Application of Artificial Intelligence in Maintenance Production for Productivity Improvement

The intricate nature of the combinatorial complexity of manufacturing operations stems from the distinctive characteristics of engineering synthesis and analysis. In the scientific domain, investigations typically focus on natural systems where inputs are controllable or measurable, outputs are observable, and the primary objective is to discern the inherent laws governing these systems. In stark contrast, engineering synthesis entails a different set of objectives. With knowledge of the natural laws, a partial specification of input and output requirements, and adherence to the experimental principles of sound engineering synthesis, engineers are tasked with crafting an artificial system that adheres to all specified constraints, ensuring manufacturability. The research paper explores the transformative impact of Artificial Intelligence (AI) on maintenance production processes and its consequential effect on overall productivity improvement. In the contemporary industrial landscape, the demand for increased efficiency has prompted a paradigm shift towards integrating AI technologies in maintenance practices. The research adopts a qualitative research approach to explore and explain the integration of AI into existing maintenance frameworks, workforce upskilling, and organizational readiness for embracing AI-driven solutions. This research highlights AI as a pivotal tool for enhancing maintenance production practices, leading to substantial improvements in productivity, resource utilization, and operational efficiency.