Demand Forecasting: Evidence-based Methods

J. Scott Armstrong () and Kesten Charles Green ()

No 24/05, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We looked at evidence from comparative empirical studies to identify methods that can be useful for predicting demand in various situations and to warn against methods that should not be used. In general, use structured methods and avoid intuition, unstructured meetings, focus groups, and data mining. In situations where there are sufficient data, use quantitative methods including extrapolation, quantitative analogies, rule-based forecasting, and causal methods. Otherwise, use methods that structure judgement including surveys of intentions and expectations, judgmental bootstrapping, structured analogies, and simulated interaction. Managers' domain knowledge should be incorporated into statistical forecasts. Methods for combining forecasts, including Delphi and prediction markets, improve accuracy. We provide guidelines for the effective use of forecasts, including such procedures as scenarios. Few organizations use many of the methods described in this paper. Thus, there are opportunities to improve efficiency by adopting these forecasting practices.

Keywords: Accuracy; expertise; forecasting; judgement; marketing. (search for similar items in EconPapers)
JEL-codes: C53 M30 M31 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cbe, nep-ecm, nep-for and nep-mkt
Date: Written 2005-09
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