AI Agents with MCV Architecture in Supply Chain Management: Toward Autonomous and Collaborative Networks

Abstract

Demand volatility, logistical interruptions, and linked worldwide networks define the remarkable complexity of modern supply chains. Classic centralized management solutions find difficulty in offering real-time solutions to changing operational problems. For designing distributed, intelligent, and self-organizing supply chain ecosystems, artificial intelligence agents combined with Model-Control-View (MCV) architectures provide transformational possibilities. These autonomous computational entities span three functional layers: view interfaces enable monitoring and interaction, control mechanisms govern decision-making and optimization, and model components represent digital twins of supply chain entities. Multi-agent coordination enables decentralized yet coherent operations through the negotiation and collaboration of agents representing suppliers, production, logistics, and retail, all of which adhere to standardized protocols. Applications include demand forecasting, intelligent logistics, stock optimization, supplier partnering, and flexible disruption response. While reducing reliance on centralized control systems, the framework enhances resilience, scalability, openness, and operational efficiency. Challenges in implementation include organizational adaptation needs, cybersecurity vulnerabilities, and data integration complexity. Future advances in autonomous and cooperative supply chain systems will include explainable artificial intelligence, quantum-enhanced optimization, edge computing powers, and blockchain-enabled trust mechanisms.