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Managing Sales Forecasters

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Author Info

  • Bert de Bruijn

    (Erasmus University Rotterdam)

  • Philip Hans Franses

    (Erasmus University Rotterdam)

Abstract

A Forecast Support System (FSS), which generates sales forecasts, is a sophisticated business analytical tool that can help to improve targeted business decisions. Many companies use such a tool, although at the same time they may allow managers to quote their own forecasts. These sales forecasters (managers) can take the FSS output as their input, but they can also fully ignore the FSS out- comes. We propose a methodology that allows to evaluate the forecast accuracy of these managers, relative to the FSS, while taking aboard latent variation across managers' behavior. We show that the results, here for a large Germany-based pharmaceutical company, can in fact be used to manage the sales forecasters by giving clear-cut recommendations for improvement.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 12-131/III.

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Date of creation: 03 Dec 2012
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Handle: RePEc:dgr:uvatin:20120131

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Web page: http://www.tinbergen.nl

Related research

Keywords: Forecast Support System; Sales forecasters; Forecast accuracy;

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References

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  1. Konstantinos Nikolopoulos, 2008. "On the accuracy of judgmental interventions on Statistical Forecasts," Working Papers, University of Peloponnese, Department of Economics 0021, University of Peloponnese, Department of Economics.
  2. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, Elsevier, vol. 25(1), pages 3-23.
  3. Goodwin, Paul, 2000. "Improving the voluntary integration of statistical forecasts and judgment," International Journal of Forecasting, Elsevier, Elsevier, vol. 16(1), pages 85-99.
  4. Bunn, Derek W. & Salo, Ahti A., 1996. "Adjustment of forecasts with model consistent expectations," International Journal of Forecasting, Elsevier, Elsevier, vol. 12(1), pages 163-170, March.
  5. Franses, Philip Hans & Legerstee, Rianne, 2009. "Properties of expert adjustments on model-based SKU-level forecasts," International Journal of Forecasting, Elsevier, Elsevier, vol. 25(1), pages 35-47.
  6. Philip Hans Franses & Rianne Legerstee, 2010. "Do experts' adjustments on model-based SKU-level forecasts improve forecast quality?," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 29(3), pages 331-340.
  7. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, Elsevier, vol. 22(3), pages 493-518.
  8. Goodwin, Paul, 2002. "Integrating management judgment and statistical methods to improve short-term forecasts," Omega, Elsevier, vol. 30(2), pages 127-135, April.
  9. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E. & Fildes, Robert & Goodwin, Paul, 2009. "The effects of integrating management judgement into intermittent demand forecasts," International Journal of Production Economics, Elsevier, Elsevier, vol. 118(1), pages 72-81, March.
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