Michael de la Torre, Yelena Ionova, Barry Price
Today's external manufacturing landscape demands more than retrospective audits and subjective traffic-light risk scores. With many CMO and API partners and fragmented oversight processes, quality organizations often discover problems only after they have escalated into supply disruptions or regulatory actions.This session will explore how predictive intelligence can modernize supplier oversight. By unifying structured and unstructured data sources - inspections, deviations, recalls, enforcement documents, audits, batch records, changes - into a single system of intelligence, we can surface early signals of risk. Hazard models and AI-based risk scoring now make it possible to quantify the likelihood of recalls, enforcement actions, or supplier disruptions weeks to months in advance.Real-world use cases will illustrate how large pharma organizations are reducing surprises, better targeting audits, and improving supplier selection decisions. The presentation will highlight the shift from static dashboards to near real-time monitoring, automated alerts, and data-driven next-best-action recommendations.