The industrial sector is undergoing a significant transformation driven by the adoption of
Artificial Intelligence (AI) technologies. Research has documented the positive impact of AI
on various aspects of industrial management, including efficiency, productivity, and
decision-making This paper builds upon this existing body of knowledge by investigating
the specific applications of AI in predictive maintenance, automation, supply chain
optimization, and quality control.This work aims to explore the transformative potential of
AI in industrial management. It delves into the specific functionalities of machine learning
and predictive analytics in enhancing various industrial processes. The objectives are to
analyze the impact of AI-powered predictive maintenance on reducing downtime and
extending equipment lifespan. Another one is to investigate how AI-driven automation
streamlines production lines, improves safety, and allows human workers to focus on more
complex tasks. Next we evaluate the effectiveness of AI algorithms in optimizing supply
chain management through inventory control, demand forecasting, and route optimization.
Last but not least we assess the role of AI-driven quality control in ensuring product quality
and reducing waste through real-time defect detection.This research employs a
comprehensive literature review to examine existing studies on AI applications in industrial
management. The review will focus on recent advancements in machine learning
algorithms, sensor technologies, and their integration with industrial processes.
Additionally, case studies of successful AI implementations in various industries will be
analyzed to understand the practical challenges and benefits.Understanding how AI can
optimize industrial processes is crucial for both theoretical and practical reasons.
Theoretically, this research contributes to a deeper understanding of the transformative
potential of AI in manufacturing and production. Practically, the findings can guide
industrial managers in implementing AI solutions to enhance efficiency, reduce costs, and
improve overall productivity. The research revealed significant positive impacts of AI on
predictive maintenance, automation, supply chain optimization, and quality control. It is
anticipated that the findings will demonstrate reduced downtime, improved safety,
optimized inventory levels, and enhanced product quality through AI-driven solutions. The
paper proposes a research project that investigates the transformative power of AI in
industrial management. By analyzing the applications of AI in predictive maintenance,
automation, supply chain optimization, and quality control, the research aims to provide
valuable insights for both academics and industrial practitioners. The anticipated findings
will contribute to a more comprehensive understanding of how AI can reshape the future of
manufacturing and production, leading to increased efficiency, sustainability, and overall
industrial success. One way to approach the question of the implementation of AI in
industrial management is to consider the different factors that influence it, such as
productivity, quality, efficiency, safety, innovation, and sustainability. Then, we can try to
quantify how AI can affect each of these factors, and how they are interrelated.