The traditional approach to supply chain analytics is purely predictive. You feed historical transit variance and weather data into an ML model, and it produces a dashboard that tells a human dispatcher, "Route 7 has a high probability of failure today." In a modern AI engineering context, a dashboard is a failure of automation.
We build Supply Chain Resilience Agents that close the loop. When a weather API or a port congestion webhook fires an alert, a LangGraph orchestrator spins up. It uses MCP to securely read the active manifests from your Oracle TMS. It identifies which shipments are at risk of breaching their SLAs.
Instead of alerting a human, the agent runs a cost-benefit simulation. It recalculates the Friction-Adjusted Delay Probability, identifies a consolidation opportunity with an alternate carrier, and generates the new route. The output is a fully prepared, optimal alternative manifest pushed directly back into the TMS, requiring only a single click from the dispatcher to execute. Intelligence without execution is just overhead.