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The efficacy of using judgmental versus quantitative forecasting methods in practice

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  • Sanders, Nada R.
  • Manrodt, Karl B.

Abstract

In an era where forecasts drive entire supply chains forecasting is seen as an increasingly critical organizational capability. However, business forecasting continues to rely on judgmental methods despite large advancements in information technology and quantitative method capability, prompting calls for research to help understand the reasons behind this practice. Our study is designed to contribute to this knowledge by profiling differences between firms identified as primary users of either judgmental or quantitative forecasting methods. Relying on survey data from 240 firms we statistically analyzed differences between these categories of users based on a range of organizational and forecasting issues. Our study finds large differences in forecast error rates between the two groups, with users of quantitative methods significantly outperforming users of judgmental methods. The former are found to be equally prevalent regardless of industry, firm size, and product positioning strategy, documenting the benefits of quantitative method use in a variety of settings. By contrast, the latter are found to have significantly lower access to quantifiable data and to use information and technology to a lesser degree.

Suggested Citation

  • Sanders, Nada R. & Manrodt, Karl B., 2003. "The efficacy of using judgmental versus quantitative forecasting methods in practice," Omega, Elsevier, vol. 31(6), pages 511-522, December.
  • Handle: RePEc:eee:jomega:v:31:y:2003:i:6:p:511-522
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    References listed on IDEAS

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    1. Dalrymple, Douglas J., 1975. "Sales forecasting methods and accuracy," Business Horizons, Elsevier, vol. 18(6), pages 69-73, December.
    2. Mady, M. Tawfik, 2000. "Sales forecasting practices of Egyptian public enterprises: survey evidence," International Journal of Forecasting, Elsevier, vol. 16(3), pages 359-368.
    3. Fok, Dennis & Franses, Philip Hans, 2001. "Forecasting market shares from models for sales," International Journal of Forecasting, Elsevier, vol. 17(1), pages 121-128.
    4. Abramson, Bruce & Finizza, Anthony, 1991. "Using belief networks to forecast oil prices," International Journal of Forecasting, Elsevier, vol. 7(3), pages 299-315, November.
    5. Lawrence, Michael, 2000. "What does it take to achieve adoption in sales forecasting?," International Journal of Forecasting, Elsevier, vol. 16(2), pages 147-148.
    6. Adya, Monica & Armstrong, J. Scott & Collopy, Fred & Kennedy, Miles, 2000. "An application of rule-based forecasting to a situation lacking domain knowledge," International Journal of Forecasting, Elsevier, vol. 16(4), pages 477-484.
    7. Lim, Joa Sang & O'Connor, Marcus, 1996. "Judgmental forecasting with time series and causal information," International Journal of Forecasting, Elsevier, vol. 12(1), pages 139-153, March.
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    2. repec:eee:proeco:v:191:y:2017:i:c:p:85-96 is not listed on IDEAS
    3. Balakrishnan, Jaydeep & Hung Cheng, Chun, 2009. "The dynamic plant layout problem: Incorporating rolling horizons and forecast uncertainty," Omega, Elsevier, vol. 37(1), pages 165-177, February.
    4. Leitner, Johannes & Leopold-Wildburger, Ulrike, 2011. "Experiments on forecasting behavior with several sources of information - A review of the literature," European Journal of Operational Research, Elsevier, vol. 213(3), pages 459-469, September.
    5. Danese, Pamela & Kalchschmidt, Matteo, 2011. "The role of the forecasting process in improving forecast accuracy and operational performance," International Journal of Production Economics, Elsevier, vol. 131(1), pages 204-214, May.
    6. repec:wsi:ijitdm:v:17:y:2018:i:02:n:s0219622017500456 is not listed on IDEAS
    7. 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.
    8. Eksoz, Can & Mansouri, S. Afshin & Bourlakis, Michael, 2014. "Collaborative forecasting in the food supply chain: A conceptual framework," International Journal of Production Economics, Elsevier, vol. 158(C), pages 120-135.
    9. Sinan Gönül & Dilek Önkal & Paul Goodwin, 2009. "Expectations, use and judgmental adjustment of external financial and economic forecasts: an empirical investigation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 19-37.
    10. Violetta Bacon-Gerasymenko & Russell Coff & Rodolphe Durand, 2016. "Taking a Second Look in a Warped Crystal Ball: Explaining the Accuracy of Revised Forecasts," Journal of Management Studies, Wiley Blackwell, vol. 53(8), pages 1292-1319, December.
    11. Danese, Pamela & Kalchschmidt, Matteo, 2011. "The impact of forecasting on companies' performance: Analysis in a multivariate setting," International Journal of Production Economics, Elsevier, vol. 133(1), pages 458-469, September.
    12. Thomson, Mary E. & Pollock, Andrew C. & Gönül, M. Sinan & Önkal, Dilek, 2013. "Effects of trend strength and direction on performance and consistency in judgmental exchange rate forecasting," International Journal of Forecasting, Elsevier, vol. 29(2), pages 337-353.
    13. 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.
    14. repec:eee:ejores:v:264:y:2018:i:2:p:558-569 is not listed on IDEAS
    15. Bacchetti, Andrea & Saccani, Nicola, 2012. "Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice," Omega, Elsevier, vol. 40(6), pages 722-737.
    16. Anqiang Huang & Han Qiao & Shouyang Wang & John Liu, 2016. "Improving Forecasting Performance by Exploiting Expert Knowledge: Evidence from Guangzhou Port," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 387-401, March.
    17. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.
    18. Brent Moritz & Enno Siemsen & Mirko Kremer, 2014. "Judgmental Forecasting: Cognitive Reflection and Decision Speed," Production and Operations Management, Production and Operations Management Society, vol. 23(7), pages 1146-1160, July.
    19. Mostard, Julien & Teunter, Ruud & de Koster, René, 2011. "Forecasting demand for single-period products: A case study in the apparel industry," European Journal of Operational Research, Elsevier, vol. 211(1), pages 139-147, May.
    20. Mirko Kremer & Brent Moritz & Enno Siemsen, 2011. "Demand Forecasting Behavior: System Neglect and Change Detection," Management Science, INFORMS, vol. 57(10), pages 1827-1843, October.
    21. 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, vol. 22(3), pages 493-518.

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