XIV International Conference on Industrial Engineering and Environmental Protection – IIZS 2024, str. 213-219
АУТОР / AUTHOR(S): Peko Lakatuš
DOI: 10.46793/IIZS24.213L
САЖЕТАК / ABSTRACT:
This paper explores the integration of AI-driven project management and predictive maintenance strategies within industrial technical systems. It discusses the adaptation of methodologies like Agile, Lean, and Waterfall to the specific demands of industrial environments, emphasizing the need for tailored approaches to optimize efficiency and reliability. The paper also examines the implementation of AI in predictive maintenance, highlighting the importance of data quality, sensor technology, and machine learning in preventing equipment failures and reducing downtime. The balance between cost and efficiency in maintenance practices is analyzed, with insights drawn from case studies in manufacturing and energy sectors. Additionally, the paper suggests strategies for governments, enterprises, and individuals to foster the development of integrative AI systems, including investments in research, cross-sector collaboration, and workforce training. The research questions focus on effective methodology adaptation and the important factors influencing AI-driven maintenance success, providing a roadmap for achieving seamless industrial automation.
КЉУЧНЕ РЕЧИ / KEYWORDS:
Project Management, Maintenance Optimization, Industrial Technical Systems, Predictive Maintenance, AI Integration, Agile Methodology, Lean Management, Industrial Automation.
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