Digital twins have the power to revolutionise the maritime industry – particularly in the management of workboats and ferries – by driving down operational expenditures (OPEX) through data-driven decisions. By creating a virtual representation of physical assets, processes, and systems, digital twins bridge the gap between the physical and virtual worlds, providing a dynamic view of operations. However, there is still a mysticism around what a digital twin is and the components it is made from. A digital twin is not just one tool, it is a collection of integrated systems which work together to deliver the operational and asset insights you need. In the context of ferries specifically, digital twins enable predictive vessel maintenance and operational planning. They collect and analyse real-world data from various sensors and systems, helping to identify inefficiencies, predict maintenance needs, and optimise operations – ultimately reducing OPEX.
This presentation will draw on a collection of live examples from a wide range of sectors and how those lessons can be applied to ferry twins. For maritime operations, one demonstrator is the BMT DEEP decision support tool used in the North Sea, which combines the metocean data with onboard sensors to provide users unprecedented insight for planning approach and offloading operations. The presentation will also explore vessel-maintenance opportunities, drawing on critical infrastructure case studies: for example, how supervisory control and data acquisition (SCADA) data in BMT DEEP has enabled predictive maintenance of the drainage pumps on international metro networks. This condition based approach replaces the calendar-based schedules, improving efficiency and delivering tangible cost savings.
Whichever way you look at it, real-world data is the backbone of digital twin. By continuously feeding data from the physical vessels into the digital twin, operators can gain the proactive insights needed for smarter decision-making. Machine Learning algorithms can then process vast amounts of data to uncover patterns and trends that might elude human analysis. By applying lessons learned from real world data systems already in use across multiple sectors by systems like BMT DEEP, we can get to the core insights quicker. In summary, digital twins can take many forms, but what matters the most is that they deliver the insights you need. By starting with the end in mind and planning for the key insights you need, you can build cost-effective digital systems that drive real results.
