Timely and effective maintenance of seagoing ships is crucial for the maritime industry to transport cargo safely and efficiently. Proper maintenance reduces accidents and delays and ensures crew safety. While it incurs costs, it prevents more expensive repairs and improves fuel efficiency, benefiting both the company and the environment. The shift to data-driven condition-based maintenance (CBM) allows for tailored maintenance schedules, optimizing resource use and minimizing operational disruptions.
The report covers common shipboard maintenance methodologies, focusing on traditional condition based maintenance. It also delves into data-driven condition based maintenance, its challenges and improvement plans, plus exploring evolving technologies and lessons from aviation, including machine learning.
Industry drivers are identified for data-driven maintenance, outlining the steps required for implementing data-driven condition based maintenance processes. The report concludes by examining incentive alignment and new business models, contrasting routine and radical innovation within maritime technology.
This report is part of LR's Digital Transformation Research Programme and was written in partnership with NYK Line , Monohakobi Technology Institute and edited by the Smart Maritime Network.
