AI-DRIVEN ANOMALY DETECTION AND SELF-HEALING IN SUPPLY CHAINS: A TECHNICAL DEEP DIVE

Abstract

This article presents a comprehensive technical framework for an AI-powered anomaly detection and self-healing system designed specifically for supply chain operations. The system integrates cutting-edge technologies including Apache Flink for stream processing, Kafka Streams for message handling, and Kubernetes with KNative for containerization and orchestration. Advanced anomaly detection is achieved through Graph Neural Networks and Transformer-based models that analyze complex network relationships and sequential data patterns, while explainable AI components ensure transparency and operational trust. The self-healing capabilities leverage event-driven workflows through Apache Airflow, automated inventory rebalancing algorithms, and blockchain-based smart contract validation via Hyperledger Fabric. Human-AI collaboration is facilitated through ChatOps integration with conversational interfaces and continuous learning mechanisms. Implementation results demonstrate substantial improvements in fraud prevention, supply chain resilience, inventory optimization, and anomaly detection speed. This framework represents a transformative approach to supply chain risk management that delivers measurable operational and financial benefits across diverse industry sectors through the seamless integration of real-time AI capabilities with automated remediation processes.