Systematic literature review identifies multi-criteria decision-making and artificial intelligence frameworks in manufacturing systems, highlighting a shift toward predictive maintenance.
Key Points
Investigate current methodologies, frameworks, and decision-making criteria used by industrial managers to select optimal maintenance strategies.
Conducted a systematic literature review evaluating traditional, condition-based, and predictive maintenance selection approaches alongside emerging machine learning and artificial intelligence models.
Multi-criteria decision-making methods—primarily AHP, TOPSIS, and DEMATEL, frequently integrated with fuzzy logic—dominate maintenance strategy selection.
Industrial practices are transitioning from static, corrective, and time-based models toward data-driven, condition-based, and AI-supported predictive frameworks.
Decision criteria increasingly emphasize environmental, social, and governance factors, including energy consumption and sustainability, alongside traditional economic and technical metrics.