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

Fangyu Zhao; Shuo Han; Rui Li; Zelin Lu

doi:10.2991/978-94-6239-719-4_34

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

Authors

Fangyu Zhao¹, Shuo Han², Rui Li¹, Zelin Lu³^{, *}

¹China Academy of Industrial Internet, Beijing, China

²Beijing University of Posts and Telecommunications, Beijing, China

³China Mobile Communication Co., Ltd., Beijing, China

^*Corresponding author. Email: luzelin@chinamobile.com

Corresponding Author

Zelin Lu

Available Online 9 July 2026.

DOI: 10.2991/978-94-6239-719-4_34 How to use a DOI?
Keywords: Artificial Intelligence; Manufacturing Cost Optimization; Production Decision-Making; Learning Curve
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.
Copyright: © 2026 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2026 6th International Conference on Enterprise Management and Economic Development (ICEMED 2026)
Series: Advances in Economics, Business and Management Research
Publication Date: 9 July 2026
ISBN: 978-94-6239-719-4
ISSN: 2352-5428
DOI: 10.2991/978-94-6239-719-4_34 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Fangyu Zhao
AU  - Shuo Han
AU  - Rui Li
AU  - Zelin Lu
PY  - 2026
DA  - 2026/07/09
TI  - Research on AI-Driven Production Decision-Making in the Manufacturing Industry
BT  - Proceedings of the 2026 6th International Conference on Enterprise Management and Economic Development (ICEMED 2026)
PB  - Atlantis Press
SP  - 293
EP  - 305
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-719-4_34
DO  - 10.2991/978-94-6239-719-4_34
ID  - Zhao2026
ER  -

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