AI-POWERED INTELLIGENT AUTOMATION FOR OPTIMIZING INDUSTRIAL PROCESSES AND MANUFACTURING SYSTEMS

Authors

  • Dr. Pham Quang Minh

    Faculty of Artificial Intelligence and Data Analytics Hanoi Institute of Smart Technologies, Hanoi, Vietnam

    Author

  • Dr. Le Thu Trang

    Department of Computer Science and Intelligent Systems Vietnam Digital Research University, Da Nang, Vietnam

    Author

Keywords:
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Abstract

The rapid evolution of Industry 4.0 has accelerated the integration of artificial intelligence (AI), machine learning, cloud computing, edge intelligence, and automation technologies into modern manufacturing ecosystems. Traditional industrial processes often suffer from limitations associated with manual decision-making, delayed fault detection, inefficient resource utilization, and fragmented operational intelligence. This research paper investigates AI-powered intelligent automation as a transformative approach for optimizing industrial processes and manufacturing systems through predictive analytics, autonomous decision-making, intelligent resource allocation, and real-time operational control. The study adopts a research and review methodology by synthesizing recent advancements in AI-driven automation architectures, cloud-native infrastructures, predictive maintenance frameworks, digital twins, cybersecurity mechanisms, and intelligent orchestration systems using the provided literature.

The proposed conceptual framework integrates AI models with industrial Internet of Things (IIoT), edge computing, cloud platforms, event-driven architectures, and autonomous operational workflows to improve productivity, reliability, and scalability. The analysis highlights how intelligent automation enables manufacturers to transition from reactive operational models toward proactive, self-optimizing systems capable of predicting equipment failures, dynamically allocating resources, and improving production efficiency. Existing studies demonstrate significant potential of AI-based predictive maintenance, real-time analytics, and automated decision systems; however, challenges remain regarding data quality, cybersecurity risks, interoperability, computational requirements, and workforce adaptation.

The findings indicate that AI-powered automation provides substantial improvements in manufacturing agility, operational resilience, cost optimization, and sustainability. The research contributes a comprehensive understanding of AI-enabled industrial transformation by establishing technological relationships between intelligent automation frameworks and next-generation manufacturing requirements. Future industrial systems are expected to increasingly rely on autonomous AI agents, digital twins, and secure edge-cloud ecosystems to achieve adaptive and intelligent production environments.

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Published
2026-07-23
Issue
Vol. 14 No. 07 (2026): Volume 14 Issue 07
Section
Articles
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Copyright (c) 2026 Dr. Pham Quang Minh, Dr. Le Thu Trang (Author)

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How to Cite

Dr. Pham Quang Minh, & Dr. Le Thu Trang. (2026). AI-POWERED INTELLIGENT AUTOMATION FOR OPTIMIZING INDUSTRIAL PROCESSES AND MANUFACTURING SYSTEMS. IFSMRC African International Journal of Research in Management, 14(07), 26-52. https://ifsmrc.org/index.php/aijrm/article/view/478

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