Summary of Learning Production Functions For Supply Chains with Graph Neural Networks, by Serina Chang et al.
Learning production functions for supply chains with graph neural networks
by Serina Chang, Zhiyin Lin, Benjamin Yan, Swapnil Bembde, Qi Xiu, Chi Heem Wong, Yu Qin, Frank Kloster, Alex Luo, Raj Palleti, Jure Leskovec
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Social and Information Networks (cs.SI)
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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 research paper proposes a novel approach to inferring production functions in supply chain networks using graph neural networks (GNNs) combined with a temporal inventory module. The model learns attention weights and a special loss function to capture hidden relationships between nodes’ inputs and outputs, improving supply chain visibility and forecasting accuracy. The authors evaluate their models on real-world data and generated data from the SupplySim simulator, outperforming baselines by 6%-50% for production function inference and 11%-62% for future transaction forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new type of model that can infer production functions in supply chain networks, which is important for improving visibility and forecasting future transactions. The approach uses graph neural networks (GNNs) combined with an inventory module to learn about the relationships between inputs and outputs. This helps to make better predictions about what will happen in the future. |
Keywords
* Artificial intelligence * Attention * Inference * Loss function
