A Hybrid QFD Framework for New Product Development

Transcription

1 A Hybrd QFD Framework for New Product Development Y C Tsa*, K S Chn* and J B Yang** * Department of Manufacturng Engneerng and Engneerng Management, Cty Unversty of Hong Kong, Tat Chee Avenue, Hong Kong, PR Chna @student.ctyu.edu.hk ** School of Management, Unversty of Manchester Insttute of Scence and Technology, Manchester, UK. an-bo.yang@umst.ac.uk Abstract Nowadays, new product development (NPD) s one of the most crucal factors for busness success. The manufacturng frms cannot afford the resources n the long development cycle and the costly redesgns. Good product plannng s crucal to ensure the success of NPD, whle the Qualty Functon deployment (QFD) s an effectve tool to help the decson makers to determne approprate product specfcatons n the product plannng stage. Tradtonally, n the QFD, the product specfcatons are determned by a rather subectve evaluaton, whch s based on the knowledge and experence of the decson makers. In ths paper, the tradtonal QFD methodology s frstly revewed. An mproved Hybrd Qualty Functon Deployment (HQFD) s then presented to tackle the shortcomngs of tradtonal QFD methodologes n determnng the engneerng characterstcs. A structured questonnare to collect and analyze the customer requrements, a methodology to establsh a QFD record base and effectve case retreval, and a model to more obectvely determne the target values of engneerng characterstcs are also descrbed. Key Words: Qualty Functon Deployment (QFD), Analytc Herarchy Process, Case-based Reasonng, Fuzzy Lnear Regresson and optmzaton. Introducton Successful New Product Development (NPD) s essental to manufacturng companes to make proft and even survve. However, NPD has been forged n the crucble of global competton, whle the customer requrements become complcated. In order to ncrease the customer satsfacton, product plannng s an mportant task to ensure the success of NPD. Good outcomes from product plannng can reduce engneerng changes throughout the entre product development cycle, and fnally reduces the product development tme and costs whle ncreases the customer satsfactons. Qualty Functon Deployment (QFD) s a plannng tool to translate customer requrements nto product specfcatons [, 2]. It has been appled n dfferent ndustres, for examples, manufacturng, servces and software development, snce ts frst ntroducton by Prof. Yo Akao n the late 960s. QFD s usually represented n the House of Qualty (HOQ). In HOQ, there are seven rooms showng the relatonshps between customer attrbutes (performance characterstcs) and engneerng characterstcs (techncal requrements),

2 and the nterrelatonshps among engneerng characterstcs. In ths paper, we wll examne the tradtonal QFD methodology and present our fndngs n secton 2. In secton 3, we wll propose a Hybrd Qualty Functon Deployment (HQFD) framework to overcome the shortcomngs stated n the tradtonal QFD methodology. In secton 4, a structured desgn of the questonnare to acqure the customer requrements s ntroduced. In secton 5 and 6, a Case-based Reasonng (CBR) based method for determnng the engneerng characterstcs s presented. The fuzzy logc based methodology n determnng the target values of engneerng characterstcs s dscussed wth llustrated examples n secton 7 and 8. Secton 9 presents our concluson. 2. Shortcomngs of the Tradtonal QFD Methodology Havng examned the tradtonal QFD methodologes, several shortcomngs have been found. The frst one s the nput of ambguous voce of customers (VOC) nto QFD. Decsons made n the desgn stage of product development are often made on the bass of ncomplete, ll-structured and vague nformaton [0]. As customer requrements s the nput of QFD, t s very mportant to classfy the customer requrements n a systematc way. However, there s a lack of systematc methods for companes. Secondly, the degrees of the relatonshps between customer attrbutes and engneerng characterstcs, and the nterrelatonshps among engneerng characterstcs are usually fuzzy and vague [4]. In tradtonal QFD methodologes, the nterrelatonshps among engneerng characterstcs s always gnored n the determnaton of the target values of engneerng characterstcs. It leads to the naccuracy of the target values of the engneerng characterstcs, as each engneerng characterstc s not an ndependent factor that s nterrelated to other characterstcs. In order to tackle the shortcomngs of tradtonal QFD methodologes mentoned, a Hybrd Qualty Functon Deployment (HQFD) framework s proposed. 3. The HQFD Framework In ths secton, a Hybrd Qualty Functon Deployment (HQFD) framework s proposed to overcome the shortcomngs of tradtonal QFD methodologes. In the HQFD, Case-based Reasonng (CBR), Analytc Herarchy Process (AHP) and fuzzy logc are appled n an ntegrated manner. CBR s a technque of artfcal ntellgence to seek solutons for new problems by makng use of smlar cases encountered n the past [5]. CBR can support QFD n a more structured way that smlar QFD fgures based on hstorcal cases can be effcently searched to ntate a new case. AHP s employed to determne the mportance of customer requrements wth parwse comparsons [4]. Fuzzy logc s adopted to transfer lngustc data to crsp scores, whch are then used to calculate overall customer satsfacton [3]. The HQFD framework can be dvded nto four sectons, as shown n Fgure. The frst s to acqure customer attrbutes; the second s to retreve the engneerng characterstcs usng CBR; the thrd s to determne the relatonshps between customer requrements and engneerng characterstcs, and the nterrelatonshps among engneerng characterstcs; and the fourth s to determne the target value of engneerng characterstcs. When acqurng customer attrbutes, an AHP based questonnare s ntroduced. Ths questonnare ntegrates qualty dmensons, crtcal ncdents and satsfacton tems. AHP s then used to calculate the 2

3 relatve mportance (weghtngs) of each customer attrbute. The second step of the proposed framework s to retreve engneerng characterstcs correspondng to customer attrbutes. Ths step s based on the Performance Index Raton (PIR), whch ndcates how the engneerng characterstcs match the customer attrbutes. The thrd step and fourth steps can be acheved usng fuzzy lnear regresson. The detals are explaned n the followng sectons. 4. Requrements Acqustons As the customer requrement s the baselne of the product development, the ablty to correctly understand and address customer needs s key to the success of any product development effort, partcularly n an envronment of tme-based competton [5,23]. The dffculty n understandng and defnng customer needs s one of the common problems across the companes [6]. It s reported that n about 70 percents of the falure cases of NPD, the user needs are not carefully consdered and correctly dentfed by the development team [6]. In order to reduce the falure, a systematc way to acqure and analyze the customer requrements must be developed durng the product plannng stage. The mportance of customer requrements could be analyzed by the analytcal herarchy process (AHP) method. It, however, does need an effectve mean to collect the data. In ths paper, we propose to develop a systematc and structured desgn of a QFD questonnare n a 4-step approach to understand the user needs, as shown n Fgure 2. Marketng Engneerng Desgn Producton Cases Engneerng Characterstcs CBR+AHP Marketng Requrements Attrbutes Retreval cases Relatonshp Matrx Plannng Matrx (Importance and Compettve Benchmarkng) AHP & CBR CBR Prortze Evaluaton + Adaptaton Cases Product Specfcatons Fuzzy Logc Learnng Fgure. The proposed HQFD framework 3

4 In the frst step, the nternal focus group members are asked to dentfy the qualty dmensons about the product. These qualty dmensons can be determned accordng to the past smlar cases from the case lbrary. Table shows the example of the qualty dmensons. The second step s to collect the crtcal ncdents (CIs). The crtcal ncdent approach s used to obtan nformaton from customers about the servce or product they receve. The customer s asked to lst out the postve and negatve examples or elements of the product for the dfferent qualty dmensons. The postve and negatve elements of the product are the crtcal ncdents. The format to acqure the crtcal ncdents s shown as Table 2. The thrd step s to group the CIs nto satsfacton tems (see Fgure 3). For example, the CI and CI2 are group nto the satsfacton tem, whle the CI3 and CI5 are grouped nto satsfacton tem 2. Both the postve and negatve crtcal ncdents are grouped to form the satsfacton tems. The satsfacton tems can be seen as the customer attrbutes, whch are the nput of the house of the qualty. The fnal step of the questonnare s to put the satsfacton tems nto the AHP-based questonnare n order to acqure the relatve mportance of dfferent qualty dmensons and also the lower herarchcal factors (satsfacton tems). The example of the questonnare s shown as Table 3. One of the advantages of the AHP comparng wth smple scorng s that AHP allows the customers gve the drect comparson among the customer attrbutes. Smple scorng wll cause excessve naccuracy, as the customer tends to rate the all the attrbutes wth hgher scores. AHP thus provdes a more obectve udgment on the customer attrbutes. Step : Qualty Dmensons (The nternal focus group lsts the qualty dmensons about product) Step 2: Crtcal Incdents (Let the target customers to lst the postve/negatve crtcal examples under each qualty dmenson) Step 3: Satsfacton Items (The focus group groups the Crtcal Incdents nto Satsfacton Items, whch wll be the customer attrbutes nputted n to the QFD) Step 4: AHP-based Questonnare (The AHP-based questonnare s used for customers to determne relatve mportance of customer attrbutes) Fgure 2. The flowchart to acqure the customer attrbutes Table. Examples of qualty dmensons Qualty Dmensons Meanng and example Performance Prmary product characterstcs, such as the brghtness of the pcture Features Secondary characterstcs, added features, such as remote control Conformance Meetng specfcatons or ndustry standards, workmanshp Relablty Consstency of performance over tme, average tme for the unt to fal Aesthetcs Sensory characterstcs, such as exteror fnsh After the relatve mportance of the customer attrbutes are collected, the product development team can analyze the results by the AHP software tool, Expert Choce. The result of the AHP ratng llustrates the degree of mportance to whch these features nfluence customer satsfacton. 4

5 Table 2. A sample table to acqure the Crtcal Incdents from customers Name of the Product: Propose of the product: Qualty Postve Crtcal Negatve Crtcal Dmensons Incdents /Examples Incdent /Examples Performance Features Other expectatons about the product: Table 3. A Sample AHP Questonnare to get the Qualty Dmenson Qualty Dmenson 2 Qualty Dmenson 3 relatve mportance wth par comparson Qualty Dmenson Qualty Dmenson 2 Qualty Dmenson 3 5. Engneerng Characterstcs Retreval from Case Lbrary After determnng the customer attrbutes and ther relatve mportance, t s necessary to search and retreve hstorcal cases from the case lbrary that are smlar to the problems of a new case. For example, when a manufacturng frm s developng a new techncal drawng pencl, the product development team translates the customer requrements nto customer attrbutes, whch wll be used as the search ndexes to ntate the searchng process. A coarse searchng s done untl the subgroup of techncal drawng pencl s found. After matchng all cases n the subgroup of techncal drawng pencl, the hghest score of the performance mportance rato (PIR) of each customer attrbute s selected to choose the engneerng characterstcs, and then evaluate and adapt the exemplars to determne the optmzed soluton to the new case. After the product s developed, t wll be added to the case bases, whch can be consdered as a self-learnng feature of the system. The flowchart of the case retreval process s shown n fgure 4. Requrements Attrbutes CI CI2 Satsfacton Item Attrbute Retreve PIR Case Bases Engneerng Characterstcs CI3 Satsfacton Item Attrbute 2 Adaptng Exemplars CI4 Evaluton CI5 CI6 Satsfacton Item Attrbute 3 Product Specfcatons Soluton of New case Learnng Fgure 3 Generatng the customer attrbutes Fgure 4 The Generated Process of CBR 5

6 A common method to compare the smlarty between two cases s the Nearest-Neghbour (NN) matchng, whch uses the numercal functon to compute the degree of smlarty (DOS) [20, 22, 25]. However, t s not sutable to mplement DOS nto the QFD framework because ths ratonal comparson method s better for constant number of customer attrbutes [22]. By comparng the DOS of dfferent hstorcal cases, the hghest DOS s used to choose the so-called most smlar case, but there may be some new customer attrbutes that cannot be found n the most resemble case. However, t may be found n other cases. If a ratonal comparson s used, there s only one smlar case found, whch wll lead to some nformaton loss. Therefore, an obect-orented comparson s developed to tackle ths dffculty. In order to have a more obectve comparson between the new customer attrbute and past customer attrbute, the performance mportance rato (PIR) s developed. The PIR ndcates the degree of satsfacton of the customer attrbutes by consderng the performance of the correspondng engneerng characterstcs. PIR s calculated by the followng equaton: PIR = n m = S S T W (3.) where S s the weght of the th engneerng characterstc to the th customer attrbute, T s the target achevng level of th engneerng characterstc, and RI s the relatve mportance of th customer attrbute. The target achevng level of each engneerng characterstcs ranges from 0 to. It wll be compared wth the target level, where the target level s regarded as the best range for the product desgn. It s computed by the followng equaton: T = RV RV max TR TR (3.2) where T s the target achevng level of th engneerng characterstc, RV s the real value of th engneerng characterstc, and TR s the target value of the th engneerng characterstc, and RV max s the allowable maxmum level of the real value. The hghest PIR for each case attrbute s chosen for each new case. Therefore, f all the new customer attrbutes can be matched wth those n the hstorcal cases, at least one case wll be chosen for further adaptaton and evaluaton to fnd the best soluton for the new case. For each customer attrbute, ts mportance of the maor compettors can also be generated from the hstorcal cases. If the new case s not exactly same as the hstorcal cases, the adaptaton s needed. 6. Case Adaptaton and Evaluaton to Determne the Target Value of Engneerng Characterstcs Adaptaton s the second step of the CBR approach. Snce no hstorcal case s ever exactly same as a new one, hstorcal cases must usually be adapted to a new stuaton [5]. After the smlar-case set s chosen, the adaptaton process should be taken to evaluate the best engneerng characterstc from the dfferent cases. And f the new customer attrbutes cannot be found from the hstorcal cases, t should be determned by udgment. After generatng the techncal requrements from the hstorcal cases or by nnovatve udgment, the exemplars can be bult up, and the evaluaton s then done to determne the proper solutons so as to satsfy the customer attrbutes of the new case. 6

7 Evaluaton s the process of udgng the goodness of a proposed soluton. Here, evaluaton s to fnd out the most sutable engneerng characterstcs to the new case. Comparng and contrastng the proposed soluton wth other smlar solutons, dfferences can be spotted. For example, f there are unsuccessful cases smlar to the new case, the decson maker must consder whether the new stuaton has the same problems. Evaluaton can pont out the need for addtonal adaptaton of the proposed soluton. Fuzzy Logc wll be used here to evaluate both the quanttatve and qualtatve data. The qualtatve data s normalzed nto fuzzy term and then evaluated to choose the most sutable level of the engneerng characterstcs. The fuzzy lnear regresson wll be appled n ths research to determne the target value of the engneerng characterstcs. The detals are shown n secton Determnaton of the Target Values of Engneerng characterstcs 7. Current research n ths area Researchers have proposed several methods to mprove the tradtonal QFD usng dfferent methods, for examples, AHP [7], Neural Network [3, 8, 24], knowledge-based system [8, 35], CBR [8, 9,, 3, 30] and expert systems [28,30]. Fuzzy logc s another popular method to help the decson makers determne the target values of engneerng characterstcs. Fuzzy logc can model vagueness. Usually, the voce of customers contans ambguty and dfferent meanngs. Fuzzy logc can descrbe such nformaton provded n terms of symbolc descrptons n lngustc varables by fuzzy numbers [3]. Many researchers have appled fuzzy logc to the determnaton of the target values of engneerng characterstcs. For examples, Vanegas and Labb [3], Km et al. [4], Wang [32], Dawson and Askn [2], Fung et al. [7], Moskowtz and Km [9], and Schmdt [2] have appled fuzzy sets to decde the target engneerng characterstcs levels. But few researchers have attempted to develop a systematc approach to fnd the optmum engneerng characterstcs targets. Many researchers treat engneerng characterstc as an ndependent factor and assume that they are not affected by each other. For example, Vanegas and Labb [3] appled fuzzy sets to determne the target values of engneerng characterstcs. They consdered constrants on cost, techncal dffculty and customer satsfacton, but treated each engneerng characterstcs as an ndependent factor. However, t s well known that engneerng characterstcs are always affected by each other. Some researches have addressed the ssue of constructng quanttatve models of customer preferences and translatng these nto optmal techncal specfcatons [2]. Yoder and Mason [34] proposed usng regresson analyss to model the relatonshp between customer attrbutes and engneerng characterstcs. Other researchers modfed Yoder and Mason s method for determnng the target values of engneerng characterstcs; for examples, Km et al [4] and Askn and Dawson [2]. They have appled lnear regresson to determne the target values of engneerng characterstcs. Fuzzy lnear regresson was ntroduced by Tanaka et al., [26], whch s based on the possblty dstrbuton to model vague and mprecse phenomena usng the fuzzy functons defned by Zadeh s extenson prncple [36]. Fuzzy lnear regresson s a useful tool to support the users n the applcaton of QFD, snce the users must ncorporate both qualtatve and quanttatve nformaton regardng relatonshps between customer attrbutes 7

8 and engneerng characterstcs nto the problem formulaton. 7.2 Determnaton of target values by Fuzzy Lnear Regresson In the proposed hybrd framework, a fuzzy regresson model developed by Km et al. [4] s bascally followed to determne the relatonshp between customer attrbutes and engneerng characterstcs, and also the nterrelatonshp among engneerng characterstcs. Ths model s formulated usng Multattrbute value (MAV) theory. MAV s used to model the customer s preferences assocated wth multple customer attrbutes. Three separate models are developed, namely, CCC model, FCC model and FFF model, as shown n Table 4. The common elements of customer attrbutes and engneerng characterstcs are broken nto three maor components, namely, system parameters that defne the functonal nterrelatonshps between customer attrbutes and engneerng characterstcs and also among the engneerng characterstcs; obectves that are to maxmze the customer satsfacton; and constrants that encapsulate desgn tradeoffs and tradeoffs between other parameters. Table 4. The comparson of dfferent models Model CCC FCC FFF System Parameters Crsp Fuzzy Fuzzy (wth 0%, 20% and 30% flexblty) Obectves Crsp Crsp Fuzzy Constrants Crsp Crsp Fuzzy In the CCC Model, t s assumed that there s no fuzzness and vagueness. Instead, ths model requres that all customer attrbutes are optmzed and the desgn tradeoffs are seen as hard constrants wth no leeway. The FCC model ncorporates fuzzness n the system by defnng the model components usng fuzzy system parameters, where the spread of each system parameter s ntroduced to the system parameters. In the FFF model, t s assumed that there are fuzzness n the system parameters, obectves and constrans, and the fuzzy flexblty s appled to the constrants of engneerng characterstcs. Detals of these models are elaborated below. Model : CCC model In ths model, all the system parameters, obectves and constrants are assumed to be crsp. It means that there s no fuzzy data n the components n ths model, all the data nputted nto the QFD are well defned, the decson beleve that there does not nclude vagueness n the data. The process of determnng target values for engneerng characterstcs n QFD can be formulated as an optmzaton problem. Let y = customer percepton of the degree of achevement of customer attrbute, =, m, x = the target value of engneerng characterstc, =, n f = functonal relatonshp between customer attrbute and engneerng characterstcs, = ;... ;m,.e., y = f (x ;... ; x n ), g =functonal relatonshp between engneerng characterstc and other engneerng characterstcs, = ;... ; n,.e., x = g (x ;... ; x - ; x + ;... ; x n ): In the crsp case, there s an essental assumpton that the system parameters and constrants all have crsp lnear relatonshps. All the functons are assumed to be of the followng format. Y (7.) = α x + α 2x αnx + α n 0 8

9 Based on ths assumpton, total lnear regresson methods are used to determne the lnear functonal relatonshps,.e. determne the values of α 0,, α n... Once the parameters are estmated, Multattrbute value (MAV) theory s used to defne the crsp obectves. MAV functon s used to generate the overall level of customer satsfacton. Z = m = w V ( y ) (7.2) where w are scalng constants representng the relatve mportance of customer attrbutes, s the ndvdual value functon for customer attrbute. Subect to y = f x,..., x ) (7.3) ( n x = g x,... x ) (7.4) ( n Model 2: FCC Model V ( y ) In ths model, the nputs are fuzzy parameters, but the obectves and the constrants are crsp. In the model, we assume that there s some vagueness n the system parameters. Let, y = f x,..., x ), =,..., m x = g ( m ( x,..., x n ), =,..., n The bar over a symbol ndcates that the expresson or varable s fuzzy. The frst maor source of vagueness s between engneerng characterstcs and customer attrbutes, and also the nterrelatonshps among engneerng characterstcs. Ths can be accounted for by alterng the crp equaton nto a fuzzy lnear functon: Y = 0 α x + α 2x αnxn + α = αx Where Y s the dependent varable and X a vector of the ndependent varables. α =( α, α 2, α n ) are fuzzy parameters, and can be denoted n vector form as α ={(α m, α m2, α mn ), (α s, α s2, α sn )}. Here, α m s the center value of α, and α s s the wdth (or spread) of α around α m. A symmetrc trangle membershp functon s employed for fuzzy parameters n ths paper. The centre value descrbes the most possble value of α whle the spread represents the precson of α. Ths allows for vagueness n the functonal relatonshps. The spread can be found usng Tanaka s lnear programmng formulaton [26]. Subect to, Mnmze ( α, α s s 2, α sn ), y = f( x,... xn), L L y f ( x,... xn), y R f ( x,... xn), x = g ( x,..., x L L x g ( x,..., x, x, x R R x g ( x,..., x, x R f +,..., xn), +,..., xn), +,..., xn), =,..., m =,..., m =,..., m =,..., n =,..., n =,..., n (7.5) (7.6) (7.7) (7.8) (7.9) (7.0) L R L R Where,, f (and g,, ) are f real lnear vectors of mean values and left and rght spreads of the estmated fuzzy parameters of (and L R L R g ), and f,, f (and g,, ) are also real lnear vectors of mean values and left and rght spreads of Model 3: FFF Model f y (and x ). In ths model, the system parameters, obectves and constrants are fuzzy, whch ndcates that there s some vagueness or mprecson n system parameters, obectves and constrants. The fuzzy flexblty s ntroduced nto the system g g g f g 9

10 parameters. The flexblty s a number set between 0% and 00%, whch sgnfyng the flexblty allowed by wdenng the soluton set. The model employs a fuzzy obectve functon and attempts to optmze the overall degree of customer satsfacton derved from multple performance characterstcs. The fuzzy optmzaton scheme has been proven to be usually n modelng multple crtera decson-makng problems nvolvng human percepton, and can be posed as an equvalent crsp optmzaton problem as follows [37,38]. Fnd x, x 2,,x n, whch maxmze λ (7.) subect to each membershp functon λ (X ), µ Y =,,m, Where λ ( 0 λ ) represents the overall membershp functon value. Subect to, λ µ ( XY, ), f λ µ ( XY, ), L L y f ( x,...), xn R R y f ( x,...), xn L L x g ( x,..., x, x +,...,), xn R R x g ( x,..., x, x +,...,), xn G =,..., m =,..., n =,..., m =,..., m =,..., n =,..., n (7.2) (7.3) (7.4) (7.5) (7.6) (7.7) The functonal relatonshps may be used as strct (crsp) or flexble (fuzzy) constrants. When the constrants are strct, the volaton of any constrant by any amount renders the soluton nfeasble. Consderng the fact that, n practce, the estmated functonal relatonshps would probably be mprecse, permttng small volatons would be more realstc. Ths can be done by employng fuzzy (flexble) constrants. The membershp functon of a fuzzy constrant arsng from S ( X ) = as (assumng a lnear form): µ ( X ) = S S ( X ) b d b s constructed Where b s the average of the th engneerng characterstcs, and d =assgned flexblty *b. We could obtan dfferent levels of flexblty between engneerng constrans (.e. 0% and 20%) and optmum solutons are found after carryng out a MINIMAX formulzaton whereby the spreads are mnmzed by maxmzng the overall satsfacton. In the fuzzy model, λ denotes the overall degree of customer satsfacton when a fuzzy obectve functon s employed and the whole obectve of the formulaton s to maxmze ths quantty. 8. Illustrated Example 8. Requrements Acquston ABC Statonery Company s gong to develop a techncal drawng pencl. The product development team s formed for ths proect. They are from dfferent functonal departments, namely, marketng, engneerng desgn, producton department, and etc. They are also the members of the nternal focus group. They lst out all the qualty dmensons related to the new product n both performance and aesthetcs aspects. A sem-opened questonnare s used to collect the customer expectatons on the new product. The customers are asked to lst out the crtcal ncdents (both postve and negatve cases) about the product. They are lsted as shown n Table 5. Accordng to the customer requrements, the product development team translates the crtcal ncdents to the satsfacton tems, and then to customer attrbutes. In ths llustraton, under the performance dmenson, we get four customer attrbutes, namely, easy to hold, does not smear, pont lasts, and does not roll. 0

11 Table 5 The questonnare to acqure the Crtcal Incdents Name of the product: Pencl Model 23 Propose of the Product: Techncal Drawng Qualty Postve cases Dmenson Performance easy to hold Aesthetcs 2 doesn t roll 3. smear 4. Pont lasts beautful 2 Modern Importance determnaton In ths step, the customer requrements are bult nto a herarchy structure (see Fgure 5). Negatve cases Need to sharpen the pencl frequently 2 Easy to get drty 2 3. Another requrements about the product: Table 6 AHP ratngs of the qualty dmensons to product (Model 23) Performance Aesthetcs Performance 7 Aesthetcs /7 Table 7 AHP ratngs of the customer attrbutes of performance Easy to Hold Smear Pont Lasts Roll Easy to Hold Smear /4 9 3 Pont Lasts /7 /9 5 Roll /5 /3 /5 By the ad of the software tool, EXPERT CHOICE, we can get the relatve mportance (weghtngs) of the four customer attrbutes of performance dmenson, as shown n Table 8. Performance Easy to hold Satsfacton on Model 23 Aesthetcs Streamlne Table 8 Relatve mportance (weghtngs) of each attrbutes customer attrbute of the performance dmenson Easy to Hold Smear Pont Lasts Does not Roll smear Applcable Length Weght Pont lasts roll Attrbute 5 Attrbute 6 Fgure 5. The herarchy structure of customer requrements The AHP-based questonnare s then developed to determne the customer mportance. Table 6 ndcates the ratngs between the qualty dmensons of aesthetc and performance attrbutes. Table 7 shows the AHP ratngs results of the customer attrbutes of the performance dmenson. 8.3 Determnaton of the engneerng characterstcs and ts symbols After the determned customer attrbutes are then used as the searchng ndex to search the best ft from the hstorcal cases n order to fnd the correspondng engneerng characterstcs. In ths research, the Performance Index Raton (PIR) method s used. The PIR ndcates the degree of satsfacton of the customer attrbutes by consderng the performance of the related engneerng characterstcs. Each PIR of customer attrbute s

12 calculated by Equaton 3., From the prevous secton, we got four customer attrbutes to retreve the engneerng characterstcs from the case lbrary. Accordng to the PIR, we fnd two engneerng characterstcs from case (Easy to Hold, and Pont Lasts), and one engneerng characterstc from case 2 ( Roll). However, we cannot fnd the same customer attrbute ( Smear) from the hstorcal cases (see Table 9). The correspondng engneerng characterstcs found from each case are shown as Table 0. Table 9. The PIR of the four customer attrbutes attrbute Easy to Hold Smear Pont Lasts Does not Roll PIR Case No. Not found n the hstorcal cases. 2 engneerng characterstcs, and the nterrelatonshp between the engneerng characterstcs are shown by the symbol. Now, we determne the target value of each system parameter, and the overall customer satsfacton of the dfferent models. By applyng the dfferent optmzaton functons of the overall customer satsfacton, and constrants to the equatons, we can get the overall customer satsfacton. The defntons and the formulas used n the models can be summarzed as n Tables and 2. Table. The equatons appled n dfferent Models Model Obectves Constrants CCC Eqs. (7.2) (7.3), (7.4) FCC Eqs. (7.2) (7.5)-(7.0) FFF Eqs. (7.) (7.2)-(7.7) Table 0. Correspondng engneerng characterstcs and ts symbols Case Tme between sharpenng CA: Pont last CA2: Roll Case 2 CA: Easy hold to Lead dust generated Shape Mnmal erasure resdue Length of pencl Tme between sharpenng Shape Mnmal erasure resdue Now, by ntegratng the two cases and the new customer requrement, we can generate an exemplar to show the relaton between the new customer attrbutes and the engneerng characterstcs, whch s shown n Fgure 6. In the proposed framework, the relatonshp between the customer attrbutes and Table 2 Result of dfferent models Models Characterstc CCC FCC FFF (0%) FFF (20%) Y Y Y Y X X X X4 3 X Z λ

13 EC Length of pencl EC2 Tme between sharpenng EC3 Lead dust generated EC4 Shape EC5 Mnmal erasure resdue Degree of Importance () Our Company s Company A s Company B s Company C s Mn Max CA Easy to hold a a CA2 smear a a a CA3 Pont lasts a a a a CA4 roll a a Measurement Unts mm Mns g g Our past smlar product Company A s Product Company B s Product Company C s Product Mnmum Maxmum For the EC4, the shape of the pencl, where ndcates hexagon, 2 ndcates round, and 3 ndcates octagon. Fgure 6. Example of QFD fgure 3

14 From the results, the hghest Z (overall customer satsfacton) can be obtaned n CCC model. However, n determnng the target value of the CCC model, we found that we have to determne fve target values of the fve engneerng characterstcs respectvely, but there are only three constrants functons. The results may be fluctuated, and t s dffcult to the decson maker to decde whch one s the most optmstc soluton to the model. On the other hand, f the number of constrants s greater than the number of engneerng characterstcs, the optmstc soluton can be determned, as proved by Km [4] and Kumar [6]. Snce the solutons n the CCC model, the relatonshps among the engneerng characterstcs and the relatonshps between the customer attrbutes and the engneerng characterstcs have uncertantes and mprecson, fuzzy set s more approprate to be used to determne the target values of the engneerng characterstcs. After allowng for 0% flexblty n the engneerng constrants n FFF model, λ s assessed usng the SOLVER n Mcrosoft Excel. As shown n Table 2, ths resulted n an ncrease n overall satsfacton levels. Flexblty levels are set on the parameters subectvely, dependng on the credblty of the assessed parameters, technologcal feasblty and engneerng tolerances. Flexblty allows for vagueness to be accounted but too much flexblty may deterorate the valdty of the system. Hence f the margnal rate of ncrease n customer satsfacton falls on ncreasng flexblty, t s revealed that the flexblty s too much (Kumar, 200). In ths llustraton, t was deemed that 20% was the maxmum level of flexblty. The best result obtaned was at 20% flexblty and by counter checkng the values, t can be sad that the optmzed desgn values can be acheved n practce. By comparng the results generated usng the FCC and FFF models, the results generated from the FFF model are better as expected because there s a hgh degree of vagueness n the system. Takng account of the fuzzness n uncertan envronments where relatonshps are not very dscrete, usng fuzzy regresson yelds more relable parameter estmates thereby allowng for better optmzaton results. On the other hand, comparng the methodologes ntroduced n ths paper n determnng the target values of the engneerng characterstcs and the tradtonal QFD methodology, we can conclude that the man advantage s that the tradtonal QFD methodology treats all the parameters of the HOQ as crsp varables (the CCC Model), however, f the data are fuzzy and mprecse, the result wll not reflect the real stuaton. If the decson maker thnks that there s suffcent and relable nformaton to determne the product specfcatons, then the CCC model s approprate to use. However, a certan degree of fuzzness and mprecson always exst n the data nput to the QFD, and thus the fuzzy approach s recommended to determne the product specfcatons. 9. Concluson QFD s wdely used n the dfferent manufacturng companes, but the tradtonal methodology has shortcomngs. In ths paper, a hybrd approach s proposed as a modfcaton to the QFD. A systematc questonnare approach s ntroduced to acqure the customer requrements. Ths questonnare can help the manufacturng companes to acqure the customer requrements more effectvely. The product development team s recommended to determne the engneerng characterstcs by the ad of case based reasonng 4

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