Pivoting Retail Supply Chain with Deep Generative Techniques: Taxonomy,
  Survey and Insights
Yuan Wang Lokesh Kumar Sambasivan Mingang Fu Prakhar Mehrotra
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.