November 25, 2024
Considering Learnings from Other Initiatives
Looking to organisations who’ve successfully implemented digital twin programs is a good way to understand use cases and potential benefits. For example, Siemens used digital twins to simulate gas turbine production, saving resources by identifying and resolving potential issues ahead of time to deliver efficiency and significant cost savings.
Meanwhile, as part of Unilever's digital twin strategy, hundreds of manufacturing sites are being transformed into virtual models representing the entire supply chain. This approach can enhance efficiency, minimise waste, optimise material utilisation, and guarantee adherence to high compliance and quality standards. Sensors are used to oversee factory operations, transmitting information to an enterprise cloud to generate a unique digital twin. Through handheld devices, on-site personnel can access digital twin data, analyse challenges, devise solutions, and exchange information seamlessly.
Renault uses digital twins to create virtual replicas of cars, starting with a virtual design and followed by a virtual model of all technological components. This approach allows Renault to test their vehicles in advance, ensuring safety compliance and making necessary changes before final production. This also ensures the car marque can make more data-driven decisions and leverage real-time insights for problem solving prior to investment.
Once you’ve gathered enough insights in terms of use cases and potential methods for implementation and development, it’s all about defining operating and delivery model for innovative capability. This is something Trimoda has been supporting major asset owners and service providers with recently.
Continuous Learning and Innovation
The playbook for digital twins has been pioneered by some of the world’s biggest, complex companies across engineering, product development, construction, and more. For years, companies like Rolls-Royce have been running highly sophisticated digital twin models of new aircraft engines to test and optimise new products. This has allowed them not just to understand how those engines are performing and improve the design process itself, but also allows them to advise the people who use their engines over the whole lifetime of the asset. This is certainly a model that would benefit the built environment.
Driving Continuous Improvement
A digital twin can learn from and then shape the workings of a physical asset. This will often take more time and effort to develop, but the machine learning that will enable this level of self-optimisation is becoming more capable, affordable, and accessible.
“Continual improvement is an unending journey.”
Source: Lloyd Dobyns
Digital twins can be used to support ‘capital versus operating’ expenditure decisions, balancing cost, and future revenue. They can also improve the performance, availability, and safety of assets, processes, and enterprises, considering how assets are used and degraded over their lifespan to inform maintenance, repair, and overhaul decisions.
Conclusion
Digital twins can also provide assurance for the safety and resilience of assets, processes, and systems, relying on physical models and probabilistic methods to forecast behaviour with high certainty. This use case is important in periods of significant technological, social, and environmental change and when seeking life extension for high-value assets used in critical applications. It’s simply a matter of taking the right steps, precautions, and learnings to ensure a seamless implementation process.
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