Integrating FKF Spectral Analysis, Artificial Intelligence, and Digital Twins for Supply-Chain Resilience

Abstract

This study proposes a distributional FKF spectral framework for intelligent and resilient supply-chain systems. Modern supply chains generate delayed, heterogeneous, and partially observed signals involving demand, lead times, quality, custody, energy state, and disruptions. The framework combines Fourier temporal analysis with Kontorovich–Lebedev scale behavior, modified Bessel and Meijer G-function representations, and differential-operator structure. It connects these foundations with artificial intelligence, digital twins, trusted data exchange, IoT sensing, sustainable mobility, and quantum-enabled logistics optimization. The FKF representation separates temporal oscillation from scale-dependent behavior and supports analysis of multi-echelon lead times, delayed information, resonance, and nonstationary disturbances. Distributional extension accommodates irregular telemetry, while AI extracts predictive and anomaly-detection features and digital twins maintain operational context. Trusted records support provenance, and quantum optimization addresses selected combinatorial logistics decisions. The integrated framework provides a unified spectral route from operational sensing to resilient decision-making. Keywords— Distributional FKF Transform; Spectral Analysis; Supply-Chain Resilience; Intelligent Supply Chains; Multi-Echelon Supply Chains; Disruption Detection; Artificial Intelligence; Digital Twins; Trusted Data Exchange; Sustainable Logistics; Quantum Optimization; Industry 5.0..