Many industries are beginning to adopt the digital twin approach for product design; for example, the UK's £15 billion Crossrail project (above) has a digital twin model of the whole network.
Technology is redefining existing production processes, enhancing product efficiency and reducing production cost.
THE most radical industrial revolution since the mechanisation of the textile industry in the 18th century is underway, right now.
In this new revolution, rightly dubbed Industry 4.0, we are witnessing the digital transformation of the manufacturing sector. The Internet of Things is accelerating and expanding the aggregation and analysis of vast qualities of data, enabling the automation and digitalisation of both processes and products.
And at the cutting edge of the Fourth Industrial Revolution is digital twinning technology.
No more physical prototyping
Simply described, a digital twin will enable a manufacturer to dispense with physical prototyping of a new product idea. It will be possible to create a product, process or system and try every feature, test every nuance or iron out every single bug before it is even built.
As a study by Deloitte points out, a digital twin is fundamentally an evolving digital profile of the historical and current behaviour of a physical object or process that helps optimise business performance.
The digital twin is based on massive, cumulative, real-time, real-world data measurements across an array of dimensions, says the Deloitte study. These measurements can create an evolving profile of the object or process in the digital world that may provide important insights on system performance, leading to actions in the physical world such as a change in product design or manufacturing process.
Industry adoption growing
It is reported that many industries are beginning to adopt the digital twin approach for product design, including construction, and asset management of buildings and infrastructure. The UK's £15 billion (S$26.8 billion) Crossrail project, for example, has a digital twin model of the whole network.
In another high-profile example, US conglomerate General Electric relies on digital twinning to build and maintain its wind farms. Virtual models allow engineers to monitor and control the turbines, identifying problems before they occur. An energy forecasting application in the virtual plant integrates with the twins and predicts the power outputs.
And oil and gas giant BP is developing digital twins that represent physical projects like new oil fields. Digital twinning helps BP's engineers to visualise their projects and virtually simulate actions before executing on physical assets.
Better products, faster to market
Using fewer physical prototypes means organisations can create better products and reach the go-to-market phase faster. Large enterprises benefit from more efficient and cost effective product life cycles, while the barriers to entry for smaller companies are reduced, creating new jobs and business opportunities.
Here in Singapore, the government is looking to the Industry 4.0 model to combat the pressures of rising operational costs, domestic labour shortage and a weakening dollar. The country's co-ordinated Industry 4.0 strategy is moving industries and companies towards adoption, recognising that although Singapore's manufacturing sector is seeing its highest growth rate since 2009, there is an urgent need for improved productivity and sustainable growth.
To address these needs, technologies such as 3D printing and augmented reality (AR) are redefining existing production processes. AR offers manufacturers exceptional redesign flexibility by first digitising the existing machine tool or process with its virtual representation - a digital twin.
The advantages of digital twinning, in terms of both enhanced product efficiency and reduced production cost, are remarkable.
Once they have a digital twin of a device, manufacturers can test it, tweak it, and tune it throughout the process of building it. By the time it is deemed ready for industrial production, the device will have been honed to perform with maximum efficiency. And the connection does not stop there - the digital twin continues to feed back intelligence throughout the lifetime of the asset. It's not just a case of cutting prototyping or construction costs, but of predicting failure and cutting maintenance costs and downtime.
Predictive maintenance is greatly enhanced, since the virtual representation of a product accompanies the physical asset across its entire life cycle, from design and engineering to distribution and end-use.
Connected for life
The physical and digital twins are connected to one another throughout their entire life cycles, with sensors fitted to the physical product sending a constant stream of data for analysis to the virtual machine.
A management system typically gathers and stores this data, and prepares it for comparison against specifications already compiled as to how the asset would optimally function. If a discrepancy is detected, engineers can test and evaluate potential solutions directly on the digital twin.
Digital twinning creates efficiencies across the entire production system too. For example, imagine if all the equipment in a wind farm had a digital twin. The twin could be used to configure each wind turbine prior to procurement and construction. Once the farm has been built, each virtual turbine could use data from its physical equivalent to optimise power production at plant level, just by small tweaks such as adjusting turbine-specific parameters like generator torque or blade speed.
The deeper value of digital twinning will come from what is done with the insights that emerge from the data produced by the digital twins, through the applications that are written to act on that data, and through the automated systems that react to this new, valuable source of streaming data.
Thus in order for the digital twin to be useful for the resolution of business problems, the conversation needs to shift from just having access to the twin, to having the ability to run analytics on the twin.
The key to making this happen is to help companies adopt digital analytics, together with industry experts. Companies need a solution that is lightweight and open source, one that can work with anything. It must be coupled with a visualisation platform that enables its user to make sense of a complex web of dynamic data, and translate that into intelligence that can be rationalised and acted upon instantly.
The digital twinning revolution is well on its way to realise significant benefits for all participants of a value chain. It's time to let the machines speak for themselves.