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The integration of Artificial Intelligence (AI) into supply chain management (SCM) has the potential to revolutionize operational efficiency, decision-making, and cost-effectiveness. While the capabilities and applications of AI in SCM have been widely discussed, this paper addresses a critical gap by presenting a comprehensive framework that not only highlights the benefits but also explores the limitations and challenges of AI adoption in real-world supply chains. Through an in-depth analysis of various AI techniques—such as machine learning, predictive analytics, and optimization algorithms—this study offers novel insights into their applicability in solving complex supply chain problems like demand forecasting, inventory management, and logistics optimization. Additionally, a case study is provided to validate the proposed AI-driven strategies, demonstrating significant improvements in accuracy and operational performance. This research contributes to the existing body of knowledge by proposing a scalable AI model tailored to the dynamic needs of modern supply chains, advancing the theoretical and practical understanding of AI’s role in this domain. The findings suggest actionable pathways for both researchers and industry practitioners, fostering innovation and resilience in global supply chain networks.
Danach et al. (Mon,) studied this question.
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