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<p>Industry 4.0 means a paradigm shift in the manufacturing industry, which is accompanied by the combination of cyber-physical systems, the Internet of Things (IoT), and artificial intelligence (AI). Although AI holds high potential in improving an operation and real-time decision-making, several production environments are still trapped with legacy infrastructure or data silo and low adaptability. The paper is the mixed method research on strategic implementation of AI in smart production management that considers 100 surveys among manufacturing experts, 15 interviews of industry leaders. Predictive maintenance, real-time scheduling, quality control with the use of computer vision, and supply chain optimization have been discussed as some of the most important AI applications. The results indicate a high level of productivity, accuracy and decreasing downtimes, but the adoption is still limited by technical obstacles, employee resistance, and ethical issues. The paper suggests a strategic plan that involves data governance, the upskilling of the workforce, as well as policy support that is scalable to solve these issues. The study provides a theoretical approach as well as some hands-on tips to use AI in Industry 4.0 contexts encouraging a comprehensive, participatory, and sustainable approach to the process of taking a turn toward intelligent manufacturing.</p>

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Leveraging artificial intelligence for smart production management in industry 4.0

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月24日

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  • Dhruba Aritra Barua,
  • Samsul Arifeen Sami,
  • Leon Barua

摘要

Industry 4.0 means a paradigm shift in the manufacturing industry, which is accompanied by the combination of cyber-physical systems, the Internet of Things (IoT), and artificial intelligence (AI). Although AI holds high potential in improving an operation and real-time decision-making, several production environments are still trapped with legacy infrastructure or data silo and low adaptability. The paper is the mixed method research on strategic implementation of AI in smart production management that considers 100 surveys among manufacturing experts, 15 interviews of industry leaders. Predictive maintenance, real-time scheduling, quality control with the use of computer vision, and supply chain optimization have been discussed as some of the most important AI applications. The results indicate a high level of productivity, accuracy and decreasing downtimes, but the adoption is still limited by technical obstacles, employee resistance, and ethical issues. The paper suggests a strategic plan that involves data governance, the upskilling of the workforce, as well as policy support that is scalable to solve these issues. The study provides a theoretical approach as well as some hands-on tips to use AI in Industry 4.0 contexts encouraging a comprehensive, participatory, and sustainable approach to the process of taking a turn toward intelligent manufacturing.