Application Prospect Analysis and Key Issues Research of AI Technology in Service-oriented Manufacturing

  • Li Y
  • Liu C
  • Zhang J
  • et al.
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Abstract

This study provides a comprehensive review of the development of service-oriented manufacturing and explores the prospects of AI technology in this field. It includes an in-depth analysis of the history, trends, and typical cases of service-oriented manufacturing both domestically and internationally. It includes an in-depth analysis of the history, trends, and typical cases of service-oriented manufacturing both domestically and internationally. The paper highlights the current status and challenges faced by China's service-oriented manufacturing, distilling the main The paper highlights the current status and challenges faced by China's service-oriented manufacturing, distilling the main development models such as intelligent services, third-party outsourcing, and full lifecycle services. By examining specific enterprise cases, the paper reveals innovative transformation paths in China. By examining specific enterprise cases, the paper reveals innovative transformation paths in the practical implementation of service-oriented manufacturing. By examining specific enterprise cases, the paper reveals innovative transformation paths in the practical implementation of service-oriented manufacturing. The aim is to optimize service processes through AI technology, enhance production efficiency and service quality, and provide theoretical support and practical guidance for the deep integration of manufacturing and service industries. The aim is to optimize service processes through AI technology, enhance production efficiency and service quality, and provide theoretical support and practical guidance for the deep integration of manufacturing and service industries.

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APA

Li, Y., Liu, C., Zhang, J., & Ren, Y. (2024). Application Prospect Analysis and Key Issues Research of AI Technology in Service-oriented Manufacturing. Transactions on Economics, Business and Management Research, 4, 80–110. https://doi.org/10.62051/v0zgcq61

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