The Sum and Its Parts: Judgmental Hierarchical Forecasting

Mirko Kremer (), Enno Siemsen () and Douglas J. Thomas ()
Additional contact information
Mirko Kremer: Management Department, Frankfurt School of Finance and Management, 60314 Frankfurt am Main, Germany
Enno Siemsen: Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455; and Wisconsin School of Business, University of Wisconsin–Madison, Madison, Wisconsin 53706
Douglas J. Thomas: Smeal College of Business, Penn State University, University Park, Pennsylvania 16802

Management Science, 2016, vol. 62, issue 9, 2745-2764

Abstract: 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 .

Keywords: judgmental forecasting; nonstationary demand; covariation detection; behavioral operations; bottom-up forecasting; random judgment error (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2015.2259 (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:62:y:2016:i:9:p:2745-2764

Access Statistics for this article

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