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Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting

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Listed:
  • Robert Fildes

    () (Department of Management Science, Lancaster University Management School, Lancaster LAI 4YX, United Kingdom)

  • Paul Goodwin

    () (Management School, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom)

Abstract

Accurate forecasts are crucial to successful organizational planning. In 2001, 40 international experts published a set of principles to guide best practices in forecasting. Some of these principles relate to the use of management judgment. Most organizations use judgment at some stage in their forecasting process, but do they do so effectively? Although judgment can lead to significant improvements in forecasting accuracy, it can also be biased and inconsistent. The principles show how forecasters should use judgment and assess its effectiveness. We conducted a survey of 149 forecasters to examine the use of judgment based on these established principles and to investigate whether their forecasting procedures were consistent with the principles. In addition, we conducted four in-depth case studies. Although we found examples of good practice, we also discovered that many organizations would improve forecast accuracy if they followed basic principles such as limiting judgmental adjustments of quantitative forecasts, requiring managers to justify their adjustments in writing, and assessing the results of judgmental interventions.

Suggested Citation

  • Robert Fildes & Paul Goodwin, 2007. "Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting," Interfaces, INFORMS, vol. 37(6), pages 570-576, December.
  • Handle: RePEc:inm:orinte:v:37:y:2007:i:6:p:570-576
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    File URL: http://dx.doi.org/10.1287/inte.1070.0309
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    References listed on IDEAS

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    Cited by:

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    3. Mark Crowder, 2015. "Public procurement: the role of cognitive heuristics," Public Money & Management, Taylor & Francis Journals, vol. 35(2), pages 127-134, March.
    4. J. Scott Armstrong & Kesten C. Green & Willie Soon, 2008. "Polar Bear Population Forecasts: A Public-Policy Forecasting Audit," Interfaces, INFORMS, vol. 38(5), pages 382-405, October.
    5. Wan, Xiang & Sanders, Nadia R., 2017. "The negative impact of product variety: Forecast bias, inventory levels, and the role of vertical integration," International Journal of Production Economics, Elsevier, vol. 186(C), pages 123-131.
    6. repec:eee:ejores:v:273:y:2019:i:3:p:992-1004 is not listed on IDEAS
    7. repec:eee:intfor:v:35:y:2019:i:1:p:144-156 is not listed on IDEAS
    8. Cote, Joseph A., 2011. "Predicting elections from biographical information about candidates: A commentary essay," Journal of Business Research, Elsevier, vol. 64(7), pages 696-698, July.
    9. repec:pal:jorsoc:v:59:y:2008:i:9:d:10.1057_palgrave.jors.2602597 is not listed on IDEAS
    10. Victor Richmond R. Jose, 2017. "Percentage and Relative Error Measures in Forecast Evaluation," Operations Research, INFORMS, vol. 65(1), pages 200-211, February.
    11. repec:eee:ejores:v:274:y:2019:i:2:p:574-600 is not listed on IDEAS
    12. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    13. repec:eee:intfor:v:35:y:2019:i:1:p:170-180 is not listed on IDEAS

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    Keywords

    applications; forecasting;

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