Vol. 4 No. 2 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

The Application of Natural Language Processing in Enhancing Communication within U.S. Manufacturing Supply Chains: Methods and Case Studies

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  • Prof. Hao Lin
Prof. Hao Lin
Chair of AI and Machine Learning, Tsinghua University, Beijing, China

Published 07-09-2024

Keywords

  • Natural Language Processing,
  • Manufacturing Supply Chains,
  • Enhancing Communication
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Prof. Hao Lin, “The Application of Natural Language Processing in Enhancing Communication within U.S. Manufacturing Supply Chains: Methods and Case Studies”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 193–209, Sep. 2024, Accessed: Oct. 15, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/160

Abstract

With the rise of globalization and emerging markets, the manufacturing supply chain has become more strained and complex. In complementary to the globalized landscape, the COVID-19 pandemic unveiled numerous vulnerabilities in the country's supply chain network. As the demand for manufactured goods and industrial supplies increases in the United States, understanding and using the latest technologies for improved communication with supply chain partners becomes a necessity. Increased communication with supply chain partners may assist in mitigating the risks arising from the rapid change in the manufacturing landscape. Recent advances in Natural Language Processing (NLP) and its supporting Artificial Intelligence (AI) technologies have promoted several innovative tools and solutions for enhanced communication with supply chain networks. However, such tools and technologies have been tricky to adopt in the manufacturing industry due to the industrial barriers and challenges. This paper discusses recent studies and case studies related to the implementation and adoption of Natural Language Processing (NLP) technologies and tools to augment communication within the primarily unstructured communication landscape of the U.S. Manufacturing Supply Chain Network.

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