“Houston, we’ve had a problem,” was the (actual) line transmitted over 330,000 km from the Apollo 13 spacecraft to Mission Control in April, 1970.1
What followed next was, in hindsight, a proto-example of the power of what would come to be known as a digital twin.
After an explosion in the spacecraft’s oxygen tanks crippled its main engine and leaked oxygen into space, the crew found themselves unable to steer and running out of air. They were as far away from other humans as they could get, but they weren’t alone. With two-way voice communication and telemetry data linking the damaged craft, NASA’s mission control rushed backup crew to the 15 simulators used to train the stranded astronauts, reprogramming them to the new, catastrophic, situation parameters.
By figuring out processes and procedures that should work based only on the equipment available on the spacecraft, they relayed the maneuvers to the Apollo 13 crew who faithfully replicated them, allowing them, miraculously, to get back safely.
The digital twin was born.
The idea of a digital twin has since grown more sophisticated. Using sensors connected to the Internet of Things (IoT) and cloud, combined with the advancements of 5G, simulation and artificial intelligence, digital twins have taken the next step (or giant leap, to mix Apollo metaphors) towards widespread accessibility.
A digital twin, in its simplest sense, is a linked virtual model of a physical object. By connecting the real-time data of the physical object or process into its digital representation — programmed with physics, mathematical models, AI and pattern recognition to faithfully recreate its sibling — the digital twin comes to life.
The living replica, constantly updated with data from its physical twin, enables a user to analyse data, monitor systems and run simulations exactly as if they were working with the physical asset.
While such twins were initially used mainly in manufacturing, to prototype products or create efficiencies, they are now branching out, being used to model business processes, organisational transformations and even entire smart city ecosystems.2
Having a twin can be fun, and like seeing whether a particular haircut would look good on you by first seeing it, risk-free, on your sibling, a digital twin can be used to model different scenarios without the risk of taking the scissors to your business or product.
With actual data being fed into the digital twin and analysed as if it were the real thing, it is possible to gain great insight into existing systems. This could be in terms of your physical asset as it stands today, such as knowledge of its working condition and maintenance needs, its performance or in identifying inefficiency. With the addition of scenario modelling, the twin could even show you what your business could be like tomorrow.
The benefit of being able to ask ‘what if?’ when it comes to making strategic decisions is obvious. Unlike in the real-world, actions can be explored without risk (physical, economic, etc.), without associated costs, and in a fraction of the time otherwise needed. Proposed changes to technology, process or strategy can be tested before investment, investigated for unexpected flow-on effects, and outcomes assessed for viability.
Digital twins can also be used to simulate prototypes — both in testing refinements to a physical version, or creating an entirely digital version to perfect before production — vastly cutting down on wasted time, money and effort.
There are already many use cases of digital twin technology, and the list is growing:
So far, many of the use cases of digital twin technology have been in the solid world of physical assets and associated processes. However, as PwC’s own Digital Twin offering shows, we are starting to see the use of digital twins in the less tangible arenas of organisational dynamics.
The traditional approach to change or transformation, as outlined in an article in strategy+business, is a cumbersome one. CEOs decide to implement new strategies or examine growth-limiting factors and then proceed to put significant time and effort into deep-dives and pain points. It takes months, and significant investment, to make recommendations, let alone roll out the changes — and the end result is far from certain.
Moreover, this process, as slow and limited as it already is, also only gives an overview of where a business is at a specific point in time. It may not encompass the understanding behind historical trends, or include future forecast scenarios. With an organisational digital twin a business can understand where it sits, and where it could sit, from multiple lenses such as organisational efficiency and effectiveness, culture and behaviour, workforce and costs.
Being able to scenario model changes with all parts of the business allows business to turn strategy into reality, avoiding unseen roadblocks and optimising the chance for success and ultimately, growth. The use of digital twin technology is even more beneficial in situations such as the unpredictable and unforeseen circumstances currently faced in the COVID-19 pandemic — requiring difficult decisions outside the comfort zone and experience of most C-suite executives.
Digital twin technology may not be able to predict the future, but with advances in AI algorithms, real-time data from affordable and increasingly ubiquitous sensors and enabled by ultra fast, low-latency 5G, it’s as close as it has ever been before. From duplicating processes and procedures, to prototyping products and recreating complex systems, digital twins promise to take the risk, cost and time out of making essential decisions for the future of your business.
Interested in PwC’s Digital Twin? Try our free ‘Digital Twin lite’ diagnostic tool to find out in minutes what elements are hindering or enabling your strategy execution. Or for information on digital transformation for your business, including the use of digital twin technology, visit our Connected Digital Enterprise site.
With thanks to Martin Van Holten, Chris Greenwood and Alastair Pearson.
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