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Measuring and Mitigating the Costs of Stockouts


Author Info

  • Eric T. Anderson

    (Kellogg School of Management, Northwestern University, 2001 Sheridan Road, Evanston, Illinois 60208)

  • Gavan J. Fitzsimons

    (Fuqua School of Business, Duke University, 1 Towerview Drive, Durham, North Carolina 27708)

  • Duncan Simester

    (Sloan School of Management, Massachusetts Institute of Technology, Room E56-305, 38 Memorial Drive, Cambridge, Massachusetts 02142)

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    There is now an extensive theoretical literature investigating optimal inventory policies for retailers. Yet several recent reviews have recognized that these models are rarely applied in practice. One explanation for the paucity of practical applications is the difficulty of measuring how stockouts affect both current and future demand. In this paper, we report the findings of a large-scale field test that measures the short- and long-run opportunity cost of a stockout. The findings confirm that the adverse impact of a stockout extends to both other items in the current order as well as future orders. We show how the findings can be used to provide input to inventory planning models and illustrate how failing to account for the long-run effects of a stockout will lead to suboptimal inventory decisions. We also demonstrate how the findings can be used in a customer lifetime value model. Finally, the study investigates the effectiveness of different responses that firms can offer to mitigate the cost of stockouts. There is considerable variation in the effectiveness of these responses. Offering discounts to encourage customers to backorder rather than cancel their orders is widely used in practice, but that was the least profitable of the responses that we evaluated. The findings have important implications for retailers considering the use of discounts as a response to stockouts.

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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 52 (2006)
    Issue (Month): 11 (November)
    Pages: 1751-1763

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    Handle: RePEc:inm:ormnsc:v:52:y:2006:i:11:p:1751-1763

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    Keywords: inventory; long run; stockouts;


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    Cited by:
    1. Becerril-Arreola, Rafael & Leng, Mingming & Parlar, Mahmut, 2013. "Online retailers’ promotional pricing, free-shipping threshold, and inventory decisions: A simulation-based analysis," European Journal of Operational Research, Elsevier, vol. 230(2), pages 272-283.
    2. Rosato, Antonio, 2013. "Selling Substitute Goods to Loss-Averse Consumers: Limited Availability, Bargains and Rip-offs," MPRA Paper 47168, University Library of Munich, Germany.
    3. Katsuhiko Shimizu, . "New Strategy Implementation and Learning: Importance of Consensus," Working Papers 0034, College of Business, University of Texas at San Antonio.
    4. Rezaei, Jafar & Davoodi, Mansoor, 2011. "Multi-objective models for lot-sizing with supplier selection," International Journal of Production Economics, Elsevier, vol. 130(1), pages 77-86, March.
    5. Liberopoulos, George & Tsikis, Isidoros & Delikouras, Stefanos, 2010. "Backorder penalty cost coefficient "b": What could it be?," International Journal of Production Economics, Elsevier, vol. 123(1), pages 166-178, January.
    6. Sachs, Anna-Lena & Minner, Stefan, 2014. "The data-driven newsvendor with censored demand observations," International Journal of Production Economics, Elsevier, vol. 149(C), pages 28-36.
    7. Samii, Amir-Behzad & Pibernik, Richard & Yadav, Prashant, 2011. "An inventory reservation problem with nesting and fill rate-based performance measures," International Journal of Production Economics, Elsevier, vol. 133(1), pages 393-402, September.
    8. Lejeune, Miguel A., 2013. "Probabilistic modeling of multiperiod service levels," European Journal of Operational Research, Elsevier, vol. 230(2), pages 299-312.


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