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Control of a Stochastic Production System with Estimated Parameters

Author

Listed:
  • Eric V. Denardo

    (Operations Research Department, Yale University, New Haven, Connecticut 06520-8267)

  • Christopher S. Tang

    (Anderson Graduate School of Business, University of California at Los Angeles, 405 Hilgard Avenue, Los Angeles, California 90024-1481)

Abstract

For an uncertain production system, the rule that controls the flow of material relies on parameters, such as the yield rates and the demand rate. These parameters are estimates, and they are usually inaccurate. In this paper, we analyze a type of "pull" rule called a proportional restoration rule. We show that these rules are stable; they do not lead to erratic behavior even when the estimation error is significant. We also show that these rules localize the effect of mis-estimates; e.g., underestimating the demand rate lowers the level of finished goods inventory, but has scant effect within the line. We show that these rules exhibit other desirable attributes---that they are efficient, that they are easy to interpret, and that they recover from disruptions quickly.

Suggested Citation

  • Eric V. Denardo & Christopher S. Tang, 1997. "Control of a Stochastic Production System with Estimated Parameters," Management Science, INFORMS, vol. 43(9), pages 1296-1307, September.
    • Handle: RePEc:inm:ormnsc:v:43:y:1997:i:9:p:1296-1307
    DOI: 10.1287/mnsc.43.9.1296
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    Citations

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    Cited by:

    1. Hadjinicola, George C. & Soteriou, Andreas C., 2003. "Reducing the cost of defects in multistage production systems: A budget allocation perspective," European Journal of Operational Research, Elsevier, vol. 145(3), pages 621-634, March.
    2. Tang, Christopher S., 2006. "Perspectives in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 103(2), pages 451-488, October.
    3. Ouyang, Yanfeng, 2007. "The effect of information sharing on supply chain stability and the bullwhip effect," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1107-1121, November.
    4. John S. Hollywood, 2005. "An approximate planning model for distributed computing networks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(6), pages 590-605, September.
    5. Christopher S. Tang, 2017. "OM Forum—Three Simple Approaches for Young Scholars to Identify Relevant and Novel Research Topics in Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 338-346, July.

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