Logistics Predictive Model

A defense contractor required a predictive system for supply chain optimization that could operate entirely on-premise with zero data egress due to security requirements. We designed and deployed an air-gapped machine learning system that analyzes historical supply chain data, geopolitical signals, and logistics patterns to predict disruptions up to six weeks in advance. The model reduced forecast errors by 40% while maintaining complete data isolation.