Toward Intelligent Supply-Chain Resilience: An AI-Driven Framework for Risk Prediction and Mitigation
Abstract
The increasing complexity and interdependence of global supply networks require more proactive approaches to disruption prediction and resilience management. This paper proposes a conceptual AI-enabled PREDICT–MITIGATE framework integrating machine learning, IoT sensing, digital twins, blockchain traceability, intelligent transportation, FKF/FKL spectral methods, green logistics, and quantum logistics optimization. The framework establishes a closed-loop architecture that converts multi-source supply-chain observations into predictive risk profiles and subsequently supports constraint-aware mitigation and continuous feedback. Artificial intelligence enables disruption forecasting and decision support; FKF/FKL spectral methods contribute temporal and multi-scale features; digital twins enable counterfactual scenario evaluation; blockchain records provenance for trusted traceability; green-logistics objectives constrain environmentally feasible recovery; and quantum optimization provides an emerging option for computationally intensive logistics problems. Cybersecurity, energy availability, and human oversight are incorporated to improve practical feasibility. The study identifies the complementary roles and different maturity levels of these technologies and highlights future requirements for explainable AI, multi-tier visibility, interoperable digital twins, autonomous mitigation, and responsible governance. The proposed framework offers a pathway toward intelligent, adaptive, and continuously learning supply-chain resilience. Keywords— supply chain risk management; supply chain resilience; artificial intelligence; digital twin; FKF transform; FKL transform; spectral analysis; multi-echelon lead time; quantum optimization; quantum annealing; blockchain traceability; Industry 5.0; Internet of Things; green logistics; electric mobility.