Adaptive Maintenance Strategies for Cargo Ships: Digital Approaches and Cost Optimization

This article addresses the critical challenge of optimizing ship machinery maintenance systems by integrating digital strategies. It examines adaptive maintenance approaches that consider real-time equipment condition, predictive analytics, and cost efficiency. A comparative analysis of existing methodologies is presented, highlighting their advantages and limitations. The study introduces an enhanced framework that incorporates machine learning models, sensor-based monitoring, and an improved decision-making process for maintenance scheduling. Experimental results demonstrate the effectiveness of adaptive strategies in minimizing downtime, reducing operational costs, and extending the lifespan of ship machinery. Practical applications and long-term implications for maritime industry stakeholders are also discussed. Additionally, the article explores the economic and operational impacts of changes in technical conditions, emphasizing cost-effectiveness and time optimization in servicing practices. The findings advocate for adaptive maintenance approaches that ensure efficient ship operation, reduce downtime, and enhance the overall productivity of maritime transport.