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Production capacity investment with data updates

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  • Philip Kaminsky
  • Ming Yuen

Abstract

This article presents a model of the capacity investment problem faced by pharmaceutical firms and other firms with long and risky product development cycles. These firms must balance two conflicting objectives: on one hand, the delay in scaling up production once the product is approved must be minimized and, on the other hand, the risk of investing in ultimately unused capacity must be minimized. A stylized model of this type of capacity investment problem is developed and analyzed. In this model the firm re-evaluates its capacity investment strategy as information about the potential success of the product is continually updated (for example, via clinical trial results in the case of the pharmaceutical industry). Motivated by observations of current practices in the biopharmaceutical industry, a computational study is used to explore how practices such as more frequent re-evaluations of investment decisions, stopping and restarting of projects, and the use of alternative types of capacity can, under certain conditions, help the firm reduce both the delay of the commercial launch of the new product and the risk of lost investment.

Suggested Citation

  • Philip Kaminsky & Ming Yuen, 2014. "Production capacity investment with data updates," IISE Transactions, Taylor & Francis Journals, vol. 46(7), pages 664-682.
    • Handle: RePEc:taf:uiiexx:v:46:y:2014:i:7:p:664-682
    DOI: 10.1080/0740817X.2013.849838
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    Cited by:

    1. Anyan Qi & Hyun-Soo Ahn & Amitabh Sinha, 2017. "Capacity Investment with Demand Learning," Operations Research, INFORMS, vol. 65(1), pages 145-164, February.
    2. Aditya Vedantam & Ananth Iyer, 2021. "Capacity Investment under Bayesian Information Updates at Reporting Periods: Model and Application," Production and Operations Management, Production and Operations Management Society, vol. 30(8), pages 2707-2725, August.
    3. Anyan Qi & Hyun-Soo Ahn & Amitabh Sinha, 2017. "Capacity Investment with Demand Learning," Operations Research, INFORMS, vol. 65(1), pages 145-164, February.

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