  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%.
