Customer Needs and Technology Analysis in New Product Development via Fuzzy QFD and Delphi

Transcription

1 Customer Needs and Technology nalyss n New Product Development va Fuzzy QFD and Delph ZON-YU LEE 1, CHUNG-CHE PI 2 1 Graduate Insttute of Project Management, 2 Department of r Transportaton Kanan Unversty No.1 Kanan Road, Luzhu Shang, Taoyuan TIWN 1 zylee.mt88g@mal.knu.edu.tw, 2 ccpa@mal.knu.edu.tw CHIN-FU YNG Department of Industral Engneerng and Management Natonal Chao Tung Unversty No Unversty Road, Hsnchu 300 TIWN cfyang@cc.nctu.edu.tw bstract: - The advanced technology n terms of nteracton between human and machne as well as consderable Emoton Interacton Products (EIP) promoted by companes have facltated a new era. For example, voce recognton and synthess, hand wrtng and hand sgnal dentfcaton and vrtual realty technologes have enabled the connecton between human and computer nteracton. Moreover, the applcaton s expended from PC, Personal Dgtal ssstant (PD) to moble phone. Future technologcal products are expected to respond human emoton and mprove human lfe qualty. Ths research centers on EIP and compares 20 customer needs and 14 technologes usng Qualty Functon Deployment (QFD). ddtonally, experts and professonals are nvted to express ther opnons towards EIP through Fuzzy Delph Method (FDM) to dscuss the dmensons and crtera to be fulflled. The results demonstrate whch customer needs and technologes should be heavly focused, and contrbute to desgn and manufacture EIP effectvely. Key-Words: - Emoton Interacton Product (EIP), Human Machne Interacton, Qualty Functon Deployment (QFD), Fuzzy Delph Method (FDM) 1 Introducton Numerous electronc frms have devoted ther attenton to the research and development of Emotonal Interacton Product (EIP). The ncreasng dgtal devces become avalable n the market to emphasze artfcal ntellgence as well as emotonal nteracton, whch boosts more purchasng. More frms also produce technologcal toys wth the characterstcs such as human-lke appearance, hgh nteracton, multple reacton, educaton and emotonal ntellgence. It seems a promsng market n the future. The market scale of technologcal toys and electronc pets was up to 38 bllon US dollars n 2008, whch frstly surpassed tradtonal toys n a scale of 36.4 bllon US dollars. Electrc Pets provde a new development and stress on emoton nvolved nstead of fun based cheap products. It s estmated that artfcal ntellgence would lead the future trend. For example, Foxconn also produced a popular dnosaur toy named Pleo n 2007 and expect to brng 30 bllon revenue. In ths context, Foxconn set up new department of robot and well prepare to take advantage of the great busness opportunty. Besdes Foxconn, BenQ, MS and SUS also partcpate n the robot ndustry and hopefully create another dversty strategy other than declnng OEM. Tawanese hgh-tech ndustry has converted from OEM to ODM and OBM, they have abltes to produce the major components for EIP, such as semconductor, communcatons kt, nformaton kt, electronc components and materal except the crtcal core technology. There s a need for Tawanese hghtech ndustry to assess the optmal soluton for EIP. The am of the research s usng QFD to transform customer needs to technology, and undertakng Fuzzy Delph Method (FDM) to analyze EIP n terms of relatve mportance among dmensons and crtera. The results can contrbute to polcy decson makng and product development strategy. E-ISSN: Issue 1, Volume 9, January 2012

2 QFD s based on the concept of the House of Qualty. Several studes used HP to determne consumer s weght n terms of relatve mportance (rmacost et al. 1994; Park & Km 1998) [[1], [24]]. There are also some surveys addressed fuzzy measures to represent mprecse decson process (Chan et al. 1999; Vanegas & Labb 2001; Chan & Wu 2005; Fung et al. 2006) [[4], [32], [3], [10]]. Other researches adopted fuzzy set, fuzzy computaton or defuzzy technque to solve the mprecson and complex n QFD (Khoo & Ho 1996; Zhou 1998; Wang 1999; Km et al. 2000; Shen et al. 2001; Kahraman et al. 2006; Chen et al. 2006) [[16], [37], [33], [17], [27], [14], [6]]. Karsak (2004) [[15]] and Chen & Weng (2006) [[5]] utlzed lngustc scales to measure the mportance level, relatonshp ntensty and correlaton ntensty, and subsequently created a fuzzy mult-goal programmng to dentfy mplementaton of desgn requrements. Ths study assocates QFD wth FDM to survey expert opnons towards EIP and ther mportance based on customer needs and technology assessment. Secton 2 llustrates the percepton n relaton to emotonal nteracton and lterature revew. Secton 3 descrbes research method. Secton 4 establshes research model. Secton 5 explans the analyss and results n terms of customer needs and technology respectvely. Secton 6 concludes ths research and suggestons are also ncluded. 2 Lterature Revew The frst part revews emotonal nteracton percepton and related ssues. It s followed by comparng and analyzng the assessment crtera of EIP n terms of customer needs and technology. The rest of ths secton dscusses current market scale of technologcal toys. 2.1 Emotonal nteracton percepton Developng consumer electronc products wth emoton lke human has been the major goal n the area of nteracton between people and computer. Laurel (1990) [[18]] and Sproull et al. (1996) [[29]] ponted out nterface wth human nature can convey easy-to-use and comfortable feelng easly, and rase user satsfacton throughout the nteracton. Besdes complex computaton va artfcal ntellgence, Socal Cues provdes another alternatve to be easer and more feasble. Ths technology s used to make people feel vrtual realty and human-lke feature of dgtal product n mddleware desgn. Many studes showed people not only regard computer as a tool, but also another nteracton of socal emoton (Reeves & Nass 1996) [[26]]. The hot topcs n human-computer nteracton have shfted from the usage to socal support and emoton (Pcard & Wexelblat 2002; Short et al. 1976) [[25],[28]]. Through the feelng of vrtual realty, computer can facltate socal relatonshp rather than humancomputer relatonshp, and can be helpful for the closeness between people and computer. There s also more emotonal product research n the area of product desgn, such as nternatonal conference on desgn and emoton, DPPI (conference on desgnng pleasurable products and nterfaces), emotonal engneerng symposum and so forth. Some study argued that the emotonal response and nteracton wth users not only brng people dfferent feelng, but also nfluence ther work performance and ablty of solvng problem (Desmet 2002; Overbeeke et al. 2002; Wensveen et al. 2000) [[9],[23],[35]]; Isen 1993; Norman et al. 2003; Norman 2004) [[12],[21],[22]]. Therefore, the emotonal feelng towards product can be connected wth usablty and performance (Norman et al. 2003; Tractnsky 1997) [[21], [31]]. The role of product desgn has gone beyond pure tool, and becomes part of human lfe. Users have created dfferent expectaton on EIP on the bass of market observaton. 2.2 The assessment crtera for customer needs and technology EIP apples a number of hgh-tech technologes on the functons and appearance of product. For example, electronc toy (named IBO) created by SONY, model wth dressng concept can functon to show users feelng desgned by Phlp (Bubelle & Frson), electronc dnosaur (named PLEO) nvented by UGOBE, talkng Mckey Mouse robot made by SEG, emotonal nteracton mouse (named MD300) nvented by BenQ, Electronc Drum Kt Shrt nvented by Thnkgeek, nteractve Nabaztag produced by Volet, and Emoton Sensor necklace desgned by VIBE. 2.3 The market scale of technologcal toys Tawanese frms provde approxmately 90% medum and low end toy IC, and they stll reman actve under fast market change. From the vewpont of the sales of components and IC, Tawan contrbuted 1 bllon US dollars n toy ndustry n Currently the proporton of electrc toy lke Pleo, SD card, W-F, sensor and servo motor of E-ISSN: Issue 1, Volume 9, January 2012

3 ICT s hgher. Tawanese frms can foster the development of technologcal toy or electronc toy based on ther electronc manufacturng ablty and experence on tradtonal toys. It s expected there are more hgh-tech frms partcpate ths market and develop hgh end components n the future. 2.4 Summary In terms of customer needs, 5 dmensons and 20 crtera have been nduced by above revew. Namely, fve dmensons of entertanment, educaton & learnng, perceptual feedback, health & securty and operaton mechansm. Twenty crtera nclude vvd nteracton, novel content, seral story, personfcaton behavor, learnng & growth, dgtal vdeo, memory aded, multlngual translaton, voce recognton & response, scrpt dsplay, dagram nterface, warm reply, rsk sensor, stress down devce, emoton management, physologcal test, portablty, convenent operaton, frendly desgn and dentfcaton. Furthermore, there are four dmensons and fourteen crtera n dmenson of product manufacture technology. The four dmensons contan appearance desgn, humanmachne nteracton, artfcal ntellgence and communcatons technology. The fourteen crtera consst of appearance desgn, human engneerng, physcal trat, facal expresson sensor, posture recognton, voce dstngushng, touch percepton, envronment percepton, temperature percepton, emoton recognton, emotonal expresson, learnng ntellgence, natural language process and wreless communcatons. 3 Research Method The constructon of research method starts wth lterature revew whch bulds up a comprehensve understandng towards prevous research, and then develops QFD model between customer needs and technology. Questonnare uses Fuzzy Delph Method (FDM) to collect experts opnon n relaton to the relatve mportance among varous customer needs of EIP, and proceed correlaton analyss between customer needs and technologes. 3.1 Lterature revew To ensure the central theme of ths study, theoretcal underpnnng s carred out to understand the characterstcs of EIP and collect valuable experence and useful nformaton from experts. The analytcal framework s establshed wth the help of expert ntervew, and combnes 20 customer needs wth 14 technology requrements. There are 5 fundamental steps for lterature revew, namely, generalzaton, nducton, abstract, crtcsm and suggeston. The source from prevous research can be categorzed nto prmary nformaton, secondary nformaton and bblographc nstruments. 3.2 QFD Qualty Functon Deployment (QFD) was ndcated by Mtsubsh s Kobe Shpyard Ste n Japan to promote qualty from 1966 to Bossert (1991) [[2]] regarded QFD as a structural method to establsh organzatonal qualty nsttuton and more understand customer needs. QFD can convert customer needs to approprate producton technology n every sngle product development stage. It also combne R&D, manufacture, management, customer needs, engneer features and product desgn qualty to deploy product component qualty as well as engneer elements (Sullvan 1986; Wasserman 1993) [[30],[34]]. Hauser & Clausng (1988) [[11]] presented that QFD s a structural approach to assocate customer requrements wth product development. They especally outlned House of Qualty s the basc desgn tool of QFD. Based on ths concept, product desgn should reflect customer needs. Therefore, busness professonals ncludng marketng, R&D, manufacturng should cross-department plan and communcate customer needs through the House of Qualty at very begnnng stage of product development. In other words, QFD uses systematc method to proceed all knds of processes such as desgn, component, manufacture and costng. The transformaton s undertaken by bnary matrx technology to facltate customer needs orented product development. No matter what knd of product s created by ndustres, the end users are customers. Only by lstenng to the voce of customers, organzatons can ncrease compettve advantage, sustan ther busness and grasp busness opportuntes. QFD can ntegrate customers voce and requrements wth the desgn, component, component, manufacture and cost processes, so that engneers can understand the assocaton and mportance easly and quckly. Bossert (1991) [[2]] explaned the structure of House of Qualty as shown n Fgure1. The essental structure of House of Qualty s prncpally dvded nto sx parts. Namely, Voce of Customer (VOC), Voce of Engneerng (VOE), Correlaton Matrx, Correlaton nalyss, Benchmarkng and Prorty. E-ISSN: Issue 1, Volume 9, January 2012

4 VOC Correlaton nalyss VOE Correlaton Matrx Prorty Benchmarkng Fgure 1. Structure of House of Qualty (Bossert, 1991) (1) VOC: t descrbes the requrements and expectatons of customers. The nformaton can collect from questonnare and ntervew. (2) VOE: t s the strategy or technology servce planned, communcated and n tune wth related departments wthn organzaton. (3) Correlaton Matrx: t nterprets the relatonshp between VOC and VOE. (4) Correlaton nalyss: the relatonshp among VOE. (5) Benchmarkng: compare n-house product wth other compettors. (6) Prorty: t can understand whch technologes are more crtcal through the rankng of VOE, and use ths bass to ntroduce new technology and resource allocaton. 3.3 Fuzzy Delph Delph was proposed by Dalkey and Helmer (1963) [[8]] as a method to express experts opnons systematcally. Murray, et al. (1985) [[20]] frst combned Fuzzy Set wth Delph. Ishkawa et al. (1993) [[13]] utlzed the concepts of accumulated frequency dstrbuton and fuzzy ntegral to ntegrate experts opnons wth fuzzy set ttled Fuzzy Delph Method (FDM). FDM can be one of crtera measurement tools wth more advantages than tradtonal Delph: (1) decrease the tmes of nvestgaton; (2) express experts opnons more completely; (3) the experts knowledge can be more ratonal and meet user requrements through the applcaton of fuzzy theory and (4) more economc n terms of tme and cost. Bascally, there are three procedures to adopt FDM: (1) establsh a set of assessment factors to nfluence decson-makng; (2) collect experts or decson groups opnons; (3) calculate assessment value usng FDM. Conventonal Delph Method s a predcton approach based of experts judgments. It belongs to the area of group decson and s subjectvely forecasted by experts judgments through questonnare survey and meetng (Lnstone 1978) [[19]]. The basc prncples are based on structural nformaton dstrbuton, anonymous group decson and experts judgments. FDM flters assessment crtera for the purpose of understandng the agree level of experts, and then prortze t usng nalytcal Herarchy Processng (HP). HP has been wdely used to measure varous ssues recently. Its advantage s to decompose complcated problems nto logc and herarchcal framework carefully. HP also can quantfy desred goals, abstract experence and socal value by way of comparson, and flter useful questonnare through consstent test to control valdty. Researchers ntegrate and sort the opnons from experts every tme, and feedback to experts for ther new judgments to come up wth new statement. Repeatng ths process many tmes, the qualty of experts judgments can be acheved. However, conventonal Delph approach usually spends a long tme wth a hgh cost durng the process of collectng and ntegratng experts opnons repeatedly. t tmes questonnares wth low return rate or unable returned wll nfluence the qualty of judgments, even twst experts ntentons. Ths s one of the barrers of tradtonal Delph. In ths context, ths research adopts Fuzzy Delph Method (FDM), whch has hgher stablty wth small sample and trangle fuzzy set used by Huang et al (2001) to overcome the drawback of Delph. Trangle fuzzy set s used to represent the fuzzy functon of decson consensus. It also uses the maxmum and the mnmum of average functon as the two ponts of trangle fuzzy set stands for the common vew of experts. Geometrc average represents the majorty consensus by decson makers, the defntons are as follows. U : The maxmum of decson consensus L : The mnmum of decson consensus X 0 : Geometrc average Fgure 2 shows the trangle fuzzy functon of decson consensus. Ths trangle functon covers the opnons toward ssue by decson group. The hghest pont means the greatest evaluaton by decson group ( U pont). In other words, there s no possblty to have better evaluaton than ths pont. The lowest pont means the smallest evaluaton by decson group ( L pont). In other words, there s no possblty to have worse E-ISSN: Issue 1, Volume 9, January 2012

5 evaluaton than ths pont. Therefore, the evaluaton by decson group s wthn the hghest and the lowest ponts. The advantages of FDM contan ndvdual opnon would be consdered, decrease the repettve survey and reduce tme and cost. The formulas of fuzzy trangle functons are as follows. ~ µ = ( L, M, U ) (1) L = Mn( X ), = 1,2,3... n (2) M = ( X (3) 1/ n 1, X 2,..., X n ) U = Max( X ), = 1,2,3... n (4) ( x L) /( M L) IF L x M ~ µ ( x) = ( U x) /( U M ) IF M x U (5) 0 otherwse µ ~ ( x) 1 0 L M U x Fgure 2: Trangle fuzzy functon stands for decson consensus means evaluaton crtera, means expert, ~ µ means fuzzy set of mportance, X means the evaluaton towards crtera by th expert, L means the mnmum evaluaton towards crtera, M means the geometrc average evaluaton towards crtera, U means the maxmum evaluaton towards crtera. 3.4 Fuzzy lngustc varable It s qute complex and dffcult to dentfy and state fuzzy stuaton by tradtonal quantfcaton methods, so that t s essental to have artfcal language varable n fuzzy envronment (Zadeh 1975) [[36]]. µ ~ ( x) 1 Fuzzy meanng varable can correspond dfferent meanngs by human language. The objectve of fuzzy meanng s to measure the ntensty of preference towards crtera by partcpants. That s, the measurement of nomnal scale s represented by the meanng ntensty of fuzzy theory. The scale can be classfed nto 5 levels, namely, low relevant, slghtly low relevant, medum relevant, slghtly hgh relevant and hgh relevant. Ths research even uses 9 pont scale to be more precse, namely, very low relevant, lower relevant, low relevant, slghtly low relevant, medum relevant, slghtly hgh relevant, hgh relevant, hgher relevant, very hgh relevant. The dstrbuton of membershp functon for 9 pont scale s shown n Fgure Fgure 3: Membershp functon of 9 pont scale x E-ISSN: Issue 1, Volume 9, January 2012

6 The number calculated by above procedures s stll fuzzy set. To compare the correlaton rankng among crtera convenently, the procedure of Defuzzy s requred. Therefore, ths research adopts Center of rea (CO) to transfer fuzzy set to Nonfuzzy Value. That s, ths non fuzzy synergy s so-called the value of BNP (Best Nonfuzzy Performance) for the purpose of rankng. In terms of ( LR, UR, MR ), the computaton of BNP s as follows (Chou & Tzeng, 2002)[[7]]. Fnally, accordng to the results of above calculaton, nput QFD model for rankng crtera and ensure ther correlaton. BNP = [( UR-LR ) + ( MR-LR )]/ 3 + LR (6) 4 Research Methodology Ths paper s based on research objectves to ndentfy drecton and focus, and ntegrate lterature revew and chosen research methods to establsh research methodology. Secton 4.1 explans how to organze measurement crtera for EIP. Secton 4.2 llustrates the processes of research methodology and provdes the operaton bass for secton 5 accordngly. 4.1 Identfyng assessment crtera Several technologes can be appled on EIP. Through the analyss done n secton 2 n terms of customer needs and technology, fve dmensons and twenty crtera are obtaned. That s, fve dmensons nclude entertanment, educaton & learnng, perceptual feedback, health & securty and operaton mechansm. Twenty crtera consst of vvd nteracton, novel content, seral story, personfcaton behavor, learnng & growth, dgtal vdeo, memory aded, multlngual translaton, voce recognton & response, scrpt dsplay, dagram nterface, warm reply, rsk sensor, stress down devce, emoton management, physologcal test, portablty, convenent operaton, frendly desgn and dentfcaton. In terms of producton technology, four dmensons and fourteen crtera are acqured. The dmensons contan appearance desgn, human-machne nteracton, artfcal ntellgence and communcatons. The crtera ncludes appearance desgn, human engneerng, physcal trat recognton, facal expresson sensor, posture recognton, voce dstngushng, touch percepton, envronment percepton, temperature percepton, emoton recognton, emotonal expresson, learnng ntellgence, nature language process and wreless communcatons. 4.2 Establshng research model Ths model s frst concluded by lterature revew and experts suggestons to have a set of crtera. QFD, fuzzy theory and fuzzy lngustc scale are then used to conduct FDM based questonnare. We combne ths questonnare wth trangle functon of experts evaluatons to buld up fuzzy weghts toward varous crtera. The last step s usng QFD model to analyze crtera measurements and correlaton rankng as shown n Fgure Fuzzy Weght of VOC 20 VOC Crtera 14 VOE Crtera Fuzzy Theory QFD Correlaton nalyss between VOC and VOE Correlaton nalyss among Technologes Fgure 4. Research Model 5 Emprcal nalyss ccordng to the research model bult n secton 4, ths secton demonstrates the emprcal analyss. Frstly, we use QFD and FDM through questonnare survey to consult 10 experts n the area of EID. The mportance level and consensus regardng dmenson and crtera of EID s acqured, and then rank them n E-ISSN: Issue 1, Volume 9, January 2012

7 terms of customer requrement and technology wth four steps. Step 1: sort and analyze fve dmensons and four crtera n terms of functon and feature by returned questonnare. Step 2: analyze and transform the fuzzy weght of twenty crtera. Step 3: use QFD to dscuss the correlaton analyss and rankng between customer requrements and exstng technology. Step 4: analyze the correlaton between technologes. 5.1 Fve Dmensons Through the FDM and questonnare to 10 experts we regard the mportance of fve dmensons ncludng entertanment, educaton & learnng, perceptual feedback, health & securty and operaton mechansm. The fuzzy weght (W ) and defuzzy value (S ) are calculated based on expert s answers. The results show the dmenson of perceptual response has the hghest score (74.4), whle the dmenson of educaton & learnng has the lowest one (47.5). s a result, the perceptual response s hghly emphaszed and followed by entertanment. On the other hand, educaton & learnng s less mportant. The calculaton s shown n Table 1. Table 1. nalyss of fve dmensons Educaton & Perceptual Health & Operaton Dmenson Entertanment Learnng Feedback Securty Mechansm Expert No. L M U L M U L M U L M U L M U W (fuzzy weght) S (Defuzzy value) P.S.: the range of s from 1 to 5 and stands for each dmenson. L: mnmum, M: medum, U: maxmum Entertanment Dmenson Through the FDM and questonnare to 10 experts we regard the mportance of 4 crtera n entertanment dmenson. The crtera nclude vvd nteracton, novel content, seral story and personfcaton behavor. The fuzzy weght (W ) and defuzzy value (S ) are calculated based on expert s answers. The results show the personfcaton behavor has the hghest score (69.6), whle seral story has the lowest one (59.8). s a result, the personfcaton behavor s hghly emphaszed and followed by novel content. On the other hand, seral story s less mportant. The calculaton s shown n Table Educaton & Learnng Dmenson Through the FDM and questonnare to 10 experts we regard the mportance of 4 crtera n educaton & learnng dmenson. The crtera nclude learnng & growth, dgtal vdeo, memory aded and multlngual translaton. The fuzzy weght (W ) and defuzzy value (S ) are calculated based on expert s answers. The results show the dgtal vdeo has the hghest score (58.2), whle multlngual translaton has the lowest one (48.6). s a result, the dgtal vdeo s hghly emphaszed and followed by learnng & growth. On the other hand, multlngual translaton s less mportant. The calculaton s shown n Table 3. E-ISSN: Issue 1, Volume 9, January 2012

8 Expert No. Crtera Table 2. Crtera for entertanment dmenson Personfcaton Vvd Interacton Novel Content Seral Story Behavor L M U L M U L M U L M U W (fuzzy weght) S (Defuzzy value) P.S.: the range of s from 1 to 4 and stands for each crtera of entertanment dmenson. L: mnmum, M: medum, U: maxmum Expert No. Table 3. Crtera for educaton & learnng dmenson Multlngual Crtera Learnng & Growth Dgtal Vdeo Memory ded Translaton L M U L M U L M U L M U W (fuzzy weght) S (Defuzzy value) P.S.: the range of s from 1 to 4 and stands for each crtera of educaton & learnng dmenson. L: mnmum, M: medum, U: maxmum Perceptual Feedback Dmenson Through the FDM and questonnare to 10 experts we regard the mportance of 4 crtera n perceptual feedback dmenson. The crtera nclude voce recognton & response, scrpt dsplay, dagram nterface and warm reply. The fuzzy weght (W ) and defuzzy value (S ) are calculated based on expert s answers. The results show the warm reply has the hghest score (75.8), whle scrpt dsplay has the lowest one (52.7). s a result, the warm reply s hghly emphaszed and followed by voce recognton & response. On the other hand, scrpt dsplay s less mportant. The calculaton s shown n Table 4. E-ISSN: Issue 1, Volume 9, January 2012

9 Expert No. Crtera Table 4. Crtera for perceptual feedback dmenson Voce Recognton Scrpt Dsplay Dagram Interface Warm Reply & Response L M U L M U L M U L M U W (fuzzy weght) S (Defuzzy value) P.S.: the range of s from 1 to 4 and stands for each crtera of perceptual feedback dmenson. L: mnmum, M: medum, U: maxmum Health & Securty Dmenson Through the FDM and questonnare to 10 experts we regard the mportance of 4 crtera n health & securty dmenson. The crtera nclude rsk sensor, stress down devce, emoton management, physologcal test. The fuzzy weght (W ) and defuzzy value (S ) are calculated based on expert s Expert No. answers. The results show the stress down devce has the hghest score (61.3), whle physologcal test has the lowest one (57.5). s a result, the stress down devce s hghly emphaszed and followed by emoton management. On the other hand, physologcal test s less mportant. The calculaton s shown n Table 5. Table 5. Crtera for health & securty dmenson Stress Down Emoton Crtera Rsk Sensor Physologcal Test Devce Management L M U L M U L M U L M U W (fuzzy weght) S (Defuzzy value) P.S.: the range of s from 1 to 4 and stands for each crtera of health & securty dmenson. L: mnmum, M: medum, U: maxmum E-ISSN: Issue 1, Volume 9, January 2012

10 5.1.5 Operaton Mechansm Dmenson Through the FDM and questonnare to 10 experts we regard the mportance of 4 crtera n operaton mechansm dmenson. The crtera nclude portablty, convenent operaton, frendly desgn and dentfcaton. The fuzzy weght (W ) and defuzzy value (S ) are calculated based on expert s answers. The result shows the frendly desgn has the hghest score (73.4), whle dentfcaton has the lowest one (57.3). s a result, the frendly desgn s hghly emphaszed and followed by convenent operaton. On the other hand, dentfcaton s less mportant. The calculaton s shown n Table 6. Expert No. Crtera Table 6. Crtera for operaton mechansm dmenson Convenent Portablty Frendly Desgn Identfcaton Operaton L M U L M U L M U L M U W (fuzzy weght) S (Defuzzy value) P.S.: the range of s from 1 to 4 and stands for each crtera of operaton mechansm dmenson. L: mnmum, M: medum, U: maxmum 5.2 Fuzzy weght transformaton of 20 crtera Through the FDM and questonnare to 10 experts we regard the mportance of 4 crtera n each dmenson. The study normalzes defuzzy value (S) and obtans the rankngs of 20 crtera. The results show the hghest weght s warm reply (0.061), and followed by frendly desgn (0.059) and convenent operaton (0.058), whle multlngual translaton has the lowest one (0.037). s a result, the warm reply s hghly emphaszed and followed by frendly desgn and convenent operaton. On the other hand, multlngual translaton s less mportant. The transformaton s shown n Table 7. Table 7. The transformaton of fuzzy weght for 20 crtera Crtera\Weght S (Defuzzy Value) Weght Weght Rankng of Dmenson Vvd Interacton Novel Content Seral Story Personfcaton Behavor Learnng & Growth Dgtal Vdeo Memory ded Multlngual Translaton E-ISSN: Issue 1, Volume 9, January 2012

11 Table 7. The transformaton of fuzzy weght for 20 crtera (cont d.) Crtera\Weght S (Defuzzy Value) Weght Weght Rankng of Dmenson Voce Recognton and Response Scrpt Dsplay Dagram Interface Warm Reply Rsk Sensor Stress Down Devce Emoton Management Physologcal Test Portablty Convenent Operaton Frendly Desgn Identfcaton P.S.: the range of s from 1 to 20 and stands for 20 crtera. The calculaton of weght uses mathematcal average approach. 5.3 Correlaton analyss between customer needs and exstng technology Ths study uses QFD to dscuss the relatonshp between customer needs and exstng technology. In term of customer needs, the results show frendly desgn has the hghest relatonal rankng (793), and followed by convenent operaton (788) and personfcaton behavor (754), whle multlngual translaton has the lowest one (461). s a result, from the perspectve of customer needs, frendly desgn s more mportant, and followed by convenent operaton and tree down devce. On the other hand, multlngual translaton s less mportant. In terms of exstng technology, the results show human engneerng s ranked the hghest (1089), and followed by appearance desgn (1078) and emoton expresson (1042), whle temperature sensor s ranked the lowest (751). s a result, from the aspect of exstng technology, human engneerng should be the frst prorty, and followed by appearance and emoton expresson, whle temperature sensor s later consdered. The calculaton s shown n Table 8. Table 8. Correlaton analyss between customer needs and exstng technology Customer Needs Exstng Technology ppearance Desgn Human Engneerng Physcal Trat Facal Expresson Sensor Posture Recognton Voce Dstngushng Touch Percepton Envronment Percepton Temperature Percepton Emoton Recognton Emotonal Expresson Learnng Intellgence Natural Language process Wreless Communcatons Total Rankng Vvd Interacton Novel Content Seral Story Personfcaton Behavor Learnng & Growth Dgtal Vdeo E-ISSN: Issue 1, Volume 9, January 2012

12 Table 8. Correlaton analyss between customer needs and exstng technology (cont d.) Customer Needs Exstng Technology ppearance Desgn Human Engneerng Physcal Trat Facal Expresson Sensor Posture Recognton Voce Dstngushng Touch Percepton Envronment Percepton Temperature Percepton Emoton Recognton Emotonal Expresson Learnng Intellgence Natural Language process Wreless Communcatons Total Rankng Memory ded Multlngual Translaton Voce Recognton and Response Scrpt Dsplay Dagram Interface Warm Reply Rsk Sensor Stress Down Devce Emoton Management Physologcal Test Portablty Convenent Operaton Frendly Desgn Identfcaton Total Rankng Correlaton analyss between technologes Ths study uses QFD to dscuss the relatonshp and rankng between 14 exstng technologes. The results show emoton recognton s ranked the hghest (766), and followed by human engneerng (699) and learnng ntellgence whle wreless communcatons s ranked the lowest (488). s a result, techncally the emoton recognton has more mpact and requres to be developed frst or acqure key technology. The rankng s followed by human engneerng and learnng ntellgence, whle wreless communcatons has smaller mpact. The calculaton s shown n Table 9. E-ISSN: Issue 1, Volume 9, January 2012

13 Expert No. Exstng Technology Table 9. Correlaton nalyss between Technologes ppearance Desgn Human Engneerng Physcal Trat Facal Expresson Sensor Posture Recognton Voce Dstngushng Touch Percepton Envronment Percepton Temperature Percepton Emoton Recognton Emotonal Expresson Learnng Intellgence Natural Language process Correlaton Value Rankng Wreless Communcatons 5.5 Dscusson Based on above analyss and computaton, t can be noted that perceptual feedback and entertanment are hghly stressed among fve dmensons. In customer dmenson, warm reply, frendly desgn and convenent operaton are most emphaszed among twenty crtera. In terms of correlaton analyss between customer needs and technology, frendly desgn, convenent operaton and personfcaton behavor are ranked hgher. s a result, frendly desgn, convenent operaton and personfcaton behavor should be hghlghted to look after customer needs. The correlaton rankng between technology and customer needs reveals human engneerng, appearance desgn and emoton expresson as the top prortes. s a result, the R&D technology should focus on those three functons. Regardng the correlaton analyss between technologes, emoton recognton, human engneerng and learnng ntellgence are ranked the hghest, so that these technologes should develop as soon as possble. 6 Conclusons and Suggeston Ths secton contans the research outcomes and contrbuton, suggestons, future research and lmtaton. 6.1 Concluson Ths study appled QFD to establsh a research framework and converted customer needs nto technology development strategy, so that frms can master a future leadng edge. It can be concluded that frendly desgn, convenent operaton and personfcaton behavor should be heavly focused to facltate the EID producton and meet customer requrements among 20 customer needs and 14 technologes. From the vewpont of relatonshp between 14 technologes and 20 customer needs, human engneerng, appearance desgn and emoton expresson should be frst developed. Wth regard to the relatonshp among 14 technologes, emoton recognton, human engneerng and learnng ntellgence have major mpacts and should be acqured urgently 6.2 Suggeston There are several ponts are suggested by ths research. (1) The mportant functons and features requred by customers have sgnfcant mpacts on QFD weght and correlaton rankng between crtera. Future research can be based on ths framework to nvestgate dfferent products and consumers. Wth a broader samplng and statstc testng procedure, the E-ISSN: Issue 1, Volume 9, January 2012

14 devaton n dfferent market can be hghlghted. (2) Ths survey doesn t nclude hgh-tech companes. ctually hgh-tech companes can consult ths model to analyze the functons and features requred by customers, and set up busness strategy to deploy market segment. The analyss also can be used to benchmark compettors strategy and come up wth dfferent strategc plannng of compettve advantage. (3) Tawanese hgh-tech frms has been shfted from OEM orented to ODM and OBM orented, they have producton ablty on the EIP components such as semconducton, communcatons kt, nformaton kt, electronc component and materal. If the core technology of emoton percepton and algorthms are requred to develop themselves or acqure from overseas, there s a need to assess the optmal beneft soluton carefully. (4) Tawanese frms are more compettve on nformaton key components and producton technology, whle less compettve on brand and channel. Therefore, Tawanese frms can algn wth Chnese frms strategcally usng polcy negotatons, so as to obtan EIP related patents, brand, channel, R&D and personnel to promote the compettve advantage of Tawanese hgh-tech frms. 6.3 Future Research In the aspect of correlaton analyss between customer needs and technology, crtera and technology assessment to each company can be done based on the requrement of crtera and technology relatonshp. In ths way, the manstream product, level of customer satsfacton and technology advantage as well as maturty can be enhanced. In the aspect of technology relatonshp, patent database can be appled to cross analyze technology and patent, and add on the technology and functon used for manstream product. The patent map of ndustral technology, trend development of manstream technology can be planned, whch can be the bass of establshng Tawan s technology polcy and frm s ndustral strategy. Furthermore, the market segment can be analyzed through the nformaton and quantfed QFD by market survey to come up wth dfferent rankng of dversfed busness strategy. 6.4 Lmtaton Ths study has been strvng for objectve and careful research, some lmtaton stll needs to be addressed. That s, the content of questonnare mght have bas resulted from the professonalsm of partcpants and cogntve dfference towards questons. Ths may cause nsuffcent objectvty and mpact the data collecton and precse analyss References [1] rmacost, R. L., Componaton, P. J., Mullens, M.. and Swart, W. W. (1994), n HP framework for prortzng customer requrements n QFD: an ndustralzed housng applcaton, IIE Transactons, 26(4), [2] Bossert, J. L. (1991), Qualty Functon Deployment - Practtoner s pproach, SQC Qualty Press Inc, New York. [3] Chan, L. K. and Wu, M. L. (2005), systematc approach to qualty functon deployment wth a full llustratve example, Omega - The Internatonal Journal of management Scence, 33, [4] Chan, L. K., Kao, H. P., Ng,. and Wu, M. L. (1999), Ratng the mportance of customer needs n qualty functon deployment by fuzzy and entropy methods, Internatonal Journal of Producton Research, 37(11), [5] Chen, L. H. and Weng, M. C. (2006), n evaluaton approach to engneerng desgn n QFD processes usng fuzzy goal programmng models, European Journal of Operatonal Research, 172(1), [6] Chen, Y., Fung, R. Y. K. and Tang, J. (2006), Ratng techncal attrbutes n Fuzzy QFD by ntegratng fuzzy weghted average method and fuzzy expected value operator, European Journal of Operatonal Research, 174(3), [7] Chou, Hua-Ka and Tzeng, Gwo-Hshung (2002), Fuzzy Multple-Crtera Decson- Makng pproach for Industral Green Engneerng, Envronmental Management, Vol. 30, No. 6, [8] Dalkey, N. C. and Helmer, O. (1963), n expermental applcaton of the Delph method to the use of experts, Management Scence, 9(3), [9] Desmet, P. M.. (2002), Desgnng Emotons. Doctoral thess, Delft Unversty of Technology. [10] Fung, R. Y. K., Chen, Y. and Tang, J. (2006), Estmatng the functonal relatonshps for qualty functon deployment under uncertantes, Fuzzy Sets and Systems, 157(1), [11] Hauser, J.R. and Clausng, D. (1988), The House of Qualty, Harvard Busness Revew, 66(3), [12] Isen,. M. (1993), Postve ffect and E-ISSN: Issue 1, Volume 9, January 2012

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