AGENTIC AI IN SUPPLY CHAIN MANAGEMENT: PATHWAYS TO AUTONOMOUS LOGISTICS
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Abstract
Current supply chain management faces significant challenges from increasing volatility and complexity, resulting in limitations of traditional management methods that rely on human decision-making and periodic forecasting. This paper presents the application of Agentic AI in driving autonomous logistics systems. The study examines four key characteristics: operational autonomy, contextual understanding, continuous learning and adaptation, and the ability to scale and integrate with other systems. The findings indicate that integrating Agentic AI can elevate operational efficiency towards automation across multiple dimensions, including real-time demand forecasting, adaptive production and delivery, supplier relationship management, automated warehouse operations, and intelligent transportation systems. However, implementing this technology presents challenges that require consideration, including infrastructure, workforce acceptance, and ethical and regulatory issues. This paper also presents recommendations for future research, particularly in developing hybrid human-AI decision-making models, enhancing sustainability, and strengthening system resilience against cyber threats, to accelerate the transition towards efficient and sustainable autonomous logistics systems.
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