ML-Enhanced AI Framework for SAP Risk Management and Anomaly Detection in Resilient Supply Chains

Abstract

Efficient and secure supply chain management is critical for modern enterprises operating in complex and dynamic environments. This paper presents a machine learning (ML)-enhanced AI framework for SAP-based risk management and anomaly detection, designed to strengthen resilience across supply chain operations. The framework leverages ML models to identify irregularities, predict potential disruptions, and detect anomalous patterns in transactional, operational, and logistical data. AI-driven analytics optimize decision-making, enabling proactive mitigation of risks and ensuring continuity in supply chain processes. By integrating with SAP systems through the Smart Connect ecosystem, the framework facilitates seamless data exchange, real-time monitoring, and adaptive responses to emerging threats. Experimental results demonstrate improved anomaly detection accuracy, faster response times, and enhanced overall supply chain resilience. The proposed approach provides a robust foundation for intelligent, secure, and agile supply chain operations.