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Enhancing marketing with engineering: Optimal product line design for heterogeneous markets

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  • Michalek, Jeremy J.
  • Ebbes, Peter
  • Adigüzel, Feray
  • Feinberg, Fred M.
  • Papalambros, Panos Y.

Abstract

Successful product line design and development often require a balance of technical and market tradeoffs. Quantitative methods for optimizing product attribute levels using preference elicitation (e.g., conjoint) data are useful for many product types. However, products with substantial engineering content involve critical tradeoffs in the ability to achieve those desired attribute levels. Technical tradeoffs in product design must be made with an eye toward market consequences, particularly when heterogeneous market preferences make differentiation and strategic positioning critical to capturing a range of market segments and avoiding cannibalization.

Suggested Citation

  • Michalek, Jeremy J. & Ebbes, Peter & Adigüzel, Feray & Feinberg, Fred M. & Papalambros, Panos Y., 2011. "Enhancing marketing with engineering: Optimal product line design for heterogeneous markets," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 1-12.
    • Handle: RePEc:eee:ijrema:v:28:y:2011:i:1:p:1-12
    DOI: 10.1016/j.ijresmar.2010.08.001
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    References listed on IDEAS

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    3. Choi, Hyunhong & Koo, Yoonmo, 2023. "New technology product introduction strategy with considerations for consumer-targeted policy intervention and new market entrant," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    4. Stelios Tsafarakis, 2016. "Redesigning product lines in a period of economic crisis: a hybrid simulated annealing algorithm with crossover," Annals of Operations Research, Springer, vol. 247(2), pages 617-633, December.
    5. Sarangi, Subrat & Chakraborty, Abhishek & Triantis, Konstantinos P., 2021. "Multimarket competition effects on product line decisions – A multi-objective decision model in fast moving consumer goods industry," Journal of Business Research, Elsevier, vol. 133(C), pages 388-398.
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    7. Pantourakis, Michail & Tsafarakis, Stelios & Zervoudakis, Konstantinos & Altsitsiadis, Efthymios & Andronikidis, Andreas & Ntamadaki, Vasiliki, 2022. "Clonal selection algorithms for optimal product line design: A comparative study," European Journal of Operational Research, Elsevier, vol. 298(2), pages 585-595.
    8. Xiaojie Liu & Xuejian Gong & Roger J. Jiao, 2022. "Low-Carbon Product Family Planning for Manufacturing as a Service (MaaS): Bilevel Optimization with Linear Physical Programming," Sustainability, MDPI, vol. 14(19), pages 1-24, October.
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    10. Kappe, Eelco & Stadler Blank, Ashley & DeSarbo, Wayne S., 2018. "A random coefficients mixture hidden Markov model for marketing research," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 415-431.
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