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Do Inventory and Gross Margin Data Improve Sales Forecasts for U.S. Public Retailers?

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  • Saravanan Kesavan

    () (Kenan-Flagler Business School, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599)

  • Vishal Gaur

    () (Johnson Graduate School of Management, Cornell University, Ithaca, New York 14853)

  • Ananth Raman

    () (Harvard Business School, Harvard University, Boston, Massachusetts 02163)

Abstract

Firm-level sales forecasts for retailers can be improved if we incorporate cost of goods sold, inventory, and gross margin (defined by us as the ratio of sales to cost of goods sold) as three endogenous variables. We construct a simultaneous equations model, estimated using public financial and nonfinancial data, to provide joint forecasts of annual cost of goods sold, inventory, and gross margin for retailers using historical data. We show that sales forecasts from this model are more accurate than consensus forecasts from equity analysts. Further, the residuals from this model for one fiscal year are used to predict retailers for whom the relative advantage of model forecasts over consensus forecasts would be large in the next fiscal year. Our results show that historical inventory and gross margin contain information useful to forecast sales, and that equity analysts do not fully utilize this information in their sales forecasts.

Suggested Citation

  • Saravanan Kesavan & Vishal Gaur & Ananth Raman, 2010. "Do Inventory and Gross Margin Data Improve Sales Forecasts for U.S. Public Retailers?," Management Science, INFORMS, vol. 56(9), pages 1519-1533, September.
  • Handle: RePEc:inm:ormnsc:v:56:y:2010:i:9:p:1519-1533
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    File URL: http://dx.doi.org/10.1287/mnsc.1100.1209
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    References listed on IDEAS

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    Cited by:

    1. Crouzet, Nicolas & Oh, Hyunseung, 2016. "What do inventories tell us about news-driven business cycles?," Journal of Monetary Economics, Elsevier, vol. 79(C), pages 49-66.
    2. Manikas, Andrew S. & Patel, Pankaj C., 2016. "Managing sales surprise: The role of operational slack and volume flexibility," International Journal of Production Economics, Elsevier, vol. 179(C), pages 101-116.
    3. Hyunseung Oh & Nicolas Crouzet, 2013. "Can news shocks account for the business-cycle dynamics of inventories?," 2013 Meeting Papers 504, Society for Economic Dynamics.
    4. Chuang, Chia-Hung & Chiang, Chung-Yean, 2016. "Dynamic and stochastic behavior of coefficient of demand uncertainty incorporated with EOQ variables: An application in finished-goods inventory from General Motors׳ dealerships," International Journal of Production Economics, Elsevier, vol. 172(C), pages 95-109.
    5. repec:eee:proeco:v:191:y:2017:i:c:p:253-266 is not listed on IDEAS
    6. Saravanan Kesavan & Vidya Mani, 2013. "The Relationship Between Abnormal Inventory Growth and Future Earnings for U.S. Public Retailers," Manufacturing & Service Operations Management, INFORMS, vol. 15(1), pages 6-23, May.
    7. Talebian, Masoud & Boland, Natashia & Savelsbergh, Martin, 2014. "Pricing to accelerate demand learning in dynamic assortment planning for perishable products," European Journal of Operational Research, Elsevier, vol. 237(2), pages 555-565.
    8. Williams, Brent D. & Waller, Matthew A. & Ahire, Sanjay & Ferrier, Gary D., 2014. "Predicting retailer orders with POS and order data: The inventory balance effect," European Journal of Operational Research, Elsevier, vol. 232(3), pages 593-600.

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    Keywords

    sales forecasting; retail; inventory; empirical;

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