Next-Generation Supply Chains: Leveraging Blockchain and Autonomous AI Agents for Real-Time Resilience

Abstract

Global supply networks face rising exposure to disruptions such as pandemics, geopolitical tensions, climate shocks, and cyber threats. Centralized models tend to be less transparent, less agile, and less predictive in these environments. This study proposes a multi-layered architecture integrating blockchain and Artificial Intelligence (AI) to create decentralized, resilient, and disruption-immunized supply networks. The system utilises IoT-driven data collection, blockchain-based immutable records for trust and traceability, and autonomous AI agents with sophisticated methodologies like Mixed-Integer Linear Programming (MILP), Transformer-based time-series forecasting, and Reinforcement Learning (RL) to facilitate anticipatory decision-making.

Through case studies on agriculture, automotive, and pharmaceutical industries and simulations, the system records significant improvements in traceability, inventory accuracy, service level compliance, and lead time reductions. Scalability, governance, and interoperability challenges are considered, and strategic directions like federated learning, hybrid blockchain architectures, and incentive-based governance models are suggested. The research highlights that the intersection of blockchain with autonomous AI not only improves operational efficiency but creates a basis for future-proof, self-optimizing supply chains that can predict and autonomously manage disruption.