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Do Experts’ SKU Forecasts Improve after Feedback?

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  • Rianne Legerstee
  • Philip Hans Franses

Abstract

We analyze the behavior of experts who quote forecasts for monthly SKU-level sales data where we compare data before and after the moment that experts received different kinds of feedback on their behavior. We have data for 21 experts located in as many countries who make SKUlevel forecasts for a variety of pharmaceutical products for October 2006 to September 2007. We study the behavior of the experts by comparing their forecasts with those from an automated statistical program, and we report the forecast accuracy over these 12 months. In September 2007 these experts were given feedback on their behavior and they received a training at the headquarters' office, where specific attention was given to the ins and outs of the statistical program. Next, we study the behavior of the experts for the 3 months after the training session, that is, October 2007 to December 2007. Our main conclusion is that in the second period the experts' forecasts deviated less from the statistical forecasts and that their accuracy improved substantially.
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  • Rianne Legerstee & Philip Hans Franses, 2014. "Do Experts’ SKU Forecasts Improve after Feedback?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 69-79, January.
    • Handle: RePEc:wly:jforec:v:33:y:2014:i:1:p:69-79
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    File URL: http://hdl.handle.net/10.1002/for.2274
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    Cited by:

    1. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    2. Fildes, Robert & Goodwin, Paul & Onkal, Dilek, 2015. "Information use in supply chain forecasting," MPRA Paper 66034, University Library of Munich, Germany.
    3. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    4. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    5. Petropoulos, Fotios & Fildes, Robert & Goodwin, Paul, 2016. "Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 842-852.

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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