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|Title: ||Estimating Demand Uncertainty Using Judgmental Forecasts|
|Authors: ||Gaur, Vishal|
Fisher, Marshall L.
|Issue Date: ||15-Apr-2005|
|Publisher: ||Stern School of Business, New York University|
|Series/Report no.: ||OM-2005-08|
|Abstract: ||Measuring demand uncertainty is a key activity in supply chain planning. Of various methods of
estimating the standard deviation of demand, one that has been employed successfully in the
recent literature uses dispersion among expertsâ forecasts. However, there has been limited
empirical validation of this methodology. In this paper we provide a general methodology for
estimating the standard deviation of a random variable using dispersion among expertsâ forecasts.
We test this methodology using three datasets, demand data at item level, sales data at firm level
for retailers, and sales data at firm level for manufacturers. We show that the standard deviation
of a random variable (demand and sales for our datasets) is positively correlated with dispersion
among expertsâ forecasts. Further we use longitudinal datasets with sales forecasts made 3-9
months before earnings report date for retailers and manufacturers to show that the effects of
dispersion and scale on standard deviation of forecast error are consistent over time.|
|Appears in Collections:||IOMS: Operations Management Working Papers|
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