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The Impact of Supplier Inventory Service Level on Retailer Demand

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Listed:
  • Nathan Charles Craig

    (Harvard Business School)

  • Nicole DeHoratius

    (University of Chicago)

  • Ananth Raman

    (Harvard Business School, Technology and Operations Management Unit)

Abstract

To set inventory service levels, suppliers must understand how changes in inventory service level affect demand. We build on prior research, which uses analytical models and laboratory experiments to study the impact of a supplier's service level on demand from retailers, by testing this relationship in the field. We analyze a field experiment at the supplier Hugo Boss to deter- mine how the supplier's inventory service level affects demand from its retailer customers. We find increases in historical fill rate to be associated with statistically significant and managerially substantial increases in current retailer orders (i.e., demand, not just sales). Specifically, a one percentage point increase in fill rate, measured over the prior year, is associated with a statistically significant 11% increase in current retailer demand, controlling for other factors that might affect retailer demand. We explore the drivers of this demand increase, including changes in retailer assortment and order frequency. We discuss features of a retail buyer's decision context identified through our field work that may explain the magnitude of the relationship we observe.

Suggested Citation

  • Nathan Charles Craig & Nicole DeHoratius & Ananth Raman, 2010. "The Impact of Supplier Inventory Service Level on Retailer Demand," Harvard Business School Working Papers 11-034, Harvard Business School, revised Jan 2016.
    • Handle: RePEc:hbs:wpaper:11-034
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    References listed on IDEAS

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    1. James D. Dana, Jr. & Nicholas C. Petruzzi, 2001. "Note: The Newsvendor Model with Endogenous Demand," Management Science, INFORMS, vol. 47(11), pages 1488-1497, November.
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    Cited by:

    1. Ruomeng Cui & Dennis J. Zhang & Achal Bassamboo, 2019. "Learning from Inventory Availability Information: Evidence from Field Experiments on Amazon," Management Science, INFORMS, vol. 65(3), pages 1216-1235, March.
    2. Pol Boada-Collado & Victor Martínez-de-Albéniz, 2020. "Estimating and Optimizing the Impact of Inventory on Consumer Choices in a Fashion Retail Setting," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 582-597, May.
    3. Ruth Beer & Hyun-Soo Ahn & Stephen Leider, 2022. "The Impact of Decision Rights on Innovation Sharing," Management Science, INFORMS, vol. 68(11), pages 7898-7917, November.
    4. K Mercy Makhitha & Brian Soke, 2021. "Investigating the challenges for the development of independent retailers in South Africa," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 10(7), pages 16-26, October.
    5. Felipe Caro & A. Gürhan Kök & Victor Martínez-de-Albéniz, 2020. "The Future of Retail Operations," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 47-58, January.
    6. Yue Dai & Tianjun Feng & Christopher S. Tang & Xiaole Wu & Fuqiang Zhang, 2020. "Twenty Years in the Making: The Evolution of the Journal of Manufacturing & Service Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 1-10, January.
    7. Anna Timonina‐Farkas & René Y. Glogg & Ralf W. Seifert, 2022. "Limiting the impact of supply chain disruptions in the face of distributional uncertainty in demand," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3788-3805, October.
    8. Christian Terwiesch & Marcelo Olivares & Bradley R. Staats & Vishal Gaur, 2020. "OM Forum—A Review of Empirical Operations Management over the Last Two Decades," Manufacturing & Service Operations Management, INFORMS, vol. 22(4), pages 656-668, July.
    9. Jiankun Sun & Dennis J. Zhang & Haoyuan Hu & Jan A. Van Mieghem, 2022. "Predicting Human Discretion to Adjust Algorithmic Prescription: A Large-Scale Field Experiment in Warehouse Operations," Management Science, INFORMS, vol. 68(2), pages 846-865, February.
    10. Gel, Esma S. & Salman, F. Sibel, 2022. "Dynamic ordering decisions with approximate learning of supply yield uncertainty," International Journal of Production Economics, Elsevier, vol. 243(C).
    11. Chen, Zhen & Archibald, Thomas W., 2024. "Maximizing the survival probability in a cash flow inventory problem with a joint service level constraint," International Journal of Production Economics, Elsevier, vol. 270(C).

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