The rise of digital twins: delivering on their promise
Digital twins are about optimising the life and operations of hugely complex and expensive assets in a wide variety of industrial sectors. It is a digital replica that provides complete visualisation of an asset, relying on data technology to deliver previously unavailable and remote insight into almost every aspect of its design, operation and performance.
The insight of what-are, what-if and what-will scenarios enables 21 st Century digital twins to improve the planning and construction process to make it as time and resource-efficient as possible. Feeding the twin’s information into advanced analytics and data models gives operators the ability to develop predictive maintenance schedules, minimise downtime, and prevent potential issues from becoming actual problems.
Elsewhere, digital twins can improve environmental performance and productivity of individual buildings and plants once in operation. They can help asset operators manage end-of-life and decommissioning effectively and efficiently. And they can help upskill blue-collar workers without sending them to hazardous or remote locations. This last point has become even more pertinent during a time of pandemic when any location in which two or more people gather for work can be regarded as a potential hazard.
What’s more, the digital twin capacity can be retrofitted to a broad range of existing assets to improve performance and productivity – from immersive technology in the design phase, to informing the supply chain in the operations phase.
The concept of a digital twin therefore is a mature one, and its ability to deliver real value is already proven in the manufacturing sector, for example. So why are digital twins yet to deliver on their promise in key sectors like energy or the built environment?
As-built vs. as maintained
In part, digital twins have succeeded in manufacturing because building them for repeatable, modular asset design typical in the sector is a more straightforward and cost-efficient process than building them for the unique, location-specific structures found in sectors such as energy.
Equally, although design, build and operations specialists have shown themselves to be proficient in creating 3D images of even the most complex, multi-billion-dollar assets, that is usually where the application of digitization has stopped.
Oil rigs and wind turbines to petrochemical plants and pharmaceutical environments, even cities, have all been digitally rendered, and that process has driven significant efficiencies into the planning and construction processes. Crucially, digital twins are almost always ‘as-built’. Once the asset is handed over to operations, there is very little information on how to run and maintain it at an optimal level and even less on how they are running in real-time.
In other words, the ‘as maintained’ picture is still largely absent. Once fatigue and degradation take their inevitable toll, digital twins no longer match their physical counterparts. What’s the right amount of chemical injection needed to maintain planned productivity levels? What is causing energy consumption and carbon emissions to spike? Where is the maintenance budget most efficiently deployed? What is the right investment to make as the asset approaches end of life? Digital twins can provide all these answers – but all too often haven’t done so.
That’s not to say that work has not been done in this area. There are pockets of brilliance around the world, often buried in different projects, that are looking at bringing more effective data analysis into the mix so that operators can use and make sense of real-time process information. There has also been work on developing more predictive models, using data coming in from the asset through smart sensors. But to date, this has been more akin to digital dabbling than a fully developed digital strategy, and it hasn't really proven to be a scalable reference point on which to build consistent value.
One part of the solution, therefore, is to extend ‘digital twin’ thinking out from the design-and-build stage and into operations and maintenance. The next challenge then is to decide who is best placed to deliver the capability needed to create successful solutions for the real world.
At a technical level, real-world digital twins can be significantly more sophisticated. The 3D visualisation, often developed with drone and laser scanners, is more like a very granular and advanced version of Google Maps than a holographic representation but it can be easily manipulated to produce extraordinary insight.
For example, as a user ‘walks’ through a remote asset, layers of information can be added to provide real-time or near-real-time status updates. Potential issues can be highlighted as red, amber or green and classified as process or asset problems. Filters allow users to view specific details – floor plans, maintenance schedules, previous works, equipment serial numbers, costs, schedules, outputs – and make decisions without ever setting foot on the asset.
IoT technology (sensors, connectivity, analytics and cyber-security) create an automatic feedback loop so the asset itself can provide updates on its own status to keep it in-sync with its digital twin. Multiple systems, new and old, are orchestrated, managed, and brought together to create value.
There isn’t a single provider that can deliver all of that. So, in addition to orchestrating individual technologies, operators must orchestrate multi-faceted teams of vendors and experts.
A new hope
The capability is there – so is the expertise. The answer lies in bringing them together effectively. That means adopting more of an ecosystem approach to digital twins and working with partners who can integrate not just the various systems, but the underlying design, build and operate expertise that informs development.
As a systems integrator (SI), Wood has put in the hard yards with the hard hats, which means that the team understands the physical reality as much as its digital adjunct. This kind of ‘industry-first’ expertise is essential to bringing together all the components and skills needed in both the energy sector and the built environment.
The industry-first approach can make fully informed contributions to the design and construction of the proposed asset, or even take on the build-in its entirety. Equally, it can determine which assets would benefit from retrofitting and the technology that would be required so that investment can be directed where it delivers the most value. Crucially, operators like Wood can contextualise and mediate some of the wilder claims made by technology providers as to their ability to deliver.
This is particularly important where complex assets are concerned. In the five years between investment decisions and asset go-live, technology will advance significantly. The SI must be able to help asset operators and building owners navigate that time lag, and so a ‘future-ready now’ mindset is a key success factor.
Finally, there is the all-important CAPEX vs OPEX question. Renewed capital constraints have placed the issue front and centre, but a successful digital twin strategy is based more on a TOTEX – total expenditure – approach. Digital twins can reduce operational planned and unplanned expenditure by a significant amount, with direct consequences for profitability. But getting the necessary technical specifications to deliver on those requirements will almost certainly re-balance the CAPEX and OPEX calculation. Again, this is something that the right SI will be able to support.
The problem with digital twins to date has not been one of technology. It has been one of approach: of technology first rather than industry first, of traditional financing, and over-dependence on single suppliers. Above all, successful digital twins are about a successfully led ecosystem; no single company can do all this alone. Instead, strength comes from networks and a real alliance. That is how science fiction becomes a digital reality.