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Statistical Learning of Service-Dependent Demand in a Multiperiod Newsvendor Setting


  • Tianhu Deng

    () (Department of Industrial Engineering, Tsinghua University, Beijing, China 100084)

  • Zuo-Jun Max Shen

    () (Department of Industrial Engineering and Operations Research and Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California 94720; and Department of Industrial Engineering, Tsinghua University, Beijing, China 100084)

  • J. George Shanthikumar

    () (Krannert School of Business, Purdue University, West Lafayette, Indiana 47907)


We study an inventory system wherein a customer may leave the seller's market after experiencing an inventory stockout. Traditionally, researchers and practitioners assume a single penalty cost to model this customer behavior of stockout aversion. Recently, a stream of researchers explicitly model this customer behavior and support the traditional penalty cost approach. We enrich this literature by studying the statistical learning of service-dependent demand.We build and solve four models: a baseline model, where the seller can observe the demand distribution; a second model, where the seller cannot observe the demand distribution but statistically learns the demand distribution; a third model, where the seller can learn or pay to obtain the exact information of the demand distribution; and a fourth model, where demand in excess of available inventory is lost and unobserved. Interestingly, we find that all four models support the traditional penalty cost approach. This result confirms the use of a state-independent stockout penalty cost in the presence of demand learning. More strikingly, the first three models imply the same stockout penalty cost, which is larger than the stockout penalty cost implied by the last model.

Suggested Citation

  • Tianhu Deng & Zuo-Jun Max Shen & J. George Shanthikumar, 2014. "Statistical Learning of Service-Dependent Demand in a Multiperiod Newsvendor Setting," Operations Research, INFORMS, vol. 62(5), pages 1064-1076, October.
  • Handle: RePEc:inm:oropre:v:62:y:2014:i:5:p:1064-1076
    DOI: 10.1287/opre.2014.1303

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    References listed on IDEAS

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    Cited by:

    1. repec:eee:ejores:v:267:y:2018:i:2:p:612-627 is not listed on IDEAS
    2. Alain Bensoussan & Pengfei Guo, 2015. "Technical Note—Managing Nonperishable Inventories with Learning About Demand Arrival Rate Through Stockout Times," Operations Research, INFORMS, vol. 63(3), pages 602-609, June.


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