Task Decomposition and Newsvendor Decision Making

• 成果类型: Article
• 署名作者: Lee, Yun Shin; Siemsen, Enno
• 署名单位: Korea Advanced Institute of Science & Technology (KAIST); University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2016.2521
• 发表日期: 2017
• 页码: 3226-3245
• 关键词: Behavioral Operations; task decomposition; newsvendor; loss function pull; overconfidence; attribute substitution; random judgment error; Decision Support

摘要

We conduct three behavioral laboratory experiments to compare newsvendor order decisions placed directly to order decisions submitted in a decomposed way by soliciting point forecasts, uncertainty estimates, and service-level decisions. Decomposing order decisions in such a way often follows from organizational structure and can lead to performance improvements compared with ordering directly. However, we also demonstrate that if the critical ratio is below 50%, or if the underlying demand uncertainty is too high, task decomposition may not be preferred to direct ordering. Under such conditions, decision makers are prone to set service levels too high or to suffer from excessive random judgment error, which reduces the efficacy of task decomposition. We further demonstrate that if accompanied by decision support in the form of suggested quantities, task decomposition becomes the better-performing approach to newsvendor decision making more generally. Decision support and task decomposition therefore appear as complementary methods to improve decision performance in the newsvendor context.