The close integration of physical and digital worlds has become a hallmark of our time. In mining, construction and other sectors, this is expressed through the use of digital twinning to represent objects and systems virtually, and to assist in decision-making.
Thanks to telematics, the Internet of Things (IoT), and the emergence of sensors that can gather performance data from real-world systems, we are now able to replicate those systems digitally, and simulate the impact of a range interventions.
What is a digital twin
Put simply, a digital twin is a computer programme that acts as a virtual replica of a physical object or process. Imagine a new car in development. Now imagine the car has a real-time digital counterpart. Using the virtual model of this car, manufacturers can run simulations to test its performance and solve any issues before the actual, physical vehicle is built and hits the road.
It is estimated that there are already billions of active IoT devices in operation worldwide. These devices are generating vast amounts of data, all of which can be employed to enhance the understanding of assets and technology performance.
With digital twinning, equipment and systems are represented virtually, and can be updated in near real time, to reflect changing conditions. Machine learning (ML) can be deployed to build knowledge about system responses, with artificial intelligence (AI) used to replicate the effects of proposed interventions, before organisations sustain the expense of building them in the real world.
Digital twinning allows businesses to rapidly boost efficiency and effectiveness. They can refine and perfect their systems ahead of deployment and make accurate decisions before committing to major investments. Technology-supported decisions are more likely to be the right ones, which helps to remove risk from operations. Digital Twin can be positioned more to improve operational efficiency in real world by playing around with parameters to visualize in the virtual world.
For emerging economies, like South Africa’s, this offers an enormous opportunity.
In the past, perceived risk has been the reason some global players have been reluctant to invest in emerging markets. On a micro, enterprise level, digital twinning helps to reduce the risks associated with major investments.
The ML capability of digital twinning allows the outcomes of new technology investments to be simulated and mapped. A strong business case can then be advanced to support these decisions.
This serves to de-risk investments in emerging market businesses, levelling the playing field and allowing developing markets to leapfrog developed-world enterprises using technology. Already, South Africa is globally competitive in several industries, as technology has narrowed the gap with rivals in established western markets.
While digital twinning might sound like a solution that is only feasible in developed countries, it’s essential to the growth of developing nations. By applying this sort of model, we can develop ways to optimise what we already have while preparing for the future.
Digital twinning is used in our mining industry to support efficient and effective decision-making. The automotive sector uses digital twinning to refine its manufacturing processes. The technology is being used to simulate the impacts of new designs in urban planning.
But this technology can also be used to combat common frustrations that we experience on a regular basis, for instance, potholes. Using a digital twin, we can quickly find the best way to safely divert traffic and free the road so that the pothole, or other hazard, can be attended to.
In the built engineering sector, South Africa has no accurate overview of the state’s assets. This makes it very difficult to keep track of how to prioritise maintenance and optimise the capacity of each entity. While, ideally, assets should be created as digital twins long before the first sod is turned in the construction process.
On the national level, building this way offers great opportunities to de-risk infrastructure builds, the foundation on which Africa’s economic resurgence must be built. Rail and road networks, urban development hubs, telecoms and IT networks, water and electricity transmission… This kind of infrastructure is highly expensive, but also essential for national development.
Cutting red tape
Digital twinning can cut through tedious red tape, that often hinders investment and slows down development. Having a constant data stream feeding a digital twin model negates the need for paper documents and ensures that everyone works off the most recent data set.
It can also help simulate the usage patterns and effectiveness of proposed infrastructure, so that African policymakers and private-sector developers can then make the right decisions and provide infrastructure that suits our needs. This will help to build institutional memory, promote trust and transparency and position Africa for future growth.
The South African job market could also benefit from the creation and implementation of this technology. There’s a lot to be done to build functioning digital twins which leads to an opportunity to create a different kind of labour force. Once established, these models need to be monitored, maintained and protected.
Digital twinning is already an essential part of planning in many sectors – especially construction. In the United Kingdom, for example, a national digital twin programme was launched by the government, in partnership with Cambridge University, to gather data, build digital twins and improve how infrastructure is built, managed, operated and eventually decommissioned.
Once outcomes can be mapped and understood, investments become less risky to governments, large enterprises and public-private consortiums. Digital twinning thus offers a great opportunity for Africa to compete with – and even leapfrog – other economies.
However – and this is a critical consideration – digital twinning requires sophisticated support and expertise. Creating an accurate digital twin, defining the parameters, and ensuring that the twin accurately reflects its real-world equivalent, is a highly specialised field.
Digital twinning requires data mining and analytics, in addition to modelling, optimisation, and control. Multiple information technology (IT) systems – including cloud and IoT capabilities – must be built to collect and store data.
This requires frameworks for end-to-end engineering analysis and recommendations to improve operations, design new materials and schedule predictive maintenance.
With the right digital twinning expertise and IT capacity, African companies can improve productivity, throughput, product quality and energy consumption, while reducing production costs, accidents and emissions.
Any South African organisation with ambitions to become globally competitive therefore needs to incorporate digital twinning into its strategic planning, and to build partnerships that will advance this vision.