Learning production functions for supply chains with graph neural networks

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%.

Fault Point Detection for Recovery Planning of Resilient Grid

Large-scale meteorological disasters are increasing around the world, and power outage damage by natural disaster such as typhoons and earthquakes is increasing in Japan as well. Corresponding to the need of reduction of economic losses due to power outages, we are promoting research of resilient grids that minimizes power outage duration. In this report, we propose PACEM (Poles-Aware moving Cost Estimation Method) for determining travel costs between failure points based on the tilt angle and direction of electric poles obtained from pole-mounted sensors and road condition data. Evaluation result shows that the total recovery time can be reduced by 28% in the target area.