Developer Builds Probabilistic GNN to Strengthen Circular Supply Chains Under Crisis

A software developer shared how a single simulated node failure in a graph neural network exposed the brittleness of deterministic models during supply chain disruptions. Over six months, they researched probabilistic graph neural inference to address uncertainty in circular manufacturing supply chains — systems that reuse and recycle materials. The resulting framework uses Bayesian GNNs, which output probability distributions rather than fixed predictions, enabling risk quantification during mission-critical recovery windows such as post-disaster logistics. Built with PyTorch and PyTorch Geometric, the implementation features a Bayesian graph convolutional layer that models stochastic edge weights representing uncertain material flows. The project aims to help supply chain AI systems make reliable decisions within tight time constraints, such as a 48-hour disaster recovery window.