Demand Forecasting Behavior: System Neglect and Change Detection

Authors:
Mirko Kremer;Brent Moritz;Enno Siemsen
Affiliations:
Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802;Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802;Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455
Venue:
Management Science
Year:
2011

Citing 9
Cited 2

A Single-Item Inventory Model for a Nonstationary Demand Process

Manufacturing & Service Operations Management
Forecasting software in practice: use, satisfaction, and performance

Interfaces - Wagner prize papers
Decision Bias in the Newsvendor Problem with a Known Demand Distribution: Experimental Evidence

Management Science
Detecting Regime Shifts: The Causes of Under- and Overreaction

Management Science
Intuitions About Combining Opinions: Misappreciation of the Averaging Principle

Management Science
Learning by Doing in the Newsvendor Problem: A Laboratory Investigation of the Role of Experience and Feedback

Manufacturing & Service Operations Management
Inference from Streaks in Random Outcomes: Experimental Evidence on Beliefs in Regime Shifting and the Law of Small Numbers

Management Science
Do Random Errors Explain Newsvendor Behavior?

Manufacturing & Service Operations Management
Reference-Point Formation and Updating

Management Science

Biased Judgment in Censored Environments

Management Science
Bounded Rationality in Service Systems

Manufacturing & Service Operations Management

Quantified Score

Hi-index	0.02

Visualization

Abstract

We analyze how individuals make forecasts based on time-series data. Using a controlled laboratory experiment, we find that forecasting behavior systematically deviates from normative predictions: Forecasters overreact to forecast errors in relatively stable environments, but underreact to errors in relatively unstable environments. The performance loss that is due to such systematic judgment biases is larger in stable than in unstable environments. This paper was accepted by Martin Lariviere, operations management.