AI-Powered Supply Chain Risk Intelligence for Consumer Protection in CPG Distribution Networks

The complexity of modern Consumer Packaged Goods (CPG) distribution networks requires risk intelligence that can anticipate disruptions and protect consumers through timely detection, traceability, and targeted intervention. This paper contributes an original integrative artifact the AI-Powered Supply Chain Risk Intelligence (SCRI) framework for consumer protection derived through a systematic literature review and thematic analysis of research from 2010 to 2023. The framework formalizes how multi-source risk signals (operational, IoT/quality telemetry, and external indicators) are transformed by an AI risk-intelligence layer comprising anomaly detection and risk sensing, predictive analytics, digital twin simulation, and real-time monitoring/control. It further provides a structured taxonomy linking AI capabilities to consumer-protection mechanisms, including recall precision, provenance transparency, product integrity assurance, and resilience-driven availability. In addition to synthesizing evidence on AI-enabled disruption detection and adaptive response, the paper reframes implementation barriers (data quality, organizational readiness, model interpretability) and ethical risks (privacy, bias, accountability) as design constraints for trustworthy deployment. The resulting contribution is a consumer-centric blueprint and research agenda for building explainable, governance-aligned AI systems that improve safety outcomes and strengthen trust across CPG distribution ecosystems.