One thing we’re seeing a lot more this year is digital twinning. This is a physical asset with an exact copy in the digital world. With a model that is both digital and dynamic, we can accurately conduct tests on how a certain asset will perform in the real world across the duration of its lifecycle.
Digital twinning has previously been thought of as a technology reserved exclusively for large-scale projects, an innovative practice used only for very high-value assets. However, digital twinning starts to become more widely adopted across more industries and sectors than ever before, taking the technology from the aspirational, to the everyday.
For instance, the new Crossrail project in the UK – the construction of a new high frequency, high capacity railway for London and the South East of England – has its own digital twin. Engineers and architects can use a virtual model of the structure and conduct computer-generated tests to learn about the behaviour of components being used, optimise designs and minimise waste. As computer power has become ubiquitous, the number of scenarios and alternatives you can analyse are endless. This type of synthetic learning will result in a better built environment.
Historically, design components had no intelligence, and we couldn’t work out how changes to different elements or environmental factors impacted on the design. Businesses had no means of establishing how changes in weather, or temperature, might affect unidentified vulnerabilities in the model. By using data from sensors attached to the physical asset, however, organisations can analyse efficiency, errors or anomalies, to improve and optimise performance, and understand the condition of the asset more accurately.
Digital twinning & machine learning
A digital twin continuously learns and updates itself from multiple sources to represent its near real-time status, working condition or position. The ultimate goal is to gain insight into the behaviour and overall performance of physical objects by exploring their digital representation. The clear advantage of digital twinning is that you can experiment in the digital world much less destructively than in the physical one, and at enormous scale.
As part of what has been dubbed “the fourth industrial revolution” (or Industry 4.0), this form of machine learning will hugely impact the manufacturing landscape, with 85% of Internet of Things-enabled platforms having digital twin functionality within the next five years, according to a recent global report conducted by Research and Markets. Teams put in place to manually maintain these assets in the past will now be less vulnerable to an unplanned failure or crisis and will, instead, have more time to make changes rather than conducting checks on the asset in person.
For our partners with a value-added sales approach, who understand what it takes to work in IT across many different industries, digital twinning represents an enormous opportunity. It pushes out the technology envelope and embodies a far more sophisticated way to understand behaviours of the physical world. Those who understand how to connect the virtual and the physical worlds are in an excellent place to be successful going forward.