The Impact of Natural Language Processing on Workflow Efficiency in American Tech Product Manufacturing: Methods and Case Studies

Authors

  • Dr. Ingrid Gustavsson Associate Professor of Human-Computer Interaction, University of Gothenburg, Sweden Author

Keywords:

Natural Language Processing, Tech Product Manufacturing

Abstract

Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on enabling machines to understand, interpret, and respond to human language in a valuable manner. Its applications in the manufacturing industry have been pivotal in enhancing workflow efficiency and communication processes. NLP enables machines to process and analyze large volumes of unstructured data, such as customer feedback, product specifications, and maintenance reports, leading to improved decision-making and streamlined operations [1].

Moreover, recent advancements in deep learning methods have significantly contributed to the automation of semantic analysis, further enhancing the capabilities of NLP in understanding and processing human language. As a result, NLP has become an indispensable tool in American tech product manufacturing, offering opportunities to optimize various processes and improve overall efficiency.

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Published

14-10-2024

How to Cite

[1]
Dr. Ingrid Gustavsson, “The Impact of Natural Language Processing on Workflow Efficiency in American Tech Product Manufacturing: Methods and Case Studies”, Distrib Learn Broad Appl Sci Res, vol. 10, pp. 254–269, Oct. 2024, Accessed: Dec. 03, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/141

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