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Estimating Demand Uncertainty Using Judgmental Forecasts

Listed author(s):
  • 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)

Registered author(s):

    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.

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    Article provided by INFORMS in its journal Manufacturing & Service Operations Management.

    Volume (Year): 9 (2007)
    Issue (Month): 4 (April)
    Pages: 480-491

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    Handle: RePEc:inm:ormsom:v:9:y:2007:i:4:p:480-491
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    1. Ajinkya, Bipin B & Gift, Michael J, 1985. " Dispersion of Financial Analysts' Earnings Forecasts and the (Option Model) Implied Standard Deviaitons of Stock Returns," Journal of Finance, American Finance Association, vol. 40(5), pages 1353-1365, December.
    2. Harvey, A C, 1976. "Estimating Regression Models with Multiplicative Heteroscedasticity," Econometrica, Econometric Society, vol. 44(3), pages 461-465, May.
    3. Christian Terwiesch & Christoph H. Loch, 1999. "Measuring the Effectiveness of Overlapping Development Activities," Management Science, INFORMS, vol. 45(4), pages 455-465, April.
    4. Morris A. Cohen & Teck H. Ho & Z. Justin Ren & Christian Terwiesch, 2003. "Measuring Imputed Cost in the Semiconductor Equipment Supply Chain," Management Science, INFORMS, vol. 49(12), pages 1653-1670, December.
    5. Werner F. M. De Bondt & William P. Forbes*, 1999. "Herding in analyst earnings forecasts: evidence from the United Kingdom," European Financial Management, European Financial Management Association, vol. 5(2), pages 143-163.
    6. Burton G. Malkiel, 1982. "Risk and Return: A New Look," NBER Chapters,in: The Changing Roles of Debt and Equity in Financing U.S. Capital Formation, pages 27-46 National Bureau of Economic Research, Inc.
    7. Munier, Francis & Ronde, Patrick, 2001. "The role of knowledge codification in the emergence of consensus under uncertainty: empirical analysis and policy implications," Research Policy, Elsevier, vol. 30(9), pages 1537-1551, December.
    8. George T. Milkovich & Anthony J. Annoni & Thomas A. Mahoney, 1972. "The Use of the Delphi Procedures in Manpower Forecasting," Management Science, INFORMS, vol. 19(4-Part-1), pages 381-388, December.
    9. Norman Dalkey & Olaf Helmer, 1963. "An Experimental Application of the DELPHI Method to the Use of Experts," Management Science, INFORMS, vol. 9(3), pages 458-467, April.
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