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An integrated framework for product line design for modular products: product attribute and functionality-driven perspective

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  • Mohit Goswami
  • Yash Daultani
  • M.K. Tiwari

Abstract

The purpose of this research is to facilitate original equipment manufacturers operating in a single market segment to frame their product line design strategy that pertains to offering right product attributes with right attribute level in the right product profile within a market segment. Through this research, we attempt to establish a link between functional level design of product attributes with commercial objectives of the enterprise. Initially, by deriving the functional importance of product attribute levels of individual product attributes within a product profile, demand and functional importance data are generated. Utilising the function-based cost estimating framework and multi-linear regression methodology, we determine the cost and product development time coefficients for respective product attributes. Finally, a mixed integer quadratic programming-based mathematical formulation is developed that includes maximisation of product premium and minimisation of various costs as major objectives under the assumption that manufacturer seeks to offer optimal number of product profiles within the market segment. Employing the commercial solver LINGO, the integrated framework is solved. The entire framework is illustrated using the operator cabin of heavy construction machinery.

Suggested Citation

  • Mohit Goswami & Yash Daultani & M.K. Tiwari, 2017. "An integrated framework for product line design for modular products: product attribute and functionality-driven perspective," International Journal of Production Research, Taylor & Francis Journals, vol. 55(13), pages 3862-3885, July.
  • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:13:p:3862-3885
    DOI: 10.1080/00207543.2017.1314039
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    References listed on IDEAS

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    1. 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.
    2. Gregory Dobson & Shlomo Kalish, 1988. "Positioning and Pricing a Product Line," Marketing Science, INFORMS, vol. 7(2), pages 107-125.
    3. Albritton, M. David & McMullen, Patrick R., 2007. "Optimal product design using a colony of virtual ants," European Journal of Operational Research, Elsevier, vol. 176(1), pages 498-520, January.
    4. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    5. Hans Sebastian Heese & Jayashankar M. Swaminathan, 2006. "Product Line Design with Component Commonality and Cost-Reduction Effort," Manufacturing & Service Operations Management, INFORMS, vol. 8(2), pages 206-219, May.
    6. G. E. Fruchter & A. Fligler & R. S. Winer, 2006. "Optimal Product Line Design: Genetic Algorithm Approach to Mitigate Cannibalization," Journal of Optimization Theory and Applications, Springer, vol. 131(2), pages 227-244, November.
    7. Mohit Goswami & Saurabh Pratap & S.K. Kumar, 2016. "An integrated Bayesian-Game theoretic approach for product portfolio planning of a multi-attributed product in a duopolistic market," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 6997-7013, December.
    8. Liang Guo & Juanjuan Zhang, 2012. "Consumer Deliberation and Product Line Design," Marketing Science, INFORMS, vol. 31(6), pages 995-1007, November.
    9. Kyle D. Chen & Warren H. Hausman, 2000. "Technical Note: Mathematical Properties of the Optimal Product Line Selection Problem Using Choice-Based Conjoint Analysis," Management Science, INFORMS, vol. 46(2), pages 327-332, February.
    10. Richard D. McBride & Fred S. Zufryden, 1988. "An Integer Programming Approach to the Optimal Product Line Selection Problem," Marketing Science, INFORMS, vol. 7(2), pages 126-140.
    11. Rajeev Kohli & R. Sukumar, 1990. "Heuristics for Product-Line Design Using Conjoint Analysis," Management Science, INFORMS, vol. 36(12), pages 1464-1478, December.
    12. A. Ye(scedilla)im Orhun, 2009. "Optimal Product Line Design When Consumers Exhibit Choice Set-Dependent Preferences," Marketing Science, INFORMS, vol. 28(5), pages 868-886, 09-10.
    13. John E. Ettlie, 1995. "Product-Process Development Integration in Manufacturing," Management Science, INFORMS, vol. 41(7), pages 1224-1237, July.
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    Cited by:

    1. Gauss, Leandro & Lacerda, Daniel P. & Cauchick Miguel, Paulo A., 2022. "Market-Driven Modularity: Design method developed under a Design Science paradigm," International Journal of Production Economics, Elsevier, vol. 246(C).
    2. Leandro Gauss & Daniel P. Lacerda & Paulo A. Cauchick Miguel, 2021. "Module-based product family design: systematic literature review and meta-synthesis," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 265-312, January.

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