IOT-Enabled FKF–FKL Spectral Analysis for Intelligent and Resilient Supply Chains
Abstract
Modern multi-echelon supply chains generate heterogeneous Internet of Things (IoT) telemetry containing lead-time fluctuations, irregular sampling, transportation delays, intensity variations, and operational disruptions. This study proposes an FKF–FKL spectral framework for constrained resilience analysis that separates observational effects from genuine structural change. Fourier analysis captures temporal dynamics, while the Kontorovich–Lebedev (FKL) representation characterizes scale-dependent behavior. Shift-invariant properties distinguish temporal displacement from structural change, whereas exponential modulation and spectral differentials characterize intensity variation and localized disruptions. A distributional formulation on a testing-function space accommodates irregular IoT observations, and sequential convergence of the two-sided FKF transform supports continuous spectral-state updating inside digital twins. Compact spectral features feed resilience scoring, AI-based decision-making, green logistics and sustainable mobility planning, hash-chain (blockchain) provenance, and hybrid quantum-classical logistics optimization (quantum annealing / QUBO). The framework provides an interpretable route for detecting and classifying disturbances across procurement, production, warehousing, unit-load-device handling, and intelligent transportation systems under Industry 5.0 human-centric automation. Keywords— FKF/FKL transforms;; sequential convergence; testing-function spaces; time-shift/modulation; residue resonance; shift-invariant spectral analysis; multi-echelon lead times; resilient supply chains; digital twins; green logistics; blockchain/hash-chain traceability; swipe control; quantum logistics/annealing; QUBO–ULD optimization; IoT; intelligent transportation; Industry 5.0; human-centric automation; hybrid-electric/solar EV mobility; battery sizing; power-line communication; anti-theft; disruption detection; anomaly diagnosis..