelopment analyss, Omega: Internatonal Journal of Management Scence, 37:

9 Rdder, R., R.C. Almeda, P. Bongers, S. Brun and S.D. Flapper, Mult-Crtera Decson Makng n Product-drven Process Synthess. 18th European Symposum on Computer Aded Process Engneerng - ESCAPE, 18. Saat, S., Determnng a common set of weght n DEA by solvng a lner programmng. Journal of Industral Engneerng Internatonal, Islamc Azad Unversty, South Tehran Branch, 4(6): Saat, S., A. Memaran, Reducng weght flexblty n fuzzy DEA, Appled Mathematcs and Computaton, 161: Theodore, T. Allen, Introducton to Engneerng Statstcs and Lean Sgma Statstcal Qualty Control and Desgn of Experments and Systems, Second Edton, ISBN , Sprnger-Verlag: London. ang, H., M. Xe, T.N. Goh, A comparatve study of the prortzaton matrx method and the analytc herarchy process technque n qualty functon deployment, Total Qualty Management, 9(6):