Demand Forecasting: Evidence-based Methods
AbstractWe 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.
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Bibliographic InfoPaper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 24/05.
Length: 17 pages
Date of creation: Sep 2005
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Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- M30 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - General
- M31 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - Marketing
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-10-16 (All new papers)
- NEP-CBE-2005-10-21 (Cognitive & Behavioural Economics)
- NEP-ECM-2005-10-18 (Econometrics)
- NEP-FOR-2005-10-16 (Forecasting)
- NEP-MKT-2005-10-16 (Marketing)
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