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Diffusion of Innovations Under Supply Constraints

Author

Listed:
  • Sunil Kumar

    (Graduate School of Business, Stanford University, Stanford, California 94305-5015)

  • Jayashankar M. Swaminathan

    (The Kenan-Flagler Business School, University of North Carolina, Chapel Hill, North Carolina 27599-3490)

Abstract

In this paper we present a canonical setting that illustrates the need for explicitly modeling interactions between manufacturing and marketing/sales decisions in a firm. We consider a firm that sells an innovative product with a given market potential. The firm may not be able to meet demand due to capacity constraints. For such firms, we present a new model of demand, modified from the original model of Bass, to capture the effect of unmet past demand on future demand. We use this model to find production and sales plans that maximize profit during the lifetime of the product in a firm with a fixed production capacity. We conduct an extensive numerical study to establish that the trivial, myopic sales plan that sells the maximal amount possible at each time instant is not necessarily optimal. We show that a heuristic “build-up” policy, in which the firm does not sell at all for a period of time and builds up enough inventory to never lose sales once it begins selling, is a robust approximation to the optimal policy. Specializing to a lost-sales setting, we prove that the optimal sales plan is indeed of the “build-up” type. We explicitly characterize the optimal build-up period and analytically derive the optimal initial inventory and roll-out delay. Finally, we show that the insights obtained in the fixed capacity case continue to hold when the firm is able to dynamically change capacity.

Suggested Citation

  • Sunil Kumar & Jayashankar M. Swaminathan, 2003. "Diffusion of Innovations Under Supply Constraints," Operations Research, INFORMS, vol. 51(6), pages 866-879, December.
  • Handle: RePEc:inm:oropre:v:51:y:2003:i:6:p:866-879
    DOI: 10.1287/opre.51.6.866.24918
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    References listed on IDEAS

    as
    1. Suresh P. Sethi, 2021. "Optimal Control Theory," Springer Texts in Business and Economics, Springer, edition 4, number 978-3-030-91745-6, October.
    2. Teck-Hua Ho & Sergei Savin & Christian Terwiesch, 2002. "Managing Demand and Sales Dynamics in New Product Diffusion Under Supply Constraint," Management Science, INFORMS, vol. 48(2), pages 187-206, February.
    3. Ouardighi, Fouad El & Tapiero, Charles S., 1998. "Quality and the diffusion of innovations," European Journal of Operational Research, Elsevier, vol. 106(1), pages 31-38, April.
    4. Hermann Simon & Karl-Heinz Sebastian, 1987. "Diffusion and Advertising: The German Telephone Campaign," Management Science, INFORMS, vol. 33(4), pages 451-466, April.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Gustav Feichtinger & Richard F. Hartl & Suresh P. Sethi, 1994. "Dynamic Optimal Control Models in Advertising: Recent Developments," Management Science, INFORMS, vol. 40(2), pages 195-226, February.
    7. Dan Horsky, 1990. "A Diffusion Model Incorporating Product Benefits, Price, Income and Information," Marketing Science, INFORMS, vol. 9(4), pages 342-365.
    8. Shlomo Kalish, 1985. "A New Product Adoption Model with Price, Advertising, and Uncertainty," Management Science, INFORMS, vol. 31(12), pages 1569-1585, December.
    9. Dipak Jain & Vijay Mahajan & Eitan Muller, 1991. "Innovation Diffusion in the Presence of Supply Restrictions," Marketing Science, INFORMS, vol. 10(1), pages 83-90.
    10. Abbas A. Kurawarwala & Hirofumi Matsuo, 1996. "Forecasting and Inventory Management of Short Life-Cycle Products," Operations Research, INFORMS, vol. 44(1), pages 131-150, February.
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