Energy-Feasible and Traceable Recovery through a Green AI–FKF Risk Engine

Abstract

Freight systems now absorb concurrent shocks from geopolitics, cyber intrusion, climate extremes, lane congestion, and tightly coupled operational dependencies. This study develops an integrated framework for AI-driven supply-chain risk prediction and mitigation by combining artificial intelligence, IoT, digital twins, blockchain, intelligent transportation systems, FKF/FKL spectral analysis, and quantum optimization. The proposed PREDICT–MITIGATE framework represents resilience as a continuous feedback process in which heterogeneous operational data are transformed into disruption indicators, evaluated through predictive and simulation-based methods, and converted into feasible mitigation actions. FKF/FKL techniques provide complementary multi-scale and temporal representations, while digital twins support scenario analysis and optimization methods assist adaptive decision-making. Blockchain strengthens data provenance and accountability, whereas sustainability and energy constraints ensure that predicted responses remain physically feasible. The study synthesizes the selected research corpus, identifies major technological relationships, and highlights challenges involving explainability, interoperability, cybersecurity, governance, and human–AI collaboration. PREDICT–MITIGATE is therefore stated as a conceptual path from after-the-fact recovery toward predictive, adaptive, secure, and energy-feasible resilience. Keywords— artificial intelligence; supply chain risk management; predictive analytics; digital twin; blockchain; quantum computing; Industry 5.0; FKF/FKL transform; resilience; mitigation