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<p>This study introduces a novel AI-enhanced inventory optimization framework that integrates reinforcement learning, Kalman filtering, blockchain-enabled verification, and genetic algorithm-based multi-objective optimization to address the complex challenges of modern multi-echelon supply chains. The model uniquely combines predictive analytics with decentralized supplier validation, enabling accurate demand forecasting, adaptive pricing, secure transactions, and sustainable operations. Unlike existing approaches, this hybrid framework incorporates perishability, emission constraints, and traceability into an intelligent decision-making system. Simulation results demonstrate marked improvements in inventory efficiency, cost optimization, supply chain resilience, and sustainability compliance compared to traditional methods. The model dynamically adjusts to demand fluctuations, optimizes replenishment cycles, reduces operational risks, and enhances supplier trustworthiness through blockchain mechanisms. Its robustness across volatile market conditions and its capability to enforce eco-efficiency objectives establish its practical relevance in retail, pharmaceuticals, e-commerce, and logistics. Overall, this research offers a transformative solution for data-driven, transparent, and environmentally responsible supply chain management.</p>

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A hybrid AI and blockchain framework for intelligent supply chain optimization with reinforcement learning-based inventory management, stochastic demand forecasting, and sustainable supplier selection

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Lalji Kumar,
  • Uttam Kumar Khedlekar

摘要

This study introduces a novel AI-enhanced inventory optimization framework that integrates reinforcement learning, Kalman filtering, blockchain-enabled verification, and genetic algorithm-based multi-objective optimization to address the complex challenges of modern multi-echelon supply chains. The model uniquely combines predictive analytics with decentralized supplier validation, enabling accurate demand forecasting, adaptive pricing, secure transactions, and sustainable operations. Unlike existing approaches, this hybrid framework incorporates perishability, emission constraints, and traceability into an intelligent decision-making system. Simulation results demonstrate marked improvements in inventory efficiency, cost optimization, supply chain resilience, and sustainability compliance compared to traditional methods. The model dynamically adjusts to demand fluctuations, optimizes replenishment cycles, reduces operational risks, and enhances supplier trustworthiness through blockchain mechanisms. Its robustness across volatile market conditions and its capability to enforce eco-efficiency objectives establish its practical relevance in retail, pharmaceuticals, e-commerce, and logistics. Overall, this research offers a transformative solution for data-driven, transparent, and environmentally responsible supply chain management.