Task Decomposition and Newsvendor Decision Making

Yun Shin Lee () and Enno Siemsen ()
Additional contact information
Yun Shin Lee: Korea Advanced Institute of Science and Technology, Dongdaemun-gu, Seoul, Korea 02455
Enno Siemsen: University of Wisconsin–Madison, Madison, Wisconsin 53706

Management Science, 2017, vol. 63, issue 10, 3226-3245

Abstract: 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.

Keywords: behavioral operations; task decomposition; newsvendor; loss function pull; overconfidence; attribute substitution; random judgment error; decision support (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
https://doi.org/10.1287/mnsc.2016.2521 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:63:y:2017:i:10:p:3226-3245

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().