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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14636
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| Title: | Estimating Demand Uncertainty Using Judgmental Forecasts |
| Authors: | Gaur, Vishal
Kesavan, Saravanan
Raman, Ananth
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. |
| URI: | http://hdl.handle.net/2451/14636 |
| Appears in Collections: | IOMS: Operations Management Working Papers
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