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%.

翻译：全球经济依赖于供应链网络上的商品流动，其中节点代表企业，边代表企业间的交易。尽管我们可以观察到这些外部交易，但它们受到不可见的生产函数的支配，这些函数决定了企业如何将其接收的输入产品内部转化为其销售的输出产品。在此背景下，推断这些生产函数具有极高的价值，有助于更好地理解和改进供应链，并更准确地预测未来交易。然而，现有的图神经网络（GNNs）无法捕捉节点输入与输出之间的这些隐藏关系。本文针对这一场景引入了一类新模型，通过将时序图神经网络与新颖的库存模块相结合，利用注意力权重和特殊损失函数来学习生产函数。我们在真实供应链数据以及基于我们新开源模拟器SupplySim生成的数据上广泛评估了我们的模型。我们的模型成功推断出生产函数，相较于基线模型有6-50%的性能提升，并在真实和合成数据上预测未来交易，性能优于基线模型11-62%。