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Estimating Demand Uncertainty Using Judgmental Forecasts

Author

Listed:
  • Vishal Gaur

    () (Leonard N. Stern School of Business, New York University, 44 West Fourth Street, New York, New York 10012)

  • Saravanan Kesavan

    () (Harvard Business School, Morgan Hall, Soldiers Field, Boston, Massachusetts 02163)

  • Ananth Raman

    () (Harvard Business School, Morgan Hall, Soldiers Field, Boston, Massachusetts 02163)

  • Marshall L. Fisher

    () (The Wharton School, University of Pennsylvania, Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, Pennsylvania 19104-6366)

Abstract

Measuring demand uncertainty is a key activity in supply chain planning, but it is difficult when demand history is unavailable, such as for new products. One method that can be applied in such cases uses dispersion among forecasting experts as a measure of demand uncertainty. This paper provides a test for this method and presents a heteroscedastic regression model for estimating the variance of demand using dispersion among experts' forecasts and scale. We test this methodology using three data sets: demand data at item level, sales data at firm level for retailers, and sales data at firm level for manufacturers. We show that the variance of a random variable (demand and sales for our data sets) is positively correlated with both dispersion among experts' forecasts and scale: The variance increases sublinearly with dispersion and more than linearly with scale. Further, we use longitudinal data sets with sales forecasts made three to nine months before the earnings report date for retailers and manufacturers to show that the effects of dispersion and scale on variance of forecast error are consistent over time.

Suggested Citation

  • Vishal Gaur & Saravanan Kesavan & Ananth Raman & Marshall L. Fisher, 2007. "Estimating Demand Uncertainty Using Judgmental Forecasts," Manufacturing & Service Operations Management, INFORMS, vol. 9(4), pages 480-491, April.
  • Handle: RePEc:inm:ormsom:v:9:y:2007:i:4:p:480-491
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    File URL: http://dx.doi.org/10.1287/msom.1060.0134
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Vilkkumaa, Eeva & Liesiö, Juuso & Salo, Ahti, 2014. "Optimal strategies for selecting project portfolios using uncertain value estimates," European Journal of Operational Research, Elsevier, vol. 233(3), pages 772-783.
    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. He, Yuxuan & Liu, Nan, 2015. "Methodology of emergency medical logistics for public health emergencies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 79(C), pages 178-200.
    4. Thomas Post & Katja Hanewald, 2010. "Stochastic Mortality, Subjective Survival Expectations, and Individual Saving Behavior," SFB 649 Discussion Papers SFB649DP2010-040, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    6. Önkal, Dilek & Zeynep Sayım, K. & Lawrence, Michael, 2012. "Wisdom of group forecasts: Does role-playing play a role?," Omega, Elsevier, vol. 40(6), pages 693-702.
    7. Post, Thomas & Hanewald, Katja, 2013. "Longevity risk, subjective survival expectations, and individual saving behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 86(C), pages 200-220.
    8. Stößlein, Martin & Kanet, John Jack & Gorman, Mike & Minner, Stefan, 2014. "Time-phased safety stocks planning and its financial impacts: Empirical evidence based on European econometric data," International Journal of Production Economics, Elsevier, vol. 149(C), pages 47-55.
    9. Marcelo Olivares & Gérard P. Cachon, 2009. "Competing Retailers and Inventory: An Empirical Investigation of General Motors' Dealerships in Isolated U.S. Markets," Management Science, INFORMS, vol. 55(9), pages 1586-1604, September.
    10. Wanke, Peter F., 2008. "The uniform distribution as a first practical approach to new product inventory management," International Journal of Production Economics, Elsevier, vol. 114(2), pages 811-819, August.
    11. Kate J. Li & Duncan K. H. Fong & Susan H. Xu, 2011. "Managing Trade-in Programs Based on Product Characteristics and Customer Heterogeneity in Business-to-Business Markets," Manufacturing & Service Operations Management, INFORMS, vol. 13(1), pages 108-123, October.

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