IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v252y2016i1p1-26.html
   My bibliography  Save this article

Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A

Author

Listed:
  • Babai, Zied
  • Boylan, John E.
  • Kolassa, Stephan
  • Nikolopoulos, Konstantinos

Abstract

Supply Chain Forecasting (SCF) goes beyond the operational task of extrapolating demand requirements at one echelon. It involves complex issues such as supply chain coordination and sharing of information between multiple stakeholders. Academic research in SCF has tended to neglect some issues that are important in practice. In areas of practical relevance, sound theoretical developments have rarely been translated into operational solutions or integrated in state-of-the-art decision support systems. Furthermore, many experience-driven heuristics are increasingly used in everyday business practices. These heuristics are not supported by substantive scientific evidence; however, they are sometimes very hard to outperform. This can be attributed to the robustness of these simple and practical solutions such as aggregation approaches for example (across time, customers and products).

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:252:y:2016:i:1:p:1-26
    DOI: 10.1016/j.ejor.2015.11.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221715010231
    Download Restriction: Full text for ScienceDirect subscribers only

    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. Andrea Silvestrini & David Veredas, 2008. "Temporal Aggregation Of Univariate And Multivariate Time Series Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(3), pages 458-497, July.
    2. Fotios Petropoulos & Nikolaos Kourentzes, 2015. "Forecast combinations for intermittent demand," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(6), pages 914-924, June.
    3. Hau L. Lee & Kut C. So & Christopher S. Tang, 2000. "The Value of Information Sharing in a Two-Level Supply Chain," Management Science, INFORMS, vol. 46(5), pages 626-643, May.
    4. Brannas, Kurt & Hellstrom, Jorgen & Nordstrom, Jonas, 2002. "A new approach to modelling and forecasting monthly guest nights in hotels," International Journal of Forecasting, Elsevier, vol. 18(1), pages 19-30.
    5. Kenneth Gilbert, 2005. "An ARIMA Supply Chain Model," Management Science, INFORMS, vol. 51(2), pages 305-310, February.
    6. Meddahi, Nour & Renault, Eric, 2004. "Temporal aggregation of volatility models," Journal of Econometrics, Elsevier, vol. 119(2), pages 355-379, April.
    7. Dangerfield, Byron J. & Morris, John S., 1992. "Top-down or bottom-up: Aggregate versus disaggregate extrapolations," International Journal of Forecasting, Elsevier, vol. 8(2), pages 233-241, October.
    8. 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.
    9. 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.
    10. 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.
    11. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    12. Maryam Mohammadipour & John Boylan & Aris Syntetos, 2012. "The Application of Product-Group Seasonal Indexes to Individual Products," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 26, pages 20-26, Summer.
    13. Weiss, Andrew A., 1984. "Systematic sampling and temporal aggregation in time series models," Journal of Econometrics, Elsevier, vol. 26(3), pages 271-281, December.
    14. Athanasopoulos, George & Ahmed, Roman A. & Hyndman, Rob J., 2009. "Hierarchical forecasts for Australian domestic tourism," International Journal of Forecasting, Elsevier, vol. 25(1), pages 146-166.
    15. Nicole DeHoratius & Ananth Raman, 2008. "Inventory Record Inaccuracy: An Empirical Analysis," Management Science, INFORMS, vol. 54(4), pages 627-641, April.
    16. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
    17. Tobias, Justin & Zellner, Arnold, 2000. "A Note on Aggregation, Disaggregation and Forecasting Performance," Staff General Research Papers Archive 12024, Iowa State University, Department of Economics.
    18. Luna, Ivette & Ballini, Rosangela, 2011. "Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 708-724.
    19. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E. & Fildes, Robert & Goodwin, Paul, 2009. "The effects of integrating management judgement into intermittent demand forecasts," International Journal of Production Economics, Elsevier, vol. 118(1), pages 72-81, March.
    20. Porras, Eric & Dekker, Rommert, 2008. "An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods," European Journal of Operational Research, Elsevier, vol. 184(1), pages 101-132, January.
    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. Marshall Fisher & Kumar Rajaram, 2000. "Accurate Retail Testing of Fashion Merchandise: Methodology and Application," Marketing Science, INFORMS, vol. 19(3), pages 266-278, June.
    23. John Boylan, 2010. "Choosing Levels of Aggregation for Supply Chain Forecasts," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 18, pages 9-13, Summer.
    24. Spithourakis, Georgios P. & Petropoulos, Fotios & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2015. "Amplifying the learning effects via a Forecasting and Foresight Support System," International Journal of Forecasting, Elsevier, vol. 31(1), pages 20-32.
    25. Widiarta, Handik & Viswanathan, S. & Piplani, Rajesh, 2009. "Forecasting aggregate demand: An analytical evaluation of top-down versus bottom-up forecasting in a production planning framework," International Journal of Production Economics, Elsevier, vol. 118(1), pages 87-94, March.
    26. Hyndman, Rob J. & Lee, Alan J. & Wang, Earo, 2016. "Fast computation of reconciled forecasts for hierarchical and grouped time series," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 16-32.
    27. Franses, Philip Hans & Legerstee, Rianne, 2013. "Do statistical forecasting models for SKU-level data benefit from including past expert knowledge?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 80-87.
    28. Ali, Mohammad M. & Boylan, John E. & Syntetos, Aris A., 2012. "Forecast errors and inventory performance under forecast information sharing," International Journal of Forecasting, Elsevier, vol. 28(4), pages 830-841.
    29. Willemain, Thomas R. & Smart, Charles N. & Shockor, Joseph H. & DeSautels, Philip A., 1994. "Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method," International Journal of Forecasting, Elsevier, vol. 10(4), pages 529-538, December.
    30. Anderson, O. D., 1975. "On a lemma associated with Box, Jenkins and Granger," Journal of Econometrics, Elsevier, vol. 3(2), pages 151-156, May.
    31. Lutkepohl, Helmut, 1984. "Forecasting Contemporaneously Aggregated Vector ARMA Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 201-214, July.
    32. Dekker, Mark & van Donselaar, Karel & Ouwehand, Pim, 2004. "How to use aggregation and combined forecasting to improve seasonal demand forecasts," International Journal of Production Economics, Elsevier, vol. 90(2), pages 151-167, July.
    33. Rob J. Hyndman & George Athanasopoulos, 2014. "Optimally Reconciling Forecasts in a Hierarchy," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 35, pages 42-48, Fall.
    34. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 146(1), pages 185-198.
    35. Srinivasan Raghunathan, 2001. "Information Sharing in a Supply Chain: A Note on its Value when Demand Is Nonstationary," Management Science, INFORMS, vol. 47(4), pages 605-610, April.
    36. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    37. Withycombe, Richard, 1989. "Forecasting with combined seasonal indices," International Journal of Forecasting, Elsevier, vol. 5(4), pages 547-552.
    38. Mohammad M. Ali & John E. Boylan, 2010. "The Value of Forecast Information Sharing in Supply Chains," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 18, pages 14-18, Summer.
    39. John Boylan & Aris Syntetos, 2006. "Accuracy and Accuracy Implication Metrics for Intermittent Demand," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 4, pages 39-42, June.
    40. Philip Hans Franses & Rianne Legerstee, 2010. "Do experts' adjustments on model-based SKU-level forecasts improve forecast quality?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 331-340.
    41. Tim Januschowski & Stephan Kolassa & Martin Lorenz & Christian Schwarz, 2013. "Forecasting with In-Memory Technology," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 31, pages 14-20, Fall.
    42. Kostas Nikolopoulos & F. Petropoulos, 2015. "Forecasting, Foresight and Strategic Planning for Black Swans," Working Papers 15003, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    43. Zotteri, Giulio & Kalchschmidt, Matteo, 2007. "A model for selecting the appropriate level of aggregation in forecasting processes," International Journal of Production Economics, Elsevier, vol. 108(1-2), pages 74-83, July.
    44. Mohammadipour, Maryam & Boylan, John E., 2012. "Forecast horizon aggregation in integer autoregressive moving average (INARMA) models," Omega, Elsevier, vol. 40(6), pages 703-712.
    45. Heinecke, G. & Syntetos, A.A. & Wang, W., 2013. "Forecasting-based SKU classification," International Journal of Production Economics, Elsevier, vol. 143(2), pages 455-462.
    46. Giulio Zotteri & Matteo Kalchschmidt & Nicola Saccani, 2014. "Forecasting by Cross-Sectional Aggregation," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 35, pages 35-41, Fall.
    47. Fotios Petropoulos & Nikolaos Kourentzes, 2014. "Improving Forecasting via Multiple Temporal Aggregation," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 34, pages 12-17, Summer.
    48. Regattieri, A. & Gamberi, M. & Gamberini, R. & Manzini, R., 2005. "Managing lumpy demand for aircraft spare parts," Journal of Air Transport Management, Elsevier, vol. 11(6), pages 426-431.
    49. McCullough, B.D. & Heiser, David A., 2008. "On the accuracy of statistical procedures in Microsoft Excel 2007," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4570-4578, June.
    50. Rob J. Hyndman & Andrey V. Kostenko, 2007. "Minimum Sample Size requirements for Seasonal Forecasting Models," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 12-15, Spring.
    51. E. Shlifer & R. W. Wolff, 1979. "Aggregation and Proration in Forecasting," Management Science, INFORMS, vol. 25(6), pages 594-603, June.
    52. Xiaolong Zhang, 2004. "Evolution of ARMA Demand in Supply Chains," Manufacturing & Service Operations Management, INFORMS, vol. 6(2), pages 195-198, April.
    53. J E Boylan & H Chen & M Mohammadipour & A Syntetos, 2014. "Formation of seasonal groups and application of seasonal indices," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(2), pages 227-241, February.
    54. Chen, Huijing & Boylan, John E., 2008. "Empirical evidence on individual, group and shrinkage seasonal indices," International Journal of Forecasting, Elsevier, vol. 24(3), pages 525-534.
    55. Zotteri, Giulio & Kalchschmidt, Matteo & Caniato, Federico, 2005. "The impact of aggregation level on forecasting performance," International Journal of Production Economics, Elsevier, vol. 93(1), pages 479-491, January.
    56. Raghunathan, Srinivasan, 2003. "Impact of demand correlation on the value of and incentives for information sharing in a supply chain," European Journal of Operational Research, Elsevier, vol. 146(3), pages 634-649, May.
    57. Syntetos, Aris A. & Boylan, John E., 2006. "On the stock control performance of intermittent demand estimators," International Journal of Production Economics, Elsevier, vol. 103(1), pages 36-47, September.
    58. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    59. Franses, Philip Hans & Legerstee, Rianne, 2009. "Properties of expert adjustments on model-based SKU-level forecasts," International Journal of Forecasting, Elsevier, vol. 25(1), pages 35-47.
    60. Holweg, Matthias & Disney, Stephen & Holmström, Jan & Småros, Johanna, 2005. "Supply Chain Collaboration:: Making Sense of the Strategy Continuum," European Management Journal, Elsevier, vol. 23(2), pages 170-181, April.
    61. Babai, M. Zied & Ali, Mohammad M. & Nikolopoulos, Konstantinos, 2012. "Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis," Omega, Elsevier, vol. 40(6), pages 713-721.
    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. Davis, Lauren B. & Jiang, Steven X. & Morgan, Shona D. & Nuamah, Isaac A. & Terry, Jessica R., 2016. "Analysis and prediction of food donation behavior for a domestic hunger relief organization," International Journal of Production Economics, Elsevier, vol. 182(C), pages 26-37.
    2. repec:eee:joreco:v:31:y:2016:i:c:p:174-181 is not listed on IDEAS
    3. repec:eee:ejores:v:266:y:2018:i:2:p:395-414 is not listed on IDEAS
    4. repec:eee:ejores:v:263:y:2017:i:2:p:412-418 is not listed on IDEAS
    5. repec:eee:intfor:v:33:y:2017:i:4:p:864-877 is not listed on IDEAS
    6. Sarker, Ruhul & Essam, Daryl, 2017. "A quantitative model for disruption mitigation in a supply chainAuthor-Name: Paul, Sanjoy Kumar," European Journal of Operational Research, Elsevier, vol. 257(3), pages 881-895.
    7. Zhou, Maosen & Dan, Bin & Ma, Songxuan & Zhang, Xumei, 2017. "Supply chain coordination with information sharing: The informational advantage of GPOs," European Journal of Operational Research, Elsevier, vol. 256(3), pages 785-802.
    8. repec:eee:transe:v:110:y:2018:i:c:p:122-136 is not listed on IDEAS

    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:ejores:v:252:y:2016:i:1:p:1-26. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/eor .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.