1. OBJECTIVES BACKGROUND APPROACH RESULTS DISCUSSION RESULTS CONCLUDING REMARKS RESULTS FUTURE WORK Acknowledgements Market Share Uncertainty Modeling for Decision-analytic Concept Evaluation Faculty Advisor: Dr. Shun Takai Department of Mechanical and Aerospace Engineering Student: Swithin Samuel Razu Department of Mechanical and Aerospace Engineering

2. Objectives 1 Decision-Analytic Concept Selection (1) Step 1: Construct an influence diagram –Decisions Product concepts: θ Warranty policy: α Price: p –Uncertainties Competition: ω Market share : s Market size: m Warranty cost : w Product cost : c –Prospect Profit Apply decision analysis (DA) [1,2] to consumer product concept selection Model uncertainty in consumer product decision making –Previously applied to system concept selection for a public project [3] –Modeled uncertainty of the government’s option to cancel a project

3. Decision-Analytic Concept Selection (2) Step 2: Construct a decision tree Step 3: Model uncertainties directly relevant to concept selection –Market share : s (conditioned on θ, α, p, ω) –Warranty cost : w (conditioned on θ, α) –Product cost : c (conditioned on θ) Step 4: Choose a concept with the maximum expected utility of profit Profit = (price - unit warranty cost - unit product cost) x unit sold = (price - unit warranty cost - unit product cost) x market size x market share = (p-w-c)ms Expected utility = E[ u(profit) ] 2 Scope of this research

4. Market Share Uncertainty Modeling Step 1: Model market share uncertainty (distribution) by integrating conjoint analysis and bootstrap –Conjoint analysis [4-6] Conjoint analysis enables designers to estimate each customer’s utility of a product concept from which market share is estimated –Bootstrap [7] Construct distribution from a single sample data applying sampling with replacements – Concept definition New concept versus competitor concepts C1 and C2 3 Scope of this research Versus

5. Choice-based Conjoint Analysis (CBC) Car Type Convertible 2 passengers (-1) Sedan 5 Passengers (0) SUV 8 Passengers (+1) Fuel Efficiency 10 (miles/gallon) (-1) 40 (miles/gallon) (0) 20 (miles/gallon) (+1) Warranty 3 years/36k miles (-1) 4 years/50k miles (0) 5 years/60k miles (+1) Price $ 20,000 (-1) $ 35,000 (0) $ 50,000 (+1) Conjoint analysis procedure illustration using automobiles as an example Procedure Illustration Product Product attributes Product attribute levels Step 2: Design choice sets using orthogonal array 4 factors, 3 levels L27 orthogonal array 27 choice sets with 3 choices in each set Choice sets Product concepts Step 1: Identify product attributes 2 performance attributes –Type, fuel efficiency 2 marketing attributes –Warranty, price 3 levels for each attribute 4

6. Procedure Illustration Step 3: Obtain a customer’s choice Each customer is shown 27 sets of three automobiles at a time and asked to choose one from each set 5 Step 5: Repeat Steps 3 and 4 for 50 customers Each customer chooses a concept with the largest total sum of his/her attribute utilities Step 4: Estimate a product Attribute utility using conjoint analysis Obtain product attribute utilities by applying MLE A customer’s attribute utility obtained from the choice data Concept 1 Warranty Price -1 0 +1 -1 0 +1 -1 0 +1 -1 -1 -1 0 0 0 +1 +1 +1 Concept 2 -1 0 +1 -1 0 +1 -1 0 +1 -1 -1 -1 0 0 0 +1 +1 +1 Concept 3 -1 0 +1 -1 0 +1 -1 0 +1 -1 -1 -1 0 0 0 +1 +1 +1 Concept 4 -1 0 +1 -1 0 +1 -1 0 +1 -1 -1 -1 0 0 0 +1 +1 +1 Concept 5 -1 0 +1 -1 0 +1 -1 0 +1 -1 -1 -1 0 0 0 +1 +1 +1 Concept 6 -1 0 +1 -1 0 +1 -1 0 +1 -1 -1 -1 0 0 0 +1 +1 +1 Concept 7 -1 0 +1 -1 0 +1 -1 0 +1 -1 -1 -1 0 0 0 +1 +1 +1 Concept 8 -1 0 +1 -1 0 +1 -1 0 +1 -1 -1 -1 0 0 0 +1 +1 +1 Concept 9 -1 0 +1 -1 0 +1 -1 0 +1 -1 -1 -1 0 0 0 +1 +1 +1 Predicted market share (%) Predicted market share of each concept at various warranty and price Choice-based conjoint survey

7. Bootstrap (BS) Bootstrap procedure illustration Procedure Illustration Step 2: Apply random sampling with replacements to the original dataset For example, 200 replications (B=200) Step 1: Randomly sample from the population 6 Step 3: Construct distribution of bootstrap sample mean and make an inference

8. Research Tasks Apply Bootstrap (BS) to choice-based conjoint analysis (CA) Procedure Illustration Step 2: Apply random sampling with replacements to the original dataset 200 replications (B=200) Apply CA to 50 customers in each BS sample 7 Step 3: Construct histogram of predicted market share Construct a histogram of predicted market share for chosen concept N Step 1: Randomly sample 50 customers Warranty = 4 years/50,000 miles Price = $ 50,000 SUV, 5 passengers Fuel efficiency = 40 (miles/gallon) Concept N

9. Concluding Remarks Previously CA has been used to obtain point estimate for market share Our approach integrates bootstrap and binomial inference with CA to obtain market share distributions This research demonstrates objective data utilization methods for customer preference uncertainty modeling And an integration of these uncertainty modeling methods with a decision-analytic product concept selection. 8

10. 9 References 1.Howard, R. A., 1988, “Decision Analysis: Practice and Promise,” Management Science, 34(6), pp. 679-695. 2.Keeney, R. L., and Raiffa, H., 1976, Decision with Multiple Objectives: Preferences and Value Tradeoffs, John Wiley & Sons, New York, NY. 3.Takai, S., and Ishii, K., 2008, “A Decision-Analytic System Concept Selection for a Public Project,” ASME Journal of Mechanical Design, 130(11), 111101 (10 pages). 4.Green, P. E., and Srinivasan, V., 1978, “Conjoint Analysis in Consumer Research: Issues and Outlook,” Journal of Consumer Research, 5, pp. 103-123. 5.Green, P. E., and Srinivasan, V., 1990, “Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice,” Journal of Marketing, 54, pp. 3-19. 6.Green, P. E., and Wind, Y., 1975, “New Way to Measure Consumers’ Judgments,” Harvard Business Review, 53, pp. 107-117. 7.Efron, B., Tibashirani, R., 1993, An Introduction to the Bootstrap, Chapman & Hall, London Future Work Acknowledgements This research is supported by the Intelligent Systems Center at the Missouri University of Science and Technology 9 Future work –More complex cases involving increased number of competitors and categories need to be researched –Integration of dynamic competition in decision-analytic concept selection –Compare experiment-based and simulation-based approaches