DEMAND FORECASTING: EVIDENCE-BASED METHODS Forthcoming in the Oxford Handbook in Managerial Economics Christopher R. Thomas and William F. Shughart II (Eds.) Subject to further revisions File: Demandforecasting-17-August-2011-clean.docx 17 August 2011 J. Scott Armstrong The Wharton School, University of Pennsylvania 747 Huntsman, Philadelphia, PA 19104, U.S.A. T: +1 610 622 6480 F: +1 215 898 2534 armstrong@wharton.upenn.edu Kesten C. Green International Graduate School of Business, University of South Australia City West Campus, North Terrace, Adelaide, SA 5000, Australia T: +61 8 8302 9097 F: +61 8 8302 0709 kesten.green@unisa.edu.au # words in body 10,053 (requested range was 6,000 to 9,000) ABSTRACT We reviewed the evidence-based literature related to the relative accuracy of alternative methods for forecasting demand. The findings yield conclusions that differ substantially from current practice. For problems where there are insufficient data, where one must rely on judgment. The key with judgment is to impose structure with methods such as surveys of intentions or expectations, judgmental bootstrapping, structured analogies, and simulated interaction. Avoid methods that lack evidence on efficacy such as intuition, unstructured meetings, and focus groups. Given ample data, use quantitative methods including extrapolation, quantitative analogies, rule-based forecasting, and causal methods. Among causal methods, econometric methods are useful given good theory, and few key variables. Index models are useful for selection problems when there are many variables and much knowledge about the situation. Use structured procedures to incorporate managers’ domain knowledge into forecasts from quantitative methods where the knowledge would otherwise be overlooked, but avoid unstructured revisions. Methods for combining forecasts, including prediction markets and Delphi,