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Leveraging the Power of Large Language Models to Drive Progress in the Manufacturing Industry

In: Proceedings of the 9th International Conference on Financial Innovation and Economic Development (ICFIED 2024)

Author

Listed:
  • Feng Shi

    (Cethik Group Co., Ltd.)

  • Yongjin Zhang

    (The 52Nd Research Institute of China Electronics Technology Group Corporation)

  • Chongxiao Qu

    (The 52Nd Research Institute of China Electronics Technology Group Corporation)

  • Changjun Fan

    (The 52Nd Research Institute of China Electronics Technology Group Corporation)

  • Jinqi Chu

    (The 52Nd Research Institute of China Electronics Technology Group Corporation)

  • Lei Jin

    (The 52Nd Research Institute of China Electronics Technology Group Corporation)

  • Shuo Liu

    (The 52Nd Research Institute of China Electronics Technology Group Corporation)

Abstract

In this paper, we delve into the transformative role of large language models (LLMs) in the evolving digital economy, particularly within the manufacturing industry. We begin by demystifying the fundamental principles of LLMs, aiming to deepen the reader’s comprehension. Subsequently, we explore the main strategies for adapting LLMs and integrating them into manufacturing processes, scrutinizing both their benefits and limitations. Additionally, we assess the multifaceted impacts of LLMs on various phases of manufacturing. The paper culminates in a forward-looking analysis, highlighting four emergent trends that signify the growing influence of LLMs in revolutionizing the manufacturing industry.

Suggested Citation

  • Feng Shi & Yongjin Zhang & Chongxiao Qu & Changjun Fan & Jinqi Chu & Lei Jin & Shuo Liu, 2024. "Leveraging the Power of Large Language Models to Drive Progress in the Manufacturing Industry," Advances in Economics, Business and Management Research, in: Khaled Elbagory & Zefu Wu & Hamdan Amer Ali Al-Jaifi & Shafie Mohamed Zabri (ed.), Proceedings of the 9th International Conference on Financial Innovation and Economic Development (ICFIED 2024), pages 125-133, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-408-2_15
    DOI: 10.2991/978-94-6463-408-2_15
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