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Research on AI-Driven Production Decision-Making in the Manufacturing Industry

Author

Listed:
  • Fangyu Zhao

    (China Academy of Industrial Internet)

  • Shuo Han

    (Beijing University of Posts and Telecommunications)

  • Rui Li

    (China Academy of Industrial Internet)

  • Zelin Lu

    (China Mobile Communication Co., Ltd.)

Abstract

Against the backdrop of the deep integration of artificial intelligence (AI) and the real economy, production decision-making in the manufacturing industry is transforming from traditional experience-driven to data intelligence-driven, and the application value of AI in this process is becoming increasingly prominent. Focusing on the optimization of manufacturing costs, this paper explores the cost optimization-driven path of AI empowering production decision-making. By sorting out the current application status of AI in manufacturing production, analyzing the limitations of the traditional experiential learning curve and capacity utilization models, and combining a business simulation case of a multinational mobile phone manufacturer, this paper constructs an AI-driven manufacturing cost optimization model that integrates the learning curve effect and the capacity utilization effect. It systematically analyzes the specific paths of AI empowering manufacturing production decision-making from three dimensions: cost prediction, capacity planning, and make-or-buy decision-making, and verifies the practical application value of the model through case application. Finally, it points out the shortcomings of the research and prospects the future research directions of AI empowering manufacturing production decision-making.

Suggested Citation

  • Fangyu Zhao & Shuo Han & Rui Li & Zelin Lu, 2026. "Research on AI-Driven Production Decision-Making in the Manufacturing Industry," Advances in Economics, Business and Management Research,, Springer.
  • Handle: RePEc:spr:advbcp:978-94-6239-719-4_34
    DOI: 10.2991/978-94-6239-719-4_34
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