1. CUSTOMER NEEDS ELICITATION FOR PRODUCT CUSTOMIZATION Yue Wang Advisor: Prof. Tseng Advanced Manufacturing Institute Hong Kong University of Science and Technology

2. Advanced Manufacturing Institute Background 2 Customer Needs (CNs) Functional Requirements (FRs) Design Parameters (DPs) Process Variables (PVs) Product specification definition Product design Process design CNs are expressed in explicit product specifications. Axiomatic design:

3. Advanced Manufacturing Institute  Customer needs elicitation should be  Good: predictive, customer insight  Fast: for customers and for designers  Cheap: reduce market research cost  Easy: reduce drudgery and errors Introduction 3

4. Advanced Manufacturing Institute  Can we find what people want quickly and inexpensively?  How to avoid confusing customers with too many products? Research issues 4

5. Advanced Manufacturing Institute 5 Challenges  Customers are  Impatient to specify a long list of items  Unable to articulate their needs  Unaware of latent needs  Lack of information about available options  Interlocking among attributes

6. Advanced Manufacturing Institute  Research framework  Bayesian network based preferences representation  Adaptive specification definition procedure  Recommendation for customized product Approach 6

7. Advanced Manufacturing Institute Preferences Representation  Uncertainty of the purchasing choices  Customers are heterogeneous  Choice decisions differ under various situations  The context of purchase differs  Dependency among preferences towards different attributes 7

8. Advanced Manufacturing Institute  Bayesian network Preferences Representation 8

9. Advanced Manufacturing Institute  The important considerations in this phase:  Customers are not patient enough to specify a long list of items.  The items differ a lot in terms of the amount of information they can provide. Specification Definition 9

10. Advanced Manufacturing Institute  Basic ideas:  Present the most informative query item to customers  The value of information:  :the additional information received about X from getting the value of Y=y.  Specification Definition 10

11. Advanced Manufacturing Institute  The solution for f (Blachman, 1968 # ): # N. M. Blachman, “The amount of information that y gives about X,” IEEE Trans. Inform. Theory, vol. IT-14, no. 1, pp. 27-31, Jan. 1968 Specification Definition 11

12. Advanced Manufacturing Institute  Given:  Customers preferences information  Determine:  Which products should be recommended?  In what order to present the recommendations if more than one recommendations are presented? Recommendation 12

13. Advanced Manufacturing Institute  Probability of relevance under binary independent assumption:  Probability of relevance considering first order conditional dependency: Probabilistic relevance computation 13

14. Advanced Manufacturing Institute  The idea is to rank products by their estimated probability of relevance with respect to the information obtained.  Probability ranking principle is optimal, in the sense that it minimizes the expected loss. Probability ranking principle 14

15. Advanced Manufacturing Institute Schematic framework 15

16. Advanced Manufacturing Institute  Precision rate  Recall rate Evaluation metrics 16

17. Advanced Manufacturing Institute  The recommendation based on probability ranking can guarantee the highest precision and recall rate.  If customers’ preferences to all the components are independent and the potential preferences towards all the alternatives of an attribute are random, the specification definition method based on the information gain has the highest precision and recall rate. Evaluation results 17

18. Advanced Manufacturing Institute Parameters setting Result (# of experiments in which the precision and recall rate are highest/ total # of experiments) m ~ Uniform (3, 13) N ~ Uniform (50, 100) |N i |~Uniform 9,345/10,000 m ~ Uniform (5, 15) N ~ Uniform (100, 150) |N i |~Uniform 9,325/10,000 m~Uniform (5, 15) N ~ Uniform (1000, 2000) |N i |~Uniform 9,344/10,000 m ~ Uniform (5, 15) N ~ Uniform (100, 200) |N i |~Norm(1, 1) 9,560/10,000 m ~ Uniform (5, 15) N ~ Uniform (100, 200) |N i |~Norm(1, 2) 9,603/10,000 m ~ Uniform (5, 15) N ~ Uniform (100, 200) |N i |~Norm(1, 0.5) 9,262/10,000 Evaluation results 18

19. Advanced Manufacturing Institute Evaluation by utility  Preliminaries:   Stochastically dominate: If, then approach 1 stochastically dominates approach 2. 19

20. Advanced Manufacturing Institute  The presented method  stochastically dominates other approaches.  is optimal with respect to any nondecreasing utility function. Evaluation results 20

21. Advanced Manufacturing Institute  An approach to elicit customers’ preference is presented.  The model can be used to adaptively improve definition of product specification for custom product design.  Based on the model, customized query sequence can be developed to reduce redundant questions.  Product recommendation approach is adopted to further improve the efficiency of custom product design Summary 21

22. Advanced Manufacturing Institute 22 Thank you! Your suggestions & comments are highly appreciated!

23. Advanced Manufacturing Institute Extension to binary independent assumption  Theorem: A probability distribution of tree dependence P t (x) is an optimal approximation to P(x) if and only it’s maximum spanning tree. [Chow and Liu, 1968]

24. Advanced Manufacturing Institute Why customized product design  Well calibrated customized product design can integrate customers into design activities  Mitigate the side effect of sticky information  Better meet customers’ requirements  Loyalty can be enhanced.  Help identify latent needs guide future product development

25. Advanced Manufacturing Institute  Lemma 1: Suppose approach 1 proposes n recommendations in a sequence S 1 =(r 11,r 12,…r 1n ). Each recommendation r 1i has probability p 1i to meet the customer needs. The sequence is arranged such that. Approach 2 also proposes n recommendations in a sequence S 2 =(r 21,r 22,…r 2n ). These n recommendations may be different from the ones in sequence S 1. Similarly, we also have corresponding probability serial and If for all, then X 1 stochastically dominates X 2 where X i is an indicator of the number of satisfactory recommendations by using approach i.

26. Advanced Manufacturing Institute  Lemma 2: Suppose approach 1 proposes n recommendations in a sequence S 1 =(r 11,r 12,…r 1n ). Each recommendation r 1i has probability p 1i to meet the customer needs. The sequence is arranged such that. Approach 2 also proposes n recommendations in a sequence S 2 =(r 21,r 22,…r 2n ) which is a permutation of S 1 =(r 11,r 12,…r 1n ). Then the distribution of satisfactory product for approach 1 is identical to approach 2.

27. Advanced Manufacturing Institute  Lemma 3: Let U(x) be a nondecreasing utility function where x is the number of satisfactory recommendations. Let X i be an indicator of the number of satisfactory recommendations by using approach i. If X 1 stochastically dominates X 2, then the expected utility by adopting approach 1 is greater or equal to that of approach 2, i.e.,.

28. Advanced Manufacturing Institute Evaluation m: the number of attributes n i : the number of alternatives of the ith attribute N: the total number of configurations P ijk : if the jth alternative of the ith component is selected, the probability that the kth configuration is the desired one. The entropy of the configuration space if the jth alternative of the ith component is selected: The expected entropy of the configuration space if the ith component is proposed for a customer to specify: 28

29. Advanced Manufacturing Institute Background  Competitive and changing market  Shorter product development time  Product variety proliferation  Bigger penalty cost of failing to meet customers’ needs or catch up customers’ needs changes 29

30. Advanced Manufacturing Institute 30

31. Advanced Manufacturing Institute  Probability of relevance (including first order conditional dependency):  Parameters setting: Probabilistic relevance model 31

32. Advanced Manufacturing Institute  tailor product to different needs  how to avoid confusing customers with too many products  Can we find what people want quickly and inexpensively  how to find out if a customer is interested in a virtual which doesn't exist  reducing inconsistent preferences  good: predictive, customer insight: what people buy or how many will people buy it  fast: for them and for us: it should be fast, doesn't cost so many time  cheap: reduce market research cost: should be cheat  easy reduce drudgery and errors: should be easy for both customers and designers  That's all the questions in marketing science today. 32