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 improve supply chain visibility 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 and data generated from our new open-source simulator, SupplySim. Our models successfully infer production functions, outperforming the strongest baseline by 6%-50% (across datasets), and forecast future transactions, outperforming the strongest baseline 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，但它们受制于不可见的生产函数，这些函数决定了企业如何将接收的输入产品内部转化为销售的输出产品。在此背景下，推断这些生产函数对于提升供应链可见性和更准确地预测未来交易具有极高价值。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。本文通过将时序图神经网络与新型库存模块相结合，引入了一类适用于此场景的新模型，该模型通过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及基于我们新开源模拟器SupplySim生成的数据上广泛评估了所提模型。我们的模型成功推断出生产函数，在多个数据集上优于最强基线6%-50%，并在预测未来交易方面优于最强基线11%-62%。