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

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  • 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).

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  • 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
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    1. Strijbosch, L.W.G. & Heuts, R.M.J. & Moors, J.J.A., 2006. "Hierarchical Estimation as Basis for Hierarchical Forecasting," Discussion Paper 2006-86, Tilburg University, Center for Economic Research.
    2. 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.
    3. Jacob Carstensen & Richard J. Telford & H. John B. Birks, 2013. "Diatom flickering prior to regime shift," Nature, Nature, vol. 498(7455), pages 11-12, June.
    4. P H Franses & R Legerstee, 2011. "Experts' adjustment to model-based SKU-level forecasts: does the forecast horizon matter?," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 537-543, March.
    5. 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.
    6. 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.
    7. A A Syntetos & J E Boylan & J D Croston, 2005. "On the categorization of demand patterns," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(5), pages 495-503, May.
    8. 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.
    9. 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.
    10. L W G Strijbosch & R M J Heuts & E H M van der Schoot, 2000. "A combined forecast—inventory control procedure for spare parts," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(10), pages 1184-1192, October.
    11. 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.
    12. 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.
    13. Withycombe, Richard, 1989. "Forecasting with combined seasonal indices," International Journal of Forecasting, Elsevier, vol. 5(4), pages 547-552.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. Kenneth Gilbert, 2005. "An ARIMA Supply Chain Model," Management Science, INFORMS, vol. 51(2), pages 305-310, February.
    20. Zellner, Arnold & Tobias, Justin, 1998. "A Note on Aggregation, Disaggregation and Forecasting Performance," CUDARE Working Papers 198677, University of California, Berkeley, Department of Agricultural and Resource Economics.
    21. Meddahi, Nour & Renault, Eric, 2004. "Temporal aggregation of volatility models," Journal of Econometrics, Elsevier, vol. 119(2), pages 355-379, April.
    22. Kostas Nikolopoulos & F. Petropoulos, 2015. "Forecasting, Foresight and Strategic Planning for Black Swans," Working Papers 15003, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    23. 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, July.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. Ghobbar, A.A & Friend, C.H, 2002. "Sources of intermittent demand for aircraft spare parts within airline operations," Journal of Air Transport Management, Elsevier, vol. 8(4), pages 221-231.
    29. 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.
    30. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    31. 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.
    32. K Nikolopoulos & A A Syntetos & J E Boylan & F Petropoulos & V Assimakopoulos, 2011. "An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 544-554, March.
    33. 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.
    34. M M Ali & J E Boylan, 2011. "Feasibility principles for Downstream Demand Inference in supply chains," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 474-482, March.
    35. Mohammadipour, Maryam & Boylan, John E., 2012. "Forecast horizon aggregation in integer autoregressive moving average (INARMA) models," Omega, Elsevier, vol. 40(6), pages 703-712.
    36. 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.
    37. Heinecke, G. & Syntetos, A.A. & Wang, W., 2013. "Forecasting-based SKU classification," International Journal of Production Economics, Elsevier, vol. 143(2), pages 455-462.
    38. 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.
    39. 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.
    40. Weiss, Andrew A., 1984. "Systematic sampling and temporal aggregation in time series models," Journal of Econometrics, Elsevier, vol. 26(3), pages 271-281, December.
    41. 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.
    42. H Chen & J E Boylan, 2007. "Use of individual and group seasonal indices in subaggregate demand forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1660-1671, December.
    43. 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.
    44. 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.
    45. A V Kostenko & R J Hyndman, 2006. "A note on the categorization of demand patterns," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1256-1257, October.
    46. 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.
    47. Nicole DeHoratius & Ananth Raman, 2008. "Inventory Record Inaccuracy: An Empirical Analysis," Management Science, INFORMS, vol. 54(4), pages 627-641, April.
    48. E. Shlifer & R. W. Wolff, 1979. "Aggregation and Proration in Forecasting," Management Science, INFORMS, vol. 25(6), pages 594-603, June.
    49. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
    50. 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.
    51. A A Syntetos & J E Boylan & S M Disney, 2009. "Forecasting for inventory planning: a 50-year review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 149-160, May.
    52. 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.
    53. 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.
    54. Marshall Fisher & Kumar Rajaram, 2000. "Accurate Retail Testing of Fashion Merchandise: Methodology and Application," Marketing Science, INFORMS, vol. 19(3), pages 266-278, June.
    55. 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.
    56. 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.
    57. Xiaolong Zhang, 2004. "Evolution of ARMA Demand in Supply Chains," Manufacturing & Service Operations Management, INFORMS, vol. 6(2), pages 195-198, April.
    58. 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.
    59. 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.
    60. A A Syntetos & N C Georgantzas & J E Boylan & B C Dangerfield, 2011. "Judgement and supply chain dynamics," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1138-1158, June.
    61. 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.
    62. 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.
    63. J E Boylan & A A Syntetos & G C Karakostas, 2008. "Classification for forecasting and stock control: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 473-481, April.
    64. 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.
    65. 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.
    66. 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.
    67. 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.
    68. 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.
    69. 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.
    70. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    71. 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.
    72. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    73. Anderson, O. D., 1975. "On a lemma associated with Box, Jenkins and Granger," Journal of Econometrics, Elsevier, vol. 3(2), pages 151-156, May.
    74. Lutkepohl, Helmut, 1984. "Forecasting Contemporaneously Aggregated Vector ARMA Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 201-214, July.
    75. T C E Cheng & Y N Wu, 2005. "The impact of information sharing in a two-level supply chain with multiple retailers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(10), pages 1159-1165, October.
    76. 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.
    77. 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.
    78. 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.
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