Al-Driven Predictive Maintenance in Shipyards: Enhancing Project Management Efficiency and Operational Cost Reduction through Statistical, Data-Driven Strategies for MRO Services

Abstract

A detailed investigation of shipyard operations was conducted during a laborious internship at Hindustan Shipyard in Visakhapatnam. The research uses cutting-edge methodologies to investigate shipyard operations, providing new perspectives on procedures not before examined in similar industrial settings. The study provides novel equipment maintenance and monitoring approaches by integrating real-time sensor technology, statistical analysis, Deep Learning/Machine Learning algorithms, and cutting-edge data analytics. Using these techniques, maintenance plans can be improved, prospective problems can be predicted, and equipment reliability, cost reduction and operating efficacy can all be increased. These advancements significantly improve project management efficiency by streamlining workflows, shortening turnaround times, and enabling proactive decision-making. The research employs data-driven methodologies to analyze operational parameters, detect anomalies, and predict maintenance needs, reducing emergency repairs and operational disruptions. The findings underscore the critical role of Al in modernizing shipyard Maintenance, Repair, and Overhaul (MRO) services, offering a scalable solution for cost-effective, efficient maritime operations.