The rapid evolution of Artificial Intelligence (AI) has profoundly reshaped how supply chains are conceptualized, managed, and optimized. This paper synthesizes extant literature to propose a unified, hierarchical framework for AI-driven supply chain planning that integrates demand forecasting, real‑time visibility, inventory and logistics optimization, and sustainability considerations. Drawing on empirical and conceptual studies—including hierarchical neural‑network planning, supply‑chain visibility models, and systematic reviews of AI adoption—the framework aims to address critical research gaps in current practices. Through a detailed, structured literature review, this study examines how AI techniques such as artificial neural networks (ANNs), machine learning (ML), and advanced analytics contribute to base‑level outcomes (e.g., demand forecasting, inventory control), mid‑level orchestration (e.g., logistics routing, replenishment scheduling), and high-level strategic objectives (e.g., sustainability, resilience, service-level optimization). Key findings reveal that AI-driven supply chain management (SCM) enhances responsiveness, reduces waste, and improves resource utilization, but also faces barriers including data quality, system interoperability, organizational readiness, and social considerations. The discussion explores theoretical implications, practical challenges, and future research directions—highlighting the need for longitudinal empirical validation, hybrid human–AI decision processes, and standardization of performance metrics. This paper contributes to supply chain theory by offering a comprehensive, multi-layered conceptual model that bridges short-term operational gains and long-term strategic sustainability goals via AI adoption.

Artificial Intelligence, Supply Chain Management, Neural Networks, Supply Chain Visibility, Demand Forecasting, Hierarchical Planning

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