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A Bayesian framework for large-scale geo-demand estimation in on-line retailing

  • Data Mining and Analytics
  • Published:
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Abstract

Time-specific geo-demand distribution estimation of the products in the catalog is the fundamental guiding analytics for inventory allocation in any major online retailer’s supply chain operations. Although geography-specific historical sales data is available for learning the geo-demand distributions, it is extremely sparse from a view of a product \(\times \) demand zone \(\times \) time data cube (tensor). As a result, we have to estimate the entries in a large-scale tensor with limited amount of training data. The sheer scale of the problem makes the task challenging to solve within a limited time frame. We formulate this problem in the spirit of text theme classification and view the geo-demand distributions as underlying probability distributions that govern the historical sales observations. We develop a Bayesian framework based on mixture of Multinomials for estimating the time-dependent geo-demand distributions in a collaborative manner. As a by-product, the solution provides guidance on grouping the products by their geo-demand patterns. We also provide practical solutions to counter various scalability issues. Benchmark results are provided in comparison to basic same-class methods and a state-of-the-art R package.

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Notes

  1. http://www.census.gov.

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Authors and Affiliations

  1. Didi Research America, 535 Middlefield Rd #180, Menlo Park, CA, 94025, USA

    Zhiwei Qin

  2. WalmartLabs, 850 Cherry Ave, San Bruno, CA, USA

    John Bowman & Jagtej Bewli

Authors
  1. Zhiwei Qin
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  2. John Bowman
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  3. Jagtej Bewli
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Correspondence to Zhiwei Qin.

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Qin, Z., Bowman, J. & Bewli, J. A Bayesian framework for large-scale geo-demand estimation in on-line retailing. Ann Oper Res 263, 231–245 (2018). https://doi.org/10.1007/s10479-016-2383-1

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  • DOI: https://doi.org/10.1007/s10479-016-2383-1

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