1. Learning production functions for supply chains with graph neural networks
- Author
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Chang, Serina, Lin, Zhiyin, Yan, Benjamin, Bembde, Swapnil, Xiu, Qi, Wong, Chi Heem, Qin, Yu, Kloster, Frank, Luo, Alex, Palleti, Raj, and Leskovec, Jure
- Subjects
Computer Science - Machine Learning ,Computer Science - Computers and Society ,Computer Science - Social and Information Networks - Abstract
The global economy relies on the flow of goods over supply chain networks, with nodes as firms and edges as transactions between firms. While we may observe these external transactions, they are governed by unseen production functions, which determine how firms internally transform the input products they receive into output products that they sell. In this setting, it can be extremely valuable to infer these production functions, to better understand and improve supply chains, and to forecast future transactions more accurately. However, existing graph neural networks (GNNs) cannot capture these hidden relationships between nodes' inputs and outputs. Here, we introduce a new class of models for this setting, by combining temporal GNNs with a novel inventory module, which learns production functions via attention weights and a special loss function. We evaluate our models extensively on real supply chains data, along with data generated from our new open-source simulator, SupplySim. Our models successfully infer production functions, with a 6-50% improvement over baselines, and forecast future transactions on real and synthetic data, outperforming baselines by 11-62%.
- Published
- 2024