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UNIVERSITY OF ILLINOIS LIBRARY _ OT URGANA - CHAMPAIGN ENGINEERING lof C — NOTICE: Return or renew all Library Materials! The Minimum Fee for each Lost Book is $50.00. The person charging this material is responsible for its return to the library from which it was withdrawn on or before the Latest Date stamped below. Theft, mutilation, and underlining of books are reasons for discipli- nary action and may result in dismissal from the University. To renew call Telephone Center, 333-8400 UNIVERSITY OF ILLINOIS LIBRARY AT URBANA-CHAMPAIGN
L161—O-1096
S-MODEL ASSISTED PRODUCT REALIZATION
BY
JOSHUA LOUIS FREEMAN
B.S., University of Illinois, 1998
THESIS
Submitted in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering in the Graduate College of the University of Illinois at Urbana-Champaign, 2000
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UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
THE GRADUATE COLLEGE
MAY 2000 (date)
Wren REBY RECOMMEND THAT THE THESIS BY
JOSHUA LOUIS FREEMAN
ENTITLED S-MODEL ASSISTED PRODUCT REALIZATION
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fags lo bast Acknowledgements
This work was sponsored by Caterpillar Incorporated. Without their generous support and assistance this work would not have been possible.
I would first like to thank Professor Harry E. Cook for providing me with the all the opportunities he has over the last two years. Each additional day I study under him I have a greater respect and understanding of the work he does.
I would also like to thank Dan Binz and the Caterpillar Forest Products Group especially Jeff Roszell, Mark Lasiter, John Sewell, Karl Dye and Troy Jackson. This environment has provided valuable insight and experience.
Finally I would like to ent my mom and dad, Mike, Molly and Christine for their constant encouragement and support.
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To esulsV anjerisistie hottoMou's ¥ omnoneodd 1.2.8 Lz bode’ sviwD sala S28 dudrioA soe suleV iegheM oma atric de eJ2 Jodrcl sivivl tena bas anion eionsio® . . GAO bas bboM-2 RE bw . beriieM ois & tate COMIGDA Sutin V foi 1 +, (OlzouG auleY Joi 1448 © cadhddeacmatead ame suisreied £5 b- ulsV Los oom iewuey at yinishsanty £. fae. Jiniantoon sonbes of Yovrme oni ut noitoginé svitemaitA Bas ' witeM colucdined yorwe suis V peel “i an Peer CUCL ISASCOMVIGL ULV CY Siew, MME EE ccs sy2cccedsscenoenaeesesedvocesnoacevesveseeee 33 Bere eet SUNS EDU CUS ULM Ve i anisssstenchinwnbensapeaadancnileWinsannensasadownwnpapacance 34 Ee CRS Cy SAID ITD LAID, vay fccci pacer sorts iccsscedsiassounesssusnevwodssdosansesas ones 35 eR AACN te ete hen sv octtesanaesVesseusssiaeviacdicassdeussssaiwdeves «doves so er eae AN ee Oe SN sans eas BE atmos oyadesensvenesesunceasasnae 36 es RCCL ie cee eee ee Pi A ANE ded ok ca tk angie ca ba can ehe nosensines ceases 36 SEED RES ee eT PN oni sg cane vacasuassecelsGareicassscvneseVseasserans es 37 ee OG aM AL STICCCLOINLN © meme ets icisiroesyacesacovoseuetdvalsessenswsnoassesssecsnentersssee’ ey; eC mM ToaUA Set LOOl. aeey ee tM ae BIR A wasn: Sahn oadas Train vugaeh< saree. dsvavacesevenss 38 Some et Pe oP LC eee eater eet cof ee oo0e «ce dasesya7 fase heetiays esesaraeatasoonbesiaces: 38 Chapter 5 RE ETRY Cree Cte Ce ISOS ULIULY mene estat ag 2.8 scasscvinecostessgaus pesusatistiudvveduisusiesteal she MCL VI AI KC eaters nee Oe eas feasiadess vs cxcsesusocticatosstoe sas eiastaveduusss Appendix UC SL VC y meen ete Se ea einen ccua doc Geena scasectvnuaesidevcedetesees devsowssicmnesssee 56 RT NY ee ene eae eet ean ee eo rec reabsaneiasapsnegs des (evssdeeyotxessaceddgnsensodse 64 e000 08 64eee Agree ep eae tae “v 20 Sos ete-+2e4 Loar due yore x 193 et 2 rif F ce 20a) @tmepae taee PSD ope asd 1 ak 7 ‘ _ ned road Ls +e OP Oe Fe me mae eh eC eter esto gees doeneese Bi] vk 6 Le 8h ee eG) 6 04 Ck Er oH (rbd bent eth s Se Ot +2 Re ORL EE ha kaw Fe Ge OOF F STEMS 2a 4 ERG S © > aleriat | Véodbete- » 6440058 4 ons cdbeteaene ® doo tnt oe b~ 6 whe 568 osereca VANES ee + viet fides uot | ** * **. eevee * ; | ala s sbeonaeeueytie’ oO — % . = ha “* _ bate se ab 12: Jon te ~ sacha wobbble a 7 waieyingA soft 1 jrapeacstaet OIBEA, 86 sae ugized yorwe Ie bordieM momidined & eqoinont) ale soptiee a eat anilqms? Ba ..- 26S santas A ec. ‘en is nale'¥ Senet stue mane so omit PO % o- | r a i ovine ouley on Yee 20, List of Tables Page Bee eres NG CM PETIA SP TICCEATIANY SIS (005. s51¢505-04epsharessordagssensernveseonesesnenrsoparspessrseoers 16 Table 4.1: Hypothetical Results of Direct Value Question in Figure 4.1.0.0... ee 29 Table 5.1: Scaled Market Values for Medium Wheel Skidder Segment...... 0.0 40 SE Pe Ce Let Me, oe 0M, sins nnsscxvensesevanshesateoccectbsnvessssaenessresoes> 42 er Se CHICORY ALUCS OI CICCICU A LLLIDULCS; y cersesse-n-rsosenrsoserpsortatduopanenmaesenecessonacersensy 49 Table 5.4: Future Scaled Value, Scaled Price and Market Share Changes...... 00 53 | esidaT Tom to a poe kee o2el 7. vm ms i! Soe Sewers dees eehedes ae , a a es . or Ibe vert ni i biases outa’ romidi ta a Lint Ob . ItrernwesZ hbEE od W Gineen ee OB dian ete (a: .cleday Cleuwen oh ove Cbs censsensennts POH Ob ss ccctiaates Mbirocdies nls css, oc eA. bose sie VUlthsi *y sani? teshaM buses itt bolaod ule¥ List of Figures Page es OLIGO ora AN LON SS ge pe selbst acter inc sede anapalin anon a aiadoisansiingiaadadanns 2 ROP ein tS TELCOS AL VIN CCU SN AO Boo cao tua ds tadtamsuacnnenecatses Per noainmormantdihiesaescveis 4 Be eee ee KAUN le DL YAN. CUTAN C Gs ULV OS iii.e-54.00ccedéa0ssanulenscosacsgeirtereessesuscessoecooene Z Rr eae aL Cas CTI CATANIA ier eS, oc Susans Da cnrcpepe ce nmmmnaeenas Yarnednartesssdoeen-aene 8 ee ET TOTO Var rest deme eee ce satis. ckdaes es iedavene ay dartossdasneavounsds sensing 1Z Bicesal ao ypOuicucamvidikel Valuc behavior Over Lime .is. +, spsnstgeennesh opepspaersseacesnee 16 eee EL VDOIMCLICAIME ICG ES CHAVION VEL MING soscesycncessscksscntsseseadcieiisacoosscessnebssasetens 7 Figure 3.3: Hypothetical Market Value Deviation from Average Value Over Time...... 18 Rm Rt LG CMAy GOL V ALIS WALLY CG el 62x eu tac cds cnacviicnsuas / , eweee bee oD Oe ee Eee ee mre id otc en qrehegpeeenagaveanes 4 : a-@ | “ © 0 6 raws 4o460 9o BAGEeG~ 000 400.0060 5 0dgeh eb bees LAei Gere sae re ba d ; ae /~_ tT . * eee ae ob O06 6s 60h en Oe ees eos obs be O41 ePea pda edb ho ~~ A 12 y ! 2 a fi ss POR AE EWR R hs ree Ey OPER OEE OFS TW) 001 ES Peet eee AEBS, - e Is 5 I ; tA - war eee PROC SOOT eT OMe Stee .. titer 3 Fav err tere roiveriotl cee rt sila kG teak | I sin 103 sescunspeerys OMT pra see saitlony ent 49 yOeu eV oyaravil mot nottaved sulsV t rE a dijanchenteinnses Galen cme ana a ‘a Po empe ene «2: acta aula ; iooniel crott etluea SY eb ri .. sof lerive! crt mods 2iai04 sone: noitudiwaicl 4 ok Mae icaat con. omiT Fav sule¥ tek boleve ot, hi ogarevs sod coitsivedd suleV belaod -& Jonge! oluborio® wade obatT atoubord mere4 =e Heetienines a eee ie ORD OD sidan Se | sass coneateeonedv verse ey anpreens CCD Sn Toa aia ae «iki I, nha rowndnniC vevww2 team 0, Sosteedine nee ond rer ia os no) bolas’ auniM sule¥ holese -f ee ve i ln V¥ bolas? sgeisvA mic roktervetl 4 oui voted Suit Ob ’ lw Chapter 1 Introduction The product planning process is very important to companies 1n competitive markets. Competition is always striving to make product improvements by reducing cost or increasing quality. Therefore in order to stay competitive it is essential to be constantly improving products. The product planning process, also called product realization, starts with an idea for a new product or improvement and continues throughout the useful life of the product. There are many decisions that must be analyzed throughout the process, each with the goal of improving the bottom line. The goal of this thesis is to show how the S-Model can be used as an aid in making product decisions that improve the bottom line. 1.1 Metrics In order to make sound decisions it is imperative to have the necessary information. The measures of information in product development are called metrics. The S-Model strives to tie the fundamental product metrics to the financial bottom line metrics. The fundamental metrics are those that the company has control over, such as cost, value, investment, quality and pace of innovation. The bottom line metrics include market share, return on investment, profits or any quantity useful in measuring the overall success of acompany. The bottom line metrics are driven by the fundamental metrics’. ' Cook, H.E. and R.E. DeVor. “On Competitive Manufacturing Enterprises I: The S-Model and Theory of Quality.” Manufacturing Review. 4(2), 1991: pp. 96-105. a ritiiognims Fr serene ea a ih tu '200 gacmier vd zinemeavorqial toubens ina bt yritvite m ot oF infitei2s 2711 svtiteqmes yer so sion ubong bolins cele eessony gaionely touhow sat subeia i me " ik gauniinoo brs Insmsyorqi 1a mubouy wae 8701 obi cut bie ary e ssy' sre od Jeger tert) asoiwen voRni oe sieilT 23uboy St aun u Ute leog fT cor! inofiod ofl aaivorgen To leon ont nitw foen 2eo20rg at ns toni enoteicoh kurborg anderen c ne. es bon si ges nboM-2 oe woul woe a . . ‘orl cl OvEsmomi ei 1 eneiatosl bios salanner 7am hAwl ocr ba ui iaSenqoloven hag PT 1 tRomolt 16. ent x sna ot ot comiemtoubong leinomabaul sdbeirad reyes oC tes geod! owe 2onvom lelnes mintiog of) ..olnvonnt to scsq bavi ins o2:' Yilaeuwp Yns 10 ahloig Jisaeernt No Tis ae 9 on? ‘ainsmieb v1 ov} om 22itiom and monod ofl (Neto G 10° a5 Ay «fae por? antwtodnimdlA ovi ipaq) acy" +90 3. bas Sa ae “O1-OP gq 125. (PP oma agitowt In the S-Model fundamental metrics are the independent variables with the bottom line metrics being the dependent variables. 1.2 Product Realization Product realization is achieved through a set of six steps, which begins with an assessment of customer needs and generation of a concept. Next research is performed to explore uncertainties in the concept and ensure feasibility. If the concept proves to be feasible the company will begin development. This is the final step before production and includes activities such as building prototypes and formulating marketing strategy. After the product design has been chosen in development there is a scale up to production where the product is manufactured. Next the product begins its useful life with customers in the sales and service step. After the product is no longer useful to the consumer, the product enters the disposal stage where it is often recycled. While these steps are dependent on one another it is not necessary to perform them sequentially. In order to increase the pace at which products are introduced the steps are often performed concurrently as shown by the overlap of the boxes in Figure 1.1. (em ON Set SL eeserorr A [setae A Saree A. SL Figure 1.1: Product Realization Process i iy : as ond monod od¥ diiw aeldeney teabasqsbai a a: A me mie us hiv encged cidlw 2yste xie to Jse 8 igual hovaitisn ai nok ‘ borne tq ci inge2s1 ize Jqponoo slo nodes his absent 1) zavor Apsanies ott agtthrdiesst step bows taicpincd atts jartse eK sreted qaraisafl aaa! 2idT Jnonqoloveb niged ech = moive enikehien: antelpnie? bas co lohag ginbl id es dage soiiviess + of tyr 'dla | sinh ingmngqcleveh of ncamio osod sid eaanea iw ati bvtoew att ah od Lown olive Seudosludema ee ry { zi on 8: 791 Dei 3 TA. Soe Odi vee bre eotpe avid ni iti mod secte Imogen sf ane aaa ee , Oli Cl Yinersorion eT haeions 300 0 lmabnsqeb on a ) bssi ee iio Honrtay do 208q oe? senwtoni ¢ | vigil ai arc ot to qghove alt vd nwore ae ying etn iyanete a a do 1a009H a pa. —— — a“ menses L_-— anette p -_ ui’ lee H tyaibort 20, Fgura > 1.3 Customer Needs While there are many steps within the product realization process customer needs is a unifying element. A successful product must fully address customer needs in its design. In an ideal situation, perhaps each step in the development process would be in contact with the consumer to determine needs. However, in practice most development team members are not able to be in direct communication with the customers, therefore the customer needs must be passed along the channel of development. The needs are usually found through marketing research in the form of surveys, focus groups or field follows. This work is usually done by a marketing group or even an outside consulting firm and then made available to the team. einick express their needs in a variety of ways. The most fundamental way is to assess how they value proposed product improvements. 1.4 Value The value of a product is a measure of the benefit that the product has to the end user. In simple terms value is what a customer gets in exchange for the price paid’. Value at the time of purchase is the benefit that the customer perceives the product will deliver. The higher the customer benefit or satisfaction the higher the value. It is possible for companies to influence value through product design, but the consumer is the ultimate judge of it. ? Anderson, J.C. and J.A. Narus. “Business Marketing: Understand What Customers Value.” Harvard Business Review. Nov.-Dec. 1998: pp. 53-65. abeon yomolaud 2asgo1g — towbang ol 9 cainah ati ni ebaon tomotays sss ei — tes ee o a loninoo nt od blvow aesong iresitaeolanvsl ost a qua rk "wen 2 neo) Ireniqolevsb Jeon ooitasig it evawoH ‘itis Spleen mY raetsi “3 a a of! aeloie tl 2emoleuo an dthw colssinvmues ani ni od of sida Jor Yiingay sta ebooy oT Ieomigoteveb 16 fondo oils gnokshechated reollo) bleil 20 ecnietg auaal sve to mnat-orll ii dowoest gritehen fg, bas sit grujiuanoo shelve ae Rsyo 160 quot gnveshem 6ydeno yilawen @ ivwew lo vlgnsv 6 oO) absens io) saviqzs “eda .mse) 68 oO) oldeitnvs 1b Maonaig Y Yorn, \ wh aguees CU al VEw Jair “7 swears ei Joubopeto oie hey “OMe & Iathw 2! sulev eel ote iW 4, Torneo) aio oe a flewed orf] ef cemdomgiosa if oil fo oetertes ww Yilsned ometus set wate b toubony danenett oulev sonoultal oF aeig Not rosmbel! -gatavaeM 2 sh que Al be “Dh 2d-22 0 | 800). oTvon “ys: , ea 1.5 S-Model Basics The ideas that have been introduced thus far; product realization, customer needs and value can all be tied together with the S-Model. Figure 1.2 shows the customer/company needs loop that demonstrates how customer needs are translated through the model to arrive at profits. A set of system level attributes or product designs are made to address the customer needs. Next the values of the designs are compared and the best design is chosen as the final product. Knowledge of the value and cost of a product are important to properly set its price. The demand of the product is determined by its price and value and the price and values of the products competing against it. Finally, profit is determined from demand, cost a price. This thesis will include all areas shown in Figure 1.2, but the emphasis will be on value assessment. Customer Needs Demand, Price, Cost and Investment System Attributes —> Profit Value and Price —-> Demand Value and Cost —-> Price Figure 1.2: Customer/Company Needs Loop bie hoon wekeleu> role ana a} aud f meg ouemetrs of ewnda $F melt Jobe i a } fobors wl) Pooondd. botals saves tbeen oeiiaso wold es ae reivbba 0) 20m 9 eimizoh rahorn to zocintertin isvel EW. leod orl? bow baiaqmop on ncigiaob Sd 1S viRaR nae 0h ve Jgubenq 5 lo lene Bas dulev sift Po sabolworAl sla nla uisv bite 2900 en vd boamnstaher tonbor ott lo baemeb odT) aang ath ee a 1G vient J; Giese oa ‘WoOv aJouborq 3) lp eaulew Dee wore eso sbulond Hiw arco ent? oong ban jeoo (basa mot £ li vera use cw a 54 itive 2izentcersy seit ud, 7 . \ . > fi mtn t } } j ; 4 - ~~. Z. a ; 7 f teoD sob bosmsd | Jt f tromtoval baat 7 tftorwd 4- ( oh boa sula? \ hurwet] 4 ‘ a —_ —., Si | ee > \t_- we bor sele¥ ail ait = quod aby yorqracDnanates SP aggis 1.6 Thesis Outline The outline of this thesis is as follows: Chapter 2 addresses market segment economics in order to build an economic background for the S-Model. Chapter 3 discusses the five steps of the S-Model that are applied to a market segment. Chapter 4 discusses the importance of market research and the direct value method. Chapter 5 presents a specific application of the S-Model to a product in the forestry equipment industry. Chapter 6 provides conclusions of the thesis. A 7 j te au trovyse Toshem — § valqerlo walt a i oot ia E rigs? JisboM-2 oil WA bintmgansd ileal | a pa ah > ee ; ! iors 2 hostgad) temas olen sot bellars oe iad Isbol-2 all toeqsta geil a > ywiqurl borer ouley tasibodt bre cloteseet ioohant te oii ps yueero? of) or touberg a o} lobolt-2 onl lo s moltesiiqes a) oar é » - 2eets off lo engiauionos tebrvrond 04a Chapter 2 Market Segment Economics 2.1 Value Some may call the price paid for a product its value, but the price someone would be willing to pay for the product may be higher than the price actually paid’. To define value it is useful to examine a transaction between a buyer and seller. Both the buyer and seller can receive a net benefit from the transaction, given by: Buyer Net Benefit=V — P (2713 Seller Net Benefit= P—C, | CD where P is price, V is perceived value and C is variable cost per unit. From examination of this transaction it can be stated that a buyer will only participate in the transaction if the perceived value is higher than the price. Also, the seller will only participate if the price is higher than cost. As the difference between V and P increases the chances a product will be purchased increases. While companies usually have a quantitative measure of cost and price of their product, they usually only have a qualitative measure of value. The S-Model addresses this and provides a quantitative measure of value. ' Cook, H.E. Product Management: Value, Quality, Cost, Price, Profits and Organization. London: Chapman & Hall, 1997. a ee bivew suosrmme sal orld ied; stp diinbaiatael sniob oT .bisa vilautes sonqeedi nerd wight canada a brs sei of ilod .siloa bre wy 9 neeeied noidonzieast euialxs ot i9 | ‘(a nevig sons: sll morinensd isn a 14) | y * Ni = Wened pV) em) 2=9 wiped alts rodeiuunmad neovl Jing rq loo oldii iv et"D bin oulsy bovionneg ai) asta ai nets dc 48 ia j thee ying ii 4cyud 8 teri belele od igo Wi fi staqioiniad vii llrw tlie? on .o 2th] ait chart? redeid ai suber bar ; eSoner'D rs ror bre N ooewted sono hib self 2A 209 itt = ys int 1 vilevet geinacoins alidVF aesesroni bezetiomug od Wed > weu you! oven ied? to song bam 309 tog © wi slinnup 6 exbiverq Sre-virlt 2seenbhe lohboM-é edt > ao] .oobesioes ea bap eter) 27} ila oe wanna ‘Bi ee i Pe ; 7 ia 120 100 80 60 Units 40 20 0 Zz a 6 8 10 re 14 Price Figure 2.1: Example Supply and Demand Curves Figure 2.1 shows example supply and demand curves. The cost is taken to be the price where the supply curve is zero as sellers will only sell the product if the price is higher than cost. Value is defined as the point on the demand curve where no units are purchased. The point where supply is equal to demand, the intersection of the two curves, is defined as the market clearing price. For the given example, the market clearing price is equal to the balanced price, where both the buyer and seller receive the same net benefit: V-P=P-C (2.3) p20 (2.4) —_- ar 2 breast bes vinqve olqinszd (LE ciel ti200 HFT .200mw> beanmh tne yigge siqinsxo ewodle Ls et nos ef avn yiqghe Sim : 7 nn | inion Sd) es boaheb . af ‘ outey Vie ae 1 .baemon o) inugs 2 viqgue srorlw IntogedT jie “y sattmeelo toaiec ofl en bonis le at ae sort beoan! art adi o8 lupe ai sori 1 | 7 _Silaned tsa t J+4eo59+%0- (d+) » a = 2.2 Market Segment Behavior Consider a market segment with NV competitors and assume that their products are similar and consumers have equal access to all information about the products. Companies are able to differentiate their products by making changes in value and price, which will affect demand. Assume that these changes are not too large. Figure 2.2 shows this situation with VN=3 competitors with reference price, demand and value. D Ref Demand Price Figure 2.2: Market Segment Demand In this situation, the change in demand for any competitor due to value and price changes can be expressed as: dD, = >" K,[6V, - oP], (2.5) where D; is demand, V; is individual value, P; is individual price and Kj is an elasticity coefficient for the market segment. This formula is obtained by a Taylor expansion about yin 2touborg tisd! jet musa bos ebeiaieneeat 3 | i” ; | by Sh aoe 2touborn oi node novennoiai tla of o eae sp a rt ne ~ilav hi nomondls pohdaen gd eouiey tds Sain aa | | po) lomais eontunrlss ozatl) ted? semuzaA mes at phous bramob ong saneiste: fiw moliiegmod tA dived ’ & ¢ & 2 ‘ fo wu ee oe ae oe oe Vere ee — Se f@ A o 5 , re 'ioqnros gus Td? Baameb ni ogani'o of notaniame © [28 Dodssig ks ‘ 1 A) - As = SS ribo 2) \ oeley lavbivibaiel SN bageaah 23 2 slianol euit scones eg of 401 Sepia. the reference state shown in Figure 2.2. The coefficient K is minus one times the ratio of the change in average demand, 5D , when each competitor changes price by 6P: 20 8) eee 5D (2.6) 2.6 2.3 Price Elasticity of Demand The price elasticity of demand is defined as the negative slope of a demand curve. In Figure 2-2 the negative slope of the demand curve shown is the market elasticity, which considers Ea the price, P, and average demand, D , of the whole market: a (2) (2.7) P There is also a price elasticity for the case then only one competitor changes price and the rest of the market holds price and value constant. This elasticity is larger than the market elasticity due to the fact that any price change will draw demand from the other competitors in addition to the increase due to the market price falling and market demand increasing. The individual elasticity, E;, can be expressed in terms of the market elasticity, E2: E,=N.E,. (2.8) The effective number of competitors in a segment, N,, is the number of manufacturers that are considered on the average transaction. This is not exactly the same as the number of competitors, N, which must be an integer. The difference between the actual number of competitors and the effective number of competitors is dependent Tae ; Po ae as - 7 : ehh: to Onley Off aarti ono marten 21 A tnsroilteoa AT. £6 Ol at ew - A ¥¢ song exygneds iotitegmnes foes modw,C i 5 2O.th oO 5) ; eran - i - i 7 % Sosaet lo ginteat 7 * . - . ° a, > : SvVIL0 bra if le ot 273 ‘ian or! om. De nis 7 brermab lo yiistasio oY o™= - n tigntests todrant ort at ewode ova bash sedi to sqole svitagon orl Shh tc um slodw effin. boginsb sgetsve bar “A pong sai 2188 ios ono yino nord) saee sil? 1 viotesia Song a ose vale zutT tnateooo auley one song ebtor teultnare 1% verb biiw snes eon vor tei 9a) th ch oukll . Me ianwA viii oF » getorn ol of} collbbs a i i i boreotgx od nod a yilodesls lewh ihe sat - i “7 a ’ oy AM 3 7 Tod igor © 1G eOliogings) 16 sodctira avitnelis od T isa? SULTS v8 iil ne od ute ronhw Mo aeieqnaa te wednin sien vu 9109 to wxdinide syst ot bi 4 220Kiegmo9 to tedenun Lawton i upon customer loyalty and the distribution of market share in the segment. The effective number of competitors would be the same as the actual number of competitors in a purely competitive market, where there is no customer loyalty and market share is roughly equal among all competitors. As customer loyalty increases in a segment the effective number of competitors decreases from the actual number of competitors. As market share distribution becomes uneven, the effective number of competitors again decreases from the actual number of competitors. 2.4 Value in a Market Segment When a company makes a major change to a product it is usually in an effort to increase value. It is necessary to look at demand change to determine if a value change truly occurred after introduction. The change in demand can be expressed as a function of change in value and change in price by expanding Equation 2.5 about a “cartel” point at which all the products originally had the same value and price: 6D, = K) — dP -—> (or, — 5P, ) ' (2.9) ji where N is the number of competitors that are being analyzed and must be an integer. By making a linear approximation of Equation 2.9 in the area of the reference state shown in Figure 2.2 the following equation results that uses absolute values instead of relative changes: p.=K)1,-2-L5,-F) | (2.10) Ht The coefficient K in the previous equations is the same as presented earlier, but now its value can be determined: suodtis oft Jnompoe edi on ode fedkieni tone ‘lvut) 6 co saltoanes lo eden bawling ofl ae onan oT Nz s Des - ian 4 ‘eireri wi Fil il2 : Ath i bik yiisyol norte ae ¥. , et ee ae eae a odinin evitestis oni inomeas & ni 232807: it ‘vilavol Senchiive tA - a a ‘ onerie doh 2A noiieaiagtc ntsite lstto sf mort ) eth 7 nes ‘NOH cue rings erolieqmde lo Seren ovied Rs Git sven eeraee zrolitoyinda Yo te insmyod todieli «ot \ QHh0ig 6 4) suného TOM © astem erutgined a bo) ae wiamnob ie dool of yiRessoen ai d néo basil | wloed? nooshotal wanet AciKupsd Saibpisd d sory dt sanedo Day oulsy i av ste cf Sel oiineighne slonbowg searing | | ; ye OS — = fk Mh fA ieite gitied ca Lett svolieqmoo 16 odie od eb oe oc -2.4 nonnptio sotecixoqge 1sotil & ae loads aseu Jad wluen colsups yoiwollot sd LS & -_ \ i : ia “7-945 hat :*' \ el .- » + 8 oniné cdf 2 anonaube eaonvStg ort NF Arnsialioea ‘ary ‘bonumslob od mao 9 | @ il) where D is the average demand among all competitors for the segment, P is the average price among all competitors for the segment and £; is the price elasticity for individual competitors. By substituting K into Equation 2.10 it can be seen that individual demand is dependent upon the average demand. This demonstrates that individual demand can only be solved for as a function of average demand. This means that Equation 2.10 and 2.11 can solve for market share and not for absolute demand. Market share is the percentage of segment sales that are made by the company in interest. Individual demand can be found only after a forecast of average competitor demand has been made. Equation 2.10 also simplifies to a form that makes it possible to solve for value given demands and prices: y= MB+P;), p, (2.12) K(N +1) where Dr is the total demand for the market segment. This equation can be further simplified in order to solve for value in terms of price and market share: = _ N?P(1+m,) +P, (2.13) (N+)E, where m; is the market share of the competitor in question expressed as a percentage of segment demand. Vid gore oil) 21 A ioreyes ont com tom ilsubivibru oi yiiteels song od) at (a Dw “Thmomgge od wal ax 4: bosib laubivibas Isdt poeg od.eeo 11 OLS nomeape ola Ags lac ane boemeb laubrvibal fel eldanoneb eidT Janina sym e sill oe ” [.$ bre O1.f nolteupd ind ehesrn aifi orcad oxmreve to nolan 6 48 sirsoiog ol) ei oaile IoatisM . bape, stiloeda Jor ton Dae Sabie banhssnt 13t v pbasmed lnvovihol Jeasalal nr veegenod sn yd shaun a8 init eolnia a ‘1 D2 -.obsra used aget beimals wtltsqnon metove Io amano a af weition iy SUSY 10) S¥iog 6) vichaant és Aer iar me & a ~ ay (0+ GM C+WrR =" ’ iy GOULUPS a pee. mediate ob vor Groneb. ‘ fate a a 2h é ia gay hon bas seh . to 2encol ap anisy ' 167 ovioe of teho ni : Be 7 Gn DE a ‘ : & OLAS loddecvnes oft to ouarfe isahremn onl e yy if 7 — 2.5 Profit in a Market Segment Profit for a company can be expressed in terms of demand, price, cost and investment: A, =D(P,-C,)-W,-M,, (2.14) where A; is annual profit, D; is annual demand, P; is price, C; is variable cost, W; is fixed cost and M; is investment for competitor 7. By plotting annual profit as a function of price it is possible to determine the optimal price to maximize profit. Demand is also a function of price. The relationship shown below in Figure 2.3 describes the profitability of a monopoly as a function of price for the sample supply and demand curves introduced in Figure 2.1. 300 250 200 150 100 ($) Profit T T T T 50 Ameo (6 9e10a 1 12013. 14 Cost Value Price ($) Figure 2.3: Profit in a Monopoly 12 . vay eae soak ae he Tr = bnn jecd 22ng Praca Agee a COGS Rs ito ¥ 5 wi ane Phe) ; Mae boxil #i (T! Jaco aldanny at) s0ig ai S buses Lagan 21 iBone i a Jone! 2 es How Taunt gaitiolg yt J 1olisqrn 9 101 innerieovit § boomed 201g Skinixem oF Sore lemiigo sii ailondistiok ot diac 7 2 id moi! eoditoesb CS spat ni woled aweda qidenonalen sdT .sonqiag ybevig?t ovtwo bremeb bas yiqque alqmuec set ia) Soin lo nofonal & ae VE Jin | + . a The lower curve represents the situation where an investment or fixed cost is needed, $50 in this case. Note on the lower curve that profit is negative when price is equal to cost or value. At these two points there are no transactions because supply is zero at cost and demand is zero at value. In these cases the profit is negative due to the fixed cost and investment. The upper curve illustrates profit when no investment or fixed cost is required. The maximum profit according to the model is realized when price is set using Equation 2.4, which is mutually beneficial to buyers and sellers and deena investment and fixed cost. 13 ; ; - f . t ; O22 .uebsan et ao boil 10 Nrscreenah O 008 oa ro tao2 oF Inuipe wi 239 ate empnaios rol 9 = a - bre 09 Je Gres 1 yigqque caused annie = = on = nit ra bis woo box et of sub svilngaen al Beng Np ae oy i [f= - zi aon bexil 19 Inemmesvar Gn Godw erg eusriaulli 9vIo nt or an ae enian te ol ooh codw bosilestal —— att of gnibtooae len] avanti i 1 ioubsaqobor bus eralisa bie eisai 0} ‘siofonsé ewan ef aid Jeo bari bee Chapter 3 The S-Model The purpose of the S-Model is to improve the product realization process by providing quantitative tools to aid in making trade off decisions. Companies have control over a number of the fundamental metrics of cost and quality, which drive the bottom line metrics of value, profitability, market share and return on investment. The quantitative tools provided in the S-Model forecast bottom line metrics based on improvement in the fundamental metrics. 3.1 Customer Needs Loop A key objective of any company is to make a profit. This can only happen when a product has a positive demand, which can be influenced by value. Companies usually have little information on value, while it has a profound influence on demand. The value of a product increases as the company is able to address the customer needs through product redesign. The S-Model works around the customer/company needs loop shown in Figure 1.2 in order to relate customer needs to value and then value to profits. 3.2 Steps in the S-Model The strength of the S-Model lies in the use of quantitative data. While other methodologies, such as QFD, assess benefits in terms of a rating scale’, the S-Model ' Akao, Y. ed. Quality Function Deployment: Integrating Customer Requirements into Product Design. Cambridge: Productivity Press, 1990. 14 re te a a Sg | | ok | vil e0a9074 noilas va oun 3 oven ak OM? lovinod ove 2oins e109 .encigiseh To abet shidim mi bin ot sical vita ani) mood oll ovnb poulw yitieip hae taco lo aoriom tern a Ssviiiiinsip sot init svi AO miie: Las ome shar csi “Yi tune susie no boerd eahvom sei! miotiod Jasdo10i lsboM-2 orl? a . ot Aeon j ha qool ebasi f p veqoed vine ago ehiT- aloe senor of ai y Kabah ¥iaa to svitoojdo ved A . wieqioO splay vé beonaetion od nes dole basa ovAi20g § ¥ oy ‘i sonoulin bauctow 3 aor Nf ahirttw ,sulév no noiiesmotsi ) 'orft casibbe of cicle 4i yreqmos off ae SoeRIoni eqinucomolews of! brow aeew loboM-d nT igkeeohend P hog oF sila noth bes suley o: sbssd vemtetuo sie of ssbio ab Ce loboih-2 oat ab ee MIs BF ) vvitidoaup Jo seu ae ni oil lobo-2 odt te dtanavs od T i -boM-d ow ,s'sos gorict e lo sonnet 9) elifonsd esseen CG) ee dope 2ereelenaes —_ 07 ond 291 Db wos A SomnOle) saeresial sin ronrenlgoG noltzait alla ae V2 ‘O9Ot zac Wivitoubord bare ef Lite gives results in the units that companies are most interested in, dollars. The five steps of the S-Model are given below. 1. Determine past and present market value 2. Determine system level attributes for possible implementation 3. Determine values of system level attributes 4. Compute future market value using attribute values 5. Determine pricing and forecast future market share and profitability. 3.3 Determine Past and Present Market Value The first step of the S-Model methodology is to use demand-price (DP) analysis to compute the historical ron of the values of competing products over time. Sales are used for demand with the assumption that supply meets demand. The prices used must be the actual transaction prices, which are generally below list price. A sample calculation is given in Table 3.1. In this sample computation, K was computed as 30 using Equation 2.12 where £)=3, D=1000 units and P =$100. Competitor B has the highest price in the segment, but also has the highest demand, or market share. This demonstrates that consumers do not make decisions based on price alone. The reason for product B having the highest price and market share is that it has a high value compared to A and C. Yo noietnomelnegl ofdizawy wil Siaghsihe Svat “enue iovel trateye Yo esl aft - ~ zotlay ohudittia gertey ‘ auake vocktsan oui ae © ‘itidetitow bee sutle dagheth suited Sessorel bids stone wale oufa’ iadieM tases1? bie deed sot aylene (IC) sole-Lasnyob Saf oF 2Pypolobortom isboM-2 om legals raul in? scuii mye stouborg ge@tioames Io zonlay sdf To baett IeSROiit staan ay rere tii erin Vimaiue Jam) MLR silt dhiw b Ss 'q 12.) vaoied vilewene: ols domlw 29°ng ROSES Tame opshegite> olemar cult nl JE aldaT novig ai i on > aa ret! f ino) OCH] hee elicy 0001} O 93 oratiw SiS nomedem il bin uniod teenlygic! ." 7 of o04 Ofte Me ; Jnontese ont 1 « Sang . ’ 7 “280 —aovimeb alarh lod ob memuanoo 16m) estat tai ovarlé toohern bus s9nq eerlgul oft gneve Be Table 3.1: Sample Demand-Price Analysis Competitor Value Equation 3(690 + 3000) Se A ise 100 | eon = —— + 100 an 3(1380 + 3000) 110 1380 Se Zin) pe | smo | tm 30(3 +1) : cc G $90 930 Be COG Se boon. 30(3 +1) Significant changes in value and price are generally seen when an existing product is updated or a new product is introduced. Minor value and price changes usually occur yearly as manufacturers adjust prices and consumer demand changes. After applying demand-price eee over a number of years the value results prices can be plotted against time. These plots can reveal the impact product updates have on value. Shown in Figure 3.1 and 3.2 are hypothetical sample value and price plots respectively over time for A, B and C. 229 220 219 210 205 —#-B ($) Value 200 —A—C 195 190 185 1994 1995 1996 1997 nao oe Year Figure 3.1: Hypothetical Market Value Behavior Over Time piavlenA soit barred if _sule¥ | motinupd omkeY Ps Sl > | | poy OT « LONE + OBODE OO - (1 + EOE eit» | ot Oa cee | f Wit . OF 2? 9 4- i ‘ + pee — ore (i + £08 = — _ im penciiteadiinieati mes (is now S67 VAs eanerg 27 sui) hae ents ai sounans re sil M an nen: } jot oulgv yout -boavbeuw al tonbowg Wee 210 Bombqe ae - Se nels benao Tone Bite eon feniba sewiodhintin as Yee ee cl misey to womun 2 ovo eevlene soneebaeeteb apne ~ sl i valabou jouborg lanem sl! ‘coven aso aolq cst? amine wlcy ebycona | ctmqyrl ow O0 Sele eage 2 ben Aw am acs | —— ‘> Oss ; ers 6... . pre . ~~“ % ath e08 ee —— | aer a —™ oer np eet TA5 110 105 a —oA 8 100 —-B oO. —A-— C 95 90 85 1994 1995 1996 1997 1998 1999 Year Figure 3.2: Hypothetical Price Behavior Over Time Figure 3.3 shows another useful way of displaying value trends by plotting the deviation from the average market value for each competitor over time. This is used to judge value changes with respect to the competition. In this type of plot it is possible to decrease value deviation even if there is no decrease in value. This is possible when the average market value increases and the product value does not increase at the same rate with the market. This shows that it is important to continuously increase the value of a product. In highly competitive markets, holding value constant means losing ground. 7 : ' i -a )~-k- » a — — a RE ce Par A ll eget 82g} cc . ‘+ or aeer e@@er sear We r ) yorvar A eof? insiiodtegy A 1S. € omgit. % ye ij ey : yi ™ 3V ait! rl tr thy Hove I ‘ 921) rorliony ewok at vet . 7 trey ow"? iio U2 ouley fohncnt ogeien oc) nO ae i Horliteqiaios od! oF ipoqes iw sued Solas - a UJ tt o2auoiosb On 2t wed! hi coves GOtsiveb siisv ae > foubeng gc) brn 2o2estom otlev ferinny oe atts ) immhoony @. h lad! ewods eull Jodie 2% “MUON ,2IS7Is Sv NNSqIHOS ‘idgid al OL r oo Ooh 1998 ($) Value Difference Year Figure 3.3: Hypothetical Market Value Deviation from Average Value Over Time 3.4 Determine System Level Attributes for Possible Implementation The goal of this step is to determine all possible system level attributes that have an effect on value. These attributes can be subjective or partially objective in relation to value’. Subjective attributes are those that consumers are unable to relate to operating cost or revenue generation. Some examples of subjective attributes are product appearance and coloration. Partially objective attributes can be related to operational cost and performance. Some examples of partially objective attributes are fuel efficiency, speed, power and capacity. Totally objective attributes do not exist because value is * Alderson, S.G. “Product Management Using Value Benchmarking.” Masters Thesis, University of Illinois, 1996. 18 3eer inv nels wile VY tovtal leonodiogyt £9 heal . iS Gs gittizeat ta: coogdinnA leva t matey’ oaters sfrreaon Ile scninnalab Oz volke ard 40 [soy ot a 10 Svileowiue Sc ons 2oludiniie sent Sulsy “ot . = s fh yomwenes te sod sus estudrim 4vioopes fa 1 t “ aioe 16 ai | TsLS S01 it Notlstensg suiTSVvaA estudittis avitosplo vilsiis? notmelos haa 6a indo yllainug to eciqunsxe oo! .saneachag shidnis.ovitosido vleieT vitseqes Das wwe —_ dado mation atvasd] ula ayia U ema 9 tonberT — onry: a’ ii . - ne Lu | ” . it a mt as utes > entirely based on consumer perceptions. Attributes that are not physically part of the product should also be considered, such as warranty and service availability. Attributes must be expressed in the way the consumer’s experience them. Imagine that a decision must be made between two engine possibilities for a new product. It would not be sufficient to consider Engine A and B as attributes if the consumer does not understand how each would affect his or her experience with the product. Attributes should be performance characteristics related to the engines. This way the consumer can sense different levels of acceleration or horsepower that they can readily comprehend. The level of detail in attributes will be dependent on the current level of progress in development of the product. Attributes considered early in development will be broad and can be used to determine the areas that future development should address. Some examples of these broad attributes are performance, durability and comfort. As the development process advances the attributes considered should be more detailed. These detailed attributes will describe the effect potential changes will have on customers experience with the product. Any change of the current product that could cause a change in value should be included. This includes both positive and negative attributes. All attributes that yield possibilities for improvement should be included, especially those that have a significant effect on performance characteristics. The potential actions of competitors should also be considered because any changes to their products may also cause their products to become more competitive relative to yours thereby reducing your profits. of} To fey yi lnomevilg ton sie Mae) omni A . . i. ae ‘ola < i) lelinve saivise bas yiewrurw 2a tous ons iors sas onadt sansinoqes . eimai ait yew on} ni bom im ; pan 3 Sa - i na suboty won s tol 2odilidiaeod snigns owl a9ewisd obadtat taunt ome we hy : ’ ia a » «= Vie = o> » 40b vo asanes of) Ui zotdinda ae : bow A ontgnt / linens of iat OS ae e en yGrnA Joubeng oct iw soneiveqazs iad 1 eit jodie Bow aes ‘ = Ss gmuetion sct-vew eit? 2enkeie oelt vowel aaidalaagosmelo Sone aogs nuo vibes moo ya da wooo tonoiesisos lo alevet HE o level inst o:lt a deebaeqob od Ilivr estudisiie al Tineb 16 lovel 6 i Thwe seroraneye vi (nike Dewoienos euudtt Joubow onde tna wri? pas jolie lost tevab stu ji son] 24935 21) oni OF bate: = Hew Loan vie wiludsite beend saad} ' +y} | J bluorle barat widittte of? 20008 ¥DE eee seig | thw esovinds feieoroy toe of edhe’ Tipe Joubowry ott itv sou yu oao-bines tart! ioubous inven od) lo sume ay iin SvDugon hos yvithioy God 2obs wi oisT {ty otoeqea .bebulsai od hlvode iextnsyorqear we) sole von *) snodes lathiteg sdT 2orlenmomaic somannoiad Roe om soubui Ted) of vagieio Yor sennied bs abi inal! Ouay ob oveRini svONegm@mos sions ~ 3.5 Determine Values of System Level Attributes Once the system level attributes have been determined the values of the attributes must be determined. There are three methods for determining attribute values: 1. Direct Value Method 2. Economic Value Method 3. Value Curve Method. The direct value (DV) method can be applied to any type of attribute and it is the only method listed above for assessing value of subjective attributes. The DV method determines consumer willingness to pay for an attribute and discussed in detail in Chapter 4. There are other methods for sav io value of attributes, such as option value, but the focus will be on these three. 3.5.1 Economic Value Method The economic value method is used when an attribute has an effect on the operational costs or revenue generation of a product. Reliability, fuel efficiency or maintenance downtime are some examples of attributes that can be evaluated with the economic value method. The calculations that will be made should be similar to those that consumers make when considering attributes. The money that can be saved by using the new attribute is directly calculated using some assumptions on average usage. Consider the reliability of a machine that is used for manufacturing. The measurement of reliability is usually availability, which is found by dividing actual hours of operation by scheduled hours of operation. Assume the current availability for the machine is 94%, which means it experiences 6% downtime. When the machine goes 20 hai exipdinite adit souley pall vache lal 4 ‘gouloy stodittte gmat vo sbaibam bors on ‘ Sail io ‘ borltaM ols mh Y oH > stenes boiioM ove) ou imo off ai ti bas atodnite to sayi vine of ‘atlqqs 94 ma borisar (VO) suit ¥, boris VO sr!T 2sindiiite svex,due Io onlay gniaezezs tol Sods Batam nen a Teieh of coeenceth bag atydinia on tcl veq oF 2eoogniltiw tom av nOliG® es Jove zotudtis 16 onler o iahtnsieh wi sbedign ene & os sol! sour no od Thera dbonioM sute¥ + Sonn zed STP Be itedw ie ir Souleorn soley SU NOKOSD oi) 10 ¥ ui yvididerledl. Jovbowm 2 le aolimentog sunswen 19 afebo x nap ter , duciroic te eoiquiexs 91108 O78 SinLEweD 3 vial cm od ity inslt erroiteltelss odT —borllsarauley oF ‘sro ed? .2otuditts gunsbiaunes nodyr slain sian “TuRee witot goiey betalunlso viostib a stadnine ot boa a! ied ofidioem 3h: ‘itidailes oi wbieny . * egribivib vd boot si dordw viihdaliovs yilesan a) Viiestied to were Y Cidiiteve Ingnuts ot omuaad,-.notinage 10 zuycut Haliiteccdon ed nos : ut att net .omtmwob 0 eeonsiteges 2 ansem sie SOE. down one of two things can happen. The first possibility is that production is shut down and revenue is lost. The second possibility 1s that rental equipment is used to keep production up while the original machine is down. In either case there is a cost associated with the machine going down. Consider increasing the availability from 94% to 96% as an attribute. The machine will be scheduled for 20,000 hours over its lifetime. The cost for each hour of downtime is $100 in lost production or machine rental. The value of the improvement from 94% to 96% availability is found by multiplying the 2% availability change, cost of downtime and machine lifetime. The value of the attribute would be $40,000=(2%)(20,000 hours)($100/hour) over the lifetime of the machine according to the economic value method. 3.5.2 Value Curve Method The value curve method is applied to performance attributes or any attribute where the value of an attribute is dependent upon the level of the attribute. There are three steps in applying the value curve method: 1. Determine curve type 2. Determine ideal, critical and baseline points 3. Estimate the weighting factor. In order to determine the curve type it 1s necessary to examine the nature of the attribute. There are three types of value curves: 1. Smaller is better (SIB) 2. Larger is better (LIB) 3. Nominal is best (NIB). 21 iweb tute ai woltorbory md angilidiaseg resit 9 ; ae qasd af bseu ef inoesqiups Letiesy tet xi LG, 2 we Ss te™ ra. noo © 2: stor seso tiga ,cw6b ef Sait eee t a To 7 7 +. c ob? mot? yulidstiove oct gaiesewsat wbiano> swob grog rf 7 4 * os hos 7 : a ans , mull eh wvo0 zor OOS tit Belnberice off Hinw snistowen oifT | ie a“ oe off eino1 ooidosm 10 noitoubéng zeo! ni 001? at omiewab ig: 18 ipeo- i ‘G aniviqitlom yd biwot ab yilidaliave 980¢ of OPM od Iemeyende sturiiie - of io suley odT Sapitstil e * anisdoecn * brs seriimwoh > to Boo ; eanena: 7 nurioeim ofjdo ontosli Sa isra fohOn| * (mod 000. OL KeP£)=000,0 S| -horliaet oulev samonooe ort OF bodisi# svi sale! ie 7 udiniiie vr CNS SMETIOTENy oxvigqe «i boron sve suisy id 1 5 * Pp : 7 i i! uU tO 2V8) om fod 10 dah ai siucdtiis meio ouigy 4 , Lbowdtom avun oulay odd goiiviepasai AES. - : xy) ovo ontionmsod 7 ule oF axe bi vies i 4) oq? oruro. od! omansieb of 1siyauil : “ovis ouliy Jo toqyi owl sae say , | (12) solted atapilam? 1 : (GL) saydziemel S, AGUA) trad ai Jednimol4 € The smaller is better curve is used for attributes such as interior noise where the value increases as the attribute level decreases. The larger is better curve is used for attributes such as fuel efficiency where the value increases as the attribute level increases. The nominal is best curve is used for attributes such as acceleration where value is low at both high (risking bodily injury due to loss of control) and low (sluggish acceleration) levels. The value for NIB is maximized somewhere between the high and low levels. The three different types of curves are shown in Figure 3.4 with normalized value plotted against attribute level, g. Normalized value is V/ Vo, which expresses value increase relative to baseline value. 1.4 LIB iz SIB 1 0.8 NIB 0.6 0.4 (V/Vo) Value Normalized 0.2 0 0 3 10 15 20 Attribute Level (g) Figure 3.4: Three Types of Value Curves The ideal, critical and baseline points characterize the value curve. The ideal point is the attribute level where the attribute value is maximized. The critical point is the attribute level where value associated with the attribute is zero. The baseline point is 22 + an ta : a - ae sakaphan a ulav ot) s1dw seine 1Oltin? as dale aotorlitve 1 udtiis ol boas s svi wed ai tigelodT eoeneieet : a al * ee {T xoawerci Jovel ondemesel 2p 2S450)90t anie 1c. vol ei sadeer nod a silane fa da oye ue 79] (nauwtolooor deigonla) wok ben ‘iit inane loval Grol Das get wlb-csevriad ¢ ~oilwomas bosinixant - . botiolq sulev boulenrnendiine AS oe 79 nl ayiRee S18 BOVINS wroniet cee oulay eoeaie soirtw .o? 4 ai sulay besifemmoVi aie ; a i iain ™ a* ~ iy di er ™ \ U- - wor 4 * “h, S f 4 * a s ® 4 4 i q : A 4 velj siocdaitia gow T AE tt peailo toreg-salloeed bre teaiting Iesbi adT urs on iocir izcon et Suley otudme of! osodw Iovel sipditic sitar “ ns i ottT ) 1 OfsOTUIb of The belpinater sila xaniw level Pe the current value and the normalized value at this point is 1. The formula for a demand curve Is: “e-[ (2 in — Siaeat (ee | ; (3.1) (g crit — § ideal y Fr (g base — S ideal where g-,i;is the critical point, gideaiis the ideal point, gyase is the baseline point, g is the random variable and yis the weighting factor’. The weighting factor influences the shape of the curve and must lie between 0 and 1. Once the critical, ideal and baseline points have been found the weighting factor can be determined using the DV or economic value method to add one or more additional points and choosing the y which provides the best fit of Equation 3.1 to the data: y= nf “eter ! y a oat ME Bal | , (3.2) Ve O (oe. SF es a Ue ee a Cola where V, is the baseline value at gyase and AV is the change in value at the point g. Another way of estimating the weighting factor is to set it equal to the fraction of time the attribute is in use or has a noticeable effect to the user’. 3.6 Compute Future Market Value Using Attribute Values Once the attribute values have been determined it is possible to formulate a number of possible product configurations or updates. For each product configuration, a list of applicable system level attributes is determined. The future market value of each * Kolli, R.P. and H.E. Cook. “Strategic Quality Deployment.” Manufacturing Review. 7(2), 1994: pp. 148- 163. * McConville, G.P. “Developing Value Relationships for Automotive Attributes.” Masters Thesis, University of Illinois, 1996. 23 m ¢ ; om | eh 7 os boninsb 6 10) slurme? of? <1 2i iniogentt ts sulew rh ° ¢ 7 Pane - - é 5 { 1 3 - 2 =< . a tna 4 = uae a Cia a) } a: ‘ ofl? 2) 2 cog vailoerd orlt af yee Sitio ieobi oll 2i\ ae Ue feat me '€ — : _ 10r8) gaikiginw od) ay baw she rooted otl trum bre svi Gli To nema oi asonenlia) totaal Bans nen iodont eeasziow adi beet need avid ziniog sufleend bie Issbi Thoin snonibbe sion mm ono bbe af beariiers sulev oinronoss 3 Vel oft grtian or eb ati ot [.F conmupl to Wb tes orlt zebrvorg doulw y Sih gears >. " supmitts off 27 “ib. bos 9 Je ouley oniivand oilt al | ; ji $32 ot al $otodl a guiciigiow * of} ‘ gouges 4 To Yew ' re 4 a uff of toofie sidnsolon 6 an 1) Sen Ob ae vie otuditA gaiey guin¥ tole svi singe 21 ht bevrifprisieb aed Sve" aoulev sIndes orl? son) J 1 | o,9 109 .2oanbqy TY anoiasegiions ouborg sidigzog 16 Tae ovat utoml becinmjeb ef estodnite level motaye sidaatiqgs te ‘ « a ee eee » ¥2) "GheizeionsM " smetgolestl yileuy ogo ten Yoo? 4:9 bre “UR f Da _ a) ; ytiee sper) One 104 equi ones oan V gniqutioved ‘LO © we oM - we) glow No wings ars 1 Le rs . product configuration is found by adding all appropriate attribute values to the current baseline value determined from demand-price analysis. The system level attributes need to be chosen carefully. When different product actions are evaluated separately and not in concert, possible interactions between the actions on the attributes, favorable or unfavorable, will be missed. When interactions between product actions are expected to be significant it is important to evaluate the actions in concert. For example, the handling characteristics of a new automotive suspension system are evaluated using the baseline tires and then the handling characteristics are evaluated using the baseline suspension and a proposed set of new tires. The handling characteristics for the new suspension and new tires may or may not be a linear combination of the other two evaluations. 3.7 Determine Pricing and Forecast Future Market Share and Profitability In this S-Model step, the development team projects the bottom line metrics for the different product configurations under consideration. Each of the possible product configurations has a change in cost in addition to the change in value from the original product. These two quantities are used to determine the change in price for the product. The rule of thumb for setting price is: V+A ap = AV +AC 5 (3.3) gc Assume that a new product that is being considered has a value increase of $1000 and a cost increase of $500. The rule of thumb price would then be $750. This allows 24 eee a lasrue ot of eonliey efudiatia staheenqua £ ae soot a , 5 “sceaes founongy Joshi not liom exe tea ei a Rar il) cowed anon groin oidiaeoq . on 50 ae anoitnewini mad’! Dboeeim ad Miw ptvcvdiaaamnnaea ' steulevs 0} Inshogqmu 2) ib Inscaiiwie sd oO) NORE. 9k Womojne wots to eoitenatowindo o iibred of) algmnxs104 anilbuad orlt om bomeoit sailoand at a boinulevs oth mroleve m6 f : , 4 joe bezoquig s brie aeiavsqewe snileend od? goien boleulave sie x ' In Vert 201) ‘Weu DAB NOR 2 woe oll 10! acne eiomedagelbanr ar 2m juenleve owl waelio ont to nominees bo: sired? tole gure’ these bus sais sain lead Jngerqolgveb edi .qote loboM-2 anit ae siioraa taht arrose noo iouborg in “i h oi jibba ai-teco ni osnerts e earl anot sob of boa ote codiasup owl cesT :2! gong anwies wt dauad te 24+ 44 a =A , <= (oo gonad 2) ied Soobowy wan aie smipesA ad on denis to olin afT 002210 sesoirtt ROD & ] ’ - Ss a - the company to realize both increased margins and market share according to Equation 2.13 if competing products do not receive any price or value changes. If the price change were set at $500 then the margin on each sale would be the same as before the changes, but the quantity V-P would increase in turn causing market share to increase. If the price change were set to $1000 then the quantity V-P would remain the same, keeping market share constant, while the margin on each sale would increase. Setting the price between the value and cost change as suggested by Equation 3.3 will both increase margins and market share. It is important to consider what competitors may do in terms of the values and prices of their new products. The proj ected future demand of all products in a segment are determined using Equation 2.10: p,=K)r,-2-LO(,-P)) (3.4) along with a prediction for K. Assuming that K remains constant over time it is possible to use the previous year’s K or an average over a number of past years. The total demand for the market segment can be found by predicting K, but market share will be the same no matter what value is chosen for K. The profitability of different product configurations is found using Equation 2.14. The fixed cost of setting up the changes, W;, and investment, /M/;, must be estimated. The forecasted bottom line metrics of market share and profitability can be used to determine the product configuration that will be chosen. eo novesp4d of gnibrosos siete ghey ieee en sormiio soirg or tl .segnedo sulaiy 30 Soy yne S f aes an - - et aa Aeuunios ora soled en cnn eel oe bivow oisz dose no nignent een at its sonq iY) .seserom of ciada toda grintien er ai Sasotwth bt toFvein Scit ae] ome off ninueyblgow SN suns gsi nad OODIEE (orwied song adt gario? omestoai biuow siaa does no gape ons jacter sasovon Ted Ii 2 fe moveud yoholapais 2n sannde too bint soul le anesi ct ol yvew.motnsqmos tedw pbhieaes of ingnog ss nt iouboro ie to Season “osiony sil .2havbow, wan ena ‘Of .£ ooteupt gales bent : / 7 (s~ ) fos ; : rT 7 >? ‘[ ; ‘ < ’ A $ fj ; rT riz <“ Eh, ” rt d noreibery 4 i > , : ; . reyclgy naw ' » ty i Tr A ay ay, 2uolvaig ie 96> Nrorrase ro fmay ; bsg y : ‘ oro Yio vel oped st ; . A ) noeods 2: oglny lociw otf wustines inher insrnib h sitidantiorg A ni bas 4 2zegaads oft qu ynities yo isos baa we 2k toh lo 21 dent sat monod bsmaar w2od> , ad Iirw suet i odewugtiae FAO 3.8 S-Model and QFD The goal of the S-Model and Quality Function Deployment (QFD) are to ensure that customer needs are addressed in the development of products”. The main difference between the two is that the S-Model uses customer input to determine the benefit from design changes while QFD relies on engineering judgements. Another difference is that the S-Model presents results in bottom line metrics while QFD presents results in ratings or other measures that do not directly correspond to profitability. The S-Model methodology can be incorporated into the QFD framework to examine tradeoffs from the consumer and company perspectives of value and profitability®. ° Akao, Y. ed. Quality Function Deployment: Integrating Customer Requirements into Product Design. Cambridge: Productivity Press, 1990. ° Cook, H.E. “Enlarging QFD to Include Forecasts of Market Share and Profits in Making Tradeoffs.” To be presented at Twelfth Symposium on Quality Function Deployment. Novi, Michigan, June 5, 2000. 26 5-72 Oo) oe (1D) iaanrnlged] none! yileuD beats a ae es some istib ciem ofl “esouborq Te inom qotovab wat ait ypHoA ' . ae . tO” sitoaoed =o) snmrsieb owe wn bh oa teil) 2) canetsTtib teonA, aan isi’ yn onigte Ae aotien CAC) oth uw 2tluest insesyy CID ofidw eiojer salt moneda wtucan einen i) voM-8 od) ovttlideiiow: o§ Siodeeri09 Vilowrts fon ob tor 2 ' tarot 2tlushew sores of owen! C710 at ol betsroqiaant 9d mio sok iliieifcey be suiov lo cevilueqeieg Ynaqmes t - mm iuped! vo) gruneigans tenvoins sania Mele) es ae UL gan yeebtapbes4t : * el to reneste' shatanl ot GAs yniganiad” a4: : preg noted yitlenO> ao murigaye AAjowT te tel of akc og) Sere Chapter 4 Direct Value Method 4.1 Direct Value Method As stated earlier, the economic value and value curve methods can be used to determine value only for partially objective attributes. The direct value method can be used to determine value for subjective and partially objective attributes. The direct value method uses market research to determine the stated willingness to pay by respondents selected from the population of potential customers. 4.1.1 Direct Value Question Respondents to a direct value survey are asked to choose between a baseline product at baseline price and an alternative product having different attributes and a different price. For each attribute, the choice is offered a number of times with the alternative price changing each time. The baseline product and price are held constant throughout the survey. An example of a direct value question for adding a spoiler to an automobile is shown in Figure 4.1. Baseline Alternative Automobile without Spoiler Automobile with Spoiler $20,000 o Figure 4.1: Direct Value Question Example 27 hs. a bastqadD | hositol onla soni ent — a ct z vy ve. mk - cS Qo) by 2u od an eborliont waue.awisy ban sulay simonoos ont rails t ) = ions boom sulav fomifb ed] vastuidivile avitoaide ylisimeg i vino f ite svitoste viieineg bas svpasidiia tol sulev, Mit oO) 22-gatlinw Detese of: smiameteb.o} daisaess tohenr io! Isineg lo aouahuqear ort sate noizout) suis’ tag NA5 oi f eee ib ~ lav bmib # al aasbnoa rs 2fLYG * of MID * nie nb bas 20M nile ; b : 417 sud dons sol 2G OMe i ST Vl De ' 585 gdmncds song at? i i. 1 oo focxs nA ovine ol ihe art ni twee? at obi " enegdel ue wings jority bos r rl & wn) ONE , ey £0,082 ‘ { A000 / é sn sens y ~~ c HOD 082 7 of JoOt U00.08€ phy ail ig L.> wart Respondents are asked to choose either the baseline or alternative for each price point. Once the alternative price exceeds a respondent’s willingness to pay, the respondent will choose the baseline product for the remainder of the price points. 4.1.2 Determine Value Once the survey has been completed by the respondents, the number of respondents, r(P), choosing the alternative for each price point is calculated. The fraction of respondents choosing the alternative for each price point, f(P), is defined as: f(P) = we AL) where n is the total number of respondents. In order to find the neutral price, Py, the alternative prices, P, are plotted against nf | . This “logit” transformation of f provides improved linearity over the entire range of values that are encountered. A linear regression is used to determine the neutral price, Py, defined as the price where half the respondents choose the alternative and the other half choose the baseline. Thus when P=Py, f=0.5 and nf i Jp. The value for the attribute improvement is given by the difference between the neutral price and the baseline price: ae Pi f, (4.2) where Po is the baseline price. Table 4.2 shows the hypothetical results of 200 respondents to the direct value questions in Table 4.1. Figure 4.2 shows the determination of the neutral price. The value of adding the spoiler is $757=($20,757 - 28 Iniog sortq fick 16) ovitemeiié ao anil ch “7 iliw tinobenotmest ont. yay of conepidities 2 soon ab aarion a3hvy sit to tebninaiey edt rot es = Jo vodiwn ont ahoobaerest ods vt hedolqende ned gad ‘i luolss 21 Jt sig dons 1 Svilaniisard antaoots Sy ; bontioh at \4)\ ey one tives 208 witeantis ott gnizood 2O~(Aye 2 “ 4) balliatysthe nl zinshaoces Lo rode Ito? 9 . “igor aid: | —+-- Int reninge bonolg oui A eosin a Ti lay To Sane ocies off 19"0 (inmenil DaveTIIt out ad weiio oti b ‘ fencroila seit sa00rdo : . \ j 2 oft; jt 1] Lat —_——w I) Oe C : + ~] wit ofloesd orf ? law sory beiwon ‘ sd) Wig apewied oat -i=it = w <= © ‘2 S.4-9ideT song onitlaend oi af oh . 200i eup sulev Iosib sed oF atngbnd ? ; nog 01) enue in cule ety 20nd letwon i} Yo sot ac $20,000). Note that the independent variable, price, is on the y-axis and the dependent variable, nf i jis on the x-axis. Table 4.1: Hypothetical Results of Direct Value Question in Figure 4.1 : : : # Choosin 102 20800 20600 Price 20400 20200 20000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 1.2000 In(f/(4-f)) Figure 4.2: Analysis of Results from Direct Value Question 4.1.3 Uncertainty in Neutral Price and Value Due to the nature of the survey there is statistical uncertainty in the estimate of neutral price. The standard deviation in the position of the data points is determined from the binomial distribution: 2 tao bnagsb sf bar aixe-yodnng at aohy olda (2 yi ov note sale eC To atlvsad insitediog, ‘ oe galiewost 4 — > % ((0-1Ntjal 1 avitematiA e c 01 ge ions esvc.0 ci! 20.0 athe a co! aon a —tininh ain Deak oicaaia ae rey oO i ev Ob j ' Lo a eo a * a 5 i ) 0 4) )} Oows.0 oo ie PAT rit i Qoclu¥ toon mod ative To siayiseA “4 sar wla¥ bus 9911 levtus of yhaleheonld Eo ‘ 7 dj m vinensonn isolate ot otedd yovine onl 10 Sain ont oF osG | 2iniog sirh adi lo moitiaog off) m aoimiveh habast af? sore ine 7 natusiniaid leimonid ait) — 7 oe Messed) (4.3) A When /is in the region near 0.5 the logit function can be approximated by 4/2, therefore Mi the standard deviation of nf -) is approximately 4s, This means that the standard deviation of nf u Jp the neutral price is 4 tae . The line in Figure 4.1 can be n expressed as: Petal I- : | f }} (4.4) SE Ly where V4, is the value of the attribute and Ey is the price elasticity. The transformation of price into a dependent variable in Figure 4.1 generates a “perceived” standard deviation of price given by: Sp _ 8Vatt ALE Soe (4.5) 3Eatt n Cee Ay 2 “ee which is equal to the slope of the regression line, : att , multiplied by the standard att deviation for each price point, 4s, A standard statistical package such as the LINEST function in EXCEL can be used in conjunction with the data for price and nf i | to compute the standard deviation in the value of the attribute as V,, is the intercept of the line given in Equation 4.4. The confidence interval about the neutral price and value is: (4.6) 30 =>) nolomd .S\e vd botemtxerqas od ; and Holton it tigol 7 $6 a rabrete it lad enssoradT jub ylstenixonqas al act i i) J ts LA magrinomi sd . = —= . ¥ b zis ony Usexturart gall “eh ae {oh £ | AVA: I », ; ; ‘ ; ad ane | F gamiolsaet off oztio tealo gor orl! 2i wc baw siudiiie of) 16 soley See ae tug! 2. oioq & litres 1.6 sus ct sidarigy urobnoqeb # (\.-s7,) aoe: iS note Oui =~ Ocul ueeenye? ofl! Fo sale only or leaps abi | | usiosg inuaitele binbinke A «a Jniog song toss 16a ral slab srl Mow moitonninoo oi beey od neo JGDKE aba 6 sluciitte of! io sulev oct ni solinived bipbusls sts orn is twods levisini sonebiings ofT A nolan’ ni novis “alt aes 1 Hei if Sai = -o) < an” where ¢, is the f¢ statistic, a is the type I error, k is the number of price point and_X is the a. set of logit transformations, nf f “| 4.2 Designing the Survey to Reduce Uncertainty The design of a direct value survey can have a significant effect on the uncertainty of values that are found. The three main factors that must be considered in the design are: 1. Number of respondents, n 2. Number of price points, k | 3. Distribution of price points about value. The uncertainties syand sp are dependent upon the number of respondents. Increasing the number of respondents is one way to reduce the uncertainty in the neutral price. Increasing the number of price points, k, also reduces the uncertainty in the neutral price. The distribution of price point around the neutral price also influences the uncertainty in the neutral price. Ideally price points should be chosen so the neutral price will fall in the middle of the range of alternative prices. A pre-survey can be used with a relatively small number of respondents to estimate the neutral price for each attribute and the price points are then chosen to bracket this estimate. Figure 4.3 shows two distributions of pricing points. The neutral price from the left distribution will have less variability than the right distribution because the neutral price is located in the middle of the range of price points. The distribution on the left is preferred as it brackets the neutral price. 31 vinietieon sosbeSt of vovise sit gat ay a a A sisnson adi no jostte tesofiinbie 5 oved nao yore culév igoub Eto ay , | ee “a is no esb sill n) bewsbtiemde od fauns tact z1otost niem sewll $qT hae! ie ‘i i .einobnogest to 1dee | 2imeog song to eden Wie tocde 2uOg sono neue Oo Fae Sit) nogu INS DIGaD S18 4% beisye 250s Ue a ‘? iarionjiih Sf an | 1 YRS 90 al alsbror2st r* 7 7 nu 4) t it 1 c x ‘ HiT ‘ "4 s2FIG | rode Si 3 ve 7 91 ‘ii ovls sore Jertesa ont! bees iniog S9mq 16 ROMIeE i , bos. lo $d bivorle ainiog sod Vilksbl sore it sy +) ‘>. 2 ij A ie i } ' ii ireilp oO SiS ott To: 10 . ' Sn of) olqinues o zinsbnodest 19 voc Lowell .oletnites 2) sdoord 0} neeoiio-ned) sagan " udiitaih fal Limo oor lsttusn oT 2huOg G id a Mu 2Ig90) 2) Sant orien ort sen Aeed nomuditiaw if Pin: ancora foled oo nenudinteib dT ..2inieg Samm 7 21100 21400 21000 21300 20900 21200 20800 21100 = ® 20s00 20700 fa eee 21000 ewe 20700 ESCO 20600 20300 20500 20200 20400 -1 -0.5 0 0.5 1 -0.8 -0.6 -0.4 -0.2 0 0.2 In(f/(1-f)) In(£/(1-f)) Figure 4.3: Distribution of Price Points about the Neutral Price 4.3 Baseline and Alternative Selection In selecting the baseline and alternative attributes it is important to consider what products the respondents are familiar with. If the respondents already own the brand of product being studied, then the baseline should be that product. When the respondents consist of owners of a variety of products competing in a market segment the baseline product should be a representative product from the segment. The baseline product does not have to physically exist; for example it can be described with the average characteristics of the products within the market segment. This approach is often utilized because the use of a specific product as the baseline may bias the results as respondents may have preconceived perceptions of the product brand or manufacturer. The alternative products considered in the survey consist simply of the baseline product with one or more additional attributes. It is important to consider how the attributes are presented to the respondents. Companies may wish not to release the details of their new products. For example, instead of describing a proposed new engine 32 Se — (S> P pipe beyyad onl lave srt 1yods gino’ 2959 10 nolesiaseid ; Ck saugit noioels2 syitamiilA nie i 1 War ht galodne yvitsmetie bas “ae ot gina A N ; rrohnoceoy orld 11 wilinzal oe eanabnogast of vi q jeri! acd bi sitis2ead seria aor .,beibute Etta Ys Han nity vio lo (onsy 8 JO swirwe re nostatwly deere! iouhorg ovielnsesiqas 8 St Bivegas =m . 4 b oh a! mca ') ofyemmye 20t aige vilsotegig os 1 Ut saricw iguboig sal sd to caer * . J | 7 soilosed oil ab tower oftiveqs 8 10 san Sat cla To Stu! latborg wif 1s enoigoctg bevissnossay . we 2) ot berebievos alouberg svildaelie oar a cm atl .2etudrim lanenibbs o1om 10 ane diiw V Varn <2itoqe) atasbroness ont of botswana s16 wdh an atk noaeah to broke Sl ytiBxe 104 ARO wapraods'%o tie ot as an attribute, the survey is used to measure the value for improvements in acceleration and efficiency from the new engine. 4.4 Direct Value Survey Distribution Method The method of delivering the direct value survey can have a significant effect on the results. The two methods that have been used in the past are paper based mail surveys and computer based attended surveys. Time frame, budget, sample size and survey length are factors that determine which method of delivery will be used. 4.4.1 Paper Based Mail Surveys The main advantage of paper based surveys is that the researcher can select the group of people that will receive the survey. This is very useful when it is necessary to receive responses from all different geographical areas and subpopulations. It can be used to ensure that random sampling 1s done in proportion to the population. The time frame that is used to conduct a mail survey is broken into two pieces. The first part is development and distribution. A time will pass where the surveys are being completed by respondents and the company has no part in the survey until this is completed. A cutoff date is usually set and surveys will be accepted until that date. After the cutoff date the completed surveys are gathered and analyzed. The time between distributing and analyzing the survey can vary, but is usually on the order of a couple of months. The presentation of the direct value questions in a mail survey is similar to presentation in Table 4.1. Respondents are asked to only consider each choice, or price point, at a time, but all price points are shown at the same time. An advantage of a mail survey is that 33 noleTale-oe mi eiremeovrorgent 1Ot aolny Sal s bosiioM noitudienid x no jnotio tussitingl2 s.aved me > oe Vide oulev Jomub od tll 1 hovzed togsq out Jenga ni boou ood avail il abortents na ecin olqme: Jeybud samt omiT 2yovive bobaste boegd bozu od liw yrovileb to boric soiw satemeied Iant 2malos!] ae Tien evorine lin boul oil. Iso olaresess off Jarl ghevatwe Goad teqag tO seuinavhe nace (2 rua eo VA oui T iv iMe g fi} ov ipurt Hive andy of ' / goqdite bas saath inoitinwmosg tooreftib tp erent eeatia wn) lise i? of noreqerqun snob et emlqmee Monn Ias doin a rove linn s Jogbaéclet bealier . aw ny ISA 4260 U0 emul nouudmeab bre ised a ; zi a ou voviue wt i) (nt belqasoe od jiiw eyorme bak Jos Yileve eee ih ceowied se odf. .l musne bi bsrierites win 2Yovilz barolqumes finer OIG | oy tro vibes al idl Wey nso Yovia se Qiiy iaerre ¢ 7) vo que Tent 2 7m eroveenp ogley Penh aie none a surveys can be self-administered. This is beneficial because it does not take away from the company’s time, but the instructions must be clear and succinct to avoid confusion to the respondents. 4.4.2 Computer Based Attended Survey The computer based attended survey also has a number of advantages that must be considered when considering the distribution method. Usually the survey is deployed at a trade show or demonstration setting where a number of customers have gathered. They are asked to take part in the survey at random or after completing a screening test to ensure they are part of the population. This means that the computer survey has less control over who takes the survey than the mail survey. The time frame for conducting a computer survey can be shorter than a mail survey, but the resources required are much larger. For each survey to be completed a computer and an attendant must be available for the length of the survey. Time to complete a computer survey varies by respondent. Herington experienced times from ten to thirty minutes for completion of an attended computer survey’. The presentation of direct value questions is smoother and less cumbersome with the computer survey. For each price point in a question there is a separate screen. This causes the respondent to only focus on one price point at a time, which is less confusing compared to the mail survey. Another advantage is that once the baseline product is selected the next question is started, which reduces repetitive questions for which the answers are already known. Because of this, the number of price ' Herington, D.R. “Incorporating the S-Model into the Product Development Process.” Masters Thesis, University of Illinois, 2000. 34 mut yaws sass jor 2o0h ti saweted [etaltoued a: ail uaunin | | ary a eee o) cetesttioos SYiows gi soqooue Gon eel od falim anor f welt. i ' = a oot 3 a aan \ . e yavin2 bobasité boesd gat aa ured tec) cooeroevbs 1o todmwg gaan ov's yoviue Behpadig heaad sigue: a 2 - ae = UJ voir ai vovie od} vUavell Bodie notiedoneib oflgarsbione aeiw be lovrllay over zomotues lo ydenen « sro!vr gniiea nonetenameb 16 Won attics gc lslqunoo 13fte mm anohem te yovive oil ai ting Sila) of Bede luqios oft 16 ensom 2uiT nonshqog sd) lo. nag ae a ov. | sree saul odT weve si, «U nec) vovwe on} aoglet ody seue ot om Fe f err; 71 2) rth ire? 6 pied! isriode 9d 0 Ser me int , abrestin ces Peabormo> 6 hetsiqnmnos od at Vovig does 108 g Gride & shligmo? otonul Vevurl oeit to in val pigny hut ol no? nM | aoralt Dogasrsqus 108; et Ot IOs 10 mothe itioesig oat - Yoving SIE . . he or Hat ovine Wingmos ofl Miw samon ° - root vio ed mabeodee acl) asaves eitT wasetaee lon’ .vovuse | tRen on of beugmos gniaginod petal aes va ri oN » i 75Ti tf} bot-slee a! humriq Ory 3 " 1 nwoed vbporda uw elowens od? Golly tot emolts ' - vf iyclowsG mobo eitolol fshehba om paitinoqnool” JL notgar io Oooc ionilil W y 4 7) 7 4 ‘i points can be increased. The distribution of price points about the neutral price will be enhanced because of the increase in price points. Both of these cause the uncertainty in neutral price and value to be decreased. The final advantage to a computer assisted survey is that the attendant can instantaneously address any confusion and ensure that the consumer understands the motivation and procedure of each question. 4.5 Direct Value Survey Sampling Plan As with any type of market research it is important to develop a sampling plan that will increase chances of achieving acceptable results. Sampling plan development consists of the following steps’: 1. Determine sample population 2. Determine sample frame 3. Determine sampling procedure 4. Determine sample size. 4.5.1 Sampling Population In direct value surveys, the typical population is all consumers in a certain market segment or region. If individual companies want to know how their customers value attributes, the population will be only the company’s customers. Sometimes the population is considered anyone that has influence in purchasing decisions. * Churchill, G.A. Marketing Research: Methodological Foundations. 5" ed. Chicago: Dryden Press, 1991. ao oe 7 - 7 a a . ee ah as od Niw oan ieison alt funds ainiog s31q}0 h wo <7 a 7 7 7 1 Yminrmany oi) says oeodt to mo@ sie oon t + odio " rs RE ae ee bstaiaen wteqimod B Ol contigs. tani “iT asenb a 7 i. oil) ted? sens , dojzdinos yas seotbhis «ievoonsineta neo ovestp dors to suhasory bas nbitextogt aif nal yeliqmee ovine onlay <_e ii { In gli! Ey d rob of Yaanoco a7 ti domses 1 eaten to aqui yas: tnoargoleveb nalq am'qmpe alues: sicalqeoon gt tivoirlos Io e9dnnil> “aquia griwollat oi tet novisiuqog sight sitar = oma! slqrnae i si ‘a oq ynilarts ont 91/2 olqptine onthe oc 3a ¢ = sotaluqo? guilg ‘is 2f donaluqog lnompyi 2yevme sulsv toowbal | on Sv! ZPLMBIMIOO laubrvibni dy! omer 38 2 f a1 a yraqrnes on vino od Tiw nottsluqng oil , mod oi soreulin ened fer 2 novos bowbienas ai none J) is 4.5.2 Sampling Frame The sampling frame is the set of the population from which the sample will be taken. The sampling frame for direct value surveys is typically all or a subset of customers in a certain database, which may be internally managed by the company or externally managed. Externally managed databases are comprised of phone books and directories, industry research directories or government filings. 4.5.3 Sampling Procedure There are many different sampling procedures that can be used. Non-probability sampling consists of methods such as convenience sampling where the sample is chosen based on ease of accessibility. Probability sampling exists when each element of the population has equal probability of being sampled. The main types of probability sampling are simple probability, stratified, proportionate and cluster sampling. Simple probability sampling is when the whole population in considered as a whole and samples are randomly chosen. Stratified sampling is when the population is divided into strata and samples are taken randomly from each stratum. Proportionate sampling is when samples are made to match a certain known distribution, such as market share. Cluster sampling is when the population is divided into clusters and a certain number of clusters are randomly chosen to be sampled. The typical sampling procedure for direct value surveys is proportionate stratified sampling by manufacturers in a market segment. 36 90 liv? signe oft sloidw meth ealiulygod off h o@ teaduze to Ile clioieggt os econ sui so Toa = > vragen edt yd bogemnre ylineosteci od aun doitiw . hie 2lood actorlq to boarqmos iB sisal heqenuitn cilasceitstiell agnilil imeaunevoe vo ashotomib forse, stubaser'l gi A dedein-n04 i .boay sd m3 ie asiubooorg gailgmee Jnsiib year sae smd oilo ai oleyinna off svodw acuigpiige Sonsino nos 28 Hawa zbodsoet to sei {Fa tevger ions now sta godarine vilidsdort Vuldizaeon to vi cscong lo eaqy! ctisen sf balqmise gaiod to wilidedorq talpe aaa te voi? gaiiqme? ‘sects bre otaaattegong .bsfaste wwilidadow slquia sie oe ; v a8 borebiee a .aetelouqod stodw sc: aodvw al gaia ee - ; 7 ‘i 1 notslucct: ct maw ai gdiiames betes eeono yee reise fisjieats dy 5 f; on vicrobar! srolay OTS aolar i Piss IS ae (ove nouueote kb avwirnal malting a ojéen of abam su wshsy 6 brs sateulp ole? Gabevib 2 noteluqon silt modw Bi utah tet 0] BaligmaAg ey ott T Soigrrise sd o} neers yinnel rr AUT As onios ines vd ainiqense boriiere ofemoirogorg 2 aq oe tale 4.5.4 Sample Size The size of the sample to be used is important when determining cost of the survey. The average elasticity, E,,, , for selected attributes must be estimated using the expression derived from Equation 4.4: —2V. io Ea 3slope = ? (4. 7) where slope is the slope of the line of price versus nf . This is often accomplished by considering the elasticities or slopes of past direct value questions about similar attributes. The sample size can be determined by: 2 At, n= |, (4.8 hkE., : where k is the number of price points for each direct value question and / is the relative size of the confidence interval. For example, if / is 0.10 the desired confidence interval will be +10% of the mean value. This equation is found using Equation 4.6 assuming that the price points are evenly distributed about neutral price. As the price point distribution moves away from this condition the sample size needed will increase. 4.6 Other Sources of Uncertainty The confidence interval in Equation 4.6 only includes uncertainty from the results of the direct value method. This is the total uncertainty only if the sample is truly representative of the population. Improper sample selection and low response rates will add to this uncertainty. a a . a —. - = ; 7’ . , — wy = ual (i to Jeoa puirunroteb pedw — 2i sola sft onic felemites sd orm eatin bose wh, a aioe 7 BS nolienpe ae a wilqinooss antic 2 att | 8 - lad aieroy soft lo ont art Io agole Set als Say «% ae i vods enollzeup antsy tesiub tceq to waqole tw acttiaianis amrs a 194 od nam onid aleewe ofT a8 ‘ _ lo-todetun Sa aay ot sot daa ziniog song ' ma xs . Javiin sorwbAinos sdf o Ie wots LEE ye Pi sutav assim ali songs mn ivody boiudiveib yinsve ou etniog 2 sil) nownbi if} oot vows aver AOE viviaresnl t0 eso 102 19st ay 23 i vino o.4 notaups at mvigie oonivblteo oT enoon lel otal sid Gorton slay Tosib P i ban soitcvolo fqreins ‘sonal 1 maluog ont To ov tose einins rane ait ot ha > 4.6.1 Nonsampling Error Nonsampling error occurs when a segment of the population is omitted from the sample. This can happen when the sampling frame does not include one or more elements of the population, which may have different perceptions from the remainder of the population. This type of error is not easily quantified, but can be reduced by properly selecting the sampling frame to match the population. 4.6.2 Nonrespondent Error Nonrespondent error is the uncertainty associated with a low response rate. This uncertainty exists when certain elements of the sample are not represented. An example of nonrespondent error is the error if all owners of a certain product or all people in a region did not respond. Much like nonsampling error this uncertainty is not easily quantified. 38 oli mot) benine et noteivqog elite Inara ory soni 10 Sete sbutloni tort ab oun A giles sr hw to Tebniciue: aft mot anoliqeneg Inerrh oved yom toniw por 1 Hrsqor yd bosthe sd.aeo isd stn cn sarees HOig qoy act dole ot steit geilgrnga youn just | .sig1 seaoreo) wol s diiw bemigoses yinehsoru Sal St 1079 nrehyoqesmow sine ton sie sigmase oc) Yo cissmmols nighes maw aie iC] Ti # 1G eT owo ile Ti sons ond aor reson 2ut? RITES Brim f° oli douwM brexyest jon Bil: f > , . BE ot > |, Cones Chapter 5 Medium Wheel Skidder Case Study 5.1 Wheel Skidder Market The wheeled log skidder (wheel skidder) market for North America consists of three segments differentiated by horsepower: under 140 hp, 140 to 160 hp and over 160 hp. Skidders in the 140 to 160 hp medium wheel skidder segment comprise approximately two thirds of total annual wheel skidder sales. Skidders from three companies make up approximately 95% of annual sales in this segment. The companies will be referred to as Company A, Company B and Company C to preserve anonymity. The following sections describe the use of the S-Model in assessing the values of selected attribute changes for medium wheel skidders. 5.2 Demand Price Analysis The trends in the market values of the skidders were found using demand price analysis. Transaction prices were used as price and sales were used as demand. Table 5.1 shows the scaled value results over the past 6 years. The values were found by converting units of sales to market share assuming that Company A, B and C make up the entire segment, which only ignores approximately 5 % of sales. All dollars have been adjusted to 1999 dollars using the producer price index (PPI) and actual prices and sales are not shown to preserve confidentiality. ao (ne hal a 7 “ay iE 2 xwiqaild 7 F vbui2é ses’) wbbite oa — ne em - saienoo sorta Mihov 26) Jada (ebbiks laorter) robbiz “ »)' ove brs ql O01 of O81 el ORM ahr =awegonton yd total Sinqiioo Mnergse bine fools muibem gil 00 | oF OA of. eT F fh mon swhbol2? 2olaprsbbisla loodw surat into; to htbows poll J joe 210 ot galee Inoue lo ee ylolemixoiqqa quad Honk VV qo) D ywsqme.) ns § vneyro}.A vosomol ape ber tg loboM-e or) lo cen 6c} odingasb arog eat 2 de looiw mwuibanesdl i 7 vievied A oot be o} sow miebbine lt to zoulay tedtent sil a abarn off 29 + ae 52 bane soiree es boa stew Zong nviswenstT 4 aay © labq off vovo wlluest sulev bolgoe onl @ ) cil gnucucen cede solaet of 2ole2 lo Wiha ani az io of @ vibinen norgge esvong) ylow donlw Jasmeased snq vsavbor,; otf gniau emliob BROT ot Be Vitlannohtt dee svisesiy G nwods ion Table 5.1: Scaled Market Values for Medium Wheel Skidder Segment $176,970 $184,462 $177,470 $203,694 $205,450 $198,706 $203,180 S211, 283 $199,407 $204,209 $214,346 $208,047 $208,095 $215,691 $211,802 $207,386 $216,939 $208,147 The average product value for the market segment has been consistently increasing and individual scaled values are shown over time in Figure 5.1. In order to make a yearly comparison each company’s deviation from the average scaled value is shown in Figure a $220,000 $215,000 $210,000 $205,000 $200,000 $195,000 Scaled Value $190,000 $185,000 —@— Company A —- Company B $180,000 +— . —&— Company C $175,000 — $170,000 1993 1994 1995 1996 1997 1998 1999 2000 Figure 5.1: Scaled Market Value Over Time 40 iioutzo2 bbidz loodW emuihyM x6 . esule teh ie Bela | — > yaeqgee) — SB gag — | OTe, TViZ Sob sale ANT. BIZ De, 2082 LOB, 20 | ww ees i<2. Os1.€0S% | weet: ‘ “30 BOC? abt_b1 6? ~ 7 Teel ong. 1182 (08,2162 200,805. rence REV BISZ OBE COS2* pa Bee a - = i — a se we meet vooianos need ani teemgoe loshin ont tol oulay abo @ ti ~~ aa lay) 1 oh “ohne al . 2 gut of omis 1970 swore i ote 2ouley beleded - ci covor er ouley bolage oyayeve orf) meodt hollsivel 2°yanqmes toss Reee my ==, > ~ xm -- 4 oe - * Aen! dou wil sule¥ tovmM boleod ae $8,000 $6,000 $4,000 $2,000 , 7 | —®-Company A —l- Company B —&— Company C $0 Difference Scaled Value -$2,000 -$4,000 -$6,000 isda IIS los oeee yO, =I S fee o9G TUS) 2000 Figure 5.2: Scaled Value Deviation from Average Over Time Although the average market value for the three has been increasing over time, Company B has been the value leader throughout the time frame shown in Figures 5.1 and 5.2. Company A had a value increase from 1994 to 1995 and came close to matching the value of Company B, but could not sustain the value increase in subsequent years as the value of Company B kept increasing. 5.3 Attribute Selection Company A is considering updating their current product with a number of features that could add value. Other competitors are not expected to make major product changes in the near future. A list of the changes that Company A was proposing for the new product was compiled. The list was transformed into attributes that were expressed 41 ‘ ™~ ~~ / f =~ . < nr ay tT seer ~Neur = 6Bee aeer Bier 3 ’ ‘ rt VA cron iMmilsivat ; av holase ‘2 swyt JOM fi oe aBii i! (iol subey footie Save oi > 4+ it owone UAT S ‘h worleuowt 19besl SHlayv onl am Aa > bre FRET of BOL cote aunont obler 8 bad Aye ( i avian oulnv ad? misieue ion bigeo jod | yneqnea ae anlaio ni jos & yoga eee , i aciiMlie? vadith. | inernrs ted) grtmbe aehobienoo ai A yanemGl ii 2vollledmnos w?wihO .sulav bie bleed jedt es (nego \ ‘ rs Jacl asgneds ont lo tail Ageia sen ait me eg ¢q udm ol bonnciemer esw teilady .beliqmies gaw loubony ne : a a in - (' : in terms that customers could easily understand. For example, changes to fuel tank capacity and fuel efficiency were combined and expressed to the customer as a change in the average amount of time the skidder could operate on a tank of fuel. Table 5.2 shows these attributes along with other categories that were selected. The actual attributes are more detailed, but only a general description is given to preserve confidentiality. Table 5.2: Attribute Selection Attribute | Description AMINES, cas ae a Ss | [ar ee | Availability teat Metis Aictribuned geal y ied sR ER eens a ae eee Each of the attributes was assigned a direct value question. Two additional direct value questions were added that addressed the brand name value with Company A being the baseline and Companies B and C being the alternatives. 42 Lr ‘Lae! : 5 ; - 7 A Ans) Intit of asgaesta elqinaxs wo bnsieobair Villsiinabiines ovisesig ob fsvig et noliqioesb mens 8) . onsois2 atudimA :& Loldat =e ngingiigea] | aqudiaa, | a lavretal 9} anet) i | TAL s eonned jog i vit yaqa An nT tow Al LA Yonge. ais ia BA —— a rolgic rolswEe | pf é3 i ~ Ji i197 } A, . — - _ - ” ae om —} ee ED EE ricctaraey | Ou ‘didaiievl | . 21S aA | deD nT | rg "238 cinevbA sviii A ote ” 4 } ‘ Js es My 4 3 den A i iD}. ___ anor = nvr i ; giduS | Gt. joa raqolsvac 7X suit a 1 Hensel yingrigW Tie vin WES | -_ ““ —— = / a 4 it " i on | = > ii aigoH | Ly _ ahs ia ssitaaalgs ho iy inoy 4 ib leno T noisy solsy henwib s barviers ew eludrate okie x ngerect - iw ouiev ian beeved orf) bozestbbs Jas} babba saw eae eo Vismrails ofj griod D-bna & esinagmeD Ban - Ch t ns - a a = 5.4 Value Analysis 5.4.1 Survey Design The direct value method was used to determine the values of the attributes. The survey had three parts: ratings/perceptions, direct value questions and demographics. The ratings/perceptions part was used to find the perception consumers had in terms of the quality, performance, reliability, resale price and preference of the products from the three companies. The demographics section was used to determine the geographical area of the respondents, how many machines the respondent operates and years of experience in the industry. 5.4.2 Distribution Method The survey was distributed in the form of a paper based mail survey. This consisted of an outgoing envelope, return envelope with prepaid postage, cover letter and survey. Both of the envelopes used the University of Illinois as the return address so consumers would not be biased by knowing which company was sponsoring the research. The cover letter, printed on University of Illinois letterhead, expressed the importance of responding to the survey. In previous mail surveys to industrial respondents response rates had been very low. A past survey on wheel skidder yielded only a 14% eeponse rate’. A more recent survey on wheel loaders yielded an even lower response rate of 3%". Both of these ' Woods, A.W. “Managing Total Quality Using Value Benchmarking.” Masters Thesis, University of Illinois, 1995. * Alderson, S.G. “Product Management Using Value Benchmarking.” Masters Thesis, University of Illinois, 1996. 43 oT 2sirdivyuc off to cooley ae onatbtoh of haem anw Donimar . gs el ~e Joiilesiaore > bos enobesup 4 eelev joni ,enoiiqeaieeiagaiien .— ‘2 7s aimee: a who sf) lo sonsyiteaty bow song sieesr viritdettss 6 ee! “ woos af! oninreveh a beri-eaw nordsee aoiekesrzorr1d oft e : é = ) eo. bite 2oietedo Jnsbnocen ol zoriisem yan wor 2iasbaee ? bortelf noited 3 | | yo » ; Tha (2q 9 rice. a iti iS) dvb enw Yor - i : . 1. Citw sqolovir: (moto .qolerns gmGRI® HETGy ) > Wil tO View a i ott heen ssn0lavas ni teed v : - a Tit » fourlw an vrord yd beesid sd jon Blaow iam . . * nor: ibe BOAT lo yitersviol! no bolang Aersi ; 7 vovwue ort of anibng rainohaoqeet latiewbnt of 2vseiue liam svoneng iil ae bi e ? ylno tobisty tebbid: jsorlw op Yovibe ed wae anon wwol cove te bobigiy abso! pad ao yet li reversion? sale wield vuled> tet guyana” 37 0 AN ARI A svit) ext? iif qnietoovonoS culaV gait} iovargaieM MobLar?” D2 nese ET. a . ~ a é 7 eae 7 ek. i. pV keel response rates fall below the target of 50% to ensure that the sample is representative of the population® . Two methods for increasing response rate were employed here. The first of these was to include a gift with the survey mailing to encourage responses. This came in the form of a business card sized magnet that included the schedule of forest products trade shows for 2000. This magnet has been enlarged for readability in Figure 5.3. The magnet could be easily added to the to the survey envelope and did not show a preference toward any particular company. The shows on the schedule were evenly distributed between all major logging regions in North America. The second way of attempting to increase the response rate was to manually put a stamp on each of the outgoing envelopes. This was to make the mailing look more like a personal letter to the consumer than a mass mailing with a postmark. 2000 Forest Products Trade Shows Oregon Logging Conference Forest Expo 2000 February 24-26 Eugene, Oregon May 11-13 Prince George, BC Timber Harvesting Southeastern Expo East Coast Logging & Equipment Expo April 14-15 Baxley, Georgia May 19-20 Richmond, Virginia Northeastern Loggers Congress & Demo 2000 International Equipment Expo September 14-16 Kelowna, BC May 5-6 Springfield, Massachusetts Figure 5.3: Forest Products Trade Show Schedule Magnet > Babbie, E. The Basics of Social Research. Belmont: Wadsworth, 1999. 44 lo svieinsesiqe ar olgmur of? rng) guran o8 Breil bs rohan SIO SIFT Sane jAat aniessrodi : snciwodae of gnilient youme oat bi i acl} bobeloui Jarl » toagam baste Samo casniand sto =a 10) Bowzalos geod and Jongant eit onot wit avods obatt 39 rane sqolsvite yavrue orl o7 on? of babbe yliens of bidoo isngemtedT E29 1 , ti no eworls ofl .ynenqmon isivothed yar Dap someon won ‘novi ai 2noyen ghigeed yotem iis nsewted baludrizih yinevs omy mnsias dq vVUlaunsen of env Oey Gunes: a) onestani of galiqauroiie loyewt & ot! szanc: Hoo! eniliaun on aaleut 09 esw entT .23qolsvas Bnivgiio oat . o 2 iti aniline events ned! tomenes ort a) 3. of? sheaett gtaubos4 32910 GG0L : vi \°a ferro I a Van sine) glggad tt vont neg ansgull OSS ys 57 aiegad tao) tend on?! wives. ived pithorae wdale © | AH OS-0t VERA jonos0) veined 2PM liaga mt (OOS Geadhe % wargne) enggad neiaadligh © : ralrtina . oq 4 3 tne giap@ ‘hace tlolwaha? &t wi a a Po i <¥ >, *, + 4 od , be 5.4.3 Direct Value Questions A direct value question was developed for each of the selected attributes. The baseline machine was described in the instructions to the direct value questions. Figure 5.4 shows the attributes of the baseline machine as presented in the survey instructions. The baseline machine attributes are representative of a typical product in the medium wheel skidder market. This ensures that there is no bias toward any single manufacturer as the baseline. Baseline Machine $160,000 170 HP Gross ROPS/FOPS Single Function Arch Decking Blade Llefs Bunching Grapple 32,000 Ib. Operating Weight 110” Grapple Opening 138” Wheelbase Winch 28L x 26 Tires Enclosed Cab with Air Conditioning Figure 5.4: Baseline Machine Configuration Instructions were given to consider this machine as the baseline in all direct value questions. For each attribute, the direct value question was given a baseline and alternative description with a number of price points. Figure 5.5 shows a sample direct value question used in the survey. Baseline Alternative Original Oil Change Interval Improved Oil Change Interval $160,000 < Choose One $160,500 $160,000 < Choose One-> $161,000 $160,000 < Choose One—> $161,500 $160,000 < Choose One-> $162,000 $160,000 Oo OyeUP < Choose One fel Cela eed $162,500 Figure 5.5: Sample Direct Value Question Five price points were used for each direct value question. The baseline price was set equal to the baseline machine price of $160,000 for all price points in all questions. The 45 surgi enoioup sulev mouib sp ene at apolauiasl yavine od oi batreasg a8 on) owen saiionad oalto'as Bi intibsmn ofl} ai tovborg lesiqy) # IO oviteiasesiqgs one daneteaie OF warsdtiucse siggie vee biewos paid on ai srodhiads esata 2idT 21ONZIOR no # oH" biG gonial) « dsl, action of oW gnitewaQ) dl OOOSE- + siqamD gnidonod “Rh” oadloolW “BEL -« grins siqqmo OH vi) OC KXGEL Of ; ar uninoitbaoD tA diiw dad & raitl rea siden A oullees€ 2 surat suiny jootib tit nts gc.od) @ enijonro aii 2btertos Ol navig SW iw cetewup suluv sib oct sluditia igetet e -towght -aleqeaing jo tsdama s isi ara iM voviua ad) ni bora noleeai OITA sutlseed 1 9BitG 22 | lovistal seme) iD fee O02 Oai7 oi) woot) —- - po OWO0aLg =i' lid @ <=3n0 eines MOOS: 49 ; C.tGk< |r} Secure 2 —4* C OOO, 0612 a on sont) —-— coc WO0dlZ - & ) 20a <4 re 4 OW08i2 ei SuloV toni signif 3¢.¢ cgi tileeadd od? anothy . sulay tooth Hone 1 tng ok a a coup iin ai crime cong ia 2 OOD DATE Io vain omistanun anitsesd sa of li i i ; 14 Fa ov 7 ,' | ae Orb 5 Wuseereen alternative price started near the baseline price for the first price point. The incremental increase in price for the alternative attribute was dependent upon the estimated value of the attribute. The marketing group of one of the companies in the segment estimated the values. These estimates were used to create preliminary price points. The survey was then pretested with a small sample in the marketing group in order to determine if the price points were properly distributed about the neutral price. The best distribution of price points would have an equal number of price points above and below the value or have the middle price point near the value. 5.4.4 Sampling Plan The population for the survey was owners of medium wheel skidder that would likely purchase another. Owners were used as the population to ensure that there was enough experience with medium wheel skidders to understand the attributes being presented. This population was limited to those owners in North America. Two sampling frames were used to obtain samples from the population. The Uniform Commercial Code (UCC) filings were used for respondents in the United States. UCC filings exist for all purchases that are financed and are categorized by equipment. The medium wheel skidder category of UCC filings was used as one sampling frame. UCC and similar filings are not kept in Canada so the Yellow Pages were used. All companies in the logging section were included in the frame. The elements of the population that are excluded from the sampling frame are owners in the United States that did not finance their purchase and owners in Canada not listed in the Yellow Pages. Some of the elements in the sampling frame may not be in the population including 46 lahiornar sfT Into Song tert off at oon io sulev betemites oll nog Inshaegsh enw oiut ofl) bent Intsingot Ont oi eolmbiqnes orld 16 9110 RO ee asw yevine “tT 2iniog sonq cunning. sth OY beau Se : fh sanensteb Oo} yabao ar tt pintanaale noida teed off cong einen od? toda nalacntaib ¥I 0 sulsv od? woled brs sveds 2itieg 2014 to tdi nae a ove daa if ices ae as lesw umber te manwo 20 avin ht al jatr al ONBIGOG isl 24 boon syow BreawO 7 fi. son) onsiasbrw Gl fwehbide lesdve eaibore dit ‘ ny 2enwe s2on! oF betinil ear roisataqar ett Ki? 1 2diqempe minide 0 beer sw came onilerasa o ‘et 7 q2o7 10h bony ong gnitit (YOU) shod ine hoonnrn ois Jat) sordid The wt Jekko-mge nilit DIU 10 viegsias tbbole iserive ml iwollis'! olf o2 shanti? oa iq tow om eqnilla wale i in ain ore? so: ai babnlen! stew.qolise 28 SAiagol onl ant eit ‘het li 0: «wave is omen gctlqnms silt moi beiiions Sas iat nor oh ¥ ott 1 vor ome an aeevro Byte vagelouie visi) conan?) ioet Be lugod ond af od ton yaronmed gitgmee otal aimee atl los 7 —- logging companies that do not own a wheel skidder and past owners who no longer own a log skidder. US dollars were converted to Canadian dollars for all surveys sent to Canada. Stratified proportionate sampling by region was used. North America was divided into three regions that contained the three major areas of logging activity: southeast, northeast and west. Figure 5.6 shows the regions that were used. Figure 5.6: Regions of Survey Distribution After the sampling frame was divided by region a number of samples were randomly chosen from each of the regions. The number of samples chosen for each region was proportionate to the percentage of demand from each region. 47 8 wo ywynol on ow eianwo F of te0 ayowuse ie 102 rath ib oh ; ae 7 ~ were opw sonomA dno! .beey ee oer a igh oe 4 aripgol Yo ekeas Ole swt ail ikea _boan stow Jed! enowen orf ewods 3.2 ugrl- ital ~G, $37 “4 ) of | 2 tudimintl yovi? te eniiged 16.4 swylt : : a 110! jie to wider 2 nolgs ¥d bobivib. cew omett anilames Sat ; > aa neige foes 11 nveads eeiqmeasgy ‘nun oT .2n0igs: oft lo nbs Ino noize dase moth boemob Te spemogte Onl ob lane! 7 : r ye ~“, - The most recent previous direct value survey on medium wheel skidders was performed by Bush*. The attributes studied in that survey are similar to the currently selected attributes. The average elasticity, Ea , was found from the individual elasticities to be 1.85. It was determined that approximately 95 surveys needed to be completed to have an average confidence interval of +10% of the value of the attribute. A response rate was assumed to be approximately 7% in order to determine the total of 1440 surveys to be sent in order to receive 95 returns. 5.4.5 Response Rate Surveys were sent and a deadline of two months was placed on all returns. Of the 1440 surveys sent, 100 were undeliverable because addresses were incorrect and 4 were returned after the deadline. There were 161 total responses before the deadline yielding a response rate of 12.1% for those surveys that reached prospective respondents. The response rate is lower than anticipated and lower than the suggested level of 50% to ensure the sample is representative of the population. This low response rate means that some level of nonrespondent error is present in the data. This error is not represented in the uncertainty of the values from direct value method analysis. While this response rate is low, the number of responses 1s approximately 10% of the annual demand of 1600 units in the medium wheel skidder segment. The promise of a larger gift after the survey has been completed and returned could perhaps be used to improve response rates in the future. * Bush, C.A. “Comparison of Strategic Quality Deployment and Conjoint Analysis in Value Benchmarking.” Masters Thesis, University of Illinois, 1998. 48 as? aobbils beodw mwibens mo ysvine orl i a vinehwe asi) of whiinie os yove tend a nana adh levbivibni ot mot baud anw , tial ats sd ot botesh ayovme ¢@ ‘inlet sarit caaheklae otudmité otf lo culev off lo oO LE Yo levies! sonsbiinos sgeisve to ‘2fo? orlt sninnaiob o} sho aes (aletn xeanagges od ot honweas Baw 9! ; erate: 20 syisss7 arabe nr insasd of : 7: 0 vamos His to cooshg anv gefieet ov7} te oniiheed 2 Ok Ee are ee viow b bur ogrioom stew sseznbie faueced oidisiev ‘aban swwe OO nae iibpeb sa! stole aoe Isial ial anw godt onifeash Aiea : 7 7 noqes) oviizaeing Deraies tari ayovine seo) 161 SPE a t* Yasguue i of) fea) ‘ dwolbns f : Loisqiovas q¢ nell * Tar er = oun ee 2B so woletil oneluqod edio ovishwsesyqal 2) alge San cio? Su nF Inoesig ar tone Inebnogesinen le Svaie ‘isne bodient spie¥ ioeub mod aoulav. srt io yink ome alt }o OF victwenerqas ei 2eanoges io wweinge Sed liv occ! slo selnoyg oT deeteee tebbile sod gibsar ei) are non VOT Doau of agechod Dluoo Demuin bus hoisiqmio2 res u ved, Irina bos inavelqeQ yilav? sips? 16 ee aa i 2iow Tl lo yliervinl! Raed T sree“ pcstony. 5.4.6 Data Analysis The data was then analyzed to determine the values of the attributes. Table 5.3 shows these scaled values. The distribution of price point about the neutral price was good for most of the attributes, with the neutral price in the middle of the price point range. Figure 5.7 shows the distribution of values for each attribute on a on a relative scale. Relative value is determined by: oe PP Relative Value = —_™<_, (Sel) ee a Cie. where PP iq is the middle price point, PPjoy is the low price point and PPhig, is the high price point. Table 5.3: Scaled Values of Selected Attributes $ : . 49 AsiduT .estudrvln orf) ie eonlaved) ommTsiab © 2nw o0ng lsviver pdiauedis hong 0 cach ot inioq sana ort) to sibbun sim S24 feusay eet ahi ai ‘ales eno a 90 stludntic dose ta eontey lo nolluditeib Sp 7 wed bontrraisb a ane (25 : ets al =onia: a 4g AS lorfl 2) awa'hh brie Infog sore wel gall “"l iniog sorta slbbisn ot tay “nie betaaiec Te ehule ¥ baleed sb2 fda fisioe woudl | “sat avioia!l sgnat Dio - OLA : 2 —- = J fiengeo a... ns feel anal Easel Tout 1 | 7 . Pave nw T uy Qi- THe : 92 * iil cts f . — a - Na Hl _— , a i dx i : i. 2 wi yer | - | “F dteqed vn naw £ dignet vingwsW. | 5» Rh? 2 t ‘yet (aos Ww ls | ened tnsmeseiqoxt | i q [S. fa0.4 c; aru) Irvarroag ig on | q - em A 1.5 Upper ee Price Point Middle Price Point Value Relative Lower Price Point Pipette) AOLAtenoIoieos GC) C2 01 ET EZ E3°ki FZ Attribute Figure 5.7: Price Point Distribution For two attributes, C2 and D1, the value was negative and outside the price points. This may have been caused by the respondents not understanding the nature of the attributes as described by the survey. This reinforces the fact that the alternative and baseline attributes must be both explained well and popular enough that the respondents know what they are being asked about. The difference between value and cost for an attribute is a measure of the degree of economic opportunity in making the attribute change. Figure 5.8 displays this difference for each attribute except the two that had negative value. 50 =~ en > pm ing-+ bhp ‘ S 1 $5 #9 'Q49 10 Sd 14 GA VA GA GA BA BA SATA pari ta covas raya mos oor] | zt mgr : tal avilege: 2s. ovlev off 10 bre 2D iri = yt rf iboxtrrabnw hot pe beuorresr? Oi} aa bison 2 Hi: oft thelt Jost odd aedrolniot cid] ovine ya he i a0 iguoce wulyqog bns | lov Sontelqxe. sVod 66 Feamns suisv noowied someehth siT svode bodes paied ae . oi gona of vitae oisrorec lo semsb sali Siena 7 %y { G Oe te spar foe? uw) Jw wiih arti evsi¢eib 62 . oO: Ad a $2,500.00 $2,000.00 $1,500.00 $1,000.00 $500.00 Scaled (V-C) $0.00 -$500.00 -$1,000.00 Attribute Figure 5.8: Scaled Value Minus Scaled Cost 5.5 Compute Future Value Assume Company A is considering adding attributes Al, A2, A4, AS, A6 and A7 to the update to the current wheel skidder. This will cause a scaled value increase of $7463.82 bringing them to approximately $215,300 in future scaled market value. The scaled cost of making these attribute changes is $2,916.67. Companies B and C are not expected to make any major value changes in the near future so the values will be held constant. Figure 5.9 and 5.10 show the market values and value deviations from average value including the predicted values for 2000. 51 A 9 A A OR Wl E: oo ta eA : aA GA. DA BA SATA “O00 5 eS 90.000, sprit 20°) boigod auniM ouis V belaod -8.¢ sige sale’ opie \ eoludna pribbe canehinnes ai A ynagMngs roidT xhbila isedw hisTmS af o} / bgt 1 OVE CIS2 yisiemiomiggs 0) f ior} qciznnd SB 2 al zdunils suidiie seal guideons to 10% (1 git aggoarin sulacv voy you solar OF8 ules Sibin of) wore O1.¢ bre Sic swght rat O0OS 10 eoulsy be )sibeag esl) gebaonine $220,000 $215,000 4— $210,000 $205,000 $200,000 $195,000 Value Scaled $190,000 $185,000 : —@ Company A —- Company B $180,000 + —&— Company C $175,000 $170,000 1993 1994 1995 1996 1997 1998 1999 2000 2001 Figure 5.9: Future Scaled Market Value $8,000 $6,000 $4,000 $2,000 $0 —@ Company A —- Company B -$2,000 Difference Value Scaled —&— Company C -$4,000 -$6,000 1993 1994 1995 1996 1997 1998 1999 2000 2001 Figure 5.10: Future Scaled Value Deviation from Average Scaled Value 52 ——} OBO,A8~ 7 a yey 000,88 > geer 2eRt .eCRP Seer univ] guia Y bélao? sasT 01.2 aught 7 i sz - a, tae The predicted situation for 2000 is very similar to 1995 where Company A is making a value increase and approaching the value of Company B. 5.6 Forecast Future Price, Demand and Profit The rule of thumb change in scaled price for Company A is $5,190.25, halfway between the change in scaled cost and scaled value. Company A is assumed to use this price for the future. There is no information that Company B will be changing price so it will be held constant. Company C will be increasing scaled price by $5037. The changes in future scaled value, scaled price and market shares are shown in Table 5.4. Market share changes are found using Equation 3.4. Table 5.4: Future Scaled Value, Scaled Price and Market Share Changes / J Seated Value Change | Sealed Price Change | Market Share Change Pie | [Company] iC TOO CCC Both Company A and B are benefiting from Company C increasing price and not value. Company A will see the largest increase in market share because they are increasing value with a rule of thumb price increase. The profitability of all or a combination of attributes can be found using Equation 2.14. 20 a yaidam af A yasqenoD sid eer on ualenia Coy sono wee Mord bre basaed .aint | —_—.- ‘ yawtiad <8.0 21.22 at A yanqmeD al song volave ai ger aut sil? o@y oF beonuyess al A yeqimi@). palev bslene baw ison balese sik BN ri song gnignsido od live Gl yomgetel te: ‘oeondtn ‘ on os, et 2 oedT ; sours 7 > 7 CFPOr? (J song Leleog gmenstorni od [liv D yasameD dnsieios by ; se lotweM .}.2 oldaT mi nworde oe eoterle tochenr bas s3hng bolése Julev Gate ? £ aoitsepS aries bouel ome " unit) owne } / Sone sant bois? coleV boise? swiiod be oi ’ d? jan ied ort & no? go and: : aula bslase ke