Intelligent Supply Chain Management Using Artificial Intelligence: Models, Challenges, And Future Prospects
TL;DR
The basics of how supply chain management works and the ability of AI-powered models to help optimize various aspects of the supply chain such as demand forecasting, inventory management, procurement, transportation, logistics, and risk management are covered.
Abstract
The world has become more global, markets are volatile, and the expectations of customers have grown with the increasing complexity of supply chain management and with constant disruptions to operations. Artificial Intelligence (AI) has become a game-changer for enhancing supply chain efficiency, resilience, and decision-making processes. This review covers the basics of how supply chain management works and the ability of AI-powered models to help optimize various aspects of the supply chain such as demand forecasting, inventory management, procurement, transportation, logistics, and risk management. The study covers machine learning (ML), deep learning (DL), predictive analytics and optimization techniques, which help businesses utilize vast amounts of real-time and historical data to support intelligent decision-making. Moreover, advanced supply chain concepts and practices, namely resilient supply chain, agile supply chain, green supply chain and cold supply chain, are examined in detail to discuss their role in providing sustainability and operational performance. The paper also reviews the latest research advances on AI for Supply Chain Optimization and highlights key challenges such as data quality concerns, interoperability, implementation complexity, and model interpretability. Finally, the future directions of AI-powered supply chains are explored, highlighting the need for scalable, explainable, and adaptive solutions that can contribute to building resilience, sustainability, and competitiveness in a dynamic business landscape.
