This study introduces a novel mathematical framework for optimizing multi-echelon inventory systems in Industry 4.0 (I4.0)-enabled supply chains (SCs). The model integrates decentralized decision-making, real-time data exchange, and inventory redistribution to address demand variability, storage constraints, and cost minimization across production, transportation, and stockout penalties. By formalizing interactions between SC entities through autonomous agents and Internet of thing (IoT)-driven visibility, the framework provides a structured approach to balancing operational efficiency and adaptability in complex supply networks.