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Using clustering to improve sales forecasts in retail merchandising

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  • Mahesh Kumar
  • Nitin Patel

Abstract

Given sales forecasts for a set of items along with the standard deviation associated with each forecast, we propose a new method of combining forecasts using the concepts of clustering. Clusters of items are identified based on the similarity in their sales forecasts and then a common forecast is computed for each cluster of items. On a real dataset from a national retail chain we have found that the proposed method of combining forecasts produces significantly better sales forecasts than either the individual forecasts (forecasts without combining) or an alternate method of using a single combined forecast for all items in a product line sold by this retailer. Copyright Springer Science+Business Media, LLC 2010

Suggested Citation

  • Mahesh Kumar & Nitin Patel, 2010. "Using clustering to improve sales forecasts in retail merchandising," Annals of Operations Research, Springer, vol. 174(1), pages 33-46, February.
    • Handle: RePEc:spr:annopr:v:174:y:2010:i:1:p:33-46:10.1007/s10479-008-0417-z
    DOI: 10.1007/s10479-008-0417-z
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    References listed on IDEAS

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    1. Bunn, Derek W. & Vassilopoulos, Angelos I., 1999. "Comparison of seasonal estimation methods in multi-item short-term forecasting," International Journal of Forecasting, Elsevier, vol. 15(4), pages 431-443, October.
    2. de Menezes, Lilian M. & W. Bunn, Derek & Taylor, James W., 2000. "Review of guidelines for the use of combined forecasts," European Journal of Operational Research, Elsevier, vol. 120(1), pages 190-204, January.
    3. Kumar, Mahesh & Patel, Nitin R., 2007. "Clustering data with measurement errors," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6084-6101, August.
    4. Salvatore Terregrossa, 2005. "On the efficacy of constraints on the linear combination forecast model," Applied Economics Letters, Taylor & Francis Journals, vol. 12(1), pages 19-28.
    5. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    6. Marahaj, E.A. & Inder, B., 1999. "Forecasting Time Series from Clusters," Monash Econometrics and Business Statistics Working Papers 9/99, Monash University, Department of Econometrics and Business Statistics.
    7. Fomby, Thomas B & Samanta, Subarna K, 1991. "Application of Stein Rules to Combination Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(4), pages 391-407, October.
    8. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
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    Cited by:

    1. Rodrigo Lopez Farias & Vicenç Puig & Hector Rodriguez Rangel & Juan J. Flores, 2018. "Multi-Model Prediction for Demand Forecast in Water Distribution Networks," Energies, MDPI, vol. 11(3), pages 1-21, March.
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    4. Mehmet BARAN & Sýtký SÖNMEZER & Abdülvahid UÇAR, 2015. "Estimating Financial Trends by Cubic B-Spline Fitting via Fisher Algorithm," Turkish Economic Review, KSP Journals, vol. 2(1), pages 20-25, March.

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