End-to-End Network Flow Optimisation and Demand-Supply Synchronisation: AI-Driven Supply Chain Strategies for U.S. Manufacturing Efficiency and Competitiveness

Authors

  • Maria Fox Professor of Computer Science, King's College London

Keywords:

end-to-end network flow optimisation, demand-supply synchronisation, supply chain strategies, u.s. manufacturing efficiency, machine learning

Abstract

[1]. The modern supply chain's network-based architecture and the data it generates provide a framework for the scalability of AI-driven technologies. AI algorithms excel in leveraging extensive datasets, enabling machines to derive unique insights and perform tasks more efficiently than humans.

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Published

31-12-2024

How to Cite

[1]
“End-to-End Network Flow Optimisation and Demand-Supply Synchronisation: AI-Driven Supply Chain Strategies for U.S. Manufacturing Efficiency and Competitiveness”, Human-Computer Interaction Persp., vol. 4, no. 2, pp. 1–10, Dec. 2024, Accessed: Jun. 04, 2026. [Online]. Available: https://thesciencebrigade.com/hcip/article/view/800