Ben Salama, UKI Industry X.0 Lead, Accenture Managing Director & Yen-Sze Soon, UKI Industry X.0 Delivery Lead, Accenture Managing Director
As industrial companies move from designing and creating standalone products to developing increasingly connected smart services and experiences, they face a number of common challenges. They need to innovate at speed as well as coordinate ever-more complex ecosystems of partners, suppliers and customers. And they need to make maximum use of huge and growing volumes of data. In addition to customer information, as new smart products generate insight, businesses have a means to continuously optimise operations and customer experiences. Understanding every process around the lifecycle of every product and asset is essential. It’s also a major challenge.
Part of the barrier comes from having the insight. A strong majority of professionals who need data for their roles – 70 per cent – say they don’t have access to everything they need. This is before we even consider the challenges of analysing that data.
The second challenge is around applying that initial insight in a way that can lead to the most impactful outcomes. Digital twin is one of the technologies that can help businesses to apply this insight in new ways. Establishing virtual development and testing as well as remote control can support new data-driven business models and drive new value propositions.
What is a digital twin?
The concept of Digital Twin is not a new one. As the name suggests, a digital twin is a virtual replica of an actual process, service, or product. Using the digital twin concept, companies can ‘virtualise’ almost anything. There are practically no limits: from machines to factories and offshore oil rigs, from cars and their driving characteristics to entire cities—all these and more can be captured and recreated virtually. Engineers can then change functions, test new settings, and calculate complex simulations at a fraction of the costs and time required for traditional methods.
A digital twin is not a single concept, but a number of different virtual models that are progressively more sophisticated, data-rich and intelligent. The simplest is a static 3D image of an asset such as a factory or an offshore drilling rig. Next is a twin that also contains engineering data, making it possible to look at a specific component. For example a pump for which you can see the make, model, sub components and detailed engineering properties, such as the amount of pressure it’s able to handle. The third level of sophistication comes with the integration of real-time operational data from sensors into the virtual model. This makes it possible to monitor conditions such as temperature and throughput.
And finally, an intelligent digital twin enables simulation and prediction, which means companies can carry out advanced testing of scenarios to understand how an asset will perform in different conditions, predict the likely failure rates of specific components and understand the impact of modifications or increases in production. Not only is this beneficial for testing, but also for training and re-training. This is particularly valuable where deployment is in remote or hazardous environments, as well as where the production of physical test models can be lengthy and/or expensive.
Digital twins can simulate extremely complex systems in real time, breaking down the boundaries between departments and companies. As a result, they compile data from a wide range of areas on a single platform.
This means that a car manufacturer, for example, can keep a constant overview of a supplier’s production data, a logistics partner’s component inventory, and the effectiveness of its own engine plant.
As well as day-to-day functions, digital twins help innovation. Teams of developers, researchers, designers, and scientists can work on projects together, regardless of their location. They can collaborate on a virtual model of a product, shortening development cycles and often improving the end result because coordination is much smoother.
Improvements in safety are a further core benefit to digital twins. In an industrial workplace, safety hazards and potential for human harm are significantly higher and more serious than in other industries. Digital twins can minimise the requirement for human interaction and physical monitoring within hazardous plants, where there can be a risk of explosion or exposure to chemicals. Via a digital twin these operations can be inspected remotely, assessing the live data being fed-back from the genuine model reflecting any potential faults or even have modification simulations run on them. All without the need for a human to directly interact with the hazardous location.
Twins and threads: driving the free flow of data
A digital twin ‘accompanies’ its real-life counterpart throughout its lifetime – providing a continuous cycle of day-to-day usage data and optimisation.
Feedback to the manufacturer also makes it easier to improve future products. This unimpeded flow of data between the ‘real’ machine and users’ or manufacturers’ IT systems is called the ‘digital thread’ which continuously runs alongside the physical product and creates a big value boost.
Mackevision, for example, used its CGI expertise to develop a digital twin of a Daimler motor block. This enables Daimler’s developers to simulate the behaviour of motor components at various speeds. It also facilitates cooperation between engineers and designers across departments, while simultaneously streamlining numerous work processes.
Or take GE, which developed a digital twin for each wind turbine to improve wind farms’ efficiency and power generation. Each turbine’s operation is evaluated and compared with the output data from the other turbines in the wind farm. That enables operators to optimise performance across the entire array, making turbines up to 20 per cent more efficient.
An engineering company in oil and gas, recreated digital models of old assets such as an offshore platform. They then used CAD to design new add-ons to the asset and ensure full fit and compatibility without having to go on site saving time and minimising safety risk.
On a larger scale, Dassault Systèmes has created a virtual replica of the city state of Singapore, which serves first and foremost as a sand box for the city’s planners. This digital twin allows city administrators to model a huge variety of scenarios with incredible accuracy, from the impact of construction work on traffic conditions to the noise pollution created by a new high-speed rail line.
So how can companies get started?
If companies are operating assets that were designed and built recently, they will likely have access to the digitised plans and drawings created with modern, sophisticated CAD tools. These make the development of an initial digital twin relatively straightforward. But many of today’s industrial assets, for example drilling platforms or factory production lines, were designed and developed before that. But that shouldn’t be a barrier either.
Today’s technology makes it possible to create a 3D image of any asset. Once that 3D image is available, machine learning tools can extract relevant engineering data from existing documentation and integrate it into the 3D image. Then, it’s possible to connect sensor data to provide live operational monitoring and – finally – algorithms can support advanced simulation and prediction capabilities.
Of course, that’s a journey that needs to be approached systematically. The crucial first step? Engaging the wider business in the potential value a digital twin can deliver. That means starting small, with a working twin that operational and engineering teams can engage with and see the positive impact it will have on their role. With their buy-in secured, moving ahead to create increasingly sophisticated and intelligent digital twins will become a project that the entire business can get behind and drive forward.
Contact IX.0-Zone@accenture.com to get started on your digital twin journey