Identifying and Prioritizing the Role of Artificial Intelligence in Predictive Maintenance and Repairs in the Automotive Industry
Keywords:
Predictive Maintenance, Artificial Intelligence, Automotive IndustryAbstract
This study aims to identify and prioritize the key roles of artificial intelligence in predictive maintenance and repair processes within the automotive industry from an expert-based decision-making perspective. The study adopts a positivist philosophy with a deductive approach and employs a mixed-methods case-survey design. Initially, a comprehensive review of the relevant literature was conducted to extract the principal roles of artificial intelligence in predictive maintenance and repairs. Based on this review, ten AI-related roles were identified and operationalized into evaluation criteria. Data were collected through structured pairwise-comparison questionnaires administered to a panel of 15 experts drawn from maintenance, research and development, and information technology departments in the automotive sector. To prioritize the identified roles, the Analytical Hierarchy Process (AHP) was applied. The consistency of expert judgments was assessed using the consistency ratio to ensure the reliability and logical coherence of the comparisons. The AHP results indicate that artificial intelligence plays a multidimensional role in predictive maintenance and repairs. Among the identified roles, saving time in the repair and maintenance process achieved the highest priority weight (0.196). This was followed by optimizing vehicle performance (0.185) and improving vehicle availability (0.169). Other significant roles included enhancing predictive accuracy, improving estimation of remaining useful life, reducing overall operational costs, optimizing maintenance schedules, strengthening security and privacy, and increasing customer satisfaction. The consistency ratio confirmed the acceptable reliability of the prioritization results. The findings demonstrate that artificial intelligence is a critical enabler of efficient, reliable, and proactive predictive maintenance in the automotive industry, with its greatest value perceived in reducing maintenance time and enhancing operational performance.
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Copyright (c) 2026 Saeed Aghamohammadi (Author); Amir Abbas Shojaee; Ali Akbar Akbari, Majid Nojavan (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
