Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness

Xuan Bi, Gediminas Adomavicius, William Li, Annie Qu

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Because of the accessibility of big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many businesses, especially those in retail, because of the importance of forecasting in decision making. Improvement of forecasting accuracy, even by a small percentage, may have a substantial impact on companies' production and financial planning, marketing strategies, inventory controls, and supply chain management. Specifically, our research goal is to forecast the sales of each product in each store in the near future.Motivated by tensor factorization methodologies for context-aware recommender systems, we propose a novel approach called the advanced temporal latent factor approach to sales forecasting, or ATLAS for short, which achieves accurate and individualized predictions for sales by building a single tensor factorization model across multiple stores and products. Our contribution is a combination of a tensor framework (to leverage information across stores and products), a new regularization function (to incorporate demand dynamics), and extrapolation of the tensor into future time periods using state-of-the-art statistical (seasonal autoregressive integrated moving-average models) and machine-learning (recurrent neural networks) models. The advantages of ATLAS are demonstrated on eight product category data sets collected by Information Resources, Inc., where we analyze a total of 165 million weekly sales transactions of over 15,560 products from more than 1,500 grocery stores.

Original languageEnglish (US)
Pages (from-to)1644-1660
Number of pages17
JournalINFORMS Journal on Computing
Volume34
Issue number3
DOIs
StatePublished - May 2022

Bibliographical note

Funding Information:
History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This research was partially supported by the National Science Foundation [Grants DMS-1613190 and DMS-1821198]. Supplemental Material: The supplementary materials are available at https://doi.org/10.1287/ijoc.2021. 1147.

Publisher Copyright:
© 2022 INFORMS Inst.for Operations Res.and the Management Sciences. All rights reserved.

Keywords

  • consumer demand
  • design science
  • machine learning
  • sales forecasting
  • tensor decomposition

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