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Do statistical forecasting models for SKU-level data benefit from including past expert knowledge?

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  • Franses, Philip Hans
  • Legerstee, Rianne

Abstract

We determine whether statistical model forecasts of SKU level sales data can be improved by formally including past expert knowledge in the model as additional variables. Upon analyzing various forecasts in a large database, using various models, forecast samples and accuracy measures, we demonstrate that experts’ knowledge, on average, apparently is not associated with variables which are systematically omitted from the statistical models. We also find that the formal inclusion of past judgment can be helpful in cases when the model performs poorly. This can lead to an improved interaction between models and experts, and we discuss the design features of a forecasting support system.

Suggested Citation

  • Franses, Philip Hans & Legerstee, Rianne, 2013. "Do statistical forecasting models for SKU-level data benefit from including past expert knowledge?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 80-87.
  • Handle: RePEc:eee:intfor:v:29:y:2013:i:1:p:80-87
    DOI: 10.1016/j.ijforecast.2012.05.008
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    References listed on IDEAS

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    1. Eroglu, Cuneyt & Croxton, Keely L., 2010. "Biases in judgmental adjustments of statistical forecasts: The role of individual differences," International Journal of Forecasting, Elsevier, vol. 26(1), pages 116-133, January.
    2. Paul Goodwin, 2010. "Why Hindsight Can Damage Foresight," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 17, pages 5-7, Spring.
    3. 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., vol. 29(3), pages 331-340.
    4. 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, vol. 118(1), pages 72-81, March.
    5. Bruno Biais & Martin Weber, 2009. "Hindsight Bias, Risk Perception, and Investment Performance," Management Science, INFORMS, vol. 55(6), pages 1018-1029, June.
    6. Lawrence, Michael & Goodwin, Paul & Fildes, Robert, 2002. "Influence of user participation on DSS use and decision accuracy," Omega, Elsevier, vol. 30(5), pages 381-392, October.
    7. Robert Fildes & Paul Goodwin, 2007. "Good and Bad Judgment in Forecasting: Lessons from Four Companies," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 8, pages 5-10, Fall.
    8. Stephen J. Hoch & David A. Schkade, 1996. "A Psychological Approach to Decision Support Systems," Management Science, INFORMS, vol. 42(1), pages 51-64, January.
    9. Franses, Philip Hans & Legerstee, Rianne, 2009. "Properties of expert adjustments on model-based SKU-level forecasts," International Journal of Forecasting, Elsevier, vol. 25(1), pages 35-47.
    10. Robert C. Blattberg & Stephen J. Hoch, 1990. "Database Models and Managerial Intuition: 50% Model + 50% Manager," Management Science, INFORMS, vol. 36(8), pages 887-899, August.
    11. 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, vol. 25(1), pages 3-23.
    12. Juan R. Trapero & Robert Fildes & Andrey Davydenko, 2011. "Nonlinear identification of judgmental forecasts effects at SKU level," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(5), pages 490-508, August.
    13. Goodwin, Paul, 2002. "Integrating management judgment and statistical methods to improve short-term forecasts," Omega, Elsevier, vol. 30(2), pages 127-135, April.
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    Cited by:

    1. repec:eee:proeco:v:191:y:2017:i:c:p:85-96 is not listed on IDEAS
    2. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    3. Fildes, Robert & Goodwin, Paul & Onkal, Dilek, 2015. "Information use in supply chain forecasting," MPRA Paper 66034, University Library of Munich, Germany.
    4. Alvarado-Valencia, Jorge & Barrero, Lope H. & Önkal, Dilek & Dennerlein, Jack T., 2017. "Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting," International Journal of Forecasting, Elsevier, vol. 33(1), pages 298-313.
    5. Syntetos, Aris A. & Kholidasari, Inna & Naim, Mohamed M., 2016. "The effects of integrating management judgement into OUT levels: In or out of context?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 853-863.
    6. 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.
    7. Bert de Bruijn & Philip Hans Franses, 2012. "Managing Sales Forecasters," Tinbergen Institute Discussion Papers 12-131/III, Tinbergen Institute.

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