Traditional supply chain mapping is a manual, point-in-time exercise that fails under disruption. This custom workflow automates continuous discovery by ingesting procurement data (SAP, Oracle), supplier disclosures, and third-party databases (Dun & Bradstreet, Panjiva) to dynamically model multi-tier dependencies. AI agents parse this data to construct a live network graph, identifying single points of failure and critical bottlenecks that procurement and risk teams cannot see. The operational upside is pre-emptive mitigation, reducing the cost and duration of supply shocks by enabling data-driven resilience planning.




