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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.00861 |
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| _version_ | 1866917602475900928 |
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| author | Wang, Yuan Sambasivan, Lokesh Kumar Fu, Mingang Mehrotra, Prakhar |
| author_facet | Wang, Yuan Sambasivan, Lokesh Kumar Fu, Mingang Mehrotra, Prakhar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00861 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pivoting Retail Supply Chain with Deep Generative Techniques: Taxonomy, Survey and Insights Wang, Yuan Sambasivan, Lokesh Kumar Fu, Mingang Mehrotra, Prakhar Artificial Intelligence Machine Learning 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. |
| title | Pivoting Retail Supply Chain with Deep Generative Techniques: Taxonomy, Survey and Insights |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2403.00861 |