Multi-Scale Supply-Chain Intelligence through AI, FKF Spectral Analysis, and Quantum Optimization
Abstract
Global supply chains now fail in coupled ways: demand shocks travel with lead-time resonance, cyber events travel with physical delays, and energy-system constraints travel with freight capacity. Artificial intelligence is widely proposed as the instrument that turns those couplings into forecasts and then into mitigation. This paper reconstructs a systematic-style review, a conceptual framework, and a research agenda from a closed corpus of twenty-five publications spanning digital twins, green logistics analytics, blockchain enabled transparency, Industry 5.0, IoT–AI–quantum convergence, quantum annealing for disruption, spectral and distributional transforms of multi-echelon dynamics, and adjacent work on electric-mobility energy systems and infrastructure security. The synthesis organizes AI contributions along the four classical supply-chain risk-management stages identification, assessment, mitigation, and monitoring and overlays three enabling layers: (i) sensing and human machine interaction, (ii) computational intelligence including quantum and spectral methods, and (iii) energy-and-infrastructure constraints that make logistics risk material. 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