Abstract

http://ssrn.com/abstract=2130708
 
 

References (37)



 


 



The Sum and Its Parts: A Behavioral Investigation of Top-Down and Bottom-Up Forecasting Processes


Mirko Kremer


Pennsylvania State University

Enno Siemsen


University of Minnesota - Twin Cities - Carlson School of Management

Douglas J. Thomas


Pennsylvania State University - Department of Supply Chain & Information Systems

August 16, 2012


Abstract:     
Operations planning tasks require demand forecasts at different levels of aggregation. This leaves firms with a choice. Top-level forecasts can be made based on top-level data or, instead, in a bottom-up fashion, where lower-level forecasts are summed up to create a top-level forecast. Lower-level forecasts can be based on lower-level data, or can instead be computed in a top-down fashion, where a forecast is generated based on top-level data and then apportioned to the lower level. Our study investigates the performance of such forecasting processes through a behavioral lens. A key performance driver across the investigated forecasting processes is the correlation structure among lower-level time-series. We identify two relevant biases that affect the relative performance of direct and indirect forecasting approaches: a propensity to neglect distal time-series information in lower-level forecasts, and a propensity for random judgment errors. We investigate how these biases aggregate as the forecasting process “moves” between levels of aggregation, which allows us to characterize the demand environments in which an organization should favor one forecasting process over the other.

Number of Pages in PDF File: 37

Keywords: forecasting process, exponential smoothing, covariation detection, behavioral operations, sales and operations planning

working papers series


Download This Paper

Date posted: August 17, 2012 ; Last revised: May 25, 2013

Suggested Citation

Kremer, Mirko and Siemsen, Enno and Thomas, Douglas J., The Sum and Its Parts: A Behavioral Investigation of Top-Down and Bottom-Up Forecasting Processes (August 16, 2012). Available at SSRN: http://ssrn.com/abstract=2130708 or http://dx.doi.org/10.2139/ssrn.2130708

Contact Information

Mirko Kremer
Pennsylvania State University ( email )
University Park
State College, PA 16802
United States
Enno Siemsen (Contact Author)
University of Minnesota - Twin Cities - Carlson School of Management ( email )
321 19th Ave South
Minneapolis, MN 55455
United States
Douglas J. Thomas
Pennsylvania State University - Department of Supply Chain & Information Systems ( email )
Dept. of Supply Chain & Information Systems
University Park, PA 16802-3306
United States
Feedback to SSRN


Paper statistics
Abstract Views: 543
Downloads: 83
Download Rank: 175,369
References:  37

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo6 in 0.516 seconds