The Sum and Its Parts: Judgmental Hierarchical Forecasting

• 成果类型: Article
• 署名作者: Kremer, Mirko; Siemsen, Enno; Thomas, Douglas J.
• 署名单位: Frankfurt School Finance & Management; University of Minnesota System; University of Minnesota Twin Cities; University of Wisconsin System; University of Wisconsin Madison; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2015.2259
• 发表日期: 2016
• 页码: 2745-2764
• 关键词: judgmental forecasting; nonstationary demand; covariation detection; behavioral operations; bottom-up forecasting; random judgment error

摘要

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.