Summary of Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks, by Azmine Toushik Wasi and Md Shafikul Islam and Adipto Raihan Akib and Mahathir Mohammad Bappy
Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks
by Azmine Toushik Wasi, MD Shafikul Islam, Adipto Raihan Akib, Mahathir Mohammad Bappy
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (stat.ML)
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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 GNNs have shown promise in various fields, but their application to supply chain management remains underexplored. This paper bridges the gap by discussing graph structures and GNN methodologies for optimizing complex supply chain problems. It provides mathematical definitions, task guidelines, and a real-world benchmark dataset from a leading FMCG company in Bangladesh. The authors demonstrate the effectiveness of GNN-based models on homogeneous and heterogeneous graphs across six supply chain analytics tasks, outperforming statistical ML and Deep Learning models by 10-40%. This work lays the foundation for solving supply chain problems using GNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs can be used to solve complex supply chain problems. The authors of this paper explain how GNNs can help with these problems, provide examples and definitions, and share a big dataset from a company in Bangladesh. They show that their method works better than others on six different tasks. |
Keywords
* Artificial intelligence * Deep learning * Gnn
