HOW PREDICTIVE MAINTENANCE IN LOGISTICS FLEETS IS REDUCING EQUIPMENT DOWNTIME AND OPERATIONAL LOSSES

Predictive maintenance has revolutionized logistics fleet management by reducing equipment downtime and operational losses. Traditional reactive maintenance approaches often result in inefficiencies, while predictive maintenance, powered by artificial intelligence (AI), the Internet of Things (IoT), and machine learning (ML), enables logistics firms to anticipate failures before they occur. This study explores the impact of predictive maintenance on fleet performance, cost efficiency, and operational reliability. The research utilized a secondary data analysis method, examining reports and case studies from 2018 to 2022. Key findings indicate that predictive maintenance reduces fleet downtime by 50%, lowers maintenance costs by 40%, and decreases equipment failure rates by 60%. Statistical tests confirmed the significance of these results: a paired t-test (p < 0.05) validated downtime reduction, a chi-square test (p < 0.05) established the relationship between predictive maintenance and lower failure rates, and regression analysis (R² = 0.85) demonstrated the correlation between predictive maintenance and cost savings. The study concludes that predictive maintenance is a transformative strategy that enhances logistics efficiency, reduces financial losses, and improves fleet reliability. The findings imply that logistics firms should integrate AI-driven predictive analytics to optimize maintenance planning. Governments should offer incentives to encourage predictive maintenance adoption, particularly in emerging markets. Future research should explore the role of blockchain in predictive maintenance transparency and the cybersecurity risks associated with IoT-based fleet monitoring.