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Using clustering to improve sales forecasts in retail merchandising

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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.

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Authors and Affiliations

  1. Robert H. Smith School of Business, University of Maryland, 4321 Van Munching Hall, College Park, MD, 20742, USA

    Mahesh Kumar

  2. Massachusetts Institute of Technology, Cambridge, MA, USA

    Nitin R. Patel

  3. Cytel Software, 675 Massachusetts Avenue, Cambridge, MA, 02138, USA

    Nitin R. Patel

Authors
  1. Mahesh Kumar
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  2. Nitin R. Patel
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Correspondence to Mahesh Kumar.

Additional information

The authors would like to thank ProfitLogic Inc. for providing data.

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Kumar, M., Patel, N.R. Using clustering to improve sales forecasts in retail merchandising. Ann Oper Res 174, 33–46 (2010). https://doi.org/10.1007/s10479-008-0417-z

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  • DOI: https://doi.org/10.1007/s10479-008-0417-z

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