Accelerating digital twin technology adoption

Digital twin technology is evolving manufacturing plants and many companies are taking advantage of its benefits.

By Suzanne Gill December 19, 2021
Image courtesy: Brett Sayles

Digital twin technology is being widely used in applications today – from initial design proof of concept through to controls testing, according to Richard Sturt, solution architect manager at Rockwell Automation. He attributed the key reasons behind most use cases as being risk reduction; testing and improving a design before committing to build it; simulating complex production runs to understand performance; testing control system against the digital twin to reduce on-site commissioning time; and training on a digital twin when it is not practical to train on the real system.

“Building a digital twin at the early stage of a project allows you to more accurately visualize what the system will look like and convey it to different stakeholders,” said Sturt. “It is much more than just a 3D animation. A system like Emulate3D from Rockwell Automation, for example, uses accurate physics to allow for detailed simulation. How fast can you place product on a moving conveyor before it will fall over for example. It can answer questions such as how do complex systems interact? How fast does a jam in a packaging machine propagate back through the system? If the system is a new configuration or very complex, there will be a lot to be gained from emulating the system.”

The next stage will be control testing. “You can develop code to control the emulation at the same time that the physical system is being built,” continued Sturt. “The more software testing you can do before you go to site the better. On site commissioning is usually on the critical path of a project, so if it can be shortened, then projects can start up faster. Some customers are now insisting on pre-testing using digital twins because of previous bad experience with drawn out onsite commissioning that caused delays in factory start-ups. Some systems can be completely tested before going to site but what if it is a complete new factory with automated storage, robots, AGVs, packaging lines, etc? It is  not possible to do a complete system test until all the equipment is on site – unless you test with a digital twin.”

Sturt said it is possible to go further than just controls testing, saying it is also possible to test the interaction of a manufacturing execution system (MES) with the control system and the digital twin or even all the way from an enterprise resource planning (ERP) system. Some digital twins, like Emulate3D, allow users to run simulations faster than real time which can enable weeks or even months of production to be simulated in just a few minutes.

“Once you have the plant running a digital twin is still a valuable tool,” continued Sturt. “If modifications need to be made to the system – to cope with a new product or different packaging, for example –  then it is possible to test different configurations or capacity expansion on the digital twin while the plant continues to run as normal, removing any need for downtimes to trial a variety of different configurations. The latest software tools often come with catalogues of pre-built common items to help speed up development. If you already have 3D CAD models, they can be imported and enhanced with realistic movement. If you are only going to use the model occasionally you can engage a specialist simulation company to develop the model for you. If there is a level of risk associated with a project, whether it’s a late start-up or not meeting performance criteria, it may be worth considering how a digital twin can help.”

Digital twin implementation benefits

Commenting on the benefits that industry is seeing from the implementation of digital twin technologies – both at the design stage and during the plant lifecycle – Lee Tedstone, global vice president and head of digital project procurement, construction and execution solutions at AVEVA, said that digital twins provide a platform to connect decision making. “For every physical asset you should have a digital asset integrating all the data so you can see it all at once, in real time,” he said. “This will enable you to visually consume information in a much more efficient way than by text. The benefit of this at the design stage is that you can take a concept forward and gain insight into hypothetical scenarios. Digital twins create synchronicity between the engineering and construction phases and allows for the planning and prevention of larger incidents before they occur. A digital thread runs right through every project from conception to delivery, and the design and build stage is hugely important.”

This is significant because global construction equates to 40% of CO2 emissions globally and being able to minimise that will be crucial going forward. So, digital twin technologies have the potential to capture a sustainability score for an asset from conception. This provides the knowledge to decide if the materials are the most sustainable on offer or if the logistics are as efficient as they can be. All of this will drive enhancement of the supply chain, giving users the power to procure and construct in the most sustainable manner.

Tedstone said the main challenges facing organizations in the past was lack of data. Today there is almost too much data available, and the key challenge now is putting that data into context. In order to do this the right systems need to be in place.

“The biggest challenge for most of our customers is knowing how to consume data in a meaningful way and being able to make business decisions based on it,” said Tedstone. “Digital twins can support this execution. Another challenge is having a digitally capable workforce so it is crucial to invest in training for your own people to be able to support the technology. Companies should have started on the journey to become digitally transformed and a huge part of this is having executive support for this.”

Change for the better

Mark Yeeles, vice president Industrial Automation UK & Ireland at Schneider Electric, says that the the introduction of digital twin technologies has changed industries for the better. “Its potential to revolutionize asset design, construction, operations, and maintenance has already been widely recognized, but we are only at the start of the journey,” he said.

Yeeles said the convergence of several factors makes the digital twin concept a proven business enabler for accelerating digital transformation, in turn overhauling old-school static representation into dynamic, real-time simulation enhanced by intelligent, live data.

“Digital twin technologies have powerful applications across the entire asset lifecycle,” he said. “Starting at the design phase, the concept of first-born digital twin allows engineers to test the twin of an asset before it is even constructed. This allows for the process, equipment, and operations to be analyzed and optimized for safety, reliability, and profitability.

“Once the first-born digital twin is optimized, organizations can give birth to the physical asset twin. Once the physical asset is up and running, the connection between the digital twins continues as the volume of data collected, contextualized and analyzed allows deeper insight into key performance drivers, enabling improved asset performance.”

As with any data-driven technology, it will be the quality of data, as well as the volume, that is the driving force behind asset optimization across its lifecycle. It is no different for digital twin technologies. Organizations looking to implement digital twin technology will need to ensure that the highest quality data is available across the network of sensors that the digital twin interacts with.

To address this, Yeeles advised companies create a digital fleet – a range of assets within a site all operating the digital twin concept – to amplify its effects. “Using the digital fleet concept, deep twin-to-twin comparisons can be applied to similar assets regardless of location, manufacturer or other variables,” he said. “The increased volume of data enhances the accuracy of the model’s analytics, allowing the twin to evolve based on fleet-wide experiences, making every model smarter as more equipment is connected. These comparisons help identify commonalities across highest or lowest performing assets, for potentially business-changing key performance insights and opportunities to improve operations and maintenance.”

– This originally appeared on Control Engineering Europe’s website. Edited by Chris Vavra, web content manager, Control Engineering, CFE Media and Technology,

Original content can be found at

Author Bio: Suzanne Gill is editor, Control Engineering Europe.