The Sum and Its Parts : Judgmental Hierarchical Forecasting

Firms require demand forecasts at different levels of aggregation to support a variety of resource allocation decisions. For example, a retailer needs store-level forecasts for a particular item to manage inventory at the store but also requires a regionally-aggregated forecast for managing inventory at a distribution center. In generating an aggregate forecast, a firm can choose to make the forecast directly based on the aggregated data or indirectly by summing lower-level forecasts (i.e., bottom-up). Our study investigates the relative performance of such hierarchical forecasting processes through a behavioral lens. We identify two judgment biases that affect the relative performance of direct and indirect forecasting approaches: a propensity for random judgment errors, and a failure to benefit from the informational value that is embedded in the correlation structure between lower-level demands. Based on these biases we characterize demand environments where one hierarchical process results in more accurate forecasts than the other. Further, using field data, we demonstrate how to estimate the relevant correlation structure of lower-level demands