managing a dispersed product development process

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Managing a Dispersed Product Development Process

Paper 103

Ely Dahan John R. Hauser

October 2000


By

Ely Dahan

and

John R. Hauser

October 2000

for the Handbook of Marketing

Barton Weitz and Robin Wensley, Editors

Ely Dahan is an Assistant Professor of Marketing at the Sloan School of Management, M.I.T., 38Memorial Drive, E56-323, Cambridge MA 02142. He can be reached at 617 253-0492, 617 258-7597 (fax), or [email protected].

John Hauser is the Kirin Professor of Marketing, Sloan School of Management, M.I.T., 38Memorial Drive, E56-314, Cambridge, MA 02142. He can be reached at 617 253-2929, 617258-7597 (fax), or [email protected].

This research was supported by the Center for Innovation in Product Development at M.I.T.

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Table of Contents

THE CHALLENGE OF A DISPERSED PRODUCT DEVELOPMENT PROCESS ............................... 1

PRODUCT DEVELOPMENT – END TO END............................................................................. 2

AN INTEGRATED PROCESS ............................................................................................... 2

PRODUCT DEVELOPMENT AS AN END-TO-END PROCESS.................................................. 4

THE PRODUCT DEVELOPMENT FUNNEL, STAGE-GATE, AND PLATFORMS ....................... 5

THE FUZZY FRONT END: OPPORTUNITY IDENTIFICATION AND IDEA GENERATION.............. 7

SURVEYS AND INTERVIEWS.............................................................................................. 8

EXPERIENTIAL INTERVIEWS ............................................................................................. 9

THE KANO MODEL: DELIGHTING CUSTOMERS .............................................................. 10

THE INNOVATOR’S DILEMMA AND DISRUPTIVE TECHNOLOGIES.................................... 11

EMPATHIC DESIGN AND USER OBSERVATION ................................................................ 12

UNDERLYING MEANINGS AND VALUES.......................................................................... 13

KANSEI ANALYSIS AND THE MIND OF THE MARKET ...................................................... 13

BENEFIT CHAINS ............................................................................................................ 14

FOCUSING THE DESIGN TEAM BY IDENTIFYING STRATEGIC CUSTOMER NEEDS............. 15

TEAM-BASED NEEDS-GROUPING METHODS: AFFINITY DIAGRAMS AND K-J ANALYSIS 16

CUSTOMER-BASED NEEDS-GROUPING METHODS: THE VOICE OF THE CUSTOMER......... 16

NEW WEB-BASED METHODS FOR THE FUZZY FRONT END............................................. 16

IDEATION BASED ON CUSTOMER NEEDS (AND OTHER INPUTS)...................................... 17

OVERCOMING MENTAL BLOCKS .................................................................................... 17

TRIZ (THEORY OF INVENTIVE PROBLEM SOLVING) ...................................................... 18

INVENTIVE TEMPLATES.................................................................................................. 18

SUMMARY OF METHODS FOR THE FUZZY FRONT-END ................................................... 18

DESIGNING AND ENGINEERING CONCEPTS AND PRODUCTS ............................................... 19

LEAD USERS .................................................................................................................. 19

EMPLOYEE FEEDBACK: KAIZEN AND TEIAN .................................................................. 20

SET-BASED DESIGN AND MODULARITY .......................................................................... 21

PUGH CONCEPT SELECTION ........................................................................................... 21

VALUE ENGINEERING..................................................................................................... 21

Managing a Dispersed Product Development ProcessDahan and Hauser

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QUALITY FUNCTION DEPLOYMENT AND THE HOUSE OF QUALITY ................................. 22

TRADEOFFS AMONG NEEDS AND FEATURES: CONJOINT ANALYSIS ............................... 23

NEW WEB-BASED METHODS FOR DESIGNING AND ENGINEERING PRODUCT CONCEPTS 29

SUMMARY OF METHODS FOR DESIGNING AND ENGINEERING CONCEPTS AND PRODUCTS

................................................................................................................................................... 32

PROTOTYPING AND TESTING CONCEPTS AND PRODUCTS ................................................... 32

TARGET COSTING: DESIGN FOR MANUFACTURING AND ASSEMBLY (DFMA) ............... 33

RAPID PROTOTYPING METHODS..................................................................................... 33

PARALLEL CONCEPT TESTING OF MULTIPLE DESIGNS ................................................... 33

INTERNET-BASED RAPID CONCEPT TESTING .................................................................. 34

AUTOMATED, DISTRIBUTED PD SERVICE EXCHANGE SYSTEMS .................................... 34

INFORMATION ACCELERATION....................................................................................... 35

PRETEST MARKET AND PRELAUNCH FORECASTING ....................................................... 36

MASS CUSTOMIZATION AND POSTPONEMENT ................................................................ 37

SUMMARY OF PROTOTYPING AND TESTING CONCEPTS AND PRODUCTS......................... 38

ENTERPRISE STRATEGY ..................................................................................................... 38

THE CHALLENGE OF DEVELOPING AN EFFECTIVE PRODUCT DEVELOPMENT

ORGANIZATION .......................................................................................................................... 38

BOUNDARY OBJECTS ..................................................................................................... 39

COMMUNITIES OF PRACTICE .......................................................................................... 39

RELATIONAL CONTRACTS .............................................................................................. 40

BALANCED INCENTIVES ................................................................................................. 40

DYNAMIC PLANNING ..................................................................................................... 40

DEPLOYMENT OF CAPABILITIES WITH WEB-BASED TOOLS ............................................ 40

PROCESS STUDIES OF THE ANTECEDENTS OF PRODUCT DEVELOPMENT SUCCESS.......... 40

ADJUSTING PRIORITIES TO MAXIMIZE PROFIT ............................................................... 43

A VISION OF THE FUTURE OF PRODUCT DEVELOPMENT .................................................... 45

1

The Challenge of a DispersedProduct Development Process

New product developm ent has a longhis tory in marketi ng incl udi ng research oncus tom er preferences (Green and Wind 1975,Green and Sri ni vas an 1990, S ri nivas an andS hocker 1973), product pos it ioning ands egmentati on (C urrim 1981, Green and Kri eger1989a, 1989b, Green and R ao 1972, Haus erand Koppel m an 1979), product forecas ti ng(Bass 1969, J am i es on and Bas s 1989, Kalwani and S i lk 1982, Mahaj an and Wind 1986, 1988, M cF adden 1970, Morri s on 1979), and tes tm arket ing (Urban 1970, Urban, Hauser andR obert s 1990). The appli cat ions have beenm any and vari ed and have led t o a deeperunders tandi ng of how to gather and usei nform at ion about the cus t om er in t he des ign, t es ti ng, l aunch, and managem ent of newproducts . Many integrati ve texts on productdevel opm ent from a m arket i ng pers pecti vehave been publi s hed to review the i s sues , t hem et hods, and the appl icat i ons (Dolan 1993,Lehmann and Winer 1994, M oore andP es sem ier 1993, Urban and Haus er 1993, Wi nd1982).

M arket ing, wi th it s focus on t he cus tomer,has had great s ucces s . Tools s uch as conj oi nt analys is , voi ce-of-t he-cus tomer anal ys is , perceptual mappi ng, int ent ion scali ng, port fol ioopt im i zati on, and li fecycl e forecas t ing are nowi n com mon use. Fi rm s t hat cont inuousl y andeffici entl y generate new products t hat are int une wit h their end cus tom ers’ needs and want s are m ore l i kely to t hri ve (Gri ffi n and P age1996). Di rect com municat i on wi th cust om ers all ows firm s to learn from cus t om ers and tail orproducts t o t hei r requi rem ents .

In paral lel wit h t he devel opment ofprescripti ve tools , res earchers have s tudied thecorrel at es of new product success i denti fyi ngcom municat i on between m arket ing andengineering as one of t he most im portant factors in success (C ooper 1984a, 1984b,

C ooper and Kl ei nschm i dt 1987, Doughert y1989, Griffin and Hauser 1996, Souder 1987, 1988). As a res ul t, organizat i onal process t ool ss uch as cross -functi on teams (Kuczm ars ki 1992, Souder 1980), quali t y funct iondeployment (Haus er and Cl aus ing 1988), andco-locat ion (Al l en 1986) were devel oped topromot e the s haring of ideas and the clos ei nt egrat ion of engineering deci si ons wit hcus tom er needs. P roces s ori ent ed t ext books now rout inely cons ider marketi ng is s ues andt he need t o i nt egrat e engi neeri ng wi th t hem arket ing funct i on (M cGrat h 1996, Ul ri ch andEppinger 2000).

As we move into the 21s t cent ury, newchall enges and opport unit i es are ari si ng drivenby gl obal market s, gl obal competi ti on, t heglobal dis persi on of engi neeri ng tal ent, and theadvent of new i nform ati on and com municat i ont echnologi es such as el ect roni c m ai l , the worl d-wide web, and i ncreas ed el ectroni c bandwi dt h. The new vi s ion of product devel opment is that of a highl y dis aggregat ed process wi th peopleand organi zat ions spread throughout the world(Holm es 1999). At the s am e t im e products arebecom i ng i ncreas ing com pl ex wi t h typical elect ro-mechani cal product s requi ri ng cl ose t oa m il l ion engineering deci si ons t o bri ng them tom arket (Eppinger, Whi tney, S mi t h and Gebala1994, Eppi nger 1998). Even software products s uch as Mi crosoft Word or Nets cape requi redis aggregat ed, but coordi nat ed process es i nvol ving hundreds of developers (C usumanoand S elby 1995, Cusum ano and Yoffie 1998).C om pet it ive pres sures m ean t hat t im e t o market has become as key to new product success as m arket ing’s ori ent at i on on cus t om er needs andcus tom er s ati sfact ion (Sm i th and Rei nert s en1998). Because product s are m arket edt hroughout the world, firm s face the t radeoffbet ween st andardizat i on for cos t reducti on andvariet y for s at i sfyi ng a broad set of cus tomers. Thi s has expanded the need for marketi ng tol ook beyond t he si ngl e product to focus on theproduct pl atform (Moore, Louvi ere and Verma1999).


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In thi s chapt er we l ook at t he st at e of theart i n res earch that address es thes e newchall enges for the m arket i ng comm uni ty. Webegin wi th an overvi ew of the int egrat ed end-t o-end product devel opm ent proces s indicati ngm arket ing’s rol e i n addres si ng the chall enges ofdevel opi ng profi tabl e products (and pl at forms ).The remainder of t he chapt er addres s es s pecifi cres earch chal lenges rel at i ng t o t he end-t o-endproces s. We organize t he remai ni ng sect i onsaround t he vari ous s t ages of development recognizing t hat , in pract ice, thes e s tages areoft en it erati ve and/ or int egrat ed. Speci fi cal lywe address , i n order, t he st rat egic end-t o-endproduct devel opm ent proces s, t he fuzzy frontend of cus t om er opportuni t y ident ifi cati on andi dea generati on, t he process of det ail ed desi gnand engi neeri ng of product s and proces ses , thet es ti ng phase where concepts and product s areprotot yped and tes ted, and t he enterpris e andorgani zati onal strat egy necess ary for success . We cl ose wi th a vi si on of the fut ure of res earchof product devel opment.

Product Development – End toEnd

In the l at e 1980s and earl y 1990s am arket ing focus on product developm ent s tres s ed cust om er sat is facti on. Res earchers inm arket ing bel ieved t hat t he key t o succes s was a bet t er unders t andi ng of the voi ce of t hecus tom er and a bet ter abi l it y to li nk that voi cet o the engi neeri ng deci si ons t hat are made inl aunching a product. F or exam ple, Menezes(1994) document s a case where Xerox movedfrom a focus on ROA and m arket share t o afocus on cust om er sat is facti on. Im portantres earch duri ng that peri od included new ways t o underst and t he voi ce of t he cust omer (Griffinand Haus er 1993), new ways t o devel opopt im al product profi les in the cont ext ofcom pet it ion (Green and Kri eger 1989a, 1991),m ore effici ent preference meas urements (Srini vasan 1988), and the abi l it y to handl el arger, more com pl ex cust omer inform at ion(Wi nd, Green, S hiffl et, and Scarbrough 1989).

At the s am e t im e t he qual i ty m ovement focus edproduct devel opm ent engineering on improvedrel iabil it y t hrough conti nuous im provements uch as Kai zen met hods (Im ai 1986), st at i st icalquali t y control (Dem i ng 1986), modi fiedexperi ment al des ign (Taguchi 1987), anddes ign for manufacturing (Boot hroyd andDewhurst 1994). There were many success esi ncluding a t urnaround of the maj or US aut om obi le manufacturers. M any engi neers cam e to bel ieve that the key t o s ucces s was abet ter qual it y product.

Als o during t hat t im e bot h m arket ing andengineering real ized that ti me to m arket wascri ti cal . Marketi ng saw the phenom enon ast hat of rewards to earl y ent rants (Gol der andTel li s 1993, Urban, Carter, Gas ki n, and Mucha1986) whil e engi neeri ng s aw, am ong othert hi ngs , the l os t profit s due t o del ays (S mi th andR ei nerts en 1998). B oth cust om er sat is facti onand t i me-t o-m arket became panaceas that, ifonl y the fi rm coul d achieve them, woul dguarantee succes s and profit abi li ty.

An Integrated Process

Today, both industry and academiaview successful product development as anintegrated process that must overcome manytradeoffs, as depicted in Figure 1. Customersatisfaction, time to market, and cost reductionthrough total quality management are allimportant, but none is viewed as the onlyguarantee of success.


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Figure 1: Tradeoffs in New ProductDevelopment (based on Smith and

Reinertsen 1998)

All else equal, a product will be moreprofitable if it delivers customer benefitsbetter, is faster to market, costs less toproduce, and costs less to develop. Figure 1puts research on product-development toolsand methods into perspective. Researchshould be directed to assure (1) that the firm isoperating on the efficient frontier with respect

to each of these strategic goals and (2) that thefirm is making the best tradeoffs among thesegoals.

R es earch m ust recogni ze t hat t here aret radeoffs along the effici ent front i er. Forexampl e, i f we focus on j ust t wo of the manygoals of product developm ent , then the effi ci ent front i er i n F igure 2 suggest s that there aret radeoffs bet ween cus tomer s at i sfact ion andreuse. A firm can become too com mi t ted toeit her. F or example, t he si gni fi cant reuse ofcom ponents , s oft ware and des igns may get theproduct to the market fas t er and reducedevel opm ent cos t s (e. g. , Wit ter, Cl aus ing,Laufenberg, and de Andrade. 1994), but t hefirm may s acrifi ce t he abi li ty to s ati sfycus tom er needs and m ay mi s s out on ways toreduce product cos ts . Si m il arl y, qual it yfunct i on depl oym ent (QF D) may be aneffect ive means to deli ver cus t om er benefit s, but s ome appl icati ons are too cum bersomereduci ng t i me t o m arket and increas i ngdevel opm ent cos t .

Figure 2: Quantifying the Tradeoffs in Product Development

02

46

8 100

2

4

68

10

Platform Reuse

Customer Satisfaction

Profit ($M)

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In response, product developmentteams have modified QFD to deliver the rightbenefits at the right costs. Such modificationsinclude just-in-time QFD (Tessler and Klein1993), turbo QFD (Smith and Reinertsen1998), and simplified QFD (McGrath 1996).Reuse and QFD are just examples. As wereview various product develop tools andmethods, the reader should keep in mind thatthe tools work together to enable the firm tomake the appropriate tradeoffs among the fourstrategic goals in Figure 1.

Product Development as an End-to-End Process

In order t o m ake t hes e tradeoffseffect ivel y, mos t fi rms now vi ew product devel opm ent (PD) as an end-t o-end proces s t hat draws on m arket i ng, engineering,m anufact uri ng, and human devel opm ent .F igure 3 is one representati on of an end-to-endproces s. Figure 3 is m odi fi ed from a proces sused at Xerox and advocat ed by the Center forInnovati on in P roduct Developm ent (S eeri ng1998). It summ ari zes m any of the forces onproduct devel opm ent and hi ghli ght sopport unit i es for res earch.

Figure 3: Product Development – End to End

F rom our pers pecti ve, t he fi ve forces inred on t he outer s quare of F igure 3 pres ent t heext ernal chal lenges to the P D team. All acti ons are cont ingent on these forces . For exam pl e, s peed to m arket mi ght be more cri ti cal i n t he

highl y com pet it i ve worl d of Int ernet s oft ware. R at her t han 3-year pl anni ng cycles, such fi rm s m ight adopt 3-year hori zons wi t h adapt ivei mplem entat ion strat egi es that are reviewedm onthl y or even weekl y (C usumano and Yoffie


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1998). The descripti ons i n t he seven bluerectangl es indi cat e act ions that mus t be taken.F or exam pl e, the firm m us t have a s t rategy fordeali ng wi t h technol ogy (“Technol ogyS trat egy”) and employ m et hods to underst andt he benefi t s provi ded t o cus tom ers bycom pet it ive product offeri ngs, ident ify gapswhere benefit s are demanded but not suppl ied, and unders t and how compet i ti on wi ll respond(“C om pet it i ve P osi ti oni ng”), whil e “Suppl yC hain Managem ent ” hel ps t he fi rm (andext ended enterpris e) incl ude s uppli ers i ndevel opi ng product s to meet cus tomer needs. In thi s chapt er we revi ew thos e act i ons that areof great es t i nt erest to a marketi ng audi ence, nam el y t hos e in the four sol id rect angles . In-bound marketi ng (“Voi ce of t he Cust omer, C onjoi nt Anal ys i s, et c. ”) provi des the wi ndowon the cus t om er. The m yri ad pers pecti ves from m arket ing, engi neeri ng, desi gn andm anufact uri ng t hat m ust be i nt egrat ed fors ucces sful PD m ani fes t thems el ves i n t he form of a ‘Core Cros s -F uncti onal Team. ” “Hum anR es ources” are import ant, incl udi ng the need tounders tand the context and cul t ure of theorgani zati on and t he need to develop hum ancapabi li ti es through trai ning, informati ont echnology, and comm uni ti es of pract ice(Wenger 1998). “Marketi ng, Engi neeri ng, andP roces s Tools ” enabl e t he end-t o-end P Dproces s to be both m ore effi ci ent and moreeffect ive.

The Product Development Funnel,Stage-Gate, and Platforms

The P D funnel i s at the cent er of F i gure 3. The P D funnel i s t he tradi ti onal vi ew that PDproceeds i n s tages as m any i deas are funnel edand developed i nto a few high-pot ent ialproducts t hat are launched. We have adoptedhere the s t ages of opport uni ty ident ificati on(and idea generati on), concept devel opment, des ign and engi neeri ng, t est ing, and l aunchused by Urban and Hauser (1993). Each t ext and each fi rm has sl i ghtl y different nam es fort he s t ages , but the des cri pt ion of PD as a st agedproces s is fairl y uni vers al. The keym anagement ideas are (1) that it is much less expens ive to screen products i n t he earl y s tages t han in the l at er st ages and (2) that each st agecan i m prove t he product and it s pos i ti oni ng s ot hat the l i keli hood of success increas es . Si m pl ecal cul at ions in Urban and Haus er dem onst rat et hat such a s taged proces s i s likel y t o reducedevel opm ent cos t s si gni fi cantl y. This s t agedproces s is best summ ari zed by Cooper (1990)who l abels the proces s st age-gate. Fi gure 4s um marizes a typical st age-gat e proces sadapt ed to the struct ure of thi s paper. St age-gat e provi des di scipl ine through a series ofgat es in which mem bers of the PD team areasked to j ust ify t he deci s ion to move to the nexts tage – lat er s t ages dram ati cal ly i ncreas e thefunds and effort s inves ted i n thi s get ti ng aproduct to market success ful ly.

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Figure 4: Cooper’s Stage-Gate Process

The funnel in F i gure 3 al so il lus trat es theconcept of pi pel ine management . Oft en the bes t s trat egy for a firm is to have suffi ci ent ly m anyparal l el proj ect s so that it can launch products t o the m arket at t he most profi tabl e pace.R es earch chal lenges include the ques ti ons ofhow m any parall el project s are neces sary, t het radeoffs bet ween more paral lel proj ects andfas ter t im e for each proj ect , and t he num ber ofconcepts t hat are needed in each st age of aparal l el proj ect t o produce the right pace ofproduct int roducti on. F igure 3 does not captureexpli cit ly the import ant characteri s ti c of realP D process es that st ages oft en overl ap. Forexampl e, wi th new met hods of us er desi gn andrapid prot otypi ng, i t i s pos si ble t o t es t concepts earli er in the des ign and engi neeri ng st age or t os creen i deas more effecti vel y in the concepts tage. Fi gure 3 al so does not capture expli ci t lyt he fact t hat t he ent ire proces s is it erati ve(al though we have tri ed t o i ll ust rat e that wi t ht he feedback arrows in Fi gure 4). F or example, i f a product does not t es t wel l , it mi ght becycled back for furt her development andret es t ed. In fact , many firms now tal k about a

“spiral process ” i n whi ch the product orconcepts m oves through a series of tight er andt ight er st ages (e. g. , C us umano and Sel by 1995).

The s m al l ovals in t he end-t o-end P Dproces s (F i gure 3) are ei ther individual products or product platform s. In manyi ndus t ri es , i ncl uding com plex elect ro-m echanical product s, soft ware, andpharm aceut i cals , firm s have found t hat i t i sm ore profi t able to develop product platform s. A plat form is a set of com mon elements s haredacros s products in t he pl atform fam i ly. Forexampl e, Hewl et t P ackard’s ent i re l i ne of i nk-j et printers is based on a rel ati vel y few pri nter-cartri dge platform s. By s haring elem ents , t heproduct can be devel oped more qui ckl y andwit h lower cost . Pl atform s mi ght al so l owerproducti on cost s and inventory cost s andprovi de a bas is for flexi ble m anufacturi ng. Ont he cust om er si de, pl at forms enable a fi rm tocus tom ize features i n a proces s t hat has becom eknow as mas s cus tomi zat ion (Gonzalez-Zugas t i, Ot to, and B aker 1998, Meyer andAlvin Lehnerd 1997, Sanderson and Uzum eri


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1996, Ul ri ch and S teve Eppinger 1995,B al dwi n and C lark 2000. )

F inal l y, t he ri ght s i de of t he end-t o-endproces s in Fi gure 3 il lus trat es the growingt rends t oward m etrics -bas ed managem ent ofP D. As the proces s becom es more di s pers edamong vari ous functi ons , various teams ,various suppl iers, and throughout t he world andas product s become m ore complex, there i s agreat er need to balance t op-managem ent control wi t h the empowerm ent of s el f-m anaged, cros s-funct i onal team s . To achi evet hi s bal ance, fi rm s are t urning t o a m et rics-bas ed approach in whi ch t eam s are m eas uredon st rat egi c indicat ors s uch as cus t om ers at is facti on, t i me t o m arket , producti on cost , and development cost . If the wei ght s on thes em et ri cs are s et properl y, then the teams , act i ngi n their own bes t int eres t s, wi ll t ake t he act ions and m ake t he decis ions that lead to the great est s hort - and long-term profi t (B aker, Gi bbons ,and M urphy 1999a, 1999b, Gibbons 1997).

Thi s com pl etes our m arket i ng overvi ew oft he end-to-end product devel opm ent proces s. The i m port ant l ess on, t hat we hope to il l us tratet hroughout the rem ai nder of thi s chapt er, i s thatt he proces s depends upon all of i ts el em ent s. Alt hough t he det ai led i mpl em ent at ion of eachelement varies depending upon technology, com pet it ion, cus tomers, and suppl iers, a fi rm is m ore effect ive if it underst ands al l of theseelements and can m anage t hem effect i vely.

We now exam ine res earch opport uni ti eswit hi n each s tage of the PD process bybeginning wit h the fuzzy front end ofopport unit y i denti fi cat ion and idea generat ion.

The Fuzzy Front End: OpportunityIdentification and Idea Generation

P erhaps the highes t leverage point inproduct devel opm ent is the front end whi chdefines what the product wil l be, repres ent edby the opening of the funnel i n F igure 3. Thi sdecis i on balances the firm ’s core s t rengt hs

versus com pet it i on wi th t he dem and ofpot ent ial cus tom ers. R el evant topi cs includet echnology st rat egy and readiness , cus tom eri nput , and newer, vi rtual -cust omer met hods. B ecaus e thi s is a marketi ng handbook, we wi ll focus more of t his s ect ion on the obtaini ngi nform at ion t o sat is fy cus tomer needs and oni dea generati on. We recom mend that readers i nt erest ed in t echnol ogy readi nes s reviewR ouss el, S aad, and Ericks on (1991) orM cGrat h (1996).

The fuzzy front end may be viewedt hrough the l ens of uncer t ai n search. That is , t he desi gn team must cons i der a m ul t it ude ofdes igns in order t o find an ideal s oluti on at thei nt ers ecti on of cust omer preferences and fi rm capabi li ti es. Once the fi rm has det ermi ned t hes trat egi c val ue of developing a new product wit hi n a part icular cat egory, but before it cans peci fy the det ail ed requi rements and features of the des i gn, it mus t sel ect the m ore prom is i ngdes igns to devel op and tes t so as t o m eet devel opm ent -cos t , product i on-cost , cus tom er-s at is facti on, and ti m e-to-market targets . P romi s ing des igns are t hos e that are t echni cal lydes irabl e, i. e. feas i bl e des igns that exploit thefirm’s com pet it i ve advant ages, and areatt racti ve to potent i al cust om ers . Marketi ng’srol e at the fuzzy front end of PD i s t o reduceuncert ai nt y duri ng t he des ign team’s s earch forwinni ng product concept s by accurat elycapturing cus tom ers’ point s-of-vi ew andcom municat i ng cust om er preferences to thedes ign t eam . In s om e cas es, t he process is m ore direct , wi t h engineer/des i gners obs ervingand comm uni cati ng di rectl y wit h pot ent ial cus tom ers, poss i bl y facil i tated by market ingpersonnel. The ease of comm uni cati on andi nt eract ion over t he Internet has t he pot enti al toi ncrease t he frequency and effect iveness ofs uch unfil t ered observati ons . The proces s ofl is tening to cus tomers in order t o opt im i ze anew product i s iterat ive, as depi ct ed in Figure5, consistent with the aforementioned “spiralprocess. ”


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Figure 5: Listening to Customers

Prioritize Needs

Create Concepts

Test Design on Customers

Characterize Needs

Gather Customer Voice

Recognizing the iterative nature of Figure5, we begin this section by reviewingt echni ques for gat hering raw data on cus t om erneeds . These m ethods i ncl ude direct s urveym et hods wi t h whi ch m arket i ng researchers arefam il i ar, but i ncl ude as wel l Kano’s m odel ofdel ighti ng cust omers , t he concept of dis rupti vet echnologi es, m ethods t o get at underl yi ngm eani ngs and val ues, methods for the “mi nd oft he m arket , ” and benefi t chains . We t henreview m et hods for charact erizi ng and refiningcus tom er needs bas ed on apparent pat terns andt hemes and methods for organizi ng needs andi dent i fying m arket s egm ent s. Needs mus t bepri ori ti zed and many marketi ng methods arequi te effecti ve. In the fuzzy front end we us et he s i mpler and less cost l y met hods recogni zi ngt hat any i nform ati on wi ll be refi ned i n thedes ign and prot otype phas e. Thus , we save areview of these “high-fideli ty” m et hods unt il t he next s ect ion of thi s chapt er. However, i nt hi s secti on we do revi ew some of t he morecom mon m et hods of ideat ion. We clos e thi ss ecti on by exam i ni ng how the Internet is changi ng t he way we view the proces s ofi dent i fying and meas uri ng cust omer needs .

Surveys and Interviews

There are m any cha lleng es whena tt em p ting to ca pt ure t he voice of t hecustom er, m ea sure preference, a nd p redict new p rod uct p urcha se b eha vior.

During t he fuzzy f ront end t he methodsm ust recog nize t ha t: (1 ) custom ers m ay still be f orm ing t heir preferences a nd m ay change their opinions by t he tim ea ct ua l p rod ucts ship , ( 2) it m a y bed if ficult f or cust om ers t o exp ress t heirt rue p references ( e.g . deg ree of pricesensit iv it y ) due t o socia l norm s, ( 3 ) theq uest ioning p rocess itself can beint rusiv e, so it is b est t o use m ult ip le,convergent methods, a nd ( 4 )inf orm at ion g at herers m ay “f ilt er” t hev oice of t he customer t hroug h t heirown b iases. Resea rchers hav ed ev eloped a nd v a lida t ed m ult ip lem et hod s in at tem pt s t o ad d ress theseissues.

M ahaj an and Wind (1992) s urveyed fi rms about techniques t hey used t o ident i fy cust om erneeds . They found t hat 68% of fi rm s usedfocus groups, and 42% used l im i ted productrol l-out s. In addi ti on, m any fi rm s used formal concept tes ts , conjoi nt anal ys i s, and Quali tyF unct i on Depl oym ent (QF D). The st udy als os ugges ted the foll owi ng i m provement s forcus tom er research:

• M ore quant i tati ve approaches

• M ore effici ent in-depth probing

• Great er accuracy and vali dit y

• S im pl er and bet t er cust om er feedback

• Great er cus tomer i nvolvem ent

• M ore effect ive use of l ead users andfield sales peopl e

• M et hods that address a long-term, funct i onal l y-int egrat ed s t rategy

The new met hods we revi ew at tem pt t oaddres s many of t hes e concerns . However,res earch i s s ti l l underway. Each m ethod has its l im it ati ons and the val ue of t he informat iondepends on the quali t y of executi on of t heres earch.


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Experiential Interviews

F or evol ut i onary des i gns target ed at anexi st i ng or fam i li ar cust omer bas e, focus groups (Cal der 1977, Fern 1982), provi deval uable i nform ati on. However, focus groupsare s ubj ect t o social norm s wi t hi n the groupand often focus on i nter-s ubject int eract ions and t hus m i ss m any of t he cust omer needs that are hard t o art i culat e or which t he cust omercannot expres s effect ivel y i n a group set ti ng. Thus, many fi rm s are turni ng t o experi ent iali nt erviews in which the needs and desi res ofcus tom ers are expl ored in one-on-onei nt erviews in which the cust om er des cribes hi s or her experi ence wi t h the product class . Thei nt erviewer probes deeply into the underl yi ng, m ore stabl e and long-term probl em s that thecus tom er i s t ryi ng t o s ol ve. Res earch byGri ffi n and Haus er (1993) indi cat es that ten tot went y experi ent ial int erviews per market s egment el i ci t the vast m ajori t y of cust omer

needs . Quali tat ivel y rich i nt erviews at thecus tom er’s locat ion are m ost effect i ve, but expens ive to conduct . One chal lenge i s to li m it t he s ess ion l ength, usual l y to an hour or l es s ,t hat engages, but does not i nconveni ence, t heparti cipant . In s el ect ing i nt erview candidat es, as el ect ion mat ri x t hat s egm ents the market according to type-of-us e and cust om er source, l ike the one in Tabl e 1, ens ures that a divers it yof cus tomers is cont act ed (B urchi ll andHepner-B rodie 1997, Hepner-B rodie 2000). The key concept is a represent ati ve rather thana random s ample in which the P D t eam gat hersi nform at ion from all the rel evant s egm ent s andfrom cus tom ers wit h varyi ng perspect ives oncurrent and fut ure needs. In addit i on, if thereare m ult ipl e decis ion m akers , say doct ors , labt echni ci ans , and pat i ents for a m edi cali ns trument , t hen each t ype of decis i on m akerneeds to be cons ul ted. S ee examples i n Hauser(1993).

Table 1: Customer Selection Matrix for Coffee MakersMarket S egment C ur rent

C ustom er sC om petitor s’C ustom er s

LeadU sers

U ntappedC ustom er s

LostC ustom er s

C ountertop 12-cupD ri p U sers

S peci alty (e.g.,E spresso) U sers

H igh-V ol ume (24-cup)U sers

M ul ti ple m embers of the P D t eam s houldreview t he trans cript s. For exam pl e, Gri ffinand Haus er (1993) suggest that each team m em ber recognizes approxi m at el y hal f t heneeds in a trans cript and that mult i pl e teamm em bers are very effect ive at ident i fying m oret han 95% of t he needs . Because non-verbal com municat i on i s cri t ical , m any firm s nowvideot ape int erviews in addi ti on to trans cribi ngt hem. S uch i nt erviews, often dis tri buted onC Ds t o t eam m em bers, have becom e known as t he “F ace of the C us t om er. ” For exam pl e, heari ng a cus tom er s ay “I us e Windows on mynot ebook and need an accurat e, buil t -i n

poi nt i ng devi ce that does n’t require m e to movem y hands from t he keyboard” carri es morei nform at ion t han a fi lt ered sum mary of “Goodpoi nt i ng devi ce is i m port ant .” S eei ng t he us ers truggle wi th exis ti ng poi nt ing devi ces is evenm ore persuasi ve. Many fi rms now include theact ual des i gn-engi neers i n t he intervi ewi ngproces s when the proces s is cos t-effecti ve (c. f. ,Leonard-Barton, Wi ls on, and Doyle. 1993). However, i n com plex products where the P Dt eam is oft en over 400 engineers and otherprofes si onals , key m embers obs erve thei nt erviews and use m ethods , such as the video-bas ed Face of t he Cus tomer, to carry t hi s


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i nform at ion t o the P D t eam i n a form t hat i t isused effect ivel y.

The Kano Model: Delighting Customers

“Cust omer needs ” are often verbal s tatem ents of benefi t s that cus tomers gai n fromt he product or servi ce. For exam pl e, acus tom er m i ght want a s afer car, a com put erm onit or that takes up l es s s pace on the des k, ora port able computer that makes si x hours on anairpl ane m ore pl easant. However, i n order todes ign a product , the P D team mus t map t hes eneeds into product feat ures. One wi dely-us edm et hod i s the Kano m odel.

The Kano m odel characteri zes product features accordi ng t o t hei r rel at ionship tocus tom er expect ati ons (Cl aus ing 1994) as inF igure 6. Som e features addres s “must have”needs . Such needs are us ual ly met by current t echnology and any new product must sati s fyt hese needs . However, i t is di ffi cul t todifferenti ate a product by i ncreasi ng thes at is facti on of thes e needs because they arealready sat is fi ed wel l by the com pet it ive s et ofexi st i ng product s. In other words , thecom pet it ive equi li bri um has di ctated t hat all viabl e products address t hes e needs . If the PDt eam does not m eet t he needs t hen t he product

wil l eli ci t cus t om er di ss ati sfact ion and loses al es . For exam pl e, an automobil e mus t havefour properly i nfl at ed ti res t hat do not comeapart on t he road. Ford’s recent problem s wi t hF ires t one tires suggest t hey di d not addres st hese “m us t have” needs . However, there areopport unit i es t o s ave cos t i f new creati vet echnology can addres s these needs as wel l orbet ter wit h l ower cos t. For exam pl e, Hauser(1993) gives an exam ple where a bas i c need form edical ins trum ent s – pri nti ng pati ent records –was m et wi t h a new t echnol ogy (paral lel port toconnect to the physi cian’s offi ce printer) thatwas s i gnifi cant l y les s expensi ve that exi st ingt echnology (bui l t-in therm al printers), yet m ett he need bett er.

Other needs are “m ore t he bett er”(somet im es call ed li near sat is fiers ). When newt echnology or i m proved ideas i ncreas e theamount by whi ch thes e needs are s at i sfied,cus tom er s ati sfact ion i ncreases , but us ual lywit h dim ini shing ret urns. S uch needs areusual l y rel evant when t echnology is advanci ngrapidl y, s uch as i s the case wi th t he speed of acom put er proces s or. In order to st ay on top oft he m arket , a comput er manufact urer must always be devel opi ng more powerful and easi ert o us e com put ers .

Figure 6: Kano Taxonomy of Customer Needs

Feature Level

SatisfactionLevel

Feature Level

SatisfactionLevel

Feature Level

SatisfactionLevel

MUST HAVE MORE the BETTER DELIGHTER

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F inal l y, a speci al cl as s of needs are thoseof whi ch cust om ers have di fficult y art iculati ngor rarel y expect t o have ful fi l led. Whenfeatures are included i n a product to sat is fys uch cus tom er needs, often unexpect edl y, cus tom ers experi ence “del i ght!” S ources ofcus tom er deli ght can becom e st rong mot ivators

for i nit ial purchase and for cust om ers at is facti on aft er t he sal e. Exampl es i ncl udecom pl ement ary frui t bas ket s in hotel room s, s oftware t hat anti ci pat es your next move, aut om obi les t hat rarely need s ervice, and others s uch as those i n Tabl e 2.

Table 2: Examples of Kano Feature TypesMust H ave Mor e the B etter D elighter

C ar (4) R eli abl e Ti res Gas Mi leage Free Loaner

N otebook A C Adapter H ard D ri ve Capacity B ui lt-in D V D Pl ayer

S oftware C ompatibil i ty P rocessi ng Speed A uto-Fil l-In

It is important to remember that theKano model is dynamic. Today’s “delighter”features become tomorrows “must have”features. For example, a graphical userinterface (GUI) and multi-processing wereonce “delighter” features, but today they are“must have” features for any desktopcomputer operating system. However, thebasic underlying customer needs of aneffective and easy to use operating systemremain. Antilock braking systems andpremium sound systems were once delighterfeatures for high-end cars, but today are “musthave” features for any brand competing in thehigh-end segment. New “delighter” featuresinclude automatic mapping and locationsystems, satellite-based emergency roadservice, side-view mirrors than dimautomatically, night-vision warning systems,and Internet access. However, the basicunderlying needs of safety and comfortabletransportation remain. Really successfulproducts are frequently due to newly identified“delighter” features that address those basiccustomer needs in innovative ways. Thedynamic nature of Kano’s model suggests aneed for ongoing measurement of customerexpectations over a product’s lifecycle.

The Innovator’s Dilemma andDisruptive Technologies

The Kano model cautions us that asnew technology develops, today’s excitementneeds can become tomorrow’s must-haveneeds. However, customers also evolve astechnology evolves. For example, as morecomputer users purchase laptop computersrather than desktop computers, their needs forreduced size and weight increase dramatically.If a disk-drive manufacturer is focused onlyon desktop computers, it may missopportunities when customers move to laptopcomputers.

Bower and Christensen (1995) andChristensen (1998) formalize this concept andpoint out that listening to one’s currentcustomers, while consistent with the firm’sfinancial goals, may enable entrants with newtechnologies to eventually displaceincumbents. This may happen even thoughthe new technologies are not initially aseffective as the incumbent technologies on“more-the-better” features. For example, theinitial small hard drives for portables were notas fast nor could they store as muchinformation as the larger disk drives used indesktop computers. But an emerging class ofportable users demanded them because theywere smaller and lighter in weight.


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Eventually, the smaller drives caught up onthe “more-the-better” features (storage, speed)and dominated the market because they wonon the “delighter” features (size and weight).

Firms fall into disruptive-technologytraps because current customers may notappreciate the new benefits of a newtechnology because it does not perform aswell on the traditional attributes they value.However, new users, not well known to thefirm, may value the new attributes morehighly and forgive the below-averageperformance on traditional attributes. Inaddition, new entrants may be willing to settlefor lower sales and profits than incumbents inorder to gain a foothold in the market.Eventually, as the new technology achieveshigher performance on traditional attributes,the incumbent’s old customers beginswitching and the firm loses its leadershipposition. To avoid the disruptive-technologytrap and to stay on top of the needs of allcustomers, Christensen proposes thatincumbent firms partner with (or develop)independent, “entrepreneurial” entities toexplore disruptive technologies. Christensenproposes that the entrepreneurial entities beheld to less stringent short-term financial andperformance objectives so that they mightfocus on long-term performance by satisfyingthe “delighter” needs of the new and growingmarkets.

Empathic Design and UserObservation

M any firms real i ze t hat no m at t er howrefined the res earch methodology and no mat terhow m uch data i s col l ected, som e ins ight s canonl y be gai ned by obs ervi ng cus tomers in thei rnat ural habit at (Leonard Barton, Wi l son, andDoyle 1993). This i s part icul arl y true whencus tom er needs are di fficult t o verbal ize or arenot obvi ous . The technique of em pat hi c des ignrequi res t hat m embers of the desi gn team i mm ers e thems el ves i n t he cust omerenvironm ent for a period long enough t o abs orb

t he problem s and feel ings experienced by us ers .If a product is inconveni ent , ineffi ci ent , ori nadequate, t he desi gner gai ns fi rs t -handexperi ence wi th the probl em. Empat hicm et hods are part icul arl y effect ive atdet erm ining t he ergonom ic as pects of aproduct. The em pathi c met hods can be carri edout by m em ber of t he PD t eam or m arket ingprofes si onals , but i n eit her case, rich medias houl d be used to capture the users experiences o that it can be shared wit h the enti re PD t eam .

Int ui t , makers of Qui cken, t he leadi ngpersonal fi nanci al s oft ware package on t hem arket , pi oneered the “Fol low-M e-Hom e”program in which Int uit em pl oyees observepurchasers in t hei r hom es from the mom ent t hey open the box to the tim e they haveQui cken funct ioning properly (C as e 1991). Usi ng em pat hi c des ign and us er observati on, Int ui t has st eadil y improved Quicken’s ease-of-use wi th feat ures such as auto fi ll -in ofaccounts and payees, on-s creen checks andregis t ers that look like their paper counterpart s, and push butt ons t o aut om ate comm on tasks .M ore import antl y, Int ui t took res ponsi bi l it y fort he enti re process of producing checks i ncluding worki ng to im prove printers andpri nt er dri vers even thos e t hes e were made byt hi rd part i es . Em pat hi c des ign highli ght ed t hat cus tom ers were not buyi ng Intui t’s products even though t he probl em was not Int uit ’s t echni cal res ponsi bi l it y. Intui t recogni zed t hat i t could lose it ’s share of the s oft ware market if it did not sol ve t he pri nt er manufacturers’s oftware and hardware problems . Int ui t’s focuson cus tomer needs has kept t he company on t opof a highl y com pet it i ve, ever-changi ngm arket pl ace.

Hol tzblatt and Beyer (1993) devel oped at echni que known as cont ext ual inqui ry inwhi ch a mem ber of the des i gn t eam conduct san ext ensi ve int ervi ew wi t h a cus tom er at his orher s i te whil e the cust om er perform s real t as ks. The i ntervi ewer can int errupt at any t im e t o askquest i ons about the why’s and how’s of t hecus tom er’s acti ons . The res ul t s of thes e


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contextual inqui ri es are shared wit h otherdes ign t eam m em bers and s ynt hes ized us ingaffini ty di agram s (descri bed below) and workflow chart s .

Underlying Meanings and Values

In addit ion t o exploring cus tom ers’ st at edneeds , P D teams often s eek t o unders tand theuns tat ed or “unart iculated” needs . One met hodt o get at unart i culat ed needs is wi t h in-dept hexperi enti al int ervi ews t hat s eek t o get cus tom ers to express such needs . (In ot herwords , unarti cul at ed needs are real l y jus tdiffi cul t-t o-art icul ate needs. ) In addi t ion, somefirms have expl ored ant hropological methods .

C ul tural anthropol ogy (cf. Levi n 1992) i s t he s t udy of hi dden meani ngs underl yingproducts , or meani ngs whi ch are s ought , but l eft unm et . The approach is broader t hanpsychology-based m ot i vati onal res earch i n t hat i t account s for cust omers ’ s oci al values , not just emoti onal needs . Is s ues such a com pany’s environm ent al i m pact and minori ty hi ri ngrecord gai n s ignificance.

How does cult ural ant hropology affectproduct des ign? The key is consi st ency wit ht he s oci al si gni fi cance of t he product . Forexampl e, i f cus t om ers buy zero em is s ionelect ric vehi cl es because of t hei r concern about t he envi ronment , t hey m ay object to a des ignwit h bat teries whi ch produce t oxi c was te.

Zal tm an’s (1997) M et aphor El ici tati onTechni que (ZM ET) s ugges ts that theunderl yi ng values and m eanings , whi ch dri vecus tom ers towards speci fi c product choicedecis i ons, may be uncovered through a process of vi s ual sel f-expres si on. ZM ET requi res parti cipant s to provi de pi ct ures and i mages t hat capture what they seek in the product cat egory.B ecaus e ZM ET al l ows the research st i muli tobe control l ed by t he respondent s, t hey canexpres s their feel ings, product m eanings andatt it udes.

Kansei Analysis and the Mind of theMarket

A s el ect group of product s , es pecial ly“hi gh-touch” consumer durabl es such as aut om obi les and pers onal inform at ionappli ances , are purchas ed as m uch for theemoti onal res ponses they evoke as for thefunct i on t hey provide. F or such product s ,m easuring cus tom ers’ true feel i ngs towardpot ent ial des igns, es peci all y their look and feel, m ay prove inval uable.

Kansei anal ys is may be des cribed as the“Li e Det ect or” of cus tomer res earchm et hodol ogi es . Most techniques of lis tening tot he cust om er as s um e that res pondent s provideans wers that accurat ely refl ect t hei r preferencesand percept ions . But for vari ous reas ons s uchas social press ure, vanit y, or even inaccurat es el f-percepti on, furt her probi ng is neces sary. Kansei anal ys is seeks t hes e true preferences bym easuring non-verbal respons es to product s ti mul i, m uch i n t he same way that gal vanics ki n res ponse, voi ce st res s, and breat hi ng rat eare recorded in li e det ect or t est ing. Exam pl esof ot her non-verbal res ponses that can bem easured are facial mus cl e cont ract i ons andeye m ovement and dil ati on. By meas uri ngt hese subt l e physi ol ogi cal res ponses whi l e acus tom er vi ews or int eract s wi t h a new product ,t he P D t eam gauges t he cus tomer’s feel ingsand at ti tudes . A gri mace duri ng sharp s t eeri ngm ight indi cat e poor res ponse i n a car, whil evis ual focus on a parti cul ar coffee makerprotot ype might reveal a preference for theout ward appearance of t hat des i gn. Bycorrel at ing t he non-verbal reacti ons ofcus tom ers wit h the s pecifi c st i muli that produced t hos e react i ons, cust omer preferences for a product ’s “l ook and feel ” can bedet erm ined. Si m il arl y, by obs erving det ail edcli ck st ream dat a, s oft ware and web si tedes igners can opti mi ze the user i nt erface form axim um cus tomer s at i sfact ion.

Kos sl yn, Zalt man, Thompson, Hurvi tz, and B raun. (1999) des cribe a m ethod that


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del ves even deeper i nto t he physi ol ogi cal aspect s of cust omer res ponse m echani sm s, am et hod t hey t erm , “The Mi nd of the Market .”Thi s work uti li zes brai n imagi ng of respondent sviewi ng marketi ng st i muli , i n thi s cas eaut om obi le deal ers hi p s cenarios , to as ses snegat i ve and pos it ive reacti ons . B y com paringt heir resul ts t o t hos e of anot her research teamuti li zing more conventi onal market res earchm et hods, t he accuracy of the brai n imagi ngapproach was val idat ed, at l eas t di recti onall y.

Benefit Chains

B enefi t chains focus on w hy cust omers have a part icul ar need that is not yet addres s edby exi st ing products . For exam pl e, whil e afocus group or Kano analys is m i ght det erm inet hat cus tom ers want small er, l i ghter-wei ght not ebook comput ers t hat perform fas t er, theunderl yi ng values dri vi ng thos e needs may not

be so obvi ous . Are cus tom ers so am bit ious thatt hey want to accom pl i sh t wice the am ount ofwork (notebook performance) and workeverywhere they go (l ight wei ght )? Or could i t be that cus tomers seek more lei sure ti me (i .e. ,l es s tim e worki ng), and prefer to do t hei r workout si de of the office? The underl yi ng val uesdri vi ng those needs might di ffer dramati cal lyand t he di fference i n underl yi ng val ues might i mply di fferent product -development solut ions . The workaholi c not ebook comput er us er mi ght need more features and bat tery li fe whil e t hel ei sure-seeker might need ease-of-l earni ng andl ow-price. F igure 7 il lus trat es a benefi t chainfor coffee makers. Here, the user’s work ethi cl eads to a desi re for eit her a di gi t al t i mer wit haut o-s hutoff (or anot her sol ut i on s uch as Int ernet cont rol ) that hel ps t he us er sat is fy hi sor her cul t ural work-et hi c val ues .

Figure 7: Benefit Chain Structure for a Coffee Maker

Cultural, Social

and Personal

VALUES

Desired

BENEFITS CONSEQUENCE

Product FeatureProducts

CATEGORIZEDby ValuedAttributes

Customer NEEDSProduces a for Particular

Attributes

The Benefit Chain: How VALUES lead to NEEDS

Example

“ I believe inhar d wor k.”

“ I want to getto the officeear ly ever ym or ning”

“ I would likem y coffee

r eady when Iawake”

Five coffeem aker s featur edigitaltim er s

“ I want them odel with

digital tim er and auto-s hutoff.”

R el at ed met hods incl ude a Means -EndC hain model of cus tom er choi ce behaviorGut man (1982) and a val ue-syst ems model (Rokeach 1973). Thes e aut hors vi ew cust omerneeds as a chai n reacti on begi nni ng wi th thecul tural , social , and pers onal values hel d by thei ndivi dual . The underl yi ng val ues hel d bycus tom ers then gui de thei r choi ces towardproducts t hat produce des i red benefi ts . Sincet here are num erous choi ces for a gi ven product ,

peopl e cat egori ze them int o set s or cl as s es ,t hereby si m pl ifying the deci si on. Thecat egori es creat ed by each cus t om er arei nfluenced by hi s or her val ues . Whil e thel ei sure seeker may categorize not ebookcom put ers bas ed on price, the workahol ic mayconsi der m achines grouped accordi ng toperformance.


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Gut man and Reynolds’ (1979) techniquefor m eas uri ng s uch benefi t chai n begins wit hKel ly’s (1955) repert ory gri d techni que. Aft erres pondent s have drawn di s ti nct ions wi thi n as et of t hree product s (by determi ni ngs im il ari ti es bet ween two products anddifferences wit h a t hird), t hey are as ked whi chatt ri but e they prefer. They are then as ked whyt hey prefer t hat att ribut e at higher and hi gherl evel s unt i l som e core val ues are reached. This t echni que is som et im es referred t o as ladderi ng. It is il lus trat ed in Fi gure 8.

Figure 8: Laddering Example for aNotebook Computer

Why Ask Why?: The Laddering Technique

FeelBetter

Notebook Computer Needs toweigh less than three pounds

Easier to take Places

Do Hard Things

Less Taxing

Get LessTired

Use MoreOften

FeelStronger

AccomplishMore

Why should your notebook computer be lightweight?

Focusing the Design Team byIdentifying Strategic Customer Needs

Traditional surveys and interviews,experiential interviews, Kano analysis,disruptive-technology analysis, empathicdesign, the study of meanings, Kanseianalysis, and benefit chains all identifycustomer needs and desires which, if fulfilled,lead to successful new products. However,these methods are sometimes too effectiveproducing not just a few needs, but ratherhundreds of customer needs. Even for asimple product such as a coffee maker, it isnot uncommon to generate a list of 100-200

customer needs. For complex products suchas copiers and automobiles, such lengthy listsmight be generated for subsystems (interior,exterior, drive train, electronics, climatecontrol). But the needs are not allindependent.

To proceed further in idea generation,the PD team needs focus. This focus isprovided by recognizing that the needs can begrouped into strategic, tactical, and detailedneeds. If we call the raw output of the variousneeds-generation methods “detailed needs,”then we often find that the 100-200 detailedneeds can be arranged into groups of 20-30tactical needs. For example, detailedstatements by customers of a software packageabout the on-line help systems, “wizards,” on-line manuals, documentation, telephonesupport, and Internet support might all begrouped together as a need by the customer“to get help easily and effectively when I needit.” The detailed needs help the PD teamcreate technology and other solutions toaddress the tactical need. However, thetactical need is sufficiently general so that thePD team might develop totally new ways ofmeeting that need such as communities ofpractice within large customers. The tacticalneeds might also be grouped into 5-10strategic needs such as “easy to use,” “doesthe job well,” “easy to learn,” etc. Thestrategic needs help the team develop conceptsthat stretch the product space and open up newpositioning strategies.

Later in the PD process (Figure 3) thePD team needs to decide on which strategicneed to focus or which features best fulfill astrategic need. In later sections we reviewmethods to prioritize these needs (andfeatures). However, in the fuzzy front end it ismore important that we get the grouping right,that is, it is more important that the rightstrategic and tactical groups be identified.This is because, at this stage of the process,the PD team wants to generate a larger numberof potential product concepts, each of which


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might stress one or a few strategic needs. ThePD team also wants to explore new ideas toaddress these strategic needs by solving therelevant tactical needs in new and creativeways. In this chapter we review the two mostcommon methods of grouping needs.

Team-based Needs-Grouping Methods:Affinity Diagrams and K-J analysis

The J apanes e ant hropologi s t Ji ro Kawakit a(denot ed K-J by the Japanese cust om of l ast nam e first ) developed a m ethod of s ynt hes izingl arge am ounts of dat a, includi ng voi ce of t hecus tom er data, int o manageable chunks bas edon themes that emerged from the dat at hems elves (M izuno 1988). The K-J met hoduses a t eam approach to develop af fi ni t ydiagr ams in which each voi ce-of-t he-cus tomers tatem ent is grouped wi th ot her s im i lars tatem ents . The K-J technique requi res anopen mind from each parti cipant , encourages creat i vi ty, and avoi ds cri ti ci s m of “s trange”i deas . The K-J method cl aim s to be based ons ti mul at ing t he ri ght -brai n creat ive andemoti onal centers of thought rather than relyi ngon pure cause-and-effect logic.

Typicall y, each data el em ent , preferably int he original language of the cust om er, i s recorded onto a card or P ost -i t note. Thecards are wel l shuffl ed t o eli m inat e any pre-exi st i ng order bias and are then grouped basedon feeli ngs rat her t han l ogi c. The im press ionor im age gi ven by each cus tomer s tat em ent s ugges ts t he group t o whi ch that card has t hegreat est affi ni t y rat her than any pre-conceivedcat egory. When a few cards are grouped, theyare l abeled wit h a descri pti on that capt ures theess ence of thei r m eaning. C ard groups are thenass em bled int o a l arger di agram wit hrel at i onshi ps between t he groups of cards i ndicated. The end res ul t i s a diagram showi ngt he t op fi ve to ten cus tom er needs, relat ions hipsbet ween needs , and detail ed cus tomer voi cedat a expres si ng thos e needs.

Customer-based Needs-GroupingMethods: the Voice of the Customer

Whi le affi ni ty di agram s and K-J analys is m ethods have proven t o be powerful i nm any appli cat ions, t hey can al s o suffer whent he t eam i s t oo em bedded in it s corporat ecul ture. For exam pl e, Gri ffin and Hauser(1993) com pared affi nit y diagrams developedby PD team s wit h affi ni ty di agram s devel opedby act ual cus tom ers. In bot h cas es , t he team m em bers grouped cust omer needs the way t hefirm normal ly buil ds the product. Cus tom ersi ns tead grouped the needs by t he way t hey uset he product . Griffi n and Haus er al s o appli edhierarchical cl ust eri ng (Green, C arm one, andF ox 1969, Rao and Kat z 1971) t o needsgat hered from a larger sam pl e of cus tomers. Here each cus tom er does a relat ivel y s im ples ort of needs i nto a sm al l num ber of pil es. Thehierarchy of st rat egi c, t act ical, and det ai ledneeds comes from t he st at i st ical analysi s . This m et hod, cal led bot h the Voice of the C us t om erand Vocalys t, has proven effect ive in li t eral l yhundreds of appl icat i ons. Alt hough we knowof no head-to-head compari son cus tom er-affini ty and voi ce-of-t he-cust omer met hods, cus tom er-based met hods seem to provi deusabl e s truct ures than team-bas ed m ethods andt hi s det ai l l eads to more creat ive sol ut i ons.

New Web-based Methods for the FuzzyFront End

Information pump. The methodsreviewed above provide a breadth of means toidentify customer needs, whether they arearticulated or unarticulated, individual-specificor bound in the culture, verbal or non-verbal,etc. Recently, the Internet has made itpossible for groups of customers tocommunicate directly and iteratively with oneanother and, together, produce a set of needsthat might not have been identified any otherway. The “Information Pump” is a novelmethod of objectively evaluating the qualityand consistency of respondents’ comments, inwhich “virtual focus group” participants opine


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on a common stimulus such as a new productconcept (Prelec 2000). The quality of theparticipants’ comments is judged by animpartial expert and by the other respondents.Two keys to the effectiveness of theInformation Pump method are the use of real-time Bayesian updating and theimplementation of an incentive-compatiblereward system for the respondents. A benefitof using the Information pump methodology isthat, in addition to collecting objective ratingsof individual opinions and comments about anew product concept, one gains insight intothe overall quality of each respondent.

“Listening in” to customers on theInternet. The Internet also provides the meansto identify customer needs by passivelyobserving customer purchase behavior on aweb site. By organizing the web site byagendas based on features or customer needs,a virtual engineer can listen in and observehow customers process attributes and, inparticular, when they search for attributes,features, or needs that cannot be satisfied byany extant product. Urban (2000)demonstrates this indirect method of capturingunmet customer needs by observing customerinteractions with an Internet-based salesrecommendation system for trucks. While thevirtual salesperson attempts to identify theideal, current-model truck for eachrespondent, a virtual design engineer noteswhich product attributes leave the customerthe most unsatisfied. The virtual engineerthen “interviews” the customer to betterunderstand the unmet needs and how to bestresolve the inherent tradeoffs that preventthose needs from being met.

Ideation Based on Customer Needs(and Other Inputs)

Once the PD team has identified andgrouped customer needs it must generate ideason how to address those needs (Goldenberg,Lehmann, and Mazursky 1999). In the nextsection (on designing and engineering

concepts) we discuss formal methods such asQFD by which the PD team can systematicallygenerate effective concepts. But not allconcepts can be generated systematically.Sometimes the PD team needs crazy andbizarre solutions which, when refined, solvethe customers’ needs in new and creativeways. A wide variety of ideation methodshave been proposed including brainstorming(Arnold 1962), morphological analysis (Ayres1969), group sessions (Prince 1970), forcedrelationships (Osborn 1963), systemsapproaches (Campbell 1985), variedperspectives (De Bono 1995), archivalanalysis (Altschuler 1985, 1996), andinventive templates (Goldenberg, Mazursky,and Solomon 1999a, 1999b). In this chapterwe review the three most recent proposals andrefer the reader to the references for the moretraditional ideation methods.

Overcoming Mental Blocks

De Bono (1995) outlines a method ofovercoming the mental blocks most of us havethat derive from our particular approaches toproblem solving. Figure 9 depicts De Bono’ssix hats, representing the diverse perspectivesof potential members of a product designteam. Typically, each participant in a newproduct debate feels most comfortablewearing one or two of the hats, frequentlyleading to conflict. The “six hats” exercisesrequire team members to “wear the otherguy’s hats” so as to improve communicationsand foster creative exchange. For example,one might ask members of the design team toreact to a novel situation such as, “A pill isinvented that makes people dislike the taste offatty foods,” from the perspective of each ofthe six hats. By identifying the types ofthinking each team member engages in,participants gain insight into their ownproblem solving approaches as well as thoseof others.


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Figure 9: De Bono’s Six Hats Method

TRIZ (Theory of Inventive ProblemSolving)

Altschuler (1985, 1996) developed atechnique for generating creative solutions totechnical problems by harnessing archivalknowledge, an early version of knowledgemanagement. Specifically, Altschulerreviewed tens of thousands of patents andnoticed that their genius was in applyinginventive principles to resolve tradeoffsbetween a limited set of “competing” physicalproperties (approximately 40 in number).These solutions typically resulted in notradeoff being made at all, for example theway aluminum cans are both lightweight andstrong by virtue of their cylindrical design.Altschuler organized the patents according tothe fundamental tradeoffs they resolved, andcreated tables so that future designers couldapply the inventive principles to similarproblems. More recently, others haveadvanced Altschuler’s work into otherdomains of science and technology.Marketing’s role in applying a method such asTRIZ is to represent the customer’s voice incomparing the multiple technical alternativesgenerated.

Inventive Templates

Goldenberg, Mazursky, andSolomon (1999a, 1999b, 1999c) extend

Altschuler’s methods to propose thatideation is more effective when the PD teamfocuses on five templates – well-definedschemes that are derived from an historicalanalysis of new products. The authorsdefine a template as a systematic changebetween an existing solution and a newsolution and provide a method by which thePD team can make these changes in a seriesof smaller steps called “operators:”exclusion, inclusion, unlinking, linking,splitting, and joining. For example, the“attribute dependency” template operates onexisting solutions by first applying theinclusion and then the linking operators.The authors give an example of how a newcar concept was developed by creating adependency between color and the locationof a car’s parts. Specifically, Volkswagen’s“Polo Harlequin” features differentlycolored parts and has become quite popularin Europe even though it was initiallyintended as an April Fools’ joke, Othertemplates include component control(inclusion and linking), replacement(splitting, excluding, including, and joining),displacement (splitting, excluding, andunlinking), and division (splitting andlinking).

Summary of Methods for the FuzzyFront-end

The fuzzy front-end of the PDprocess is the least well defined, but,perhaps, the most important phase of theprocess. Without good customer input andcreative ideas, the process is doomed fromthe start. Customers just will not buyproducts that do not satisfy their needs. It ishard to succeed in PD unless the PD teamhas ideas which help them to create newways to satisfy those needs. Thus, it is notsurprising that there has been significantresearch to propose and test many differentways to identify customer needs and


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generate creative ideas. In this section wehave tried to review the most commonmethods that are relevant to a marketingaudience. They are rich and varied; eachhas its own strengths and none should beused alone. For example, if PD team usesjust the Kano model it could become overlyfocused on the product’s technologicalfeatures and miss underlying psychologicalor social needs. On the other hand, a purefocus on the mind of the customer couldcause the team to miss the obvious solutionsthat will ultimately dominate the market.Good practice suggests that the PD teamconsider a variety of approaches tocustomer-need identification and use themin parallel. If the final output is subjected toa rigorous needs-grouping method such asaffinity diagrams or customer sorts, then thePD team will be able to assure that the ideasit creates solve one or more strategiccustomer needs.

Once the PD team knows thestrategic needs, it needs some ideas. Onceagain there are a variety of methods. Ourown experience suggests that different teamswill be comfortable with differentapproaches. Some teams prefer the formalsystems approaches, others need the wilderapproaches that “take a vacation from theproblem,” and still others prefer to just workalone. The organization (see enterprisestrategy below) must be conducive to thesemyriad approaches. While such creativity islauded by most PD teams, the organizationalchallenges and frustrations of dealing withtruly creative people frequently preempt thebenefits (Staw 1995). However, if ideageneration is successful, the teams willsuggest large numbers of initial ideas thatcan later be systematically engineered intoeffective concepts, prototypes, and products.We now turn to more systematic methods bywhich these ideas are shaped into conceptsand products.

Designing and EngineeringConcepts and Products

R et urning to the P D funnel at the cent er oft he P D proces s in Fi gure 3, we see t hat t hem any ideas creat ed i n opport uni ty i denti ficat i onare funnel ed to a sm all er set of concept s t hat are wi nnowed st i ll furt her t o a viable s et ofproducts or plat form s . In t hi s s ect ion weaddres s concept generat ion and the des ign andengineering proces ses t hat develop these vi abl eproducts . We begi n wit h met hods such as leaduser analys is , Kai zen and Teian anal ys is , s et -bas ed desi gn, and Pugh concept selecti on. Each of these m ethods bui l ds on t he ideat ionand cust om er-needs unders t andi ng of the fuzzyfront end.

Lead Users

S om et i mes the best i deas com e fromout si de the firm and, i n parti cul ar, from cus tom ers thems elves . In some categories t heaverage cus tomer can recogni ze and appreciatenew s oluti ons t o t hei r bas ic needs and i n othercat egori es it i s m ore difficul t . M orei mport antl y, PD team s are often embedded int heir corporate cult ure and vi ew PD through t hel ens of their current products . For exam pl e, com put er s oft ware des igners oft en focus oncurrent markets rather than em erging m arket s. Older readers m i ght rem em ber whenM ul ti M at e and WordSt ar dom inat ed the wordproces si ng market. These packages were bas edon the older Wang word process ors t hat wereused mai nl y by office s taff whose prim aryfunct i on was word process i ng. When wordproces si ng moved t o the m ainst ream, ot herpackages , such as WordP erfect, recogni zedt hose us ers who word process ed as t hey wrot e. Of cours e, WordP erfect it s el f did not move fas tenough t o Windows-bas ed product ivit y s ui t es and, in turn, l ost t he market to Mi crosoft’sWord. As wit h other di srupt ive t echnologies, t he evol ut i on of word proces si ng softwares ugges ts t hat fi rm s need to act ivel y s ol i ci t input from forward-looki ng cust omers .


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Lead user analys is recogni zes that a s mal ls et of t oday’s users face tomorrow’s problems . These us ers oft en face new problems and oft endevel op their own sol ut ions. In som e cas es t hese us ers are a very speci al i zed market , but i nm any cas es they anti cipat e t he needs of thel arger m arket . For exam pl e, aut om obi lem anufact urers foll ow NASC AR racingcarefull y becaus e the raci ng t eam s face newchall enges and oft en invent new s ol uti ons t hat can l ater apply to a more general m arket . C om put er proj ect ion sys tem s manufact urers m onit or hi gh-end uses , such as NASA’s fl i ghts im ul ators , becaus e as technol ogy advances theprobl ems faced by si m ul at or us ers wi ll s ugges t s ol ut i ons for broader m arket s such as vi deogam ers .

Von Hi ppel (1984) suggest s t hat s om e oft he best s ources of ins ight int o us er needs andpot ent ial product prototypes are these “l eadusers , ” cus tomers whose s t rong product needsi n the pres ent fores hadow the needs of t hegeneral marketpl ace in the fut ure. Thes epeopl e are “ahead of thei r t im e” and badl y needgood ans wers now t o sol ve speci fi c probl ems t hat mos t peopl e won’t experience for som et im e to com e. Von Hi ppel describes how toi dent i fy l ead us ers, and then how t o i ncorporatet heir insi ght s int o the product des i gn proces s i na five-s tep proces s:

1. Ident i fy a new market t rend or product opport unit y (e. g. great er computerportabil it y, zero em i ss ion vehi cl es , etc. ).

2. Define m eas ures of potent i al benefi t ast hey rel at e t o cus tom er needs.

3. S el ect “lead us ers ” who are “ahead of theirt im e” and who wi ll benefi t t he most from agood sol ut i on (e.g. power us ers ).

4. Ext ract informat ion from the “l ead users ”about thei r needs and pot ent ial s ol uti ons and generat e product concept s that embedt hese solut ions .

5. Tes t the concept s wi t h the broader market t o forecas t t he im pl i cati ons of l ead userneeds as t hey appl y to the m arket i ngeneral.

Urban and von Hi ppel (1988) appli ed this t echni que to com puter-aided des ign (CAD)s ys tem s. Alt hough t he convent i onal wi sdom oft he C AD devel opers was that the s ys t em s werem uch too complex for us ers t o modify, Urbanand von Hi ppel found that lead us ers who faceddiffi cul t probl ems had not onl y m odi fi ed thei rs ys tem s, but had generated s ignificant i mprovem ent s. For exam pl e, des igners ofcom pl ex, i ntegrated circui ts developed 3-dim ens ional C AD syst ems t hat coul d deal wit hcurved s urfaces , m ul t iple layers, and non-s urface-mount ed component s . When 3-D CADs oftware packages were devel oped bas ed ont hese lead-us ers ’ sol ut ions, t hey were hi ghlyrat ed by t he more general market.

Employee Feedback: Kaizen and Teian

Another source of ins ight into ways inwhi ch to addres s cus t om er needs bet t er i s t hecom pany’s own work force. In his writ ings onK ai zen, t he Japanes e concept of cont i nuous i mprovem ent , Mas aaki (1986) explains t hat each employee i s res ponsi ble for bot hm ai nt aining t he st at us quo and dest roying i t. Thi s refers t o the noti on that em pl oyees must fol low cert ai n standards, but als o eli mi nat ewas te and contri bute to i nnovat ion. One way inwhi ch em pl oyees can contri bute is by m aki ngfrequent s ugges t ions on product and process i mprovem ent s through a sys tem the J apanes ecal l T ei an. See K ai zen T ei an 1 by t he JapanHum an Relat ions As sociati on (1992). Ofcours e, the s cope of such an em pl oyees ugges ti on syst em covers more than jus tcus tom er needs, but the es sence of conti nuous i mprovem ent i s meeti ng cus tomer needs moreeffect ivel y.


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Set-based design and Modularity

In addit ion t o get ti ng ideas from l ead us ersand from t he product i on em pl oyees , the P Dt eam can purs ue syst emati c m et hods such ass et -based des ign. This m ethod generat es m ul ti ple desi gn opti ons by breaki ng a product i nt o small er subsyst ems , standardizi ng t hei nt erfaces between t hos e subsys tems , andgenerati ng one or more des ign opt ions for eachkey s ubs ys t em s. Given int erchangeabil it ybet ween the s ubs ys tem s, m ult ipl e des igns ol ut i ons becom e avai labl e t o the fi rm , lim it edonl y by the num ber of com binat i ons ofs ubsys tems that are feasi ble.

Ward, Li ker and Sobek (1996), Sobek, Ward, and Liker (1999), and Li ker, Ward, andC ri st i ano (1996) des cri be a set -bas ed des ignproces s in which t he freezing of the finalchoice of subsys tems is delayed unt i l theproduct is cl os er to launch. The fi rm can thencheck the pul se of a dynam ic m arket in order toopt im i ze t he fi nal choi ce of m odular des i gns, t hereby exploit i ng t he fl exi bi l it y inherent i n t hes et -based approach. Baldwin and Cl ark (2000)further charact eri ze fl exi bi li t y due t o productand proces s m odulari t y as form s of real opt ionsand demons t rate the pot ent iall y high val ue ofhol di ng such opt ions .

Pugh Concept Selection

When mass customization is notprevalent in an industry, the firm must narrowfrom a broad array of possible designsolutions to a few critical solutions(sometimes just one). Pugh (1996) develops amethod of winnowing multiple new productconcepts which he terms “controlledconvergence.” In essence, Pugh suggests thateach member of the design teamindependently generate conceptual solutions tothe design problem. The competing ideas arethen compared to a standard datum, selectedfor its typicality in the product category, andare evaluated as being better than, equal to, orinferior to the datum on the key dimensions

that will contribute to product success. Thegroup proceeds to eliminate weaker ideas, butalso attempts to cull the advantages of eachconcept and incorporate it into the remainingones before discarding it. In this way, the“winning” concept incorporates many of thebest ideas of all of the other concepts.Marketing’s role in this process is to identifythe key customer criteria on which conceptswill be based and to ensure that each conceptis evaluated with customer preferences inmind.

Using inputs from the ideationprocesses, lead-user analysis, set-baseddesign, and Pugh concept selection the PDteam outputs a smaller set of high-potentialproduct concepts. Following the PD funnel,the PD team then focuses on these conceptsand develops each to their greatest potential.This means linking engineering solutions tocustomer needs and vice versa.

Value Engineering

F rom a cus t om er’s poi nt -of-view, aproduct consi st s of a bundle of feat ures andbenefi ts result i ng from i t s us e, whi le from t hefirm’s pers pect i ve, the product cons is ts of abundl e of parts and proces s that res ul t in it s m anufact ure. At t he point when cos t andfeasi bil it y t radeoffs are made by t he fi rm, i t i si mport ant to connect the cus tom er and fi rmperspect ives. Ulrich and Eppi nger (2000)des cri be one met hod of doi ng s o known as val ue engi neeri ng, which rel at es the i mport ancecus tom ers place on each functi on perform ed bya product to the cos t of the part s contri buti ng tot hat funct i on. A key pri nci pl e underl yi ng val ueengineering i s that the m arginal cos t of eachpart of a product shoul d be in proport ion t o its m argi nal cont ri but ion t o cus tom er value. Toi mplem ent val ue engi neeri ng the t eam m us t know (1) t he val ue pl aced by cust om ers oneach funct i on and (2) t he cost of t he parts andm anufact uri ng t o provide that funct i on. Weaddres s (1) bel ow as it i s m os t rel evant to t he


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m arket ing audience. For great er det ai l on (2)s ee Ul ri ch and Eppinger (2000).

Quality Function Deployment and theHouse of Quality

Val ue engi neeri ng requi res t hat we linkcus tom er needs to product solut ions so t hat t heP D team can m ake i nt ell igent t radeoffs and, perhaps, fi nd creati ve sol ut ions that do notrequi re tradeoffs. Quali t y Funct ionDeployment (QFD) and it s more-recent progeny (Tess ler and Kl ei n 1993, McGrath1996, Sm it h and Reinert sen 1998) provi de them eans to m ake t his l i nkage. QF D i ts elf i s a s etof process es that li nk cus tomer needs al l t heway t hrough t o producti on requi rements .Alt hough t he ful l QF D proces s is som et im esused, most notably i n J apan, i t i s the fi rs t

m at ri x of QFD, cal led t he Hous e of Quali t y(HOQ), t hat i s used mos t oft en. The dri vingforce behi nd the HOQ is t he short , accurate,rel evant l i st of key cust omer needs ident ified i nt he fuzzy front -end and s t ruct ured int os trat egi c, tact i cal, and det ai l ed needs. In theHOQ, these needs are relat ed t o productatt ri but es which are then eval uat ed as t o howwel l they meet cus tom er needs. P roductatt ri but es are “benchmarked” agai ns t com pet it ors ’ features i n their abil i ty t o m eet cus tom er needs and t he HOQ i s used tocom pare the benchm arking on features t obenchm arki ng on cust omer needs . Fi nal ly thet ot al product i s eval uated by the abil it y of its features t o m eet cus t om er needs m oreeffect ivel y and at l ower cos ts than competi ti veproducts .

Figure 10: The House of Quality

The House of Quality

Customer needs and their

importance to the customer

Product features

How do product features affect

each other?

How well does each competitor meet each need? How well does

each feature meet each need?

Cost of each feature

Target level of each feature

How do competitors rate on each feature?

1

4

5

3

6

7

8

2

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The HOQ provides and organizes theinformation that the PD team needs to refineeach concept. It has proven effective in avariety of applications including consumerfrequently purchased goods, consumerdurables, consumer services, business-to-business products, and business-to-businessservices. A further advantage of the HOQ andrelated techniques is that it enhancescommunication among PD team members(Griffin and Hauser 1992). This is becomingeven more important as PD teams becomemore dispersed and global.

Marketing’s primary input to the HOQincludes identifying customer needs (fuzzyfront end), measuring how products fulfillthose needs (e.g., Green, Tull, and Albaum1988, Churchill 1998, Moore and Pessemier1993, Urban and Hauser 1993), andunderstanding the tradeoffs among customerneeds and among potential product features.Ultimately, the HOQ method translatescustomer priorities, as captured by aprioritized list of needs, intoengineering/design priorities by identifyingthose product features that contribute the mostto satisfying customers better than competitiveofferings.

Tradeoffs Among Needs and Features:Conjoint Analysis

Aft er cust omer needs are ident i fi ed andgrouped, after cri ti cal feat ures are i denti fi edand l i nked to cust om er needs , and after highpot ent ial concepts are devel oped, t he PDt eam’s next s tep i s to focus on t hos e features and concept s that are m os t l ikely t o i mprovecus tom er s ati sfact ion and lead to profit abl eproducts . Devel oping m et hods to measures uch tradeoffs among cust omer needs and/ orfeatures i s , arguabl y, one of the m ost s t udiedprobl ems i n m arket ing res earch. We have beenabl e to ident ify alm ost 150 art icles publ is hed i nt he t op marketi ng journal s on conjoi nt anal ys i si n the l as t t wenty years. In thi s secti on wereview s om e of the basi c ideas . See als o

reviews by Green (1984), Green and Sri ni vas an(1978, 1990). Als o, because t hey cont inue tobe us ed by PD t eam s, we i ncl ude i n our revi ews el f-expli cat ed methods s uch as t hos e reviewedi n Wi l ki e and P ess em i er (1973).

S uppos e that the P D team is devel opi ng anew l aundry product and has ident ifi ed t hes trat egi c needs of “cleans effect ively,” “s afe fordel icate cl ot hes ,” “eas y to us e i n all s i tuat i ons, ”“good for the envi ronment , ” and“inexpensi ve. ” The team now want s toevaluate a seri es of product concept s, each ofwhi ch st ret ches one of the five s trategi ccus tom er needs. Conj oint analys is , appli ed tocus tom er needs, is t he general method tom easure the cus t om ers ’ tradeoffs am ong t hos eneeds . By ident ifyi ng and quanti fyi ng t het radeoffs, perhaps by cus t om er segm ent ,conjoi nt anal ys i s hel ps t o focus the P D team ont hose concept s that have the hi ghes t pot ent ial .C onjoi nt anal ys i s can als o be appli ed to product features ; for exam pl e, the m aker of an cameram ight want to know how hi ghl y cus tom ersval ue such feat ures as 1-s tep vs. 2-st ep pi ct uret aking, st yli ng covers, automat ic vs . manualfocus i ng, and automat ic vs . control l ablel ight i ng. Conj oint analys is can tel l the P D teamwhi ch of t he features i s mos t highl y val ued (bywhi ch segm ent ) and can as s ociat e a wil li ngnes s t o pay for thos e feat ures .

Camera Example. We begin byi ll us t rati ng conjoint anal ys is wi th the mos tcom mon t ype of appli cat ion – providi ngpreferences wit h res pect to product s (or product concepts ) in whi ch t he experim ent er has variedt he feat ures (or needs ful fi ll m ent) of t heproducts s yst em ati cal ly. We t hen revi ew ot hert ypes of conj oi nt analysi s and suggest newforms that are now feas ibl e wi t h st ate-of-t he-arti nform at ion and comm uni cat ion technology.

Suppose that we have identified a setof features for a new camera from acombination of sources including experientialinterviews, empathic design, Kansei analysis,and the information Pump. In general, this


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feature list will be quite extensive, but for thepurpose of this chapter we will illustrate thefeature list with a reduced set of five features.1

We might conclude that customers have needsthat can be addressed through various levels ofthe five product features in Figure 11.

F or exam pl e, one product permut at ionwould cost $25, weigh 16oz., have automat icl ight cont rol , produce 3-i nch square pict ures , and require t he us er to focus manual ly. In al lt here are 4 x 4 x 2 x 3 x 2 = 192 perm ut ati ons ,each of whi ch m i ght be a viabl e product. Inpri nci pl e, we coul d ask a sampl e of cust omers t o evaluat e each of the 192 pot enti al product s ,but t his woul d be an extremely unwi eldy tas k. As the num ber of pot ent ial products increas es t he t ask becomes qui t e burdens ome (Green, C arrol l, and Gol dberg 1981, Green, Gol dberg,and M ont em ayor 1981, Malhotra 1986) and thequali t y of the dat a degrades (B at es on, R ei bs t ei n, and Bouldi ng 1987, Huber, Wit t ink, F iedl er, and Mi l ler 1993, Moore and Semenik1988). We must al so be concerned wi th bi as es t hat can result when the num ber of level s vari es acros s att ribut es (Wi tt ink 1989), potent i al lydrawi ng more at t enti on and i mport ance to thos eatt ri but es wi th more level s.

Factorial designs. It would be evenm ore cum bersome if we asked cus tomers toevaluate product s that varied on the t wenty-t wocam era feat ures in foot not e 1 – if thes e were am ix of t wo-level and three-l evel at t ri but es t his would yi el d alm ost 400 mi l li on potenti al

1Some attributes might include price, picture

quality, picture delivery, opening of the camera,removable cover or not, picture taking process (1 vs 2steps), light selection (3 settings vs feedback),disposable camera, camera with cartoon characters,metallic vs plastic camera, battery type: AA vs AAA,picture size, color vs. black & white, chemical vs.digital picture vs. both at the same time, zoom lensvs. regular, holster for the camera, picture has asticky backing, the film is decorated, the filmcontains some advertising to reduce cost, panoramicpictures, waterproof camera, manual control over thepicture, picture cutter included in the camera, etc.

products . Ins tead we woul d l ike t o capture thei nform at ion t hat cus t om ers woul d provi deabout tradeoffs am ong feat ures by as ki ng eachcus tom er t o eval uate a much sm all er number ofproducts . For exampl e, for the five att ribut es inF igure 1 1 we m ay not need t o ask eachcus tom er t o eval uate al l 192 feat urecom bi nat ions. Ins tead, we coul d us e a m oreeffici ent experi ment al des ign known as f ract i onal fact ori al desi gn. If we as sum e thatall of t he at tri butes are independent we cans im pl i fy t he num ber of com bi nat ions st il l further by us ing a s pecial fracti onal factori alknown as an ort hogonal ar ray. For exampl e, we mi ght us e one ort hogonal des ign cal led a“hyper-greco-lat in-s quare” des i gn and as k eachcus tom er t o eval uate just 16 careful ly chos enproducts . (A si m il ar experim ent al approach, known as Taguchi [1987] m ethods , is us ed indes cri bi ng reli abi li t y tes ti ng and stati s ti cal s am pl i ng.) The actual det ai ls of a part i cularfract i onal fact ori al desi gn, t hat i s t he speci fi cl evel s of pri ce, wei ght , light cont rol , pictures ize, and focus i ng for the 16 pot ent ial products ,can be det erm ined us i ng l i st ings com pi led byAddel m an (1962) and Hahn and S hapiro(1966) or by us i ng comput er program s produced by S AS , S ys t at , Sawtooth S oft ware, B rett on-Cl ark S oft ware, and ot hers.

Respondents’ tasks. The t ask by whi chcus tom ers expres s their eval uat ions of product svaries . S ee Cat ti n and Wi tt ink (1989) for as urvey of indus t ry practi ce. By far t he most com mon t as k i s to si m pl y ask t he res pondent st o rank order t he product profi les in terms ofpreference. For exam pl e, each respondent m ight order a s et of cards according t o his orher preferences for (or l i keli hood of buying) theproducts depi ct ed. Each card, known as a f ul l-profi l e, des cri bes a product cons is ti ng ofdiffering level s of the key at t ri but es . Ot hert as ks incl ude as ki ng the res pondent to eval uat epai rs of profil es (S rinivasan and S hocker 1973,B at es on, R eibst ein, and B oul di ng 1987) ort radeoffs among at tri butes dis played t wo at at im e (Johns on 1974, Segal 1982). The tas k can


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be rank order (Green and Wind 1975) or t hecus tom er can provi de a scaled evaluati on(Carm one, Green, and Jain 1978, Haus er andS hugan 1980, Curri m, Weinberg, and Wit ti nk1981, Leigh, MacKay, and Sum mers 1984, S ri ni vas an and Park 1997). Al l form s of data

col lecti on appear to be reli abl e (B ateson,R ei bs t ei n, and Bouldi ng 1987, Green andS ri ni vas an 1990) and none seem to domi nat eeit her practi ce or t he academi c l it erature.

Figure 11: Simplified Conjoint Attributes and Levels

P roduct Attribute A lter native Levels

P rice (P ) $15 $20 $25 $30

Weight ( W) 16oz. 20oz. 32oz. 64oz.

Light contr ol ( L) A uto 1-s tep

P ictur e size (G ) pos tage stamp 3 inch s quare s tandard

F ocus ing ( O ) A uto manual

Part worth functions. Once we have therat ings (or rankings ) for each product i n t heexperi ment al des ign, we repres ent t his i nform at ion by a uti l it y funct i on, that is, a real -val ued functi on of t he at t ri but e level s chosens uch that differences i n uti li t y representdifferences (or rank orders) i n preferenceamong the products . If the att ribut es arei ndependent , as is as sumed i n an ort hogonal array, t hen t he ut il i ty of a product i s sim pl y t hes um of t he uniat tributed uti li t ies of each of theatt ri but es . If the att ri but es are speci fied bydis crete levels as in Figure 11, then the uti li t yof each of the level s of each of the feat ures is cal led a part wort h. That i s, the uti li t y of acam era t hat cos t s $25, wei ghs 16oz. , has aut om ati c light cont rol , produces 3-inch squarepictures , and requires the user t o focus m anual ly woul d be equal t o t he part wort h of$25, plus the part wort h of 16 oz., pl us the part

worth of automat ic l i ght control, pl us t he partworth of 3-inch square pi ctures , pl us the part worth of m anual focus . (We can als o repres ent uti li t y as the product of the part worths – i t i sonl y separabi li t y that is im pl i ed byi ndependence. ) Part wort hs are i ll ust rat ed i nF igure 12a. However, if the att ri but es areconti nuous we m i ght als o repres ent the ut il it yby a more conti nuous funct ion. An ideal point m odel for att ri but es where m ore i s not al ways bet ter (e. g., pi ct ure s ize, Fi gure 12b) is ones uch conti nuous funct ion and a vect or model where more is bett er (e.g. , quali ty of pi ct ure,F igure 12c) is anot her s uch cont inuousfunct i on. Decreas ing functi ons (e. g., price), concave funct ions (e. g. , clari t y of sound), andant i-i deal point (e. g., t he tem perat ure of tea)m odel s are al so poss i bl e. S ee the dis cus si on inP ekel m an and Sen (1979).

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Figure 12: Types of Conjoint Utility Function Forms

a. Par t- Wor th b. Ideal P oint c. Vector

Level of an Attribut

Utility

Level of an Attribute

Utility

Level of an Attribut

Utility

In som e cas es t he at t ri but es m i ght not bei ndependent . F or example, t he desi re of thecus tom er for manual focus i ng m i ght dependupon the qual it y of image that can be producedwit h the camera’s lens and fil m . In t hi s cas e,conjoi nt anal ys es us e m ore com plexexperi ment al des igns that al low i nt eract i onsand es ti mat e ut i li ty funct ions that cannot berepres ented s im ply as t he sum (or product ) oft he part wort hs . See Green and Devi ta (1975), Akaah and Korgaonkar (1983), and Johns on, M eyer, and Ghos h (1989).

Estimation. In order t o est i mate the uti li t yor part worth functi ons we m us t decompos e theoveral l preference rati ng (or ranki ng) i nto t heuti li t ies of the att ribut es (wi th or wit houti nt eract ions). Earl y appl icat i ons ass um ed thatonl y the rank-order inform at ion was relevantand us ed m onotonic anal ys i s of vari ance(monanova) to es ti mat e the part wort hs (Greenand S rinivasan 1978). Al t ernat ivel y, li nearprogramm ing provides accurat e est im atesbas ed on a crit eri on of m ean absolut e ordirect ional errors (S ri ni vas an and Shocker1973, Jain, Aci t o, M alhot ra, and Mahaj an1979, Malhotra 1982). However, manyres earchers have dis covered that cus tomers canprovi de val id preference dat a whi ch has strongm et ri c properti es (Huber 1975, Haus er andS hugan 1980, Curri m, Weinberg, and Wit ti nk1981) whil e other res earchers have found that t reat i ng rank data as i f it were met ri c provi des

accurate es ti mat es (C armone, Green, and Jai n1978). Thus, m any appl icati ons use OLSregres si on or ot her met ri c m et hods. If ris k isi mport ant in the des i gn deci si on, t hen vonNeumann-Morgens t ern uti li t y es t im at i on canbe us ed by as ki ng res pondent s to evaluat eproduct profi les i n whi ch one or more of theatt ri but es is uncert ain (Hauser and Urban 1979,Eli as hberg and Hauser 1985, Farquhar 1977,Kahn and M eyer 1991).

Another com mon dat a col lecti onprocedure sim pl y pres ents the cus tom er wi ths et s of al t ernat ive products profil es chosenfrom an experim ent al desi gn and asks t hecus tom er t o s el ect t he product he or s he prefers from each set of product profi l es . This method, known as choi ce-based conj oi nt anal ysi s, us es aquant al choice model such as a logi t or probi t m odel to es ti mat e the part wort hs from t hechoice dat a. S ee El rod, Louvi ere, and Davy(1992), Carroll and Green (1995), Haai jer,Wedel , Vri ens , and Wans beek (1998), Oppewal, Louviere, and Ti m merm ans (1994), and Orme (1999).

Compositional methods. C onjoi nt analys is i s usuall y thought of as adecom pos it i onal technique in which partworths are es ti m at ed by as ki ng respondent s toevaluate potent i al product s (or reduced set s ofatt ri but es of t hos e products ). However, there i sals o a l ong t radit ion i n market ing of


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com pos it ional m ethods i n whi ch the cus tom eri s as ked t o s pecify direct ly t he im portance orpart worth of a need or feat ure. Thes e met hods, als o known as expect ancy-val ue or s elf-expli cat ed methods , were reviewed by Wil kieand P ess em i er i n 1973. In t hes e met hods , t hecus tom er rates each product on each need(rates ) and eval uates t he im portance of eachneed (weights ). The ut il i ty of a product i s thent he s um of the wei ght s ti m es t he rat es , sum medover cus tom er needs. M ore recent ly, S ri ni vas an (1988), S rinivasan and Wyner(1988), and B uckli n and S rinivasan (1991) usea m et hod call ed Casem ap i n whi ch they havem odifi ed s elf-expl icated met hods for part wort hest im ati on. Aft er an init i al conj uncti ve phas e(described below), C asemap asks cus t om ers t os peci fy the i mport ance of each feat ure or needand t he rel at ive val ue of each level of eachfeature or need (rel ati ve to a base level of thatfeature or need). The part worth i s t hen t heproduct of thes e t wo values. Such sel f-expli cat ed methods have t he advantage ofbei ng a rel at ively easy t ask for the cus t om er –for exam pl e, Cas em ap can be com pl et ed overt he t elephone and does not require that as am pl e of cus tom ers com e to a central locat ion.C as em ap and other sel f-expli cat ed m ethods have proven t o be accurat e and reli abl e(Bates on, Rei bs t ei n, and Bouldi ng 1987, Akaahand Korgaonkar 1983, Green and Hels en 1989, Hoepfl and Huber 1970, Huber, Wit ti nk, F iedl er, and Mi l ler 1993, Leigh, MacKay andS um mers 1984).

C on ju n ct ive proces ses . St andardconjoi nt anal ys i s as ks res pondent s to evaluat eproducts t hat vary on all level s of al l of thefeatures or needs. But m any researchers havehypot hes ized that not all level s of al l features orneeds are accept able to respondents . Forexampl e, a cust omer who want s a cam era for aweddi ng mi ght not accept a pos t age-s tamp si zepicture; no l evels of t he ot her att ribut es wi l lcom pensate the cus tom er enough to m ake hi mor her buy a weddi ng camera that produces s uch small pi ct ures. When att ribut es have suchm inim um acceptable l evels (at least a 3-i nch

s quare pict ure) the cus tom er i s s ai d t o fol low aconjunct ive proces s (Ei nhorn 1971, Grether andWil de 1984). C asemap and ot her m et hods(Sawt oot h 1996) have modi fied conjoi nt analys is t o account for s uch conj uncti veproces ses by fi rst as ki ng the res pondent tos peci fy those l evels of t he at t ri but es t hat areunaccept abl e. Pri or speci fi cat ion ofunaccept abl e level s improves predict ion if donecarefull y. However, Green, Kri eger, andB ansal (1988) and Kl ein (1986) caut i on t hat i ft he ques ti ons are not pre-test ed andi mplem ented carefull y, the res pondent wi l lfal sel y rej ect att ri but e level s t hat he or shewould have later accept ed.

Reducing respondent burden. Whi l e thebas ic concept of conj oi nt anal ysi s is bot hpowerful and si m pl e, a key i mpl em ent at ionbarri er has been t he respondent s’ t ask.Alt hough we can reduce the five att ribut es inF igure 11 to an ort hogonal array of 16 profi l es ,t hi s is not always t he cas e. For exam pl e, Wi nd, Green, S hi ffl et , and Scarbrough (1989) report as ucces sful appl i cati on of conj oint analys is t ot he desi gn of M arriot t’s Court yard Hot el s i nwhi ch there were 50 factors at a tot al of 167l evel s . C l earl y, we cannot as k res pondents t oevaluate even a reduced experi m ental des i gnfor s uch problem s. As a res ul t , res earchers andpract i ti oners have focused on means to reducet he burden on respondents . We have al readyreviewed t radeoff analysi s (e. g., J ohnson 1974)i n whi ch respondents compare t wo at t ri but es at a t im e, choice-bas ed conj oint analys is i n whi chres pondent s choose am ong set s of profi les (e. g., C arrol l and Green 1995), and t he el i mi nat ion ofunaccept abl e at t ri but e level s (e. g. , S ri nivas an1988). (M alhot ra [1986] als o propos es am et hod t hat s creens unaccept abl e profi les .)There has als o been much effort all ocated t overy effici ent experi ment al des igns (e.g. ,Kuhfel d, Tobi as , and Garrett 1994). Wereview here t hree ot her m ethods for reducingres pondent burden: (1) met hods that mi xi ndivi dual and market -l evel dat a (hybridconjoi nt ), (2) met hods that em ploy mul ti -st agedat a col lecti on in which the fi rs t tas k is


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s im pl i fi ed (hierarchi cal int egrat ion andcus tom ized desi gns ), and (3) m ethods t hat adapt the quest i ons to the res pondent’s earlyans wers (adapti ve conjoint anal ys is ).

Hybri d conj oi nt anal ysi s (cf. Green 1984)com bi nes s elf-expl icated met hods at the level of the i ndi vi dual res pondent wi thdecom pos it i onal methods t hat s pli t theexperi ment al des ign acros s res pondents t oreduce t he compl exit y of the data coll ect iont as k. F or exam ple, res pondent s m ightexpli cit ly rate the part worths of each att ri but eon a 1-10 scale and then order a subset of ful l-profi l e cards . The es ti mat ed ut il it i es are bas edon bot h types of dat a. Al though, at t he level oft he i ndi vi dual res pondent , hybrid conj oi ntanalys is m ay not provide the detail ed accuracyof ful l-profi le methods , hybri d m et hods haveproven qui t e accurat e at the s egm ent or market l evel (Akaah and Korgaonkar 1983, Green, Gol dberg, and M ont em ayor 1981). Recentl y,Lenk, DeSarbo, Green and Young (1996)propos e a hybri d m et hod i n whi ch they us ehierarchical Bayes (HB) m ethods t o est im atei ndivi dual ut il i ty functi ons wi th reducednum bers of ques t ions per res pondent . The HBm et hods im prove accuracy by the use of heavycom put at ion and wi ll becom e more com mon ast he cost of com put at i on decreas es dram at i call ywit h the advent of i nexpensi ve but powerful com put ers.

In hierarchical int egrat ion, res pondents evaluate product s on hi gher level at tributes, facet s , or needs . They t hen eval uat e therel at i ve i m pact of feat ures that affect thosehigher l evel const ructs . Methods for the m ore-det ai l ed eval uat ions incl ude t radit i onal conj ointanalys is (Hauser and Si mm i e 1981), hybri dconjoi nt anal ys i s (Wi nd, Green, S hi ffl et , andS carbrough 1989), choice-bas ed conj ointanalys is (Oppewal, Louviere, and Ti m merm ans 1994), and self-expl i cated m et hods (Haus er andGri ffi n 1993). In a relat ed m ethod, S ri nivas anand P ark (1997) us ed cus tom ized conj ointanalys is , a m odi fi ed met hod in whi chres pondent s use self-expl i cated m et hods to

evaluate al l at t ri but es and then evaluat e as ubset of the m ost i m port ant at tributes wit h ful lprofi l e conjoint anal ys is . The s ubs et chos en fort he dril l-down is, i n a s ens e, cust omi zed t o eachres pondent .

The t hird stream of res earch di rect ed at reduci ng respondent burden empl oys adapt i vem et hods. The m ost comm on is S awt oot hS oftware’s adapt i ve conj oi nt analysi s (ACA, S awtooth 1996). In ACA respondents are first asked a series of sel f-expli cat ed ques ti ons .They are t hen as ked to evaluat e pai rs ofprofi l es i n whi ch a subset of the at tributes vary. The m ethod is adapti ve because each ques t ionaft er the first is chos en wi th a heuri st i c thatatt em pts t o gat her t he mos t informat ion perquest i on. Fi nal ques ti ons t hen est abl is h t herel at i ve s cal es of t he sel f-expli cat ed andadapt i ve component s. ACA has provenaccurate under the ri ght circum st ances and has proven t o add i ncrem ent al informati on rel at ivet o the s el f-expl icat ed porti on of t he int ervi ew(Huber, Wi t ti nk, F iedler, and Mil ler 1993,J ohns on 1997, Orme 1999). However, Green,Kri eger, and Agarwal 1991 caut i on t hat AC Am ight not be as accurat e as the ful l profil em et hod when t he latt er is feas i bl e. J ohnson(1999) propos es that one can post -anal yzeACA data wi th hi erarchi cal B ayes analysi s t oi mprove it s accuracy.

R ecent ly, Toubi a, Si m es ter, and Haus er(2000) propos e an im proved ACA al gorit hm bas ed on t he new i nt eri or-point algori thm s inm at hem at ical program m ing. They als o ask ani ni ti al quest ion, but t hen choose s ubs equentquest i ons such that the answers m axi mall yreduce t he feas i bl e set of uti l it y param eters . Alt hough t hei r met hod has not yet been appl iedempiri call y, si m ul at i ons sugges t that (1) t hei nt eri or point algori thms can gat her as muchi nform at ion as tradi t ional ACA, but in half t henum ber of quest i ons, and (2) for manys it uat ions the ini ti al sel f-expli cat ed ques ti ons can be s ki pped for a furt her reduct i on i n t heres pondent s ’ burden.


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Summary of Conjoint Analysis. C onjoi nt analys is i s a powerful tool for unders tandi ngt he t radeoffs cust om ers m ake between vari ousfeatures of a product . C onj oi nt analysi s uses t he l i st of key cust omer needs or product features determ i ned by the t echni quesdis cus sed in the fuzzy front end. It pri orit i zest hose feat ures bas ed on t he am ount of ext rabenefi t cus tomers derive from a feat ure or fromanother way of sat is fyi ng a cus tomer need. Theres ul t s can be used to im prove desi gns ,opt im i ze t hem for val ue, and predict m arket s hare and product success .

New Web-based Methods for Designingand Engineering Product Concepts

The advent of new information andrapid-communication technologies such asextremely powerful desktop computers, theInternet, and the World Wide Web (web) areleading to new and exciting methods of

concept evaluation. We review some of thesemethods here.

Web-based conjoint analysis.McArdle (2000) reports on the application ofconjoint analysis to the design of a newcamera. The advantages of such web-basedapplications are that rich, contextual, yetvirtual media can be used to illustrateproducts. For example, in Figure 13a therespondent is shown how he or she can use thecamera – with a single click the respondentcan view animations of the camera or picturesin use. Similar screens enable the respondentto better understand the features that are beingvaried. Then the respondent is presented witha conjoint analysis task, in this case pairedcomparisons (Figure 13b). The task is madeeasier for the respondent by animating thescale and by making detailed featuredescriptions or product demonstrationsavailable with a single click.

Figure 13. Web-based Product Demonstration and Pairwise Tradeoffs for a Camera

The advantage of web-based conjointanalysis is that the respondents can completethe task remotely. For example, McArdle(2000) compared samples of respondents whoanswered the questions in the comfort of theirhomes to respondents who completed thequestions on a more-traditional, dedicatedcomputer in a mall-intercept environment.

The qualitative results were quite similar,although there was some slight variation dueto sample selection. Certainly with furtherdevelopment the methods can be made toconverge. Although McArdle used a fixed,orthogonal design, both Sawtooth and Toubia,Simester, and Hauser (2000) provide web-based adaptive methods.


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We expect such web-based conjointanalysis to grow dramatically. Already, in theyear 2000, companies are forming panels ofweb-enabled respondents who can completeconjoint tasks. NFO Worldwide, Inc. has abalanced panel of over 500,000 web-enabledrespondents, DMS, Inc., a subsidiary of AOL,uses “Opinion Place” to recruit respondentsdynamically and claims to be interviewingover 1 million respondents per year,Knowledge Networks has recruited 100,000Internet enabled respondents with randomdigit dialing methods and provides them withweb access if they do not already have it,Greenfield Online, Inc. has an online panel of3 million respondents, and Harris Interactive,Inc. has an online panel of 6.5 millionrespondents (Buckman 2000). We expect thatin the next few years easy-to-access and easy-to-use web-based adaptive algorithms willbecome widely available. This coupled with

web panels has the potential to make itpossible to complete a conjoint analysis studyin days rather than months, and at greatlyreduced cost. Already prototypes ofautomated adaptive conjoint systems are beingdeveloped that will enable an inexperienceduser to conduct an ACA study, complete withanalysis, in under a week (Faura 2000).

User design with DnD. Theinteractivity of the web coupled with rapidlyadvancing computer power makes it possibleto explore creative methods to gatherinformation on customers’ preferences. Forexample, Dahan and Hauser (2000) describe amethod of feature-based user design on theweb in which respondents drag and drop(DnD) their preferred features onto a designpalette that illustrates the fully configuredproduct, as seen in Figure 14.

Figure 14: User Design of an Instant Camera

As these choices are made, tradeoffssuch as price and performance are instantlyvisible and updated, so the respondent caninteractively learn his or her preferences andreconfigure the design until an “ideal”configuration is identified. The methodincludes full configuration logic, so that only

feasible designs can be generated, i.e., choiceson one attribute can preclude or interact withchoices on other attributes. User designprovides an engaging, interactive method ofcollecting data on customer tradeoffs. Thisdata can be used to narrow the set of features.The reduced set of features can then form the


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basis of a more extensive conjoint analysis.McArdle (2000) reports that the conjointanalysis of the camera features in Figure 13predicted well those features that customersselected with DnD (Figure 14).

The DnD interface employed in userdesign market research may presage thatwhich will be used to sell mass-customizedgoods over the Web. For example, as shown inFigure 15, customers may appreciate a highlyvisual interface in which product features are

literally dragged and dropped into place.Park, Jun, and MacInnis (1999) demonstratethat customers arrive at different “idealconfigurations” depending on whether theyare asked to add options to a base model orsubtract options from a fully loaded model,suggesting that the initial configuration of auser design web site may have high impact onthe data collected (in the case of marketresearch) or sales effectiveness (in the case ofmass-customized e-commerce).

Figure 15: User Design of a Printer/Copier

Securities Trading of Concepts(STOC). One criticism of concept testingmethods that rely on direct feedback frompotential customers is that customers may nothave the incentive to be completely truthful.Further, in most real purchase situations,customers may be influenced by others’opinions and choices, a network externalitynot easily accounted for with traditionalconcept testing methods. Chan, Dahan, Lo,and Poggio (2000) offer a potential solution tothese concerns in the form of a novel marketresearch methodology, Securities Trading ofConcepts (STOC), depicted in Figure 16.

Utilizing the communications andconceptualization technologies of the Internet,participants compete in a simulated tradinggame in which the securities traded are newproduct concepts. The prices and tradingvolumes of these securities provideinformation as to the underlying preferencesof the individual traders and of the group as awhole. The idea that the price mechanismconveys information efficiently is wellunderstood in financial contexts. Earlyapplications of the STOC method suggeststhat it can identify those concepts that have the


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highest customer-demand potential, butfurther application and validation is necessary.

Figure 16: Securities Trading of Concepts(STOC)

Summary of Methods for Designing &Engineering Concepts and Products

Marketing plays a major role in thedesign and engineering of product conceptsand in the selection of product features. Leaduser analysis, Kaizen and Teian methods, set-based design, and Pugh concept selectionprovide means by which PD teams canselectively winnow product concepts to focuson those that are most likely to succeed.However, a key criterion in any winnowingprocess is customer acceptance and customerwillingness to pay for product features thatsolve their needs. Thus, QFD and the HOQprovide the means by which product focus andcustomer focus converge.

Perhaps the most important marketinginput to the design and engineering process isthe analysis of the tradeoffs that customersmake with respect to needs and productfeatures. These methods, generally calledconjoint analysis, include bothdecompositional methods in which customersevaluate bundles of needs or features andcompositional methods in which customers

directly evaluate the needs or features. Bothmethods work well and provide valuable inputto the HOQ and other concept selection andrefinement methods. Finally, new methodsare being developed to take advantage of newcomputing and communications technologiessuch as the World Wide Web. Internet-basedconjoint analysis, new adaptive methods, userdesign methods and Securities Trading ofConcepts all have the potential to increase theeffectiveness and reduce the cost of themarketing input to concept selection andrefinement. In some cases, we expect thecosts to drop by a factor of ten and the time-to-completion of the market input to dropfrom six weeks to a few days. Thesedevelopments will enhance further the abilityof the NP team to design and engineerconcepts. However, such concepts must betested so we now turn to prototyping andtesting concepts and products.

Prototyping and Testing Conceptsand Products

R et urning to the P D funnel i n end-t o-endproces s in Fi gure 3 we see t hat aft er the productconcepts have been generat ed, winnowed andrefined, t hey need t o be tes ted before t hey canbe launched. The goal in this phas e of the P Dproces s is to eval uat e the concepts (andengineer t he fi nal product ) so that any launch i shighl y l ikely t o s ucceed. The team must maket radeoffs among the cos t of tes ti ng, t headvant age of further devel opment, and anydel ays i n product launch. A t est ing m et hods houl d be accurate and cos t effecti ve.

R ecent ly, there has been signi ficant newwork done to underst and t he rol e of test i ng andt he opti mi zat ion i n the P D proces s. The PDproces s is exam i ned as a uni fi ed ent it y sucht hat reduct ions in cost and uncertai nt y at ones tage affect cos t and uncert ai nty as s ubs equents tages . Thus , the t radeoffs between t im e-t o-m arket , devel opm ent expens e, product ion cos tand qual it y can be vi ewed as an opt i mal search


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for t he bes t product desi gn. Tes ti ng thusbecom es a search proces s under uncertaint yand can be opti m ized. Thi s secti on wi ll revi ewt hi s new work as wel l as cover many of t het es ti ng met hods in t he li t erat ure.

Target Costing: Design forManufacturing and Assembly (DFMA)

Dahan and Srinivasan (2000) highlightthe potential importance of unit productioncost in the marketing success of a newproduct. Specifically, they suggest thatinvestments to reduce unit manufacturingcosts increase profitability through sixmechanisms: (1) market share improvementsdue to lower prices, (2) primary marketgrowth due to lower prices, (3) reducedchannel costs due to greater volume, (4)learning curve virtuous cycles due to highercumulative volumes, (5) quality improvementsdue to simplified designs, and (6) strategicbenefits due to competitive disincentives. Inshort, they suggest that marketers work withtheir operations counterparts at the earlyphases of new product design to ensure thatlow costs are locked in early.

Boothroyd, Dewhurst, and Knight(1994) describe methods of dramaticallyreducing unit manufacturing costs for a broadarray of products, primarily through part countreduction and simplification of the assemblyprocess. Because cost reductions may requirechanges in the appearance or performance ofthe product itself if taken to extremes,marketing input, to the extent that it capturescost-benefit tradeoffs as customers wouldmake them, is invaluable to making thesedecisions.

Ulrich and Pearson (1998)demonstrate empirically using a method theyterm “product archaeology,” that unitmanufacturing costs for products competing inthe same category (coffee makers) varygreatly, holding quality constant andstandardizing for feature variation. They

argue that while some of these cost differencesmay be the result of local manufacturingeconomics or variations in plant efficiency, asignificant portion of the cost differencesresult directly from design decisions madeearly in the product development process.Once these design decisions are frozen,reducing the excess costs that may result fromthem is nearly impossible.

Rapid Prototyping Methods

Thomke (1997, 1998a, 1998b), andThomke, von Hippel , and F ranke (1998)dis cus s the i ncreasi ng im portance of newt echnologi es such as rapi d prot ot ypi ng,s im ul ati on and com bi nat ori al m ethods i nexploring mul ti ple t echni cal s oluti ons duri ngt he earl y phases of product devel opm ent. Iness ence, t hes e techni ques autom at icall ygenerate and tes t variati ons on a product concept theme us ing param etric desi gn. M arket ing’s rol e i n thi s context is to cont ri but et he cust om er res pons e part of the object i vefunct i on us ed t o eval uate thos e pot ent ial des igns that pas s the t echni cal s creening t es t .F or exam pl e, whi le a cert ain percent age ofpot ent ial body des igns mi ght pass a computer-s im ul ated crash test , onl y a s ubs et of t hos ebody des igns mi ght m eet ot her cus tom errequi rem ent s for aes t heti cs and performance.M et hods of test i ng m ult ipl e des igns wi thcus tom ers are needed, as des cri bed next.

Parallel Concept Testing of MultipleDesigns

Gross (1967) suggested that multipleadvertising campaigns be developed andpretested so as to improve the expectedeffectiveness of the “best” campaign.Srinivasan, Lovejoy, and Beach (1997) applysimilar thinking to PD and suggest the needfor more parallel concept testing prior to“freezing” the design of a new product. Theybase their analysis on experience with PDclasses taught at Stanford University in whichteams of students design competing products


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within the same product category. Over yearsof teaching the course, they note the difficultyof accurately predicting which student designswould fare best in simulated marketcompetitions based on actual respondentpreferences. They suggest carrying forwardmultiple concepts until a later stage ofdevelopment, testing these with potentialcustomers, and then selecting from among thewinning concepts.

Dahan and Mendelson (2001) quantifythis argument and determine the optimalnumber of concepts to test given the cost pertest and the nature of upside profit uncertainty.Their analysis suggests that the statisticaltheory of extreme values is relevant to concepttesting and that the optimal number of tests isrelated to the ratio of profit uncertainty (e.g.standard deviation of the profit distribution) tothe cost per concept test. Further, they showthat the nature of upside profit uncertainty,whether fat-tailed, exponential-tailed, orbounded, significantly impacts the degree ofoptimal parallel testing.

Internet-based Rapid Concept Testing

Dahan and Srinivasan (2000)developed and tested a Web-based method ofparallel concept testing using visual depictionsand animations, in this case of bicycle pumps.Respondents viewed eleven new productconcepts, and expressed their preferences by“buying” their most preferred concepts atvarying prices. These choices were convertedinto individual part worths for each conceptusing price and product as the only attributesin a conjoint analysis. These results werecompared to a control cell in which similarpart worth measurements were conductedusing working physical prototypes of the bikepump concepts. The results showed that boththe verbal and Web-based visual methodsidentified the top three concepts from thecontrol group, and that the Web-basedmethods measured these preferences withgreater accuracy. As in the fuzzy front end

and in the design and engineering phase, weexpect that these Internet-based methods willgrow in power and applicability over the nextfew years. With further development thesevirtual concept testing methods have thepotential to dramatically reduce the cost andtime devoted to concept testing.

Automated, Distributed PD ServiceExchange Systems

As we can tes t cus tom er respons e to newproducts m ore rapi dl y and inexpensi vel y, it i s becom i ng m ore i m port ant t hat we can desi gnand cost out these concept s rapidly andi nexpens ively. However, as product devel opm ent becomes more dis persed firms areout sourcing m ore and more servi ces. Evenwit hi n a fi rm , mem bers of the PD team who areexpert s in gatheri ng the voi ce of t he cus tomerm ight be i n C al i forni a (s ay near lead us ers foraut om obi les ), whil e expert s in the physi cal des ign of the car door mi ght be i n Det roi t, expert s in devel oping t he wi ri ng harness (fort he door) might be i n M is him a, Japan, andexpert s in wi nd tunnel si m ul at i on m i ght be inS eatt l e. Furtherm ore, each sub-t eam m ightrepres ent their experti se in a computer model t hat is not com pat ibl e wi t h the other experts . One m odel might em pl oy a spreadsheet (e. g., Excel ), another mi ght uti l ize a s tat is ti cal package (e. g. , SPS S), anot her a C AD syst em, and t he las t a mat hem at ical model ing s ys t em (e. g. , M at Lab). B ut al l mus t work toget her i ft hey are t o des i gn a car door for t he new F ordThunderbird.

Whi le in t he pas t, t hes e teams woul ds pend cons i derable t i me comm uni cati ng anddevel opi ng compati bl e anal yt ical sys tems , newaut om ated and di st ri but ed servi ce exchanges ys tem s such as DOME, depi ct ed in F i gure 17, m ake it pos si bl e t o dramat ical l y reduce thecom municat i ons cos ts and speed the product tom arket (Wal lace, Abrahams on, S eni n, andS ferro 2000). The key idea behind DOM E iss ervi ce exchange rat her t han data exchange. F or exam pl e, the voi ce-of-the-cus tom er t eam


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m ight inves t in a conjoint anal ys is of t hefeatures of a car door – sound insul at ion, eas eof openi ng and closi ng wi ndows , s tyl ing, et c. and buil d a choi ce s i mulat or t hat predict s sal es as a funct i on of t hes e features . The physi cal m odel er mi ght buil d a C AD syst em in whichphysi cal di mens i ons (height, wi dt h, lengt h, curvat ure, wi ri ng requi rem ents , etc. ) are i nputand i n whi ch shape i s out put . The wind tunnel expert m ight bui ld a si mul at or in which shapeand i nsulat ion are i nput and i n whi ch noi sel evel and drag are output . The wiri ng harnes s des igner m i ght want all of t hes e input s and canout put i nform at i on s uch as t he power del i veredt o the electric wi ndows and aut om at i c doorl ocks . Each of thes e t eam s, and many ot hers, requi re and generate informati on that is connected through a virtual web – t he voi ce-of-t he-cust om er expert cannot run the conjoi nt s im ul ator wit hout noi se l evel, the wind tunnel expert cannot produce noi s e level wi thout aphysi cal m odel, and the phys ical model ercannot produce a C AD drawi ng wi thout i nt erfacing wit h t he wi ri ng harness expert.

DOM E addres ses these comm uni cat ionprobl ems by s et t ing up a “servi ces” exchangeon a com mon com put er pl at form cal led aDOM E server. Each di st ri but ed expert teamneed onl y acces s a DOME envelope for i ts program (Excel, SP SS , M at Lab, etc.) and pos ti ts i nput requi rem ent s and out put s ervices. Then eit her a cent ral adm i ni st rat or, s uch as thecore PD team, can bui ld an i nt egrat ed sys tem oreach expert can trade i nform at i on i n a s ervicesm arket pl ace. In t he latt er cas e DOM E provi des t he m eans for t he market to funct ion andi nform at ion i s exchanged in a free market ofs ervi ces .

Information Acceleration

Internet-based rapid concept testingprovides the means to gather customer inputabout virtual concepts, and DOME providesthe means to engineer and cost out thesevirtual concepts. However, in order to makethe demand vs. cost tradeoffs, the PD team

needs to simulate product acceptance in amarketplace where sales are affected bymarketing variables such as advertising, word-of-mouth, and sales force presentations.

Figure 17: Distributed Object ModelingEnvironment (DOME)

Furthermore, really new productsoften stretch technology and customercomprehension of the benefits of technology.For example, prior to the development of thepersonal computer, word processing was doneby professionals rather than by virtuallyeveryone, spreadsheet analysis was limited toa few financial professionals, and personalfinance was done with the checkbook ratherthan programs like Quicken. There was littledemand for web browsers before the Internetbecame widely available and little demand forhome networking before broadbandcapabilities were ubiquitous. Thus, in testingreally new products and concepts it is oftennecessary to place potential customers in newinformation states with new perspectives onthe world. Some of the early attempts atsimulating future environments for thecustomer were called information acceleration


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(IA). They relied heavily on central locationinterviewing with multimedia computers that,at the time, were high end. Today, with therapid advancement of the Internet, and withstreaming video and other multimediacapabilities, we expect that IA methods willbecome more widespread.

We illustrate IA with Figure 18 fromUrban, Weinberg, and Hauser (1996). Figure18 is a prototype electric vehicle. Customerscould view this vehicle on the computer, walkaround it virtually, and even (virtually) openthe hood, truck, and doors. In additioncustomers could “talk” to other consumers like

them, interact with a virtual salesperson, viewadvertising, mock-ups of consumermagazines, and other stimuli they were likelyto see when shopping for a new vehicle.Furthermore, they were accelerated into thefuture with accounts of alternative futureenvironments that were either favorable,neutral, or unfavorable towards electricvehicles. IA has been tested and validated in anumber of other environments includingmedical equipment and durable consumerproducts (Urban, Hauser, Qualls, Weinberg,Bohlmann, and Chicos 1997).

Figure 18: Example of Information Acceleration

Pretest Market and PrelaunchForecasting

Internet-based concept testing,DOME, and IA deal with product concepts orvirtual prototypes. However, once the virtualprototype is developed and the actual productis engineered, the PD team still seeks toreduce the risk of a full-scale launch. Thus,once a firm has refined its product concepts,developed prototypes, and can produce itsproducts in limited quantities, it needs to test

the full benefit proposition that includes thephysical product, service, distribution, andmarketing actions such as advertising,detailing, sales force presentations, and word-of-mouth publicity. We call such testingeither pretest market or prelaunch forecasting.

In a typical pretest market analysis,potential customers are shown products in asetting that is chosen to simulate the purchaseexperience. For consumer products theymight be shown television advertising,


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magazine articles, tapes of other consumerstalking about the product, salesforcepresentations, consumer magazine reports orwhatever media are appropriate to the productcategory. For consumer packaged goods, theymight be recruited at a mall, brought to a roomto see advertising, then brought to anotherroom that simulates a grocery store. Theywould be asked questions about perceptions,preferences, and purchase intentions andallowed to make an actual purchase in thesimulated store.

The methods of analysis includetrial/repeat analysis (Eskin and Malec 1976),recursive analysis (Pringle, Wilson, and Brody1982), norms (Blackburn and Clancy 1980),econometrics (Blattberg and Golanty 1978),logit analysis (Silk and Urban 1978), andpreference distribution modeling (Hauser andShugan 1983, Hauser and Gaskin 1984).Indeed, most of these models combinemultiple methods. For example, Silk andUrban (1978) explicitly use two parallelmodels and examine the convergent validitybetween the two before making forecasts. Seecritical reviews in Narasimhan and Sen(1983), Shocker and Hall (1986), and Ozer(1999). In general, the models predict welland have proven robust across manyapplications (Urban and Katz 1983). In recentyears the set of product categories hasbroadened tremendously. For example, theAssessor group at MARC, Inc. reportsapplications from frequently purchasedproducts (candy, soda, deodorants) toconsumer durables (personal computers).

In some situations, such asautomobiles, the PD team wants to test themarket before launch, but after the productionfacilities have been built. Although volume isless of an issue, the team needs to know whatit will take to sell the production. That is, itneeds an estimate of the required investmentin advertising, dealer incentives, andpromotion. To extent that production isflexible, it also wants to know how many

items it will likely produce with each set offeatures such as global-positioning andmapping systems, premium sound systems, oreven metallic paint. In this case, prelaunchmethods are used (e.g., Urban, Hauser, andRoberts 1990). In a typical prelaunchapplication, consumers are shown advertising,magazine articles, and word-of-mouthsimulations. This is similar in nature topretest market analyses. However, customersare also allowed to experience the productthrough test drives (for automobiles) or homeuse. “What-if” models are built to forecast theimpact of market variables and diffusionmodels are used to forecast the timing of sales(Bass 1969, Mahajan, Muller, and Bass 1990,Mahajan and Wind 1986, 1988).

Mass Customization andPostponement

In some cases, the best strategy mightbe expeditionary marketing in which manyproducts are placed on the marketsimultaneously allowing the market to decidewhich are fit to survive (Ham el and Prahal ad1991). However, expeditionary marketingonly makes sense if the firm can easily rampup production on the successful products andif the cost of failure is low (both directly andin loss of goodwill). The combination of e-Commerce, product and process modularity,and flexible manufacturing systems are nowmaking these conditions common in a varietyof product categories. In particular, we areseeing an explosion in the sale of custom-configured goods that are only assembled afterdemand is observed. Perhaps the prototypicalapplication of this system has been the build-to-order system used by Dell Computer todominate the market for personal computers(Rangan and Bell 1998). Gilmore and Pine(1997) characterize four approaches to “masscustomization” and suggest that the optimalapproach depends heavily on the nature of theproduct and the markets it serves. Theseconcepts represent a radical shift not only inmanufacturing strategy, but also in the


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marketing thinking underlying productdevelopment. Specifically, once a firm adoptsa mass-customization approach, the designproblem shifts from searching for optimalbundles of attributes and optimal product linesto defining optimal common platforms,attributes, and level options, component-by-component pricing and custom-configurationuser interfaces. Feitzinger and Lee (1997)provide extensive examples of products thatbenefit from modularity and postponement ofat least some aspects of production until afterdemand is observed.

However, it is still vitally importantthat the PD team use the methods describedearlier to winnow the concepts and featuresthat are made available to the customer.Customers are limited in their ability to searchamong features – without some focus, eventhe best mass customization process mightfail.

Summary of Prototyping and TestingConcepts and Products

In most cases the launch of a newproduct is very costly in terms ofcommitments to production, distribution, andmarketing. It is good management strategy toinvest earlier than launch to refine the productand its marketing and to reduce the risk offailure. This includes methods to reduce costand risk (DFMA), methods to develop and testprototypes rapidly, and parallel developmentto manage the pipeline. As the product nearsmarket, PD teams have used effectivelypretest and prelaunch forecasting experimentsto get early readings on how well the marketwill accept the product or product line. Thesemethods have been in use for almost thirtyyears and have proven accurate in a widevariety of circumstances.

With the advent of new informationand communications technologies, improvedprototyping and testing methods are becomingavailable. Internet-based rapid concept

testing, information acceleration, andautomated and distributed service exchangesystems such as DOME are enabling PDteams to work together and develop productsfaster and more profitably. The trueadvantage of these methods is that they can beused with virtual prototypes, thus enabling thePD team to forecast market response inparallel (or prior to) the development of thephysical product. We expect these trends tocontinue as the Internet and the worldwideweb are truly transforming the finaldevelopment and testing of concepts andproducts.

Enterprise Strategy

P revi ous s ect ions have address ed theconcept of an end-to-end product devel opm entproces s wi t h st ages of opportunit yi dent i fi cat ion and i dea generat ion (the fuzzyfront end), t he desi gn and engi neeri ng ofconcepts and product s , and t he prot otypi ng andt es ti ng of concept s and product s. In each s taget here are many tools and met hods avail abl e tounders tand the cus tom er and to make us e oft hat inform at ion t o devel op profi table product s. However, product developm ent i s not done in avacuum . R ather, i ts success or fai l ure dependsheavi l y on the organi zati on in which i t isembedded. (R evi ew F i gure 3. ) To bes ucces sful , t he fi rm must al ign i ts cult ure,i ncent ives , and process es to ensure that thepeopl e i nvolved in t he process can do their j obs effect ivel y. Whil e tools and met hods mi ght work wel l in theory or in demonst rat ionprograms , to make a difference they must i mprove the effect iveness of t he ent ireorgani zati on. And t his i s not trivi al .

The Challenge of Developing anEffective Product DevelopmentOrganization

Des pi t e many new t ool s and m et hods in thedes ign of an end-t o-end product developm ent proces s, and des pi te the new Internet-bas ed t ool s, m any organi zati ons are st ruggl i ng wi th t he


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execut ion of those proces s es . For exampl e, Wheel wri ght and Cl ark (1995) document thatdes pi t e the fact t hat t hei r suggest i ons in earli erwork have produced i s ol at ed success es, m anyorgani zati ons have faced diffi cul ty in us ing thesem et hods on an ongoing bas i s. Repenning andS term an (2000) docum ent t his phenom ena furt hers ugges ti ng that whil e new process es mi ght beexcel l ent, it i s difficul t t o implem ent them in realorgani zati ons faci ng the chall enge of act uall yget ti ng product s out the door. Of cours e t hi s i n not l im it ed to product development as m any previousm anagement pract ices have had troubl e wi t hi mplem entat ion including cus tom er s ati sfact ionprograms , quali t y ci rcl es , t ot al quali tym anagement , bus i ness process re-engi neeri ng, ands om e inform at ion t echnology (Anders on, F ornel l ,and Lehm ann 1994, Howe, Gaeddert, Howe 1995,Klein and Sorra 1996, Lawl er and Mohrm an 1987, Orl ikows ki 1992).

One reas on for fai lure has beencom municat i on and sus pi ci on – a new tool orm et hod chal lenges those t eam m embers who haveenj oyed the rewards of bei ng an expert i n t he us eof the old tool . New m et hods and priori t ies shi ft t he i m port ance of peopl e and functi ons . Anot herreason for fail ure i s t hat t oo much is expect ed toos oon. New tool s and methods t ake t i me t o l earnand t hey di vert energies from the t ask of get t ing aproject out t he door. If the benefi ts of t he new toolare obvi ous right from the begi nning i t wil l beadopt ed qui ckly. But i f the benefi t s of the newt ool are s pread over future project s whi l e the cos t i s incurred on the current proj ect, then team m em bers may not have the incent ives to i nvest . S ee exam pl es in Griffin (1992). Indeed, if t hei nves t ment in t he new t ool i s so great t hat i t det racts from current performance, then theproces s can ent er a death spiral of self-confi rm ingatt ri but ion errors . That is , team mem bers see t hat t he current proj ect takes longer or is m ore di fficult and t hey at tribute t his t o t he new tool or met hod(Repenni ng 1999). M anagers at t ri but e the del ays or cos ts t o t he team members , whi ch leads m anagers t o s ugges t the us e of more tool s (beyondopt im al al l ocat i ons), whi ch jus t rei nforces t he

del ays and cost s (Repenni ng and S terman 2000a, 2000b).

To overcom e s om e of the problem s ofi mplem entat ion res earchers have propos edboundary object s , com muni t ies of pract ice,rel at i onal cont racts , and balanced incent ives . Wereview each i n turn.

Boundary Objects

C arli l e (1999) sugges ts t hat t ool s and m ethods are m ore l i kely to be used i n a real organi zat ions ifdifferences and dependenci es across boundaries i nt he fi rm are represented and unders t ood by product devel opm ent t eam m em bers. He sugges ts t hat certai n obj ects , cal l ed boundary obj ects , effect com municat i on by all owi ng team members t ol earn di fferent thought worl ds , propos ealt ernat ives, t est t radeoffs , trans form knowl edge, and create new sol ut i ons. F or exam ple,C AD/C AE tools , by generat i ng frequent vi rtual protot ypes duri ng the developm ent proces s , hel pass ure t hat com ponent s are i nt egrat ed and t hat devel opm ent proceeds in a cl os ely coordi nat edm anner. S i mi larly, Cus um ano and Sel by (1995)des cri be a process at M icros oft where di s parat eprogramm ers provide code to the com m onprogram (s ay Mi crosoft Office). Frequent ly andperiodical l y, t he ent ire program is “bui l t” i n ordert o synchronize and s t abil i ze t he ent ire package ofcode. Each com ponent of code mus t, in t his proces s, work wi th t he ent ire package. Final l y, C arli l e and Lucas (1999) argue that “t echni cal work in cross -functi onal set ti ngs wi ll be m oreeffect ive when it: (1) es t abli s hes a com m on m odeland l anguage, (2) us es that logical st ructure andl anguage as a s hared team process t o expl oreconst rai nt s and ri sks , and (3) keeps t he interacti veproces s al i ve by cont inui ng to carry forwardalt ernat ives and s om e degree of des i gn fl exibi li ty. ”

Communities of Practice

Organi zati onal studi es suggest that process knowl edge is oft en deeply em bedded in socialgroups wit hin t he organizati on (B rown andDugui d 1991, Lave and Wenger 1991, Wenger


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1998). There are new chal lenges to tap suchi mpli cit and tacit knowledge about proces ses sot hat hum an capabil it i es i n product devel opm ent t ools and met hods di ffuses wit hin organi zat ions. Thi s includes encouragi ng such comm uni ti es ofpract i ce t hrough m echanis m s by which t hey cans hare knowl edge. In the com ing years we expectt hat int erest i n t hi s area wil l expl ode as the Int ernet and i nform ati on technol ogy m akes pos si bl ei nt ernal m arket s (and ext ernal markets ) throughwhi ch such im pl i ci t proces s knowl edge is shared.

Relational Contracts

R ecent res earch in agency theory suggest s t hat formal i ncent ive m echanis m s are not s uffi cient to “i nduce t he agent t o do the right thi ngat the right ti m e (Gi bbons 1997, p. 10). ” Thus formal m echanis m s are oft en suppl em ent ed orreplaced wi th l ong-t erm i m pl ici t rel at ionships i nwhi ch deci s ions are inform al ly delegat ed to s elf-m anaged PD team s t hrough sel f-enforcingrel at i onal cont racts . By vi ewi ng t he rel at ionshipas a repeat ed game, Baker, Gibbons and M urphy(1999a, 1999b) sugges t that the greateri nform at ion i nherent in t he informal rel ati ons hi ps enabl es these relati onal contract s to succeed.

Balanced Incentives

C ockburn, Henderson, and Stern (1999)s ugges t that those best practi ces t hat di ffus e arepract i ces that are complem entary to one another –t hey mus t be adopt ed as bundles rat her t hani ndivi dual l y. For exam pl e, they suggest that s ci ence-bas ed drug di scovery i n t hepharm aceut i cal indus t ry required thatorgani zati ons adopt more high-powered incenti ves wit hi n t he research organi zati on. Their researchs ugges ts t hat P D t ool s and m et hods cannot beviewed i n isolat ion, but as part of a morecom prehens i ve end-to-end proces s. The t ool s andm et hods that the organi zat ion accept s wi l l be thos et hat work wel l in the cont ext of the overal lproces s.

Dynamic Planning

R epenning and S t erman (2000a, 2000b)s ugges t that fi rms wi shing t o adopt new tools andm et hods adopt a dynam ic- rat her t han s tat ic m ent al m odel . That is , firm s shoul d view the di ffus i on ofnew t ool s and m ethods as an inves tm ent t hat i s amort i zed over mul ti ple proj ect s rat her thanrequi ring imm edi at e ret urn on a s ingle proj ect .They sugges t further that the firm study thedynam i c int errel at ionships bet ween theexpect at ions of managers and t he sel f-al l ocat i on ofeffort among product devel opment teams .

Deployment of Capabilities with Web-based Tools

The adopti on of new tools and met hods doesnot j ust happen. New capabi li t ies and knowledgeare required on the part of the productdevel opm ent t eam . F or example, F ord M ot orC om pany has a t echni cal s t aff of 25, 000profes si onals t hat are expected t o undert ake 40hours of t rai ni ng each year – a t ot al of 500 person-years of effort to l earn new proces s es . This is i ndeed a s ubs tanti al comm i tm ent . On t he ot herhand, indus try est im ates that approxim at ely 75%of the t rai ni ng materials in F ort une 100 compani es are redundant , wit h som e of these cost ing$300, 000 each t o create and $50,000 to t ransl atei nt o each addit i onal language (Learnshare 2000). Whi le tradi ti onal met hods of apprent ices hip ares ti ll im portant (Lave 1991), new opportunit ies areavail abl e wit h web-based traini ng i n whi chm at eri al s are l ess expens i ve t o reproduce, can bes hared across fi rm s (subj ect t o com pet it i ve i s sues ),and can be updat ed easi ly. For exam pl e, Fordexpect s cours es to t ake on average one-t hird les st im e for equi val ent learni ng gain (e.g. a t hree-dayi ns truct or-led cours e t akes two days on averagewit h the web-enhanced vers ion).

Process Studies of the Antecedents ofProduct Development Success

C om pl ement i ng t he st udy of t heorgani zati onal cul ture under which productdevel opm ent t eam s operate, t here is an extens i ve


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l it erature on t he ant ecedent s and cons equences ofproduct devel opm ent succes s. The goal of t hi s l it erature is t o i denti fy vari abl es and const ructs t hat are correl ated wit h succes s acros s firms . Forexampl e, earl ier i n thi s chapt er we revi ewed som eof thi s li t erat ure t o s ugges t that com municat i onbet ween the m arket ing functi on and the t echni cal devel opm ent functi on is a st rong correlat e ofproduct devel opm ent succes s. Thi s lit eraturerel ies pri m aril y on quest i onnai res sent to product devel opm ent profes si onals . Thes e profess i onal s report t hei r percept i ons of various cons t ruct s andt heir percept ions of the succes s of the productdevel opm ent proj ect. Gri ffi n and P age (1993, 1996) provi de a comprehens ive review of thi sl it erature (referenci ng 77 art i cl es ). To date, t hes trength of t hi s res earch st ream has beenexplorat ory i n nat ure – i denti fyi ng potenti al const ructs that affect success . Tabl e 3 summ ari zes ,i n al phabet ical order, ei ght y-one of t he cons t ruct s t hat have been found to affect product devel opm ent s ucces s, ei ther di rectl y, indirect ly, ori n int eract ions wi th ot her cons truct s. 2

Alt hough each of t hes e const ructs i s correl at ed wi th success t o s om e degree, fews tudi es have included a broad set of cons truct s andfew have us ed regres s ion analys is or pat h anal ys is t o ident ify t he relat ive impact of these cons t ruct s (Mont oya-Weis s and C alant one 1994, 411-412).F urtherm ore, res earchers are only now beginni ngt o model t he ful l im pli cat ions of endogenous

2 See, for example, Atuahene-Gima (1995),

Ayers, Dahlstrom, and Skinner (1997), Bonner,Ruekert and Walker (1998), Calatone and diBenedetto (1988), Cooper and de Brentani (1991),Cooper and Kleinschmidt (1993, 1994, 1995), Datar,Jordan, Kekre, Rajiv and Srinivasan (1997),Goldenberg, Lehmann and Mazursky (1999), Griffin(1997), Griffin and Hauser (1994), Ittner and Larcher(1997), Kahn (1996), Lambert and Slater (1999), Leeand Na (1994), Mishra, Kim and Lee (1996),Moorman (1995), Moorman and Miner (1997),Olson, Walker and Ruekert (1995), Rosen, Schnaarsand Shani (1988), Sharda, Frankwick, Deosthali, andDelahoussaye (1999), Song and Parry (1997a, b),Song, di Benedetto and Zhao (1999), and Song,Montoya-Weiss and Schmidt (1997).

decis i ons by fi rms . For exampl e, a hi gh-potenti al m arket i s likel y t o att ract com peti t ors, thus the net i mpact of “market pot enti al” and “m arket att racti venes s” mi ght be to nei ther heighten nordim ini sh observed success (C ooper 1985).

P erhaps the bes t s um m ary of thi s li t erat ure t odat e is the m et a-anal ys is of 47 s ci ent ifi c st udi es byM ontoya-Wei ss and Cal antone (1994). Thes eres earchers i denti fi ed the eighteen cons t ruct s t hat were report ed m ost . For ease of com paris on theygrouped these cons truct s int o four cat egori es : s trat egi c factors, development process , market environm ent , and organi zat ion. Al l eight eenconst ructs had a s ignificant i m pact on s ucces s (Fi sher com bi ned t es t ) and all but three hadm edium t o large correlati ons (0.2 t o 0.3) wit hs om e measure of success . The rem ai ningconst ructs were incl uded in onl y one s tudy (s peed-t o-market) or no s tudies (cost s , marketcom pet it iveness ). B ecaus e t hi s l it erature is st il l devel opi ng, not al l of the s tudies report edcorrel at ions, t hus t he aut hors al so report thenum ber of tim es a const ruct was found to affect am easure of success . Table 4 summ ari zes t heaverage correlat ions and num ber of studi es.

Thi s lit erature has the potent i al t o enl i ghtenand focus fut ure res earch on t he ant ecedent s ofproduct devel opm ent succes s. The greates tres earch need i s for st udi es t hat combine acom prehens i ve s et of cons t ruct s , deal wi t hendogeneit y i n a s ophis ti cat ed manner, anddevel op measures wit h greater int ernal andext ernal vali di t y. 3

3 Montoya-Weiss and Calatone (1994)

provide an excellent discussion of the threats tointernal and external validity including issues thatonly significant correlations are reported even when alarge number of constructs are included in the study,issues that respondents often are allowed to self-select successes and failures, and issues that much ofthe literature relies on respondents perceptions ratherthan objective measures.

42

Table 3. Antecedents of Product Development Successaddress cl ear needs i nformation acquisiti on product definiti on

adequate team i nnovati veness product di fferenti ati on

adequate resources i nstrumental market i nformation product-fi rm compati bil ity

clear strategy i nterdepartmental col laboration product-market fit

company resources i nternal commitment profi ciency of formal P D activi ti es

competitive i ntell igence management accountabi li ty product organizati on

conceptual market information management commi tment quali ty of launch executi on

concurrent engi neeri ng management support quali ty of servi ce

costs market competiti veness R &D , marketing i ntegration

cross-functional i ntegrati on market grow th rel ati onal norms

cul tural antecedents market i ntell igence resource avai labil ity

customer sati sfaction market ori entati on rigorous tool s

customer needs and benefi ts market potential servi ce experti se

customer service market size standards

decentrali zation marketing ori entation strategy

degree of product new ness marketing ski ll s and resources superi or product

empow erment marketing synergy suppl i er i nvolvement

entrepreneuri al cl imate new ness to fi rm synergy

environmental uncertainty new ness to market techni cal compl exi ty

evaluati on and rew ard procedures novel ty techni cal i nnovati veness

execution of marketi ng organi zati onal memory techni cal ski ll s and resources

execution of technical organi zati onal memory dispersi on techni cal synergy

experi ence wi th market pioneeri ng advantage templ ates of change

focus platform reuse time to market

formal processes pre-development profi ci ency uni queness

formal cross-functional i ntegrati on product advantage val ue to the customer

formal izati on of user i nventions product champion i nvolvement

high quali ty teams product compl exi ty

43

Table 4. Meta-analysis of Academic Literature(from Montoya-Weiss and Calantone 1994, 408, 410)

Quanti tati ve Com pari sons Quali tative C om par isons

C onstr uct

N um ber ofStudi es

AverageC or rel ation

N um ber ofStudi es

Per cent ofStudi es

Strat egic f acto rs

Technol ogi cal syner gy 6 0.27 18 67%

Pr oduct advantage 5 0.36 16 59%

Marketi ng syner gy 5 0.30 14 52%

Company r esour ces 3 0.30 9 33%

Strategy 1 0.32 8 30%

D evelo pmen t p ro cess

Pr otocol 7 0.34 19 70%

Pr ofi ci ency of technical acti vities 7 0.28 18 67%

Pr ofi ci ency of marketi ng acti vities 5 0.33 18 67%

Pr ofi ci ency of pr e- devel opm ent 5 0.29 11 41%

Top m anagement support/skil l 2 0.26 9 33%

Fi nanci al /busi ness analysis 1 0.27 8 30%

Speed to m ar ket 1 0.18 8 30%

Costs 0 – 4 15%

M arket Enviro nment

Market potenti al 4 0.24 23 85%

Envi r onment 2 0.29 15 56%

Market com peti tiveness 0 – 2 7%

Org an izatio n

Inter nal/exter nal r elati ons 3 0.31 13 48%

Or ganizati onal factors 3 0.30 12 44%

Adjusting Priorities to Maximize Profit

Alt hough proces s s tudies provi de val uabl ei ns ight wi t h res pect to t he const ructs whichcorrel at e wit h succes s acros s firms , t hey do not provi de a tool wit h whi ch to determ i ne howm uch rel at i ve em phas i s the firm s hould pl aceon these cons truct s. Thi s i s parti cul arl ychall enging becaus e the i m pact of t hes econst ructs is cont ingent upon the capabi l it ies of

t he fi rm and the cont ext in whi ch i t operat es . F urtherm ore, because the impact of theseconst ructs is l i kely to be non-li near, t he process s tudi es do not indicate t he opt im al level for aparti cul ar fi rm . As we di scus s ed at t hebeginning of thi s chapt er (F igure 2), there aret radeoffs among foci (s ay cust omer sat is facti onvs. pl at form reuse) and i t i s pos si ble t oovers hoot if too m uch emphas is is pl aced on as ingl e cons truct . F or example, t oo large an


44

emphas is on cus t om er sati s fact i on m i ght provet oo cost ly and reduce profit even as i t increases

l ong-t erm revenue.

Figure 19: The Metrics Thermostat

To address the iss ue of s elect i ng priori t iesw it hi n a f i rm, we turn to adapt ive cont rol(Haus er 2000, Li tt le 1966, 1977). The concept i s qui te s i mple. Suppose that we are operati ngwit hi n a di vi si on of fi rm . It is l i kely that thei mpli cit cult ure and relat ional cont ract s arerel at i vely homogeneous wi t hi n thi s divis i on.The product developm ent t eam s unders tand thecul ture and operat e accordingl y. Thus , theyunders tand the impli cit priori t ies that are pl acedon const ructs s uch as cus t om er sati s fact i on andplatform reus e. Even i f the fi rm were operat i ngwit h the opti mal pri ori ti es, over t i me, thechall enges it faces, both internal and external, are l i kely to s hift the respons e surface in F i gure2. Thus, at any gi ven t im e we are l i kely toobs erve the firm operat ing on som e porti on oft hi s curve as i ndi cat ed i n F igure 19 by t he bl uecircl e (l). M oreover, becaus e t he im pl i ci tcul ture is relat ivel y hom ogeneous , mos t of theobs ervat ions of cust omer sat is facti on, pl at formreuse, and profi t for l aunched product s wil l bei n the nei ghborhood of the operat ing poi nt as

i ndicated by the l ight bl ue tangent hyperpl anei n Fi gure 19. Thus, a regress ion of profi t oncus tom er s ati sfact ion and pl at form reuse wit hi nt he hyperpl ane wi ll suggest t o t he fi rm how toadj us t t hei r em phasi s on cus tom er s ati sfact ionand pl at form reuse i n order to increas e theprofi t abil i ty of t hei r product devel opmentprojects .4 Whi l e thi s example is based on twoconst ructs , cus t om er sati s fact i on and pl atform reuse, i t is readi ly extended to a larger num berof const ructs . For exampl e, adapti ve control can be appl ied to top-l evel const ructs s uch as cus tom er s ati sfact ion, pl atform reus e, and ti m et o market and, sim ul t aneousl y, to a larger

4 Figure 19 indicates the suggested change

in customer satisfaction and platform reuse. Toachieve this, the firm must adjust its priorities withrespect to these measures, thus we need a mappingfrom measures to priorities on measures. This relieson agency theory and is beyond the scope of thischapter. For more details see Hauser (2000). Formore details on agency theory see Gibbons (1997).


45

num ber of lower-level cons truct s that enabl et he fi rm t o achi eve those st rat egic priorit ies .

These methods have been appl ied t o am aj or product development fi rm (3 s t rategicconst ructs , t en enabl ing const ructs , and sevencovari at es ). B ased on dat a from 16 product devel opm ent proj ects , t he “m et rics therm ost at ”was able t o i denti fy that the firm was pl acingt oo l i tt le em phasi s on cus tomer s at i sfact ion andt oo m uch em phas i s on pl at form reuse. Becauset he result s had hi gh face vali dit y, the firmreact ed wi t h three i nit iat ives : (1) the firmcreat ed the rol e of a “marketi ng engineer” whoi s res pons i bl e for as suri ng that the voi ce of thecus tom er i s i ncorporated in the des i gn and thatt he product i s des igned t o be market ed, (2) t hefirm adj us t ed i t s channel s t o reach the cus tom erbet ter and to m atch cus tom ers wit h theappropri at e products , and (3) the fi rm undert ook a m aj or st udy of plat form reus e t oopt im i ze t hei r portfoli o wit h res pect toups tream /downst ream technologi cal devel opm ent , a bal ance of product vari ant s andm aj or redes igns , and enterpris e coherence i ns oftware development .

A Vision of the Future of ProductDevelopment

Throughout the 1980s and 1990s the focus of res earch on product devel opm ent bym arket ing academ ics has been on bri ngi ngcus tom er i nform ati on into the productdevel opm ent proces s and on usi ng thati nform at ion. S i gnifi cant st ri des were m ade i nconjoi nt anal ys i s, voice of the cus t om erm et hods, product opt i mi zat ion, demandforecast ing, and m arket t est ing. Toward theend of t he 1990s t he chal l enges of productdevel opm ent began to change as markets andcom pet it ion became m ore gl obal , asengineering and desi gn tal ent becam e m oredis persed, as i nternal product devel opmenteffort s mi grated i nt o t he extended ent erpri se, and as i nform at i on and com muni cat iont echnologi es changed the way people worked.

The new chall enges call for a productdevel opm ent proces s that is int egrat ed,i nform at ion i nt ens ive, al m os t ins tantaneous ,and m akes strong use of new technol ogi es suchas the Int ernet . We call this new vis ion i 4P D:i nt egrat ed, i nform at i on, ins tantaneous , andInt ernet .

Integrated

The research chall enges of t he next decadeare t hos e that addres s product devel opment as an int egrat ed, end-t o-end process t hat requires adet ai l ed unders t andi ng and coordi nat ion ofcus tom ers, competi ti on, and int ernal capabi li ti es. Res earch points to core t eam s thatare ei ther cros s -functi onal or have the abi li t y tom ake use of cros s-funct ional knowledgeembedded i n t he fi rm . Furtherm ore, desi gnnow m eans the desi gn of t he product , t heass em bly and manufact uring proces s, thes ervi ce del ivery proces s, the ent ire val ue chain,and t he marketi ng mat erial s – all i ntegrated toprovi de hi gh val ue t o t he cust omer. F orexampl e, research on voice-of-t he-cust om erm et hods in the next decade m us t cons ider notj us t dat a col lecti on, but how the P D t eam wil l use t hat data.

Information

Ult im ately, i t is peopl e who desi gnproducts , but as t he process becomes m orei nt egrat ed the dem ands for i nform at i on havegrown. For exam pl e, cutt i ng-edge, PD teams m us t int egrat e inform at ion from t he cust omer, t he as sembl y process , t he manufacturingproces s, t he channel deli very proces s, and them arket ing proces s. In som e cas es , thi s means new roles – s om e firm s now use “m arket ingengineers” who hel p des ign a product s o that iti s eas y to market. However, i ntegrati ondem ands informat ion – t he ri ght i nform at i on t ot he ri ght peopl e at the ri ght tim e so that theycan m ake t he ri ght deci si ons . Thus , m any oft he research chall enges i n t he next decade wi l li nvol ve met hods to as sure this informati ont rans fer. Methods s uch as DOM E are just the


46

beginning of int egrat ed i nform ati on syst ems t hat wil l lead to great er product development com pet it iveness .

Instantaneous

S peed-to-m arket has been recognized as acom pet it ive advant age. New met hods such as virtual prototypes , Int ernet voice-of-the-cus tom er m ethods , Int ernet conj oi nt anal ysi s, t he Inform ati on Pump, l is t ening i n, securit ies t radi ng of concept s, and DnD us er desi gn al lhave the potent i al t o provide inform at ion t o theP D team al m os t ins tantaneous ly. We call this ent ire s et of m ethods t he vi rt ual cust om er. F orexampl e, t radit i onal conj oint analys is s t udies t ake a m ini mum of 6-8 weeks. New Internet-bas ed methods have t he pot enti al to reduce thatt o two days , openi ng up t he pot enti al for t heP D team to have it s cus tom er-preferencequest i ons ans wered al most inst ant aneousl y. Infact, it wi ll s oon be pos s ible to get st ati st i cali nform at ion about cus tomer want s and needsalm os t as fas t as it us ed to t ake t o debate t hem .Virtual prototypes m ean t hat product s can be“creat ed” in days, and Int ernet connecti vit ym eans that thes e prot ot ypes can be tes ted wit hcus tom ers in hours . Servi ce i ntegrati onm et hods such as DOME mean that manyengineering des i gn deci si ons can be reducedfrom months t o days. Int erest i ngly, i n thefut ure we might be i n a s i tuat i on where thedecis i on on how fast to i ntroduce product sm ight be m ore of a s t rategic deci si on onproduct pos it ioning rat her t han cons trai ned byt he fi rm ’s abil i ty t o des i gn and tes t product s .

Internet

B y Int ernet we reall y m ean i nform at i onand comm uni cati ons t echnol ogies . It i s theset echnologi es that are enabli ng the proces s to bei nt egrat ed, i nform at i on i ntens i ve, andi ns tantaneous .

The i 4PD paradi gm is one perspect ive ont he future of product developm ent ; aperspect ive t hat des cri bes how the proces s wi l l

l ook. B ut we m ust not forget the human side ofproduct devel opm ent. One of m ost i m port ant i ns ights of t he late 1990s was the need to st udyt he us e of PD t ool s and m ethods w it hi n t heorgani zati on. By underst anding corporat ecul ture and i ncent ives, t he new end-to-endproces s wi l l be robus t, knowledge-based, peopl e-bas ed, and market-bas ed. By robus t wem ean a proces s that can adapt to changes in t heenvironm ent , market condi t ions , andorgani zati on. By knowl edge-bas ed werecognize that the fi rm s that wil l be mos tcom pet it ive wil l be those that can train thei r P Dt eams to desi gn and bui ld product s mos teffect ivel y. We cannot s t udy the proces s i ni solat ion of the people we are as ki ng toi mplem ent the proces s . The proces s wil l not s ucceed if we do not as sure that the t eam m em bers have the capabi li t ies to exploit it .Thi s means not onl y traini ng, but al socom munit ies of pract i ce, boundary object s , anddynam i c thi nking. B y people-based, we m eant hat the proces s res pects the teams ’ needs andt hat the m etrics and incenti ves (expli ci t andi mpli cit ) are desi gned so that team members ,act ing i n their own bes t int erest s, makedecis i ons and t ake acti ons ali gned wit h the best i nt erest s of the firm . F i nall y, by market-bas edwe mean two t hi ngs : first that the proces s wi l lbe res pons i ve t o cus t om ers and competi tors, and s econd, t hat i t empowers t eam s to maket heir own choices in the context of thei r owns peci fic expert i se and knowl edge.

In the end, we bel ieve that res earch onproduct devel opm ent in the 21s t cent ury wi ll concentrat e on unders tandi ng an end-to-endproces s t hat reall y w or ks in real or ganiz at i ons. We do not expect a fad of the month, but ratherres earch t o unders tand the s ci ence oforgani zati ons , market ing, and productdevel opm ent s o that met hods and t ool s areembedded i n a s elf-l earni ng and s el f-eval uati onproces s that is ri ght for the firm and i t s markets .

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R6

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R7

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R10

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