Estimating Demand Uncertainty Using Judgmental Forecasts

TLDR

Measuring demand uncertainty is crucial for supply‑chain planning, yet difficult without historical data; dispersion among forecasting experts offers a viable method in such cases. The study tests the dispersion‑based method and introduces a heteroscedastic regression model to estimate demand variance from expert forecast dispersion and scale. The authors evaluate the method on item‑level demand data and firm‑level sales data for retailers and manufacturers, including longitudinal forecasts made months before earnings reports, to assess dispersion and scale effects. The variance of demand and sales is positively correlated with expert forecast dispersion and scale, increasing sublinearly with dispersion and more than linearly with scale, and these relationships hold consistently over time.

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.