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@article{204150,
        author = {Mr.Shivaji D Holkar and Prof. Vidya Taval},
        title = {A Study On Impact Of AI Driven Predictive Maintenance On Manufacturing Performance},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {1708-1717},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204150},
        abstract = {This study examines the impact of AI-driven predictive maintenance on manufacturing performance using a quantitative research approach. The research integrates operational data collected before and after the implementation of predictive maintenance, along with primary survey data obtained from 110 respondents. Key performance indicators such as downtime, availability, Mean Time Between Failures (MTBF), and Mean Time To Repair (MTTR) were analysed to evaluate system performance. Statistical analysis was conducted using JAMOVI software, including descriptive statistics, paired sample t-tests, correlation analysis, and linear regression. The findings reveal a significant reduction in downtime and failure frequency following the adoption of predictive maintenance. Additionally, a substantial increase in MTBF and improvement in availability were observed, indicating enhanced equipment reliability and operational efficiency. The results demonstrate that AI-driven predictive maintenance plays a crucial role in optimizing manufacturing performance by enabling proactive maintenance strategies and reducing unexpected equipment failures. This study contributes to the existing body of knowledge by providing empirical evidence supported by statistical analysis and offers practical insights for organizations aiming to adopt data-driven maintenance systems.},
        keywords = {},
        month = {June},
        }