Semantic and Temporal Graph Neural Networks for Supply Chain Risk Quantification

Calculating supply chain risk management values requires a granular set of parameters. Failure risk at each supply chain entity is a dependent value influenced by entities within a supply chain. Predicting future risk values from historical data is based on trends and patterns therein. On the surface, historical data does not show how the data interlink and connects. To understand the problem this research starts by examining the underlying data structure that a supply chain uses to store data. Mining the data requires understanding its relationship to other data within the structure. This understanding allows the system or user to make better decisions. The research brings together four different pieces to show a risk value at each node within a supply chain network. This research generates a dataset using seed data and trend data to build a supply chain network. This research then predicts future risk values at a node level using ARMIA and a graph neural network. Node centrality values help show the importance of nodes to a supply chain. The centrality methods this research uses are betweenness, degree, eigenvector, and Katz centrality. Each one gives an importance value based on a different view of centrality. This research then looks for the vi influence of a node on a customer. Calculating an influence value by using Bayesian networks. The last value calculated is the profit loss at a customer node. All these different pieces are brought together in the risk calculation equation to give a final node-per-node risk value. The risk calculations align well with the structure of a graph. This research will show that a graph neural network and a graph database are useful for supply chain problems.