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Transforming Supply Chain Management for Efficiency and Resilience
Abstract
This paper explores the transformation of supply chain management for efficiency and resilience through AI-driven engineering. Through case studies and research insights, it investigates how artificial intelligence is revolutionizing traditional supply chain practices, including demand forecasting, inventory optimization, and logistics management. The study highlights the application of AI techniques such as predictive analytics, machine learning, and optimization algorithms in enhancing decision-making, reducing costs, and mitigating risks in supply chain operations. Additionally, it discusses the integration of AI with IoT devices, blockchain technology, and cloud computing to enable real-time visibility, traceability, and collaboration across supply chain networks. The paper also addresses challenges such as data integration, interoperability, and cybersecurity in the deployment of AI-driven engineering solutions in supply chain management. It emphasizes the importance of collaboration, transparency, and agility in leveraging AI's potential to create more responsive, adaptive, and sustainable supply chains.
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