Advertisement

Designing Optimal Products: Algorithms and Systems

  • Stelios Tsafarakis
  • Nikolaos Matsatsinis
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 258)

Abstract

The high cost of a product failure makes it imperative for a company to assess the market penetration of a new product at its early design. In this context, the Optimal Product Line Design problem was formulated thirty five years ago, and remains a significant research topic in the area of quantitative marketing until today. In this chapter we provide a brief description of the problem, which belongs to the class of NP-hard problems, and review the optimization algorithms that have been applied to it. The performance of the algorithms is evaluated, and the best two approaches are applied to simulated data, as well as a real world scenario. Emphasis is placed on Genetic Algorithms, since the results of the study indicate them as the approach that better fits to the specific marketing problem. Finally, the relevant marketing systems that deal with the problem are presented, and their pros and cons are discussed.

Keywords

Genetic Algorithm Product Line Market Share Optimal Product Attribute Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alexouda, G., Paparrizos, K.: A Genetic Algorithm approach to the product line design problem using the Seller’s Return criterion: an exhaustive comparative computational study. European Journal of Operational Research 134(1), 165–178 (2001)zbMATHCrossRefGoogle Scholar
  2. Alexouda, G.: An evolutionary algorithm approach to the Share of Choices problem in the product line design. Computers and operational research 31, 2215–2229 (2004)zbMATHCrossRefGoogle Scholar
  3. Alexouda, G.: A user-friendly marketing decision support system for the product line design problem using evolutionary algorithms. Decision support systems 38, 495–509 (2005)CrossRefGoogle Scholar
  4. Balakrishnan, P., Gupta, R., Jacob, V.: Development of hybrid genetic algorithms for product line designs. IEEE Transactions on Systems, Man, and Cybernetics 34(1), 468–483 (2004)CrossRefGoogle Scholar
  5. Balakrishnan, P., Jacob, V.: Genetic algorithms for product design. Management Science 42(8), 1105–1117 (1996)zbMATHCrossRefGoogle Scholar
  6. Belloni, A., Freund, R., Selove, M., Simester, D.: Optimizing Product Line Designs: Efficient Methods and Comparisons. Working Paper, MIT Sloan School of Management (2005)Google Scholar
  7. Belloni, A., Freund, R., Selove, M., Simester, D.: Optimizing Product Line Designs: Efficient Methods and Comparisons. Management Science 54(9), 1544–1552 (2008)CrossRefGoogle Scholar
  8. Bradley, R.A., Terry, M.E.: Rank analysis of incomplete block designs I: The method of paired comparisons. Biometrika 39, 324–345 (1952)zbMATHMathSciNetGoogle Scholar
  9. Camm, J.D., Cochran, J.J., Curry, D.J., Kannan, S.: Conjoint Optimization: An Exact Branch-and-Bound Algorithm for the Share-of-Choice Problem. Management Science 52(3), 435–447 (2006)CrossRefGoogle Scholar
  10. Chen, K.D., Hausman, W.H.: Technical Note: Mathematical properties of the optimal product line selection problem using choice-based conjoint analysis. Management Science 46(2), 327–332 (2000)CrossRefGoogle Scholar
  11. Dobson, G., Kalish, S.: Heuristics for pricing and positioning a product line using conjoint and cost data. Management Science 39(2), 160–175 (1993)CrossRefGoogle Scholar
  12. Downs, B.T., Camm, J.D.: An exact algorithm for the maximal covering problem. Naval Research Logistics 43, 435–461 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  13. Green, P.E., Carroll, J.D., Goldberg, S.M.: A general approach to product design optimization via conjoint analysis. Journal of Marketing 45, 17–37 (1981)CrossRefGoogle Scholar
  14. Green, P.E., Krieger, A.M.: Models and heuristics for product line selection. Marketing Science 4(1), 1–19 (1985)CrossRefMathSciNetGoogle Scholar
  15. Green, P.E., Krieger, A.M.: A consumer-based approach to designing product line extensions. Journal of product innovation management 4, 21–32 (1987)CrossRefGoogle Scholar
  16. Green, P.E., Krieger, A.M.: An application of a product positioning model to pharmaceutical products. Marketing Science 11, 117–132 (1992)CrossRefGoogle Scholar
  17. Green, P.E., Krieger, A.M., Zelnio, R.N.: A componential segmentation model with optimal design features. Decision Sciences 20(2), 221–238 (1989)CrossRefGoogle Scholar
  18. Holland, J.H.: Adaptation in Natural and Artificial systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  19. Kohli, R., Krishnamusti, R.: A heuristic approach to product design. Management Science 33(12), 1523–1533 (1987)CrossRefGoogle Scholar
  20. Kohli, R., Krishnamusti, R.: Optimal product design using conjoint analysis: Computational complexity and algorithms. European Journal of Operational Research 40(2), 186–195 (1989)zbMATHCrossRefMathSciNetGoogle Scholar
  21. Kohli, R., Sukumar, R.: Heuristics for product line design using conjoint analysis. Management Science 36(12), 1464–1478 (1990)CrossRefGoogle Scholar
  22. Kotler, P., Armstrong, G.: Principles of Marketing, 12th edn. Prentice Hall, New Jersey (2008)Google Scholar
  23. Krieger, A.M., Green, P.E.: A decision support model for selecting product/service benefit positionings. European Journal of Operational Research 142, 187–202 (2002)zbMATHCrossRefGoogle Scholar
  24. Land, A.H., Doig, A.G.: An automatic method for solving discrete programming problems. Econometrica 28, 497–520 (1960)zbMATHCrossRefMathSciNetGoogle Scholar
  25. Luce, R.D.: Individual choice behavior: a theoretical analysis. Wiley, New York (1959)zbMATHGoogle Scholar
  26. Manrai, A.K.: Mathematical models of brand choice behaviour. European Journal of Operational Research 82, 1–17 (1995)zbMATHCrossRefGoogle Scholar
  27. McBride, R.D., Zufryden, F.: An integer programming approach to the optimal line selection problem. Marketing Science 7, 126–140 (1988)CrossRefGoogle Scholar
  28. McFadden, D.: Conditional Logit Analysis of Qualitative Choice Behaviour. In: Zaremka, P. (ed.) Frontiers in Econometrics, Academic Press, N.Y. (1973)Google Scholar
  29. Nair, S.K., Thakur, L.S., Wen, K.: Near optimal solutions for product line design and selection: Beam Search heuristics. Management Science 41(5), 767–785 (1995)zbMATHCrossRefGoogle Scholar
  30. Radcliffe, N.J.: Forma analysis and random respectful recombination. In: Proceedings of the 4th International Conference on Genetic Algorithms (1991)Google Scholar
  31. Sawtooth Software, Advanced Simulation Module (ASM) for product optimization. Sawtooth Software technical paper series (2003)Google Scholar
  32. Shi, L., Olafsson, S., Chen, Q.: An optimization framework for product design. Management Science 47(12), 1681–1692 (2001)CrossRefGoogle Scholar
  33. Shocker, A.D., Srinivasan, V.: A consumer-based methodology for the identification of new product ideas. Management Science 20, 927–937 (1974)CrossRefGoogle Scholar
  34. Steiner, W., Hruschka, H.: A probabilistic one-step approach to the optimal product line design problem using conjoint and cost data. Review of Marketing Science working papers 1(4), 1–36 (2002)Google Scholar
  35. Steiner, W., Hruschka, H.: Generic Algorithms for product design: how well do they really work? International Journal of Market Research 45(2), 229–240 (2003)Google Scholar
  36. Zufryden, F.: A conjoint measurement-based approach for optimal new product design and market segmentation. In: Shocker, A.D. (ed.) Analytical approaches to product and marketing planning, Marketing Science Institute, Cambridge (1977)Google Scholar
  37. Zufryden, F.: ZIPMAP: a zero-one integer programming model for market segmentation and product positioning. Journal of the Operational Research Society 30, 63–70 (1979)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Stelios Tsafarakis
    • 1
  • Nikolaos Matsatsinis
    • 1
  1. 1.Department of Production and Management Engineering,Decision Support Systems LaboratoryTechnical University of CreteChaniaGreece

Personalised recommendations