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Forecasting Software in Practice: Use, Satisfaction, and Performance

Author

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  • Nada R. Sanders

    (Department of Management Science and Information Systems, Raj Soin College of Business, Wright State University, Dayton, Ohio 45435)

  • Karl B. Manrodt

    (Department of Information Systems and Logistics, College of Business Administration, Georgia Southern University, PO Box 8152, Statesboro, Georgia 30460)

Abstract

Using survey data from 240 US corporations, we evaluated practitioners' use and satisfaction with forecasting software and its performance. Despite the many commercial forecasting software packages, only 10.8 percent of the respondents reported using them. Forty-eight percent reported using spreadsheets to make forecasts. Sixty percent reported being dissatisfied with forecasting software. However, we found that those who used commercial forecasting software packages had the best forecast performance, as measured by mean absolute percentage error (MAPE). Those using commercially available packages had errors 6.7 percent lower than those using spreadsheets and 17.2 percent lower than those who used no program. Also, they were more satisfied with their software than those using spreadsheets. In fact, users of forecasting software programs reported a 12.2 percent reduction in forecast error. We found that 61 percent of respondents routinely adjusted forecasts produced by software based on their judgment. Roughly 85 percent of respondents considered ease of use and easily understandable results the most important software features.

Suggested Citation

  • Nada R. Sanders & Karl B. Manrodt, 2003. "Forecasting Software in Practice: Use, Satisfaction, and Performance," Interfaces, INFORMS, vol. 33(5), pages 90-93, October.
  • Handle: RePEc:inm:orinte:v:33:y:2003:i:5:p:90-93
    DOI: 10.1287/inte.33.5.90.19251
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    References listed on IDEAS

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    1. Ord, Keith, 2000. "Commercially available software and the M3-Competition," International Journal of Forecasting, Elsevier, vol. 16(4), pages 531-531.
    2. Tashman, Leonard J. & Leach, Michael L., 1991. "Automatic forecasting software: A survey and evaluation," International Journal of Forecasting, Elsevier, vol. 7(2), pages 209-230, August.
    3. Nada R. Sanders & Karl B. Manrodt, 1994. "Forecasting Practices in US Corporations: Survey Results," Interfaces, INFORMS, vol. 24(2), pages 92-100, April.
    4. Armstrong, J. Scott & Overton, Terry S., 1977. "Estimating Nonresponse Bias in Mail Surveys," MPRA Paper 81694, University Library of Munich, Germany.
    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. Yokuma, J. Thomas & Armstrong, J. Scott, 1995. "Beyond accuracy: Comparison of criteria used to select forecasting methods," International Journal of Forecasting, Elsevier, vol. 11(4), pages 591-597, December.
    7. Everette S. Gardner, 1984. "The Strange Case of the Lagging Forecasts," Interfaces, INFORMS, vol. 14(3), pages 47-50, June.
    8. Dalrymple, Douglas J., 1987. "Sales forecasting practices: Results from a United States survey," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 379-391.
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    7. van Bruggen, G.H. & Wierenga, B., 2005. "When are CRM Systems Successful? The Perspective of the User and of the Organization," ERIM Report Series Research in Management ERS-2005-048-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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