Estimating Demand Uncertainty Using Judgmental Forecasts

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.