Rapidly evolving consumer trends and demands are challenging today’s businesses to constantly catch up—or risk losing market share to competitors. But constant changes to business models or manufacturing lines can severely disrupt operations and impact bottom lines. How then can Australian businesses review the many opportunities out there, and make strategic transformations that pay off in the long run?
The answer may already be present in data. Digital transformation is well underway for Australian businesses, with forecasts acknowledging the impact that analytics and industrial IoT will have on Australian industries by 2024. The greatest value of this transformation lies in the steady streams of data that come from the machines, systems, and devices used by all businesses. But high-performing organizations go a step further, and “create copies” of their operations—a digital twin—which they can use as a sandbox to run simulations, tests, and experiments, without impact to real-life operations.
Industrial machines have long been hooked up to sensors and connected through telemetry for monitoring and observability. From these actions you could, for example, get information about a wind turbine’s performance—temperature, harmonic vibrations, power generated, direction of rotation, angle of the blade, etc.
And while well-instrumented, traditionally, any changes to tune the system would have to be done in real time on the real machine. That’s great if you have disposable peers for that machine, but a problem if they’re unique, or at least uniquely assigned.
A digital twin is essentially a virtual device that reflects the exact state, information, and organisation of the physical device to which it’s connected. It’s a living, telemetry-driven model of the material entity, both simulating operation and evolving with the physical source system it models. If the model is close enough, you can test changes on the model first, observe simulated changes to the physical system, then decide whether to apply the changes.
In the wind turbine example, let’s say you’re thinking about testing different blade angles for efficiency in geo-specific, turbulent flow profiles. First, the operations team makes the changes to the virtual configuration in the digital twin. Then, by observing the digital twin using the familiar metrics used to monitor the primary system, you can measure potential vibration, compare generation results, set performance expectations, and safely apply the new blade angle routine to the generator.
Monitoring is the key to ensuring the digital twin is behaving as expected and to knowing that it is, in fact, an effective analog to its physical counterpart. The more you quantify the quality of how identical the digital representation is to the physical counterpart, the fewer risks you encounter when making physical alterations. As is always the case with monitoring, high accuracy and fidelity are how admins ensure their changes are beneficial and successful.
For some time, due to the cost and scale of their machines, heavy manufacturing has led the charge to create and maintain digital twins. Operations teams enable telemetry from multiple systems to feed the data in real time into simulations built by product teams, while IT is responsible for making sure the infrastructure is providing the metrics and transport that keep the digital model working.
With enterprise technology, technology professionals are also building digital twins: setting up and configuring identical systems on which to experiment. Advanced teams wield the most sophisticated versions—model-driven digital twins. Led by developers, these environments allow programmatic instrumentation and testing of multiple digital twin models driven by machine learning. And this isn’t just an option for the big players anymore—the Australian government’s Advanced Manufacturing Growth Centre (AMCG) recently pledged $250,000 to help Australian manufacturers increase their use of digital technology to innovate, expand, and boost productivity.
It’s increasingly easy to find examples of companies who rely on digital twins. The system known as Building Information Modelling (BIM) is already revolutionising the construction industry in Australia, where a virtual building exists side-by-side with its real-world counterpart. And the virtual model is simulated before physical construction even begins, allowing prefabrication modelling that results in less air pollution, dust, and noise in real life.
And for those of us who maintain real systems for main street businesses, implementing a digital twin or two can create efficiencies and add business value. That’s especially true as we move to an increasingly software-actuated infrastructure.
We’re going to ask new questions and receive unexpected answers. At the same time, the potential scope and speed of change errors increases due to automation. Digital twins will be particularly helpful for novel occurrence remediation.
Even for fundamental operations chores, we believe potential lies in digital twinning of critical systems, such as backup, unifying, or at least co-presenting critical service delivery metrics from test and production systems.
Greater access to deep learning tools can also allow smaller companies to take advantage of digital twins. Unfortunately, many organisations simply don’t have the in-house expertise or perceive a need for digital twinning. Others are legitimately worried about significant custom development and new technology.
But surprisingly, for most enterprises, the first step of digital twinning may be taken today with immediate benefit: building the right monitoring and telemetry into applications at the beginning instead of layering it on after the fact in operation.
With more real-time data available, IT, at a minimum, can demonstrate to leadership the value of lab and tools investment and identify which systems would benefit most from safe experimentation.
And that’s really the point. Experimentation is a good thing, and in IT it’s a very good and badly needed thing. Too often we know this, but are held back by risk and fear. But while some remain sceptical on the usefulness of digital twins, they’re increasingly proving to be useful when it comes to improving the management and monitoring of real-world systems.
From a talent management point of view, the use of digital twins allows higher levels of proactiveness, entrepreneurship, critical thinking, and creativity from employees. Imagine being able to make critical product decisions on a digital twin—and immediately see the results—which can drastically reduce time from R&D to production. In turn, this would result in a more efficient way of doing things and the production of better goods and services that will benefit customers.
Perhaps one day companies may depend on digital twins as much as they do any other critical IT technology. Digital twins help deliver that most magical IT trick: accelerating change by removing risk. It’s real freedom for developers, IT, operations, and, ultimately, the business to do what leadership is asking for in the first place—innovate.