Three machine learning models operating on the product knowledge graph — predicting which assemblies will break, which components are chronically unstable, and which external triggers will force a change — before the change event occurs.
Reactive change management means discovering problems after they have already propagated. Predictive change management means preventing propagation before it begins.
The future of engineering change management is not faster reaction — it is proactive prevention. A material batch drifting out of specification will eventually cause a field failure. A supplier preparing a product discontinuation notice will eventually force a redesign. A component being revised seven times in ninety days is telling you something about your design-for-manufacturability. Foresight hears these signals before they become engineering changes.
Foresight operates three machine learning models on the Axiom product knowledge graph simultaneously: a Graph Neural Network (GraphSAGE architecture) trained on historical change propagation patterns to predict impact before BFS traversal confirms it, a natural language processing pipeline that analyzes engineering specifications for semantic drift and constraint inconsistencies, and a time-series anomaly detector that monitors revision frequency per component and flags statistical outliers. When any model generates a signal, Foresight pre-generates a draft ECR with preliminary impact analysis — routing it to the responsible engineer before the problem manifests as a formal change request.
From graph neural network inference to external obsolescence monitoring — Foresight operates eight continuous intelligence engines on the product knowledge graph.
"We found thirty-four part families that were consuming engineering capacity like a black hole. Fourteen of them traced to a single titanium casting supplier whose dimensional accuracy had been drifting for eighteen months — well within specification, but trending toward the boundary. Foresight saw the trend. We saw the supplier corrective action. The 22% reduction in ECO volume was not magic. It was simply hearing signals that had always been there."
"The GNN predicted that changing the coating specification on one component would cascade through twelve assemblies and invalidate two qualification records. The deterministic traversal confirmed eleven of the twelve assemblies and both qualification records. The twelfth assembly had been archived three months earlier — the only one the GNN got wrong. Ninety-one percent accuracy, in seconds, before anyone had to trace a single BOM relationship manually. That changes the entire dynamic of the change control board."
Submit a sample component. Watch Foresight analyze its revision history, scan for obsolescence risk, predict propagation paths, and surface the signals your engineers have been missing.