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Protecting the data-driven newsvendor against rare events: a correction-term approach

Author

Listed:
  • Gokhan Metan

    (Humana)

  • Aurélie Thiele

    (Lehigh University)

Abstract

We propose an approach to the data-driven newsvendor problem that incorporates a correction factor to account for rare events, when the decision-maker has few historical data points at his disposal but knows the range of the demand. This mitigates a weakness of pure data-driven methodologies, specifically, the fact that they under-protect the system against tail events, which are in general under-observed in the empirical demand distribution. We test the approach in extensive computational experiments and provide a summary table of the numerical experiments to help the decision maker gain further insights.

Suggested Citation

  • Gokhan Metan & Aurélie Thiele, 2016. "Protecting the data-driven newsvendor against rare events: a correction-term approach," Computational Management Science, Springer, vol. 13(3), pages 459-482, July.
    • Handle: RePEc:spr:comgts:v:13:y:2016:i:3:d:10.1007_s10287-016-0258-1
    DOI: 10.1007/s10287-016-0258-1
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    References listed on IDEAS

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