Deterring the Gray Market: Product Diversion Detection via Learning Disentangled Representations of Multivariate Time Series

Hao Lin (), Guannan Liu (), Junjie Wu () and J. Leon Zhao ()
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Hao Lin: School of Economics and Management, Beihang University, Beijing 100191, China; Key Laboratory of Data Intelligence and Management, Ministry of Industry and Information Technology, Beijing 100191, China
Guannan Liu: School of Economics and Management, Beihang University, Beijing 100191, China; Key Laboratory of Data Intelligence and Management, Ministry of Industry and Information Technology, Beijing 100191, China
Junjie Wu: School of Economics and Management, Beihang University, Beijing 100191, China; Key Laboratory of Data Intelligence and Management, Ministry of Industry and Information Technology, Beijing 100191, China
J. Leon Zhao: The Chinese University of Hong Kong, Shenzhen 518172, China

INFORMS Journal on Computing, 2024, vol. 36, issue 2, 571-586

Abstract: A gray market emerges when some distributors divert products to unauthorized distributors/retailers to make sneaky profits from the manufacturers’ differential channel incentives, such as quantity discounts. Traditionally, manufacturers rely heavily on internal audits to periodically investigate the flows of products and funds so as to deter the gray market; however, this is too costly given the large number of distributors and their huge volumes of orders. Owing to the advances in data analytics techniques, the ordering quantities of a distributor over time, which form multivariate time series, can help reveal suspicious product diversion behaviors and narrow the audit scope drastically. To that end, in this paper, we build on the recent advancement of representation learning for time series and adopt a sequence autoencoder to automatically characterize the overall demand patterns. To cope with the underlying entangled factors and interfering information in the multivariate time series of ordering quantities, we develop a disentangled learning scheme to construct more effective sequence representations. An interdistributor correlation regularization is also proposed to ensure more reliable representations. Finally, given the highly scarce anomaly labels for the detection task, an unsupervised deep generative model based on the learned representations of the distributors is developed to estimate the densities of distributions, which enables the anomaly scores generated through end-to-end learning. Extensive experiments on a real-world distribution channel data set and a larger simulated data set empirically validate our model’s superior and robust performances compared with several state-of-the-art baselines. Additionally, our illustrative economic analysis demonstrates that the manufacturers can launch more targeted and cost-effective audits toward the suspected distributors recommended by our model so as to deter the gray market.

Keywords: gray market; product diversion; multivariate time series; disentangled learning; anomaly detection (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:36:y:2024:i:2:p:571-586

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