IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v35y2019i1p129-143.html
   My bibliography  Save this article

Forecast quality improvement with Action Research: A success story at PharmaCo

Author

Listed:
  • Phillips, Christina Jane
  • Nikolopoulos, Konstantinos

Abstract

There is a gap in the forecasting research surrounding the theory of integrating and improving forecasting in practice. The number of academically affiliated consultancies and knowledge transfer projects that there are around, due to a need for improvements in forecast quality, would suggest that many interventions and actions are taking place. However, the problems that surround practitioner understanding, learning and usage are rarely documented. This article takes the first step toward trying to rectify this situation by using the specific case study of a fully engaged company. A successful action research intervention in the Production Planning and Control work unit improved the use and understanding of the forecast function, contributing to substantial savings, enhanced communication and improved working practices.

Suggested Citation

  • Phillips, Christina Jane & Nikolopoulos, Konstantinos, 2019. "Forecast quality improvement with Action Research: A success story at PharmaCo," International Journal of Forecasting, Elsevier, vol. 35(1), pages 129-143.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:1:p:129-143
    DOI: 10.1016/j.ijforecast.2018.02.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207018300402
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2018.02.005?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. John E. Mello & Robert A. Stahl, 2011. "How S&OP Changes Corporate Culture: Results from Interviews with Seven Companies," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 20, pages 37-42, Winter.
    2. 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.
    3. Goodwin, Paul & Önkal, Dilek & Thomson, Mary, 2010. "Do forecasts expressed as prediction intervals improve production planning decisions?," European Journal of Operational Research, Elsevier, vol. 205(1), pages 195-201, August.
    4. Makridakis, Spyros & Taleb, Nassim, 2009. "Decision making and planning under low levels of predictability," International Journal of Forecasting, Elsevier, vol. 25(4), pages 716-733, October.
    5. White, Leroy, 2016. "Behavioural operational research: Towards a framework for understanding behaviour in OR interventions," European Journal of Operational Research, Elsevier, vol. 249(3), pages 827-841.
    6. Katherine C. Kellogg & Wanda J. Orlikowski & JoAnne Yates, 2006. "Life in the Trading Zone: Structuring Coordination Across Boundaries in Postbureaucratic Organizations," Organization Science, INFORMS, vol. 17(1), pages 22-44, February.
    7. 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.
    8. Mahmoud, Essam & DeRoeck, Richard & Brown, Robert & Rice, Gillian, 1992. "Bridging the gap between theory and practice in forecasting," International Journal of Forecasting, Elsevier, vol. 8(2), pages 251-267, October.
    9. Moon, Mark A. & Mentzer, John T. & Smith, Carlo D., 2003. "Conducting a sales forecasting audit," International Journal of Forecasting, Elsevier, vol. 19(1), pages 5-25.
    10. Kreye, M.E. & Goh, Y.M. & Newnes, L.B. & Goodwin, P., 2012. "Approaches to displaying information to assist decisions under uncertainty," Omega, Elsevier, vol. 40(6), pages 682-692.
    11. Wang, Xun & Disney, Stephen M. & Wang, Jing, 2014. "Exploring the oscillatory dynamics of a forbidden returns inventory system," International Journal of Production Economics, Elsevier, vol. 147(PA), pages 3-12.
    12. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    13. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E., 2010. "Judging the judges through accuracy-implication metrics: The case of inventory forecasting," International Journal of Forecasting, Elsevier, vol. 26(1), pages 134-143, January.
    14. Wanda J. Orlikowski, 2000. "Using Technology and Constituting Structures: A Practice Lens for Studying Technology in Organizations," Organization Science, INFORMS, vol. 11(4), pages 404-428, August.
    15. 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.
    16. Corbett, Charles J. & Overmeer, Willem J. A. M. & Van Wassenhove, Luk N., 1995. "Strands of practice in OR (the practitioner's dilemma)," European Journal of Operational Research, Elsevier, vol. 87(3), pages 484-499, December.
    17. Luoma, Jukka, 2016. "Model-based organizational decision making: A behavioral lens," European Journal of Operational Research, Elsevier, vol. 249(3), pages 816-826.
    18. Ormerod, R.J., 2014. "Critical rationalism in practice: Strategies to manage subjectivity in OR investigations," European Journal of Operational Research, Elsevier, vol. 235(3), pages 784-797.
    19. Howick, Susan & Ackermann, Fran, 2011. "Mixing OR methods in practice: Past, present and future directions," European Journal of Operational Research, Elsevier, vol. 215(3), pages 503-511, December.
    20. Dejonckheere, J. & Disney, S. M. & Lambrecht, M. R. & Towill, D. R., 2003. "Measuring and avoiding the bullwhip effect: A control theoretic approach," European Journal of Operational Research, Elsevier, vol. 147(3), pages 567-590, June.
    21. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
    22. 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.
    23. Hämäläinen, Raimo P. & Lahtinen, Tuomas J., 2016. "Path dependence in Operational Research—How the modeling process can influence the results," Operations Research Perspectives, Elsevier, vol. 3(C), pages 14-20.
    24. Mark Moon, 2006. "Breaking Down the Barriers to Forecast Process Improvement," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 4, pages 26-30, June.
    25. Mingers, John & Brocklesby, John, 1997. "Multimethodology: Towards a framework for mixing methodologies," Omega, Elsevier, vol. 25(5), pages 489-509, October.
    26. Tako, Antuela A. & Kotiadis, Kathy, 2015. "PartiSim: A multi-methodology framework to support facilitated simulation modelling in healthcare," European Journal of Operational Research, Elsevier, vol. 244(2), pages 555-564.
    27. Robert Fildes & Fotios Petropoulos, 2015. "Improving Forecast Quality in Practice," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 36, pages 5-12, Winter.
    28. 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.
    29. Rachel Croson & Karen Donohue, 2006. "Behavioral Causes of the Bullwhip Effect and the Observed Value of Inventory Information," Management Science, INFORMS, vol. 52(3), pages 323-336, March.
    30. Richard J Ormerod, 2014. "The mangle of OR practice: towards more informative case studies of ‘technical’ projects," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(8), pages 1245-1260, August.
    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. Fildes, Robert & Goodwin, Paul, 2021. "Stability in the inefficient use of forecasting systems: A case study in a supply chain company," International Journal of Forecasting, Elsevier, vol. 37(2), pages 1031-1046.

    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. 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.
    2. 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.
    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. Baecke, Philippe & De Baets, Shari & Vanderheyden, Karlien, 2017. "Investigating the added value of integrating human judgement into statistical demand forecasting systems," International Journal of Production Economics, Elsevier, vol. 191(C), pages 85-96.
    5. Pennings, Clint L.P. & van Dalen, Jan & Rook, Laurens, 2019. "Coordinating judgmental forecasting: Coping with intentional biases," Omega, Elsevier, vol. 87(C), pages 46-56.
    6. Howick, Susan & Ackermann, Fran & Walls, Lesley & Quigley, John & Houghton, Tom, 2017. "Learning from mixed OR method practice: The NINES case study," Omega, Elsevier, vol. 69(C), pages 70-81.
    7. Mirko Kremer & Enno Siemsen & Douglas J. Thomas, 2016. "The Sum and Its Parts: Judgmental Hierarchical Forecasting," Management Science, INFORMS, vol. 62(9), pages 2745-2764, September.
    8. Van den Broeke, Maud & De Baets, Shari & Vereecke, Ann & Baecke, Philippe & Vanderheyden, Karlien, 2019. "Judgmental forecast adjustments over different time horizons," Omega, Elsevier, vol. 87(C), pages 34-45.
    9. M. Nassereddine & M. A. Ellakkis & A. Azar & M. D. Nayeri, 2021. "Developing a Multi-methodology for Conflict Resolution: Case of Yemen’s Humanitarian Crisis," Group Decision and Negotiation, Springer, vol. 30(2), pages 301-320, April.
    10. Sroginis, Anna & Fildes, Robert & Kourentzes, Nikolaos, 2023. "Use of contextual and model-based information in adjusting promotional forecasts," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1177-1191.
    11. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    12. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    13. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
    14. Enrique Holgado de Frutos & Juan R Trapero & Francisco Ramos, 2020. "A literature review on operational decisions applied to collaborative supply chains," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-28, March.
    15. Harper, Alison & Mustafee, Navonil & Yearworth, Mike, 2021. "Facets of trust in simulation studies," European Journal of Operational Research, Elsevier, vol. 289(1), pages 197-213.
    16. 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.
    17. Cedric A. Lehmann & Christiane B. Haubitz & Andreas Fügener & Ulrich W. Thonemann, 2022. "The risk of algorithm transparency: How algorithm complexity drives the effects on the use of advice," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3419-3434, September.
    18. 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.
    19. Ponte, Borja & Puche, Julio & Rosillo, Rafael & de la Fuente, David, 2020. "The effects of quantity discounts on supply chain performance: Looking through the Bullwhip lens," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    20. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.

    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:eee:intfor:v:35:y:2019:i:1:p:129-143. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.