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The value of competitive information in forecasting FMCG retail product sales and the variable selection problem

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  • Huang, Tao
  • Fildes, Robert
  • Soopramanien, Didier

Abstract

Sales forecasting at the UPC level is important for retailers to manage inventory. In this paper, we propose more effective methods to forecast retail UPC sales by incorporating competitive information including prices and promotions. The impact of these competitive marketing activities on the sales of the focal product has been extensively documented. However, competitive information has been surprisingly overlooked by previous studies in forecasting UPC sales, probably because of the problem of too many competitive explanatory variables. That is, each FMCG product category typically contains a large number of UPCs and is consequently associated with a large number of competitive explanatory variables. Under such a circumstance, time series models can easily become over-fitted and thus generate poor forecasting results.

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  • Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.
  • Handle: RePEc:eee:ejores:v:237:y:2014:i:2:p:738-748
    DOI: 10.1016/j.ejor.2014.02.022
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    as
    1. Foekens, Eijte W. & Leeflang, Peter S. H. & Wittink, Dick R., 1994. "A comparison and an exploration of the forecasting accuracy of a loglinear model at different levels of aggregation," International Journal of Forecasting, Elsevier, vol. 10(2), pages 245-261, September.
    2. Taylor, James W., 2007. "Forecasting daily supermarket sales using exponentially weighted quantile regression," European Journal of Operational Research, Elsevier, vol. 178(1), pages 154-167, April.
    3. Raju, Jagmohan S., 1995. "Theoretical models of sales promotions: Contributions, limitations, and a future research agenda," European Journal of Operational Research, Elsevier, vol. 85(1), pages 1-17, August.
    4. Preston, John & Mercer, Alan, 1990. "The evaluation and analysis of retail sales promotions," European Journal of Operational Research, Elsevier, vol. 47(3), pages 330-338, August.
    5. Fildes, Robert & Wei, Yingqi & Ismail, Suzilah, 2011. "Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures," International Journal of Forecasting, Elsevier, vol. 27(3), pages 902-922, July.
    6. Lee, Wing Yee & Goodwin, Paul & Fildes, Robert & Nikolopoulos, Konstantinos & Lawrence, Michael, 2007. "Providing support for the use of analogies in demand forecasting tasks," International Journal of Forecasting, Elsevier, vol. 23(3), pages 377-390.
    7. Lee G. Cooper & Penny Baron & Wayne Levy & Michael Swisher & Paris Gogos, 1999. "PromoCast™: A New Forecasting Method for Promotion Planning," Marketing Science, INFORMS, vol. 18(3), pages 301-316.
    8. 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.
    9. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
    10. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
    11. Lee G. Cooper & Giovanni Giuffrida, 2000. "Turning Datamining into a Management Science Tool: New Algorithms and Empirical Results," Management Science, INFORMS, vol. 46(2), pages 249-264, February.
    12. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    13. Vincent R. Nijs & Marnik G. Dekimpe & Jan-Benedict E.M. Steenkamps & Dominique M. Hanssens, 2001. "The Category-Demand Effects of Price Promotions," Marketing Science, INFORMS, vol. 20(1), pages 1-22, September.
    14. Inseong Song & Pradeep K. Chintagunta, 2006. "Measuring Cross-Category Price Effects with Aggregate Store Data," Management Science, INFORMS, vol. 52(10), pages 1594-1609, October.
    15. Foekens, E.W. & Leeflang, P.S.H. & Wittink, D.R., 1992. "Asymmetric Market Share Modelling with many Competitive Items Using Market Level Scanner Data," Papers 471, Groningen State, Institute of Economic Research-.
    16. Suresh Divakar & Brian T. Ratchford & Venkatesh Shankar, 2005. "Practice Prize Article—: A Multichannel, Multiregion Sales Forecasting Model and Decision Support System for Consumer Packaged Goods," Marketing Science, INFORMS, vol. 24(3), pages 334-350, July.
    17. Kirthi Kalyanam & Sharad Borle & Peter Boatwright, 2007. "Deconstructing Each Item's Category Contribution," Marketing Science, INFORMS, vol. 26(3), pages 327-341, 05-06.
    18. Davydenko, Andrey & Fildes, Robert, 2013. "Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 510-522.
    19. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    20. Rinne, Heikki & Geurts, Michael, 1988. "A forecasting model to evaluate the profitability of price promotions," European Journal of Operational Research, Elsevier, vol. 33(3), pages 279-289, February.
    21. Sodhi, ManMohan S. & Tang, Christopher S., 2011. "The incremental bullwhip effect of operational deviations in an arborescent supply chain with requirements planning," European Journal of Operational Research, Elsevier, vol. 215(2), pages 374-382, December.
    22. Koen Pauwels & Shuba Srinivasan, 2004. "Who Benefits from Store Brand Entry?," Marketing Science, INFORMS, vol. 23(3), pages 364-390, July.
    23. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    24. Bandyopadhyay, Subir, 2009. "A Dynamic Model of Cross-Category Competition: Theory, Tests and Applications," Journal of Retailing, Elsevier, vol. 85(4), pages 468-479.
    25. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    26. Chatfield, Chris, 1988. "Apples, oranges and mean square error," International Journal of Forecasting, Elsevier, vol. 4(4), pages 515-518.
    27. 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.
    28. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.
    29. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
    30. Ouyang, Yanfeng, 2007. "The effect of information sharing on supply chain stability and the bullwhip effect," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1107-1121, November.
    31. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
    32. Goodwin, Paul, 2002. "Integrating management judgment and statistical methods to improve short-term forecasts," Omega, Elsevier, vol. 30(2), pages 127-135, April.
    33. Kuo, R. J., 2001. "A sales forecasting system based on fuzzy neural network with initial weights generated by genetic algorithm," European Journal of Operational Research, Elsevier, vol. 129(3), pages 496-517, March.
    34. Albertson, Kevin & Aylen, Jonathan, 2003. "Forecasting the behaviour of manufacturing inventory," International Journal of Forecasting, Elsevier, vol. 19(2), pages 299-311.
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    Cited by:

    1. 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.
    2. Karray, Salma & Martín-Herrán, Guiomar & Zaccour, Georges, 2020. "Pricing of demand-related products: Can ignoring cross-category effect be a smart choice?," International Journal of Production Economics, Elsevier, vol. 223(C).
    3. Kourentzes, Nikolaos & Petropoulos, Fotios, 2016. "Forecasting with multivariate temporal aggregation: The case of promotional modelling," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 145-153.
    4. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
    5. Gur Ali, Ozden & Pinar, Efe, 2016. "Multi-period-ahead forecasting with residual extrapolation and information sharing — Utilizing a multitude of retail series," International Journal of Forecasting, Elsevier, vol. 32(2), pages 502-517.
    6. Sagaert, Yves R. & Aghezzaf, El-Houssaine & Kourentzes, Nikolaos & Desmet, Bram, 2018. "Tactical sales forecasting using a very large set of macroeconomic indicators," European Journal of Operational Research, Elsevier, vol. 264(2), pages 558-569.
    7. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2019. "Forecasting retailer product sales in the presence of structural change," European Journal of Operational Research, Elsevier, vol. 279(2), pages 459-470.
    8. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    9. Ma, Shaohui & Fildes, Robert, 2017. "A retail store SKU promotions optimization model for category multi-period profit maximization," European Journal of Operational Research, Elsevier, vol. 260(2), pages 680-692.
    10. 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.
    11. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    12. Petropoulos, Fotios & Fildes, Robert & Goodwin, Paul, 2016. "Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 842-852.
    13. Tine Van Calster & Filip Van den Bossche & Bart Baesens & Wilfried Lemahieu, 2020. "Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective," Papers 2002.00949, arXiv.org.
    14. Gheorghe CARSTEA & Razvan-Andrei CORBOS & Ruxandra-Irina POPESCU & Ovidiu-Iulian BUNEA, 2017. "Analysis Of The Influence Of Some Indicators On The Profitability Of The Fmcg Retail Market In Romania," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 11(1), pages 481-492, November.
    15. Ma, Shaohui & Fildes, Robert, 2020. "Forecasting third-party mobile payments with implications for customer flow prediction," International Journal of Forecasting, Elsevier, vol. 36(3), pages 739-760.
    16. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.

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