An FKF–AI–Digital Twin Framework for Intelligent Supply-Chain Management

Abstract

Next-generation supply chains can be viewed as distributed, partially observed stochastic systems where demand, lead times, quality, custody, energy conditions, and disruptions are revealed with delay and may be distorted across organizational boundaries. The supplied mathematical manuscript establishes a distributional-transform foundation based on Laplace and K-type kernels, modified Bessel functions, Meijer G-function representations, and second-order differential operators. This framework is extended toward intelligent and resilient supply-chain analysis by integrating the FKF spectral perspective with artificial intelligence, digital twins, trusted data exchange, Internet of Things sensing, sustainable mobility, and quantum-enabled logistics optimization. The FKF representation separates temporal oscillations from scale- or intensity-dependent behavior, enabling analysis of multi-echelon lead-time dynamics, delayed information, resonance, and nonstationary disturbances. Its distributional extension is especially suitable for irregular, impulsive, incomplete, and heterogeneous operational signals. AI models can exploit spectral features for prediction and anomaly detection, while digital twins provide continuously updated operational context. Blockchain-oriented trusted records support provenance and state consistency, and quantum optimization can address selected combinatorial logistics decisions. Keywords—Distributional FKF Transform; Spectral Analysis; Supply-Chain Resilience; Intelligent Supply Chains; Digital Twins; Swipe Controller; Artificial Intelligence; Trusted Data Exchange; Sustainable Logistics; Quantum Optimization; Industry 5.0.