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

Author

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  • Zhiwei Qin

    (Didi Research America)

  • John Bowman

    (WalmartLabs)

  • Jagtej Bewli

    (WalmartLabs)

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.

Suggested Citation

  • Zhiwei Qin & John Bowman & Jagtej Bewli, 2018. "A Bayesian framework for large-scale geo-demand estimation in on-line retailing," Annals of Operations Research, Springer, vol. 263(1), pages 231-245, April.
    • Handle: RePEc:spr:annopr:v:263:y:2018:i:1:d:10.1007_s10479-016-2383-1
    DOI: 10.1007/s10479-016-2383-1
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    2. Ian Holmes & Keith Harris & Christopher Quince, 2012. "Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-15, February.
    3. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
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