Development: Product Design

1. Development: Product Design

2. The NPD Process “Fuzzy” Front End Phase 1: Opportunity Identification and Selection Phase 2: Concept Generation/ Ideation Phase 3: Concept Evaluation & Screening Phase 4: Development Phase 5: Testing & Launch

4. What Is Design? • Has been defined as “the synthesis of technology and human needs into manufacturable products.” • In practice, design can mean many things, ranging from styling to ergonomics to setting final product specifications. • Design has been successfully used in a variety of ways to help achieve new product objectives. • One thing it is not: “prettying up” a product that is about to manufactured!

5. Aesthetic Evaluations of Consumer Products • Balance • Movement • Rhythm • Contrast • Emphasis • Pattern • Unity

6. Contributions of Design to the New Products Process

7. Purpose of Design Aesthetics Ergonomics Function Manufacturability Servicing Disassembly Item Being Designed Goods Services Architecture Graphic arts Offices Packages Range of Leading Design Applications

8. Assessment Factors for an Industrial Design

9. Psychological Responses to Product Form Product Form Behavioral Responses • Cognitive • Evaluations • Categorization • Beliefs Aesthetic Evaluations Consumer Response to Product Form (Adapted from Bloch 1995)

10. What is Product Form? • Objective Physical Properties of a Product • Form • Structure • Texture • Color

11. Psychological Responses to Consumer Products • Context • Category Membership • Functionality • What happens in the absence of context? • Design communicates, but does it do so effectively? • How does the design and its context influence: • Consumers’ reactions to the new products • Consumers’ communication strategies

12. What Does the Design Tell You?

13. What Does the Design Tell You?

17. New Product Development Sales Forecasting & Financial Analysis

18. Estimating Sales Potential

19. Sales Potential Estimation • Often used to interpret concept test results

20. The Concept Statement

21. Sales Potential Estimation • Often used from concept test results • Assumes awareness and availability • Translating “Intent” into sales potential: • Develop the “norms” carefully for a specific market and for specific launch practices • Examples: • Services: 45% chance that the “definitely would buys” actually will buy; 15% for the “probably will”s • Consumer Packaged Goods: 70-80% chance that the “definites” will buy; 33% chance for the “probably will”s

22. Sales Potential Estimation

23. Sales Potential Estimation • Translating Intent into Sales Potential • Example: Aerosol Hand CleanerAfter examining norms for comparable existing products, you determine that: • 90% of the “definites” • 40% of the “probables” • 10% of the “mights” • 0% of the “probably nots” and “definitely nots”will actually purchase the product • Apply those %age to Concept Test results:

24. Sales Potential Estimation • Translating Intent into Sales Potential • Apply those %age to Concept Test results: • 90% of the “definites” (5% of sample) = .045 • 40% of the “probables” (36%) = .144 • 10% of the “mights” (33%) = .033 • 0% of the last 2 categories = .000 • Sum them to determine the %age who would actually buy: .045+.144+.033= .22 • Thus, 22% of sample population would buy(remember: this % is conditioned on awareness & availability)

25. From Potential to Forecast • With Sales Potential Estimates: • To remove the conditions of awareness and availability, multiply by the appropriate percentages: • If 60% of the sample will be aware (via advertising, etc.) and the product will be available in 80% of the outlets, then: • (.22) X (.60) X (.80) = .11 • 11% of the sample is likely to buy

26. Sales Forecasts • With Sales Potential Estimates • A-T-A-R Models • Best used with incremental innovations • Based on diffusion theory: • Awareness, Trial, Availability, Repeat

27. ATAR

28. An A-T-A-R Model of Innovation Diffusion Figure 8.5 Profits = Units Sold x Profit Per Unit Units Sold = Number of buying units x % aware of product x % who would try product if they can get it x % to whom product is available x % of triers who become repeat purchasers x Number of units repeaters buy in a year Profit Per Unit = Revenue per unit - cost per unit

29. The A-T-A-R Model: Definitions Figure 8.6 • Buying Unit: Purchase point (person or department/buying center). • Aware: Has heard about the new product with some characteristic that differentiates it. • Available: If the buyer wants to try the product, the effort to find it will be successful (expressed as a percentage). • Trial: Usually means a purchase or consumption of the product. • Repeat: The product is bought at least once more, or (for durables) recommended to others.

30. A-T-A-R Model Application 10 million Number of owners of Walkman-like CD players x 40% Percent awareness after one year x 20% Percent of "aware" owners who will try product x 70% Percent availability at electronics retailers x 20% Percent of triers who will buy a second unit x $50 Price per unit minus trade margins and discounts ($100) minus unit cost at the intended volume ($50) = $5,600,000 Profits

31. Points to Note About A-T-A-R Model 1. Each factor is subject to estimation. Estimates improve with each step in the development phase. 2. Inadequate profit forecast can be improved by changing factors. If profit forecast is inadequate, look at each factor and see which can be improved, and at what cost.

32. Getting the Estimates for A-T-A-R Model Figure 8.7 xx: Best source for that item. x: Some knowledge gained.

33. Sales Forecasts • With Sales Potential Estimates • Diffusion of Innovations • The Bass Model: • Predicts pattern of trial (doesn’t include repeat purchases) at the category level • Works for all types of products, and can be used with discontinuous innovations

34. Bass Model Forecast ofProduct Diffusion Figure 11.4

35. The Bass Model • Estimates s(t) = sales of the product class at some future time t:s(t) = pm + [q-p] Y(t) - (q/m) [Y(t)]2Wherep = the “coefficient of innovation” [Average value=.04]q = the “coefficient of imitation” [Average value =.30]m= the total number of potential buyersY(t) = the total number of purchases by time t

36. The Bass Model • Important Feature • Once p and q have been estimated, you can determine the time required to hit peak sales (t*) • and the peak sales level at that time (s*):t* = (1/(p+q)) ln (q/p) s* = (m)(p+q)2/4q

37. Financial Analysis

38. Financial Analysis • How Sophisticated? • Depends on the quality/reliability of the data and the stage you’re in • Early Stages: • Simple cost/benefit analysis or • “Sanity Check” as 3M uses: • attractiveness index = (sales X margin X(life).5 ) / cost • sales= likely sales for “typical year” once launchedmargin = likely margin (in percentage terms)life = expected life of the product in years (sq root discounts future)cost = cost of getting to market (dev., launch, cap.ex.)

39. Financial Analysis: Later Stages • Payback and Break-Even Times • Cycle Time • Payback Period • Break-Even Time (BET) = Cycle Time + Payback Pd.

40. Financial Analysis: Later Stages • Payback and Break-Even Times

41. Financial Analysis: Later Stages • Payback and Break-Even Times • Discounted Cash Flows (DCF, NPV, or IRR) • The most rigorous analysis for new products: • year-by-year cash flow projections discounted to the present • the discounted cash flows are summed • if the sum of the dcf’s > initial outlays, the project passes • The “Dark Side” of NPV (for NPD) • Unfairly penalizes certain projects by ignoring the Go/Kill options along the way (option values not accounted for in traditional NPV)

42. Financial Analysis: Later Stages • Payback and Break-Even Times • Discounted Cash Flows (DCF, NPV, or IRR) • Options Pricing Theory (OPT) • Recognizes that management can kill a project after an incremental investment is made • At each phase of the NPD process, management is effectively “buying an option” on the project • These options cost considerably less than the full cost of the project -- so they are effective in reducing risk • Kodak uses a decision tree and uses OPT to compute the Expected Commercial Value (ECV) of a given project

43. Using OPT to find the ECV Commercial Success $PVI Pcs Technical Success Yes Launch $C Pts Yes No Development $D Commercial Failure $ECV No Technical Failure KEY: Pts = Prob of tech success $D = Development costs remaining Pcs= Prob of comm success $C = Commercialization/launch costs $ECV = Expected commercial value $PVI = Present value of future earnings

44. Using OPT to find the ECV ECV = [ [(PVI * Pcs) - C] * Pts] - D KEY: Pts = Prob of tech success $D = Development costs remaining Pcs= Prob of comm success $C = Commercialization/launch costs $ECV = Expected commercial value $PVI = Present value of future earnings

45. NPV vs. OPT: An Example Income stream, PVI (present valued) $40 million Commercialization costs (launch & captial) $ 5 million Development costs $ 5 million Probability of commercial success 50% Probability of technical success 50% Overall probability of success 25% TRADITIONAL NPV (no probabilities): 40 - 5 - 5 = 30 Decision = Go NPV with probabilities: (.25 X 30) - (.75 X 10) = 0 Decision = Kill ECV or OPT: { [(40 x .5) - 5] * .5} - 5 = 2.5 Decision = Go