The popularity of digital twin technology has grown exponentially over the past few years, with its manufacturing and supply chain role continuing to expand at scale. Connected Technology Solutions gathered three of the sector’s leading experts of the technology – John J Roffel, global solution architect, Honeywell Connected Industrial, Mark Coates, strategic industry engagements director, Bentley Systems and Mike Loughran, CTO UK, and Ireland, Rockwell Automation – to discuss the impact that the technology is having on the sector.
Let us start with your definition of what a digital twin is. There appears to be different visions from different stakeholders. What are the attributes that are essential for a digital twin?
John J. Roffel (JJR): There are multiple definitions of digital twin technology, which has created some industry confusion. According to Deloitte, the term can be defined as ’a near-real-time digital image of a physical object or process that optimises business performance’.
This technology provides an accurate digital replication of a physical object or process that provides actionable insights into behaviour and performance. In other words, a digital twin enables insights that can be translated into actions giving value. For example, knowing something is wrong may be a great insight, but not knowing exactly what to do about it does not have much worth.
Mike Loughran (ML): A digital twin is just a digital replica of a physical asset, living or non-living. It is dynamic; that is why anything in the physical world, whether that be living, static, multiple things, the digital representation should genuinely represent it. If an item is static in the real world, it can be static in the digital world, and if it is moving in the real world, it will be moving in the digital world with all the key attributes that come with it. It must be an exact replica of it to be of value.
Mark Coates (MC): The short answer is that a digital twin is a digital representation of a physical asset, process, or system, which allows us to understand and model its performance. Digital twins are continuously updated with data from multiple sources, making them different from static 3D models. Digital twins are designed to detect, prevent, predict, and optimise through real-time analytics to deliver business value. In my opinion, the five critical components of effective digital twins are geospatial and physical data about the assets in the system; direct observation or sensor data about the environment that affects the assets; performance data; analytics; and visualisation.
What are the drivers from the market? Why does it appear that there is a great demand for digital twins?
MC: The market is being driven by the need for better planning, delivery, operation, and maintenance of infrastructure assets. One of these key drivers is the increased demand for predictive maintenance. With the improved access to data, digital twins enable delivery teams and asset owners to make more precise decisions faster due to the increased level of knowledge and industry insights.
As the market focuses on critical improvements such as reducing carbon emissions, the demand for predictive maintenance is increasing. A digital twin enables companies to monitor their assets’ performance and condition during normal operations and minimise the likelihood of downtime. As we see a move away from the mindset of merely focusing on delivering a project, clients now want to pool their data. A digital twin provides a system of systems and empowers companies to make educated decisions. Using both company asset information and industry information, asset owners can reduce their liability and enhance performance for both themselves and investors, as well as for the user.
A digital twin is a perfect repository for the much-publicised golden thread of information and data in the UK. When you then link in asset performance, you have another primary industry driver for digital twins. Clients can negotiate better terms on their asset loans and insurance premiums because of the information they hold.
JJR: Businesses all share a common culture of continuous improvement, and the application of digital twins is the next technological innovation that shows promise against continuous development goals. I would point out that digital twin applications to drive process enhancements are not new and have been around for years in model-based control and model-based planning. However, what is new is the capabilities to deploy digital twins to address either many or more complex models at a scale that was unimaginable in the past.
Furthermore, digital twins have the inherent ability to capture and propagate domain expertise. This, too, is driving demand for the technology. With the industrial workforce going through a significant changing of the guard, with many seasoned experts now retiring, digital twins are being used to capture their knowledge for use by the next generation of plant personnel.
ML: In the manufacturing world, it is first the machines’ design to meet the demand required right now and design, test and deploys very quickly. Then, once it is in deployment, we can change the existing machines used for repurposing. People cannot presently get to the site because of restrictions, so there it is about taking as much time as possible out of the commissioning cycle.
What technology has allowed the digital twin to gain such importance in such a short time?
ML: Computing power, access to high-level computers via the cloud, but just general technology advancements, including things such as augmented reality, virtual reality, and those becoming a lot easier to access, as well as the computing power we have on our desks. There is also the way that many vendors have designed that technology, including Rockwell, where we have embedded in digital twin assets as part of our physical and software offering. It just becomes part of the design process, rather than being a separate thing done by one company. The cost is coming right down as well, so the cost has been a big driver.
MC: One of the leading technologies is asset performance management (APM), based on products such as AssetWise or PlantSight, to support a digital twin. With this information, based on critical components’ operational data, clients can improve planning and hold specifications to account.
JJR: Increased computing horsepower, the focus on data and analytics, machine learning, artificial intelligence and cloud computing are all playing a role. However, the need to do more with less may be the most significant factor driving innovative manufacturers to deploy digital twins.
Technology advancements make the risk for such deployment more manageable while providing, replicating and delivering solutions to scale to meet the current reality.
What are the benefits that companies gain from adopting digital twins?
JJR: The term we often see used is ‘sweating the assets’, in other words, getting more out of your existing assets by running them closer to constraints, addressing issues before they become problematic and operating as efficiently and effectively as possible. Benefits can vary significantly, but being able to unlock additional recurring value without large capital expenses delivers results directly to the bottom line.
ML: I think to do it justice, you have got to look at the three ways. From the people who are designing a machine or a line, they have the benefit of being able to run that in simulation and test before they build it, so they can iron out many of the issues or problems, or they can offer high performance. Once that goes into the actual physical world and becomes a reality, the end-user or operator can efficiently train people to run that and test it before it goes live without any problems. If they bring new operators in or new production or maintenance people, they can learn virtually before they get let loose on the real thing. From a lifecycle point of view, if your new design comes in digital ready and running, you can then tie the real-world production feedback back into that model. That allows you to get more out of the machine, change, repurpose, or improve it as required on the fly. You get the agility side of it and the product lifecycle management once it goes into place.
MC: The best place to look is live examples and projects that Bentley supports with industry partners. One example is High Speed Two, Britain’s first new intercity railway built north of London in over 100 years. As part of this £55.7 billion project, Mott MacDonald and SYSTRA are part of a joint venture for two main works civil contract lots, N1 and N2. The design teams are collaborating with the Balfour Beatty/VINCI (BBV) joint construction venture working on behalf of HS2. Mott MacDonald and SYSTRA are designing the railway route from London to Birmingham. The £4 billion Area North project is a 70 metre stretch of the 354 kilometre HS2 project and is divided into 350 assets, including 53 tunnels and 74 kilometres of cuttings associated with surrounding civil works. With 1,850 project participants, the team had to contend with 43,000 imported files and the production of 61,000 design files, not including iterations of those designs.
For this size and scope project, the project team realised that traditional methods of collaboration and information management would not work. Instead, Mott MacDonald and SYSTRA decided that the best way to manage all the data for effective collaboration was to create a digital twin. The team began by developing a connected data environment, using a template as the foundation, and enhancing it to meet the client’s requirements. Workflows built into the platform were based on British information management standards, allowing the multidiscipline project team to work with the data and create deliverables while knowing that the information was suitably checked and verified.
Using the data housed in ProjectWise, Mott MacDonald and SYSTRA implemented model-based delivery to minimise drawing production. The project team produced over 4,000 design information models, created a digital twin, and saved significant design time, allowing team members to enhance the quality of the design and improve client and stakeholder collaboration and engagement.
Are there any sweet spots for the technology in your experience, whether that be processes or sectors?
MC: From a holistic level, the work we are doing with cities like Stockholm showcases how we can use accurate data, and strong visualisation can help large groups of people. For example, plans are underway to build 140,000 new apartments by 2030 to meet the high demand. Communicating clearly and involving citizens early in the planning stages is crucial to avoid misunderstandings and formal complaints that could delay the urban planning process. The ambitious plan also has the goal of attracting investors to the region.
Stockholm quickly realised the value of using 3D visualisation to engage inhabitants in the planning process from the beginning. Stockholm is actively progressing its digitalisation to become a smarter city, and several initiatives are ongoing to reach that goal.
JJR: Honeywell has decades of experience providing innovative automation and control solutions to the process industries. We have also been at the forefront of digital twin technology for industrial applications. Since digital twins can vary significantly in size and scope, we do not believe there is a single sweet spot for this sector. Indeed, large-scale continuous processes, which can be operated more effectively and efficiently with digital twin technology, can generate significant value for the end-users. However, a simple asset model that can be replicated at scale may contribute similar value in aggregate. The sweet spot, therefore, is the right trade-off between benefit, risk, and value.
ML: When you look in the rear-view mirror, the significant benefactors out of it were typically around the intralogistics side of things; this could be baggage handling or parcel handling. Because the cost has come down with speed to adoption, it has benefits to OEM machine builders and line builders. It also sees use in manufacturing consumer-packaged goods and automotive and aerospace, which has been very prevalent for many years. It has been a bit more democratised now the cost levels have come down so more people can use it.
Can you give me a short example of how a digital twin has helped meet one of your customers’ business needs?
ML: We talk about the intralogistics side of things, packages, baggage handling and parcel handling, and the very prevalent deployment. When you look at more things like remote asset pumping or compressor stations, these are complex bits of machinery deployed in some inhospitable areas. How do you remotely monitor them, change them, tweak them, and test them without sending out personnel to the site? That is the kind of thing that is happening now as well.
MC: Bergen, Norway, is extending its light rail system to provide an optimal public transport solution that will facilitate urban development. The new NOK 6.2 billion, nine-kilometre integrated rail line features eight new stops, including a stopping place and depot situated underground and two new tunnels. Sweco Nederland (Sweco NL) designed a railway that connects with the existing city infrastructure. Faced with mountainous terrain, a limited footprint, and coordination and integration challenges, Sweco NL required an open digital solution to streamline workflows and accommodate the accelerated schedule.
The project team established an open, connected data environment to share and manage information among the 18 different engineering disciplines spread across five countries. The project team recognised that the large project scale and complexities of data integration, alignment, change management, collaboration, and communication required a new digital approach.
JJR: A customer was exploring digital solutions to achieve operating excellence. Following a review of their existing operating practices, we implemented digital twin technology for early event detection and resolution while also embedding domain expertise for drill-down capabilities, root cause analysis and constraint optimisation.
This approach supports a continuous drive towards operational best practices. For example, changing workflows helped identify a means to meet capacity by more than ten per cent. It also enabled early detection of operating changes to mitigate the impact of deteriorating equipment performance.
Is the manufacturing industry ready for digital twins, and do they have the structure and processes to take advantage of it?
JJR: There is a broad understanding that digital twin technology is foundational to digital transformation efforts. Many organisations have a C-level role with the term digital in the title. However, at the lower levels of the organisation, the readiness to adopt digital twins varies widely. Much of the industry is in a learning mode, targeting smaller applications or piloting larger scale opportunities to improve knowledge and understanding.
The digital twin vendor’s ability to enable connected solutions for off-premise deployment means that this readiness is more about the organisation’s maturity and experience than having the infrastructure for success. This also means we can anticipate a mass adoption once we get past the early adopter phase of practical and valuable digital twin applications.
ML: I think it depends on the industry, but I would suggest that any new piece of equipment or plant coming in, one of the things that should be asked for is the digital design should go with it; that should be part of the package. That way, it does not put any onus on the end-user to then re digitise. It is becoming more of a requirement than it has in the past but is a little bit behind in some industries, and that tends to be down to the lack of understanding or not knowing what to ask for from suppliers.
MC: Very much so. Off-site construction, which is increasingly popular, will benefit significantly from a digital twin. By creating a virtual representation of each physical device, manufacturers suddenly have a wealth of data on production processes and performance at their fingertips. With off-site construction, manufacturers can collect data directly from equipment and operations on the shop floor in real time, giving them complete insight into what, why, and when of downtime, cycle time, quality, and scrap. Using a digital twin leads to better products, enriched data via sensors and more accurate delivery times.
Data is the lifeblood of any advancing city or project. Making smarter decisions with data can significantly impact both the delivery and life of a manufactured asset. However, collecting data for its own sake is not beneficial. The key is to produce good, precise data from the correct services. Suppose that data can be fed into a decision-support mechanism, like real-time simulation and predictive analytics software. In that case, manufacturers can confidently understand how their network is currently behaving and how it will react under a wide range of what-if conditions.
What are the challenges that companies face when looking to use digital twins?
ML: If we start with the legacy question, which is a large part of many industries, it can seem like an impossible task to digitise your process or facility. However, I think what I would start with for those people is to ask their suppliers and machine builders who they are working with what they have got, because many of them will have been designed already in digital CAD. Many of the vendors, such as Rockwell, with our companies, can take digital CAD packages and turn them into a digital twin as part of what the software does. The fact that the technology has come on leaps and bounds could be a lot easier for them to start to build a digital twin of their physical plant than they thought, but they do need to engage their suppliers. Many OEMs will have those on the shelf; they will have the digital CAD drawing with the physical attributes. Tying that to their existing automation equipment is relatively simple, especially from Rockwell Automation; that is something we have had built-in for some time with things like Emulate3D and Ansys; we already do that. I do not think it is insurmountable as people believe, but there is a lack of understanding and knowledge out there, which can put people off.
JJR: The people side of change management is perhaps the most overlooked challenge in deploying digital twins. A digital twin application, in its deployment, will almost always change the way people work. The application will enable more effective collaboration and modify existing workflows.
People will need to resist a recurring tendency to revert to prior methods of work. Failure to address the need for end-users to use the technology has been the eventual failure of previous technological innovations. Therefore, effective change management is a crucial challenge that every company must recognise when realising the value of digital twin applications.
What is next for digital twins? What can we expect in the next decade?
JJR: Considering the technology adoption cycle, it appears that we are still in the early adopter phase for digital twins. Innovative end users are beginning to realise the value of this solution. Soon, early adopters will be joined by the followers, and then we will see a mass acceptance of the technology as a necessity to compete.
At the same time, other innovations will continue to move forward, so much so that it is difficult to predict what they may look like. AI and machine learning will play a more active role in the care and maintenance of digital twin applications. The decisive outcome is a crucial tool to enable more autonomous operations.
ML: Rather than a digital twin being part of the design process, as you design the product, the digital twin creates itself. When you put together the code, you put together the physical machine; it is inherently built into those design and implementation tools. I think it will become a lot more utilised by people on the industrial plant floor, and they will come to expect it will become the norm to test it virtually and then deploy physically. The expectation from people operating, maintaining, and looking after the equipment is that it will be an inherent part of their plant.
There has been much talk about autonomous operations in manufacturing and industry. Is the digital twin a vital step on the path to autonomous operations?
JJR: Like how model-based controllers have been used to provide automatic feedback control, the digital twin will be a crucial component of autonomous operations. Rather than a standalone tool, it will be used in combination with data, analytics, AI, machine learning and other capabilities.
ML: Even just from a check and safety point of view, nobody would want to deploy autonomous manufacturing without proving it will be right because it could be a very costly mistake if it is not. It is a significant part of it, especially if we are to meet the consumer speed requirements we see. People want the agility of the factories they are going to require in the future; it will be a must.
MEET THE PANELLISTS
John J Roffel, global solution architect, Honeywell Connected Industrial
John J Roffel has over 30 years of experience in dynamic simulation and process control applications, including more than 28 years with Honeywell. For 22 years, he was responsible for delivering and leading advanced software solutions projects and operations teams worldwide. More recently, as marketing director, he has led the global simulation and operator competency businesses before joining the America’s Honeywell Connected Industrial team to drive strategic connected and standalone digitisation software initiatives.
Mark Coates, strategic industry engagements director, Bentley Systems
Mark Coates leads Bentley System’s strategic industry engagement, keeping a keen eye on the latest industry trends and their impact on successful projects’ delivery. He is a former quantity surveyor with an extensive background in global project delivery. He has worked on numerous infrastructure projects, consulting asset owners and their advisors on technology adoption, focused on attaining better project results while being conscious of time, cost, and quality.
Mike Loughran, CTO UK, and Ireland, Rockwell Automation
Mike Loughran is CTO for the UK and Ireland at Rockwell Automation, an industrial automation and information technology provider. He has been with the company for more than 16 years, having begun in software sales and moving up the ladder to the C-suite position he now holds.