IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v33y2003i5p90-93.html
   My bibliography  Save this article

Forecasting Software in Practice: Use, Satisfaction, and Performance

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.33.5.90.19251
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.33.5.90.19251?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Armstrong, J. Scott & Overton, Terry S., 1977. "Estimating Nonresponse Bias in Mail Surveys," MPRA Paper 81694, University Library of Munich, Germany.
    4. Nada R. Sanders & Karl B. Manrodt, 1994. "Forecasting Practices in US Corporations: Survey Results," Interfaces, INFORMS, vol. 24(2), pages 92-100, April.
    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. 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.
    7. Ord, Keith, 2000. "Commercially available software and the M3-Competition," International Journal of Forecasting, Elsevier, vol. 16(4), pages 531-531.
    8. Everette S. Gardner, 1984. "The Strange Case of the Lagging Forecasts," Interfaces, INFORMS, vol. 14(3), pages 47-50, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Asimakopoulos, Stavros & Dix, Alan, 2013. "Forecasting support systems technologies-in-practice: A model of adoption and use for product forecasting," International Journal of Forecasting, Elsevier, vol. 29(2), pages 322-336.
    2. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Jason R. W. Merrick & Jill R. Hardin & Russell Walker, 2006. "Partnerships in Training," Interfaces, INFORMS, vol. 36(4), pages 359-370, August.
    5. Jiling Li & Zekai Lin & Xiaheng Zhang, 2023. "The Study on the Effectiveness of Sustainable Customer Relationship Management: Evidence from the Online Shopping Industry," Sustainability, MDPI, vol. 15(7), pages 1-21, March.
    6. 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.
    7. Hewage, Harsha Chamara & Perera, H. Niles & De Baets, Shari, 2022. "Forecast adjustments during post-promotional periods," European Journal of Operational Research, Elsevier, vol. 300(2), pages 461-472.
    8. 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.
    9. Abolghasemi, Mahdi & Hurley, Jason & Eshragh, Ali & Fahimnia, Behnam, 2020. "Demand forecasting in the presence of systematic events: Cases in capturing sales promotions," International Journal of Production Economics, Elsevier, vol. 230(C).
    10. 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.
    11. Aljuneidi, Tariq & Punia, Sushil & Jebali, Aida & Nikolopoulos, Konstantinos, 2024. "Forecasting and planning for a critical infrastructure sector during a pandemic: Empirical evidence from a food supply chain," European Journal of Operational Research, Elsevier, vol. 317(3), pages 936-952.
    12. 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.
    13. Goodwin, Paul & Gönül, M. Sinan & Önkal, Dilek, 2019. "When providing optimistic and pessimistic scenarios can be detrimental to judgmental demand forecasts and production decisions," European Journal of Operational Research, Elsevier, vol. 273(3), pages 992-1004.
    14. Theocharis, Zoe & Harvey, Nigel, 2019. "When does more mean worse? Accuracy of judgmental forecasting is nonlinearly related to length of data series," Omega, Elsevier, vol. 87(C), pages 10-19.
    15. Tliche, Youssef & Taghipour, Atour & Canel-Depitre, Béatrice, 2020. "An improved forecasting approach to reduce inventory levels in decentralized supply chains," European Journal of Operational Research, Elsevier, vol. 287(2), pages 511-527.
    16. Yalta, A. Talha & Jenal, Olaf, 2009. "On the importance of verifying forecasting results," International Journal of Forecasting, Elsevier, vol. 25(1), pages 62-73.
    17. Mashal, Ibrahim & Alsaryrah, Osama & Chung, Tein-Yaw & Yuan, Fong-Ching, 2020. "A multi-criteria analysis for an internet of things application recommendation system," Technology in Society, Elsevier, vol. 60(C).
    18. Philip Hans Franses, 2004. "Do We Think We Make Better Forecasts Than in the Past? A Survey of Academics," Interfaces, INFORMS, vol. 34(6), pages 466-468, December.
    19. Kusters, Ulrich & McCullough, B.D. & Bell, Michael, 2006. "Forecasting software: Past, present and future," International Journal of Forecasting, Elsevier, vol. 22(3), pages 599-615.
    20. Ujwal Kayande & Arnaud De Bruyn & Gary L. Lilien & Arvind Rangaswamy & Gerrit H. van Bruggen, 2009. "How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations," Information Systems Research, INFORMS, vol. 20(4), pages 527-546, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. JS Armstrong, 2004. "Forecasting for Environmental Decision Making," General Economics and Teaching 0412023, University Library of Munich, Germany.
    2. Eksoz, Can & Mansouri, S. Afshin & Bourlakis, Michael & Önkal, Dilek, 2019. "Judgmental adjustments through supply integration for strategic partnerships in food chains," Omega, Elsevier, vol. 87(C), pages 20-33.
    3. 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.
    4. 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.
    5. Goodwin, P., 1996. "Statistical correction of judgmental point forecasts and decisions," Omega, Elsevier, vol. 24(5), pages 551-559, October.
    6. Lawrence, Michael & Sim, William, 1999. "Prototyping a financial DSS," Omega, Elsevier, vol. 27(4), pages 445-450, August.
    7. Remus, William & O'Connor, Marcus & Griggs, Kenneth, 1998. "The impact of information of unknown correctness on the judgmental forecasting process," International Journal of Forecasting, Elsevier, vol. 14(3), pages 313-322, September.
    8. JS Armstrong & Fred Collopy, 2004. "Integration of Statistical Methods and Judgment for Time Series," General Economics and Teaching 0412024, University Library of Munich, Germany.
    9. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    10. Klassen, Robert D. & Flores, Benito E., 2001. "Forecasting practices of Canadian firms: Survey results and comparisons," International Journal of Production Economics, Elsevier, vol. 70(2), pages 163-174, March.
    11. Webby, Richard & O'Connor, Marcus & Edmundson, Bob, 2005. "Forecasting support systems for the incorporation of event information: An empirical investigation," International Journal of Forecasting, Elsevier, vol. 21(3), pages 411-423.
    12. Webby, Richard & O'Connor, Marcus, 1996. "Judgemental and statistical time series forecasting: a review of the literature," International Journal of Forecasting, Elsevier, vol. 12(1), pages 91-118, March.
    13. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    14. 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.
    15. De Baets, Shari & Harvey, Nigel, 2018. "Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support," International Journal of Forecasting, Elsevier, vol. 34(2), pages 163-180.
    16. Goodwin, Paul, 2002. "Integrating management judgment and statistical methods to improve short-term forecasts," Omega, Elsevier, vol. 30(2), pages 127-135, April.
    17. 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.
    18. 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.
    19. 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.
    20. F Caniato & M Kalchschmidt & S Ronchi, 2011. "Integrating quantitative and qualitative forecasting approaches: organizational learning in an action research case," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 413-424, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orinte:v:33:y:2003:i:5:p:90-93. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.