Digital Twins Are Changing the World
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Source: Irving Wladawski-Berger, CogWorld think tank member
“Digital twins are fast becoming part of everyday life,” said the lead article in the August 31 issue of The Economist. A Digital Twin is essentially a computerized companion to a real-world entity, be it an industrial physical asset like a jet engine, an individual’s health profile, or a highly complex system like a city.
“Digital twins began as basic computer models of physical objects and systems,” explained The Economist. “As computers have become more powerful, twins have become more sophisticated. Complex design and modelling software means many physical objects initially take shape in the virtual world. Small sensors, capable of measuring all sorts of things, feed twins with real-time data, ensuring that they mirror their physical counterparts.”
Digital Twins help bring the physical and digital worlds closer to each other. Digital technologies, internet connectivity, and other innovations are designed right into the products, creating a new era of smart connected products. Massive amounts of product-usage data can now be gathered, stored and analyzed by applications in the product’s cloud counterpart. The use of AI takes all this much further, allowing virtual models to both simulate and optimize activities in the real world.
In addition, digital twins make it easier to tackle highly complex problems, spot problems before they materialize, and test innovative ideas without real-world consequences. The Economist issue includes articles on three important digital twin applications: speeding up product development, making companies more efficient, and enabling scientific innovations. Let me briefly discuss each of these articles.
Speeding up product development
The first article illustrates the use of digital twins in the development of leading edge products by explaining how it’s being used in the design of Formula One racing cars, the world's fastest regulated road-course racing cars. Formula one cars depend on their aerodynamics, suspension, electronics, tires, and other highly advanced levels of engineering to safely achieve their very high speeds.
The article features the Red Bull Racing team, which employs around 1,500 people to design and build their racing cars. “Out on the track, they race in a world where mere fractions of a second over a minimum of 305 km separates winners from losers.” During a season, the cars will undergo several thousand design changes and tweaks. “These have to be done at breakneck speed, with components designed, tested, shipped and fitted in a matter of days between races. There is no room for error.”
That means that, beyond the racing track, the only way Formula One teams can maintain such a pace is by battling it out in the digital world. Using a digital twin of each of their cars, — a very detailed simulation of the entire design and production process, — problems can be discovered and corrected before they emerge in the actual physical car.
“To work, a digital twin needs to be constantly updated by its physical counterpart. This is done using real-time information gleaned from sensors that measure just about anything that can be measured. In the case of Red Bull, each of its cars’ digital twins is updated by more than 250 sensors constantly checking things like engine performance, tire temperatures and suspension movements. By the end of a race, the amount of wireless data relayed by each car back to a team’s engineers can be in the terabytes.”
Over the past few decades, advances in computer-aided design (CAD) technologies, tools and applications , —e.g. structural analysis, computational fluid dynamics, powerful supercomputers, — have transformed the development and manufacturing of cars and other complex engineering systems. The digitization and virtual simulation of a car’s development has already helped to shrink the process from conception to mass production from around five years to about two.
Digital twin technologies are already being used in jet engines, power generators and other complex, expensive products, monitoring each individual unit in order to anticipating problems and schedule the necessary preventive maintenance to minimize the products’ downtime, let alone the potential for catastrophic failures. “The advantages such twins offer in speed, reliability and cost together represent the future of manufacturing, says The Economist.
Making companies more efficient
The second article deals with the use of digital twin technologies to monitor and adjust the various operations of a company based on real time data. The article uses the example of Uber, one of the most sophisticated digital twin systems of its kind, which monitors the status of its various cars on the road, the traffic and weather conditions, and the expected demand to optimize its operations.
“If current technology trends hold, such end-to-end digital representations of a company’s inner workings— and, increasingly, its ecosystem of customers and suppliers — will no longer be the speciality of tech firms such as Uber. Artificial intelligence (AI), in particular, will make it much easier for all sorts of businesses to build virtual replicas and oversee them on a scale managers alone never could.”
“As a result, digital twins will redefine what it means to run a company. Instead of co-ordinating disparate islands of automation, as is the case today, bosses will manage a constantly churning flywheel fueled by data. With access to information from all over the company’s operations, as well as from its customers and suppliers, a corporate twin will not just help managers make better plans. It will also implement them, learn from the outcomes and optimize itself to achieve certain corporate objectives — over and over again.”
According to the article, Amazon is considered to have pushed this process the furthest. This is not surprising. Amazon is one of the most successful two-sided platform companies, bringing together consumers and producers in high-value transactions. Scale increases a platform’s value. The more products or services the platform offers, the more consumers it will attract, helping it then attract more offerings making the platform even more valuable for both vendors and consumers. Moreover, the larger the network, the more data is available to personalize recommendations, further increasing the platform’s value.
Translating network effects to better attract users and vendors doesn’t happen by accident. It requires careful attention to the design and governance of the platforms, an area where Amazon truly excels, such as optimizing different parts of its supply chain—from how many of a certain item to keep in stock to where to build new warehouses, — and bringing them together into one coherent business model.
“The past 25 years saw the rise of huge tech platforms, including Amazon, Uber, Google and Meta, most of which are marketplaces that match consumers with goods, services and content. As non-tech businesses, from carmakers to insurers, become more and more embodied in software, they will turn into large platforms. By embracing their digital twins, companies will be able to do more than just match buyers and sellers, orchestrating complex relationships between them too.”
Enabling scientific innovation
“Scientists are no strangers to computer models,” notes the third Economist article, reminding us that some of the very first uses of computers to simulate reality were built by physicists keen to understand the behavior of molecules, atoms, and sub-atomic particles. This was actually my personal experience as a physics graduate student at the University of Chicago in the 1960s under the direction of Professor Clemens Roothaan, — one of the pioneers in the use of computer models in scientific research.
Computer modeling has greatly advanced in the intervening decades, and is now an integral part of scientific research. But, it’s only recently, “that such models have become sophisticated enough to be dubbed digital twins — in-silico replicas, in other words, of their real-world counterparts, capable of modeling their behavior in real time,” adds the article. “Key to this transformation has been the improvement of sensor and imaging technologies, along with ways to collect, transfer and analyze vast quantities of data."
For example, digital twins are now yielding new insights into the human body, as part of the drive toward personalized medicine, making it possible to tailor medical treatments to the individual patient based on their predicted response. “If an individual can have an entire organ reliably simulated, goes the thinking, then the effects of a disease and the likely impact of drugs can also be modeled in detail. Such techniques are being used to model lungs and kidneys, the development of the placenta during pregnancy, and the complex interconnections of neurons within the human brain to help understand the causes of epileptic seizures.
“The organ most relevant to engineers, though, is the heart, a system of valves and chambers that squeeze and relax up to a hundred times a minute to send blood around the body,” said the article. “And whereas hearts all follow the same laws of physics, each does so in different ways. Everything from diet and lifestyle to age and physique can alter how cardiac tissue contracts in response to electrical signals, as well as how smoothly blood flows through the heart’s chambers. Understanding the impact of such changes on bodily health is key to helping patients recover from heart disease.”
“Some researchers dream of doing something similar with Earth, by combining digital twins of specific planetary processes.” A major goal of such research is to use real-time data to improve predictions of how global warming will affect the weather and clues as to how the planet might avoid environmental catastrophe.
“With so much to recommend them, digital twins are likely to become ever more integral to how science is done.”
Irving Wladawsky-Berger is a Research Affiliate at MIT's Sloan School of Management and at Cybersecurity at MIT Sloan (CAMS) and Fellow of the Initiative on the Digital Economy, of MIT Connection Science, and of the Stanford Digital Economy Lab.