Abstract
Accurate demand forecasting is crucial for companies, since both overage and shortage of products can reduce company profits. Numerous statistical measures have been proposed to evaluate the accuracy of forecasts, but these measures tend to neglect the different economic impact of forecast errors for different items. Motivated by industry practitioners’ need to evaluate their forecasting processes and identify potentials for improvement, we develop a framework to evaluate demand forecasts. The framework consists of statistical measures and a scoring model and allows for an analysis of forecast accuracy on different levels of aggregation. A case study from a large retailing company shows that the framework is easy to implement in standard software and can provide relevant managerial insights.
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References
Armstrong, J.S. (ed.) (2001) Evaluating forecasting methods. In: Principles of Forecasting: A Handbook for Researchers and Practitioners. Dordrecht, MA: Kluwer Academic Publishers, pp. 443–473.
Armstrong, J.S. and Collopy, F. (1992) Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting 8 (1): 69–80.
Catt, P.M., Barbour, R.H. and Robb, D.J. (2008) Assessing forecast model performance in an ERP environment. Industrial Management & Data Systems 108 (5): 677–697.
Enns, S. (2002) MRP performance effects due to forecast bias and demand uncertainty. European Journal of Operational Research 138 (1): 87–102.
Fildes, R., Goodwin, P., Lawrence, M. and Nikolopoulos, K. (2009) Effective forecasting and judgemental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting 25 (1): 3–23.
Fildes, R. and Kingsman, B. (2011) Incorporating demand uncertainty and forecast error in supply chain planning models. Journal of the Operational Research Society 62 (3): 483–500.
Gadd, A. and Wold, H. (1964) The Janus Quotient: A Measure for the Accuracy of Prediction. Amsterdam: North-Holland Publishing Company.
Goodwin, P. and Fildes, R. (2011) Forecasting in supply chain companies: Should you trust your judgement? OR Insight 24 (3): 159–167.
Götze, U. and Bloech, J. (1995) Investitionsrechnung: Modelle und Analysen zur Beurteilung von Investitionsvorhaben. Berlin: Springer.
Grob, R. (1983) ErweiterteWirtschaftlichkeits- und Nutzenrechnung: Duale Bewertung von Investitionen für Planungsalternativen. Verlag TÜV Rheinland: Köln.
Hyndman, R.J. and Koehler, A.B. (2006) Another look at measures of forecast accuracy. International Journal of Forecasting 22 (4): 679–688.
Keeney, R.L., Raiffa, H. and Meyer, R.F. (1993) Decisions with Multiple Objectives. Cambridge, UK: Cambridge University Press.
Kimmel, P.D., Weygandt, J.J. and Kieso, D.E. (2011) Accounting: Tools for Business Decision Making, 4th edn, Hoboken, NJ: Wiley & Sons.
Leitch, G. and Tanner, J.E. (1991) Economic forecast evaluation: Profits versus the conventional error measures. The American Economic Review 81 (3): 580–590.
Makridakis, S. (1993) Accuracy measures: Theoretical and practical concerns. International Journal of Forecasting 9 (4): 527–529.
Mathews, B.P. and Diamantopoulos, A. (1994) Towards a taxonomy of forecast error measures a factor-comparative investigation of forecast error dimensions. Journal of Forecasting 13 (4): 409–416.
Theil, H. (1966) Applied Economic Forecasting, Studies in Mathematical and Managerial Economics, Vol. 4. Amsterdam: North-Holland Publishing Company.
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Ott, K., Mensendiek, A. & Gmeinwieser, K. A framework for economic demand forecast evaluation. OR Insight 26, 203–218 (2013). https://doi.org/10.1057/ori.2013.3
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DOI: https://doi.org/10.1057/ori.2013.3