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A Bayesian Approach to Managing Learning-Curve Uncertainty

Author

Listed:
  • Joseph B. Mazzola

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708-0120)

  • Kevin F. McCardle

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708-0120)

Abstract

This paper introduces a Bayesian decision theoretic model of optimal production in the presence of learning-curve uncertainty. The well-known learning-curve model is extended to allow for random variation in the learning process with uncertainty regarding some parameter of the variation. A production run generates excess value (above its current revenue) for a Bayesian manager in two ways: it pushes the firm further along the learning curve, increasing the likelihood of lower costs for future runs; and it provides information, through the observed costs, that reduces the uncertainty regarding the rate at which costs are decreasing. We provide conditions under which one of the classical deterministic learning-curve results---namely, that optimal production exceeds the myopic level---carries over to the extended framework. We demonstrate that another classical deterministic learning-curve result---namely, that optimal production increases with cumulative production---does not hold in the Bayesian setting.

Suggested Citation

  • Joseph B. Mazzola & Kevin F. McCardle, 1996. "A Bayesian Approach to Managing Learning-Curve Uncertainty," Management Science, INFORMS, vol. 42(5), pages 680-692, May.
  • Handle: RePEc:inm:ormnsc:v:42:y:1996:i:5:p:680-692
    DOI: 10.1287/mnsc.42.5.680
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    Citations

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    Cited by:

    1. Zhang, Shichen & Zhang, Jianxiong, 2018. "Contract preference with stochastic cost learning in a two-period supply chain under asymmetric information," International Journal of Production Economics, Elsevier, vol. 196(C), pages 226-247.
    2. Ji, Xiang & Li, Guo & Wang, Zhaohua, 2017. "Impact of emission regulation policies on Chinese power firms’ reusable environmental investments and sustainable operations," Energy Policy, Elsevier, vol. 108(C), pages 163-177.
    3. Womer, K. & Li, H. & Camm, J. & Osterman, C. & Radhakrishnan, R., 2017. "Learning and Bayesian updating in long cycle made-to-order (MTO) production," Omega, Elsevier, vol. 69(C), pages 29-42.
    4. N. Bora Keskin & John R. Birge, 2019. "Dynamic Selling Mechanisms for Product Differentiation and Learning," Operations Research, INFORMS, vol. 67(4), pages 1069-1089, July.
    5. Sun, Xiaojie & Tang, Wansheng & Zhang, Jianxiong & Chen, Jing, 2021. "The impact of quantity-based cost decline on supplier encroachment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 147(C).
    6. Way, Rupert & Lafond, François & Lillo, Fabrizio & Panchenko, Valentyn & Farmer, J. Doyne, 2019. "Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves," Journal of Economic Dynamics and Control, Elsevier, vol. 101(C), pages 211-238.
    7. Ben Klemens, 2021. "Attributing Value to Patents and Trademarks in Complex Production Chains," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 12(2), pages 842-875, June.
    8. Cavagnini, Rossana & Hewitt, Mike & Maggioni, Francesca, 2020. "Workforce production planning under uncertain learning rates," International Journal of Production Economics, Elsevier, vol. 225(C).
    9. Ruiz-Aliseda, Francisco, 2009. "Misinformative advertising," IESE Research Papers D/809, IESE Business School.
    10. Alessandro Arlotto & Stephen E. Chick & Noah Gans, 2014. "Optimal Hiring and Retention Policies for Heterogeneous Workers Who Learn," Management Science, INFORMS, vol. 60(1), pages 110-129, January.
    11. Mazzola, Joseph B. & Neebe, Alan W. & Rump, Christopher M., 1998. "Multiproduct production planning in the presence of work-force learning," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 336-356, April.
    12. Tarakci, Hakan & Tang, Kwei & Teyarachakul, Sunantha, 2009. "Learning effects on maintenance outsourcing," European Journal of Operational Research, Elsevier, vol. 192(1), pages 138-150, January.
    13. Basu, Arnab & Jain, Tarun & Hazra, Jishnu, 2018. "Supplier selection under production learning and process improvements," International Journal of Production Economics, Elsevier, vol. 204(C), pages 411-420.
    14. Laura J. Kornish & Steven A. Lippman & John W. Mamer, 2011. "Search and the introduction of improved technologies," Naval Research Logistics (NRL), John Wiley & Sons, vol. 58(6), pages 578-594, September.
    15. Papineau, Maya, 2006. "An economic perspective on experience curves and dynamic economies in renewable energy technologies," Energy Policy, Elsevier, vol. 34(4), pages 422-432, March.
    16. Hossein Jahandideh & Kumar Rajaram & Kevin McCardle, 2020. "Production Campaign Planning Under Learning and Decay," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 615-632, May.

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