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

Zhiwei Qin (), John Bowman () and Jagtej Bewli ()
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Zhiwei Qin: Didi Research America
John Bowman: WalmartLabs
Jagtej Bewli: WalmartLabs

Annals of Operations Research, 2018, vol. 263, issue 1, No 12, 245 pages

Abstract: 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.

Keywords: Bayesian estimation; Geo-demand; Mixture of multinomials; Tensor completion (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10479-016-2383-1

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