Digital Twin: what is it and who’s already using it?

Digital twin technology first emerged in 2002 but remained relatively under explored until recently when it became more cost effective to implement thanks to the IoT. So what exactly is it and how does it work?

It’s best to start off by thinking of a digital twin as a bridge between the physical and digital world. It is essentially a virtual model of a process, product or service. By allowing this pairing of the virtual and physical worlds, we can enable the analysis of data and monitoring of systems to predict problems before they occur, prevent downtime, develop new opportunities and even plan for the future by using simulations. It works by using smart components that use sensors to gather data about real-time status, working conditions, or positions which are integrated with a physical item. The components are connected to a cloud-based system that receives and processes all the data the sensors monitor. This input is then analysed against business and other contextual data. This means lessons can be learned and opportunities are uncovered within the virtual environment that can be applied to the physical world.

Sounds interesting right?  Well it’s not until we apply it to some use cases that things really get exciting. Check out some of the organisations that have already benefiting from the power of digital twin technology.

Newcastle University & Northumbrian Water

In a world first, Newcastle University and Northumbrian Water have partnered up to deploy digital twin technology to create a computer replica of Newcastle city which is allowing experts to perform real-time resilience testing to see how its infrastructure will perform in the face of challenges such as climate change and population growth. Used in this way, the digital twin will not only allow the city to react in real time to such freak weather events, but also to test an infinite number of potential future emergencies.

McLaren Race Team

Winning a Formula 1 race is no longer just about building the fastest car, hiring the bravest driver and praying for luck. These days, when a McLaren team races in Monaco or Singapore, it beams data from hundreds of sensors wired in the car to Surrey. There, analysts study that data and use complex computer models to relay optimal race strategies back to the driver.

Digital twin technology is currently being employed to improve driver and car performance in the high-stakes world of Formula 1. Virtually, the digital twin runs exactly the same race as the physical car, including road conditions, weather and temperature. Such a system can help prevent costly – and potentially, life-threatening – malfunctions through enhanced predictive analysis capabilities.

The Mater Private Hospital in Dublin

As with many healthcare providers, the Mater Private Hospital (MPH) in Dublin was under pressure from growing patient demand, aging infrastructure and lack of space. The hospital was also struggling to improve the patient experience with rising waiting times, interruptions and delays causing huge problems. To overcome these challenges, MPH partnered with Siemens Healthineers to redesign the layout and infrastructure of the MPH radiology department. Using digital twin technology, the hospital was able to test different operational scenarios and layouts. Realistic 3D animations and quantitative reports made it possible to predict the operational scenarios and instantly evaluate alternative options to find the right solution to transform their care delivery.

All indications seem to predict we are on the cusp of a digital twin technology explosion. Gartner have even boldly, and quite confidently predicted that by 2021, half of large industrial companies will use digital twin technology resulting in those organisations gaining a 10% improvement in effectiveness. But it won’t be until stories like these are shared and reshared that more organisations will begin to take inspiration from these real-world successes and deploy their own digital twin technologies to shape the future.

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