Pivoting Retail Supply Chain with Deep Generative Techniques: Taxonomy, Survey and Insights
Yuan Wang, Lokesh Kumar Sambasivan, Mingang Fu, Prakhar Mehrotra
TL;DR
This paper addresses the challenge of leveraging Deep Generative Models (DGMs) to transform the retail supply chain, spanning purchase, logistics, and sell phases. It develops a taxonomy of DGMs—explicit vs implicit density models—and surveys end-to-end retail applications, from demand forecasting to ETA and customer engagement, highlighting how autoregressive, flow-based, VAE, EBM, GAN, and diffusion-based approaches can be applied. Key contributions include a structured overview of DGMs, a synthesis of current and potential retail use cases, and practical insights for integrating DGMs with reinforcement learning and large language models. The work informs researchers and practitioners about where DGMs can deliver predictive accuracy and prescriptive decisions, and where challenges remain for real-world deployment in retail networks.
Abstract
Generative AI applications, such as ChatGPT or DALL-E, have shown the world their impressive capabilities in generating human-like text or image. Diving deeper, the science stakeholder for those AI applications are Deep Generative Models, a.k.a DGMs, which are designed to learn the underlying distribution of the data and generate new data points that are statistically similar to the original dataset. One critical question is raised: how can we leverage DGMs into morden retail supply chain realm? To address this question, this paper expects to provide a comprehensive review of DGMs and discuss their existing and potential usecases in retail supply chain, by (1) providing a taxonomy and overview of state-of-the-art DGMs and their variants, (2) reviewing existing DGM applications in retail supply chain from a end-to-end view of point, and (3) discussing insights and potential directions on how DGMs can be further utilized on solving retail supply chain problems.