Summary of Pivoting Retail Supply Chain with Deep Generative Techniques: Taxonomy, Survey and Insights, by Yuan Wang et al.
Pivoting Retail Supply Chain with Deep Generative Techniques: Taxonomy, Survey and Insights
by Yuan Wang, Lokesh Kumar Sambasivan, Mingang Fu, Prakhar Mehrotra
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the application of Deep Generative Models (DGMs) in modern retail supply chains. DGMs are designed to learn the underlying distribution of data and generate new points that are statistically similar to the original dataset. The authors aim to provide a comprehensive review of state-of-the-art DGMs and their variants, as well as discuss existing and potential use cases for these models in retail supply chain management. Specifically, they will (1) provide an overview and taxonomy of current DGMs, (2) review existing applications of DGMs in retail supply chain management from a point-of-view perspective, and (3) discuss insights and potential directions on how DGMs can be further utilized to solve retail supply chain problems. By leveraging DGMs, the authors hope to improve inventory management, demand forecasting, and supply chain optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if machines could help us predict what people will buy and when they’ll need it. This paper talks about how special computer models called Deep Generative Models can do just that! These models are good at generating new data points that are similar to the original information. The authors want to explore how these models can be used in retail supply chains, which is the process of getting products from manufacturers to stores. They will look at what’s already been done with these models and discuss how they could be used even more effectively to improve things like inventory management and predicting customer demand. |
Keywords
* Artificial intelligence * Optimization
