The Sum and Its Parts: Judgmental Hierarchical Forecasting
研究了企业如何通过直接或间接方式生成不同层级的聚合需求预测，识别了影响两种方法相对表现的行为偏差，并描述了哪种层级过程在何种需求环境下更准确。
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 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. This paper was accepted by Martin Lariviere, operations management.