What would you do if you had a copy of yourself? A digital doppelgänger, identical to you in every way, in an accurate digital rendering of your home, workplace, neighborhood, or city? Even better: What if the digital version of you—your digital twin—was impervious to injury, pain, or embarrassment? The mind boggles at the possibilities. Suffice it to say, you’d probably be able to make decisions for yourself with a lot more certainty of the outcome.
In business, this heightened degree of certainty is extremely valuable—and emerging digital twinsmay help deliver it.
Put simply, a digital twin is a virtual replica of a physical object, person, or process that can be used to simulate its behavior to better understand how it works in real life. Digital twins are linked to real data sources from the environment, which means that the twin updates in real time to reflect the original version. Digital twins also comprise a layer of behavioral insights and visualizations derived from data. When interconnected within one system, digital twins can create a digital and often immersive environment that replicates and connects every aspect of an organization to optimize simulations,scenario planning, and decision making.
Creative product development leaders are increasingly enthusiastic about digital twins. McKinsey analysis indicates the global market for digital-twin technology will grow about 60 percent annually over the next five years, reaching $73.5 billion by 2027.
There are a few different types of digital twins. First, there’s a product twin, which is a representation of a product. This digital twin can include products at various stages of the life cycle, from initial concept design and engineering through full functionality—meaning you get live, real-time data on a product as if it’s in service.
Another type of digital twin is a data twin. You probably already have a great example of a data twin in your pocket: Google Maps is a digital twin of the Earth’s surface. It links real-time data on traffic to help optimize your commute.
Other types of twins include systems twins, which model the interaction between physical and digital processes, including manufacturing processes, end-to-end supply chain management, store operations, and customer journeys. And finally, infrastructure twins represent physical infrastructure such as a highway, a building, or even a stadium.
Digital twins have the potential to deliver more agile and resilient operations. And their potential isn’t lost on CEOs: McKinsey research indicates that 70 percent of C-suite technology executives at large enterprises are already exploring and investing in digital twins.
Product digital twins can be characterized by three key dimensions:
An organization’s journey with a digital twin includes each of these dimensions. A digital-twin project might start with a single, critical component and then expand to a whole product as the company’s digital-twin maturity increases.
Maintaining supply chains is a top priority across organizations. McKinsey analysis indicates that supply chain disruptions cost, on average, 45 percent of one year’s cash profit. Organizations are aware of the risk: an estimated 86 percent of companies recently surveyed are investing in supply chain transformation to respond to industry disruptions.
Digitally enabled supply chains deploy digital-twin and AI technology to drive optimization and efficiency.They take data captured from all facets of an organization’s operations and model the data to mimic physical assets, people, and processes. Based on insights from a twin, an organization’s leaders can freely experiment, increase their decision-making speed by up to 90 percent, and more.
Top-performing companies we surveyed are already aware of the benefits of this technology: among companies investing in supply chain disruption, digital twins rank in the top three investment priorities.
In general, digital twins can drive value across an organization by increasing visibility into real-time and predicted performance, while derisking and optimizing product development, operations, customer relationships, and more.
More specifically, data twins provide visibility into operations, profitability, and customer touchpoints. Data twins can improve decision making, providing real-time visibility into in-transit inventory, customer journeys, and staffing. Data twins can also help organizations adhere to increasingly common regulations requiring upstream visibility into sourcing, especially in healthcare, high tech, and government.
Systems twins help drive cost optimization, increase throughput, improve customer experience, and heighten resilience to supply-and-demand shocks. In McKinsey’s experience, companies have reduced transportation costs and labor by as much as 10 percent, while increasing consumer promise—meaning allowing organizations to more reliably deliver on their promises to customers—by up to 20 percent. All the while, digital twins help improve resiliency by creating networks that perform under a variety of supply-and-demand scenarios.
Customer digital twins can add significant value as well, particularly top-line growth and improved customer experience. In McKinsey’s experience, organizations have posted revenue increases of as much as 10 percent by developing digital twins of their customers, allowing those customers to fully interact and immerse themselves within a company’s product. Mercedes-Benz (formerly Daimler), for example, has developed customer twins that allow customers to “test drive” a vehicle without ever getting behind the wheel.
Product digital twins can significantly reduce time to market. Conversations with senior R&D leaders show that digital twins have cut development times by up to 50 percent for some users, reducing cost along the way. Product digital twins can allow for rapid iterations and optimizations of product designs—far faster than physically testing every single prototype. What’s more, these digital twins can result in significant improvements in product quality. By simulating the product throughout the manufacturing process, it’s possible to identify flaws in the design much earlier. (“The companies that harness [digital-manufacturing twins] first,” says McKinsey senior adviser Will Roper, “will really shake up the markets they’re in.”) And finally, by mirroring a product in service, it’s possible to create a single source of truth for how the design is functioning, allowing for real-time adjustment or redesign.
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A key element an organization needs for implementing digital twins is digital maturity. This essentially means data: a high-quality data infrastructure that delivers reliable data from both testing and live environments, as well as the talent needed to build and maintain that infrastructure.
Organizations in advanced industries are leading the charge: our survey data indicates that almost 75 percent of companies have already adopted digital-twin technologies that have achieved at least medium levels of complexity. These include organizations in a variety of industries, from automotive, aerospace,and defense to technology, retail, and consumer goods. Logistics, infrastructure, and energy players are more likely to be developing their first digital-twin concepts.
Interest in digital twins, combined with rapidly advancing supportive technologies, is spurring market estimates for digital-twin investments of more than $48 billion by 2026. We’re already seeing some advanced implementations:
Generative AI (gen AI) and digital twins can be used symbiotically. For its part, gen AI can structure inputs and synthesize outputs of digital twins, and digital twins can provide a robust test-and-learn environment for gen AI. By combining these two technologies, organizations can achieve exponentially more than they could by using just one.
More specifically, organizations can assign large language models (LLMs) to create code for a digital twin using a software development platform like GitHub. A digital-twin prototype created by LLMs could serve as a starting point for developers across digital-twin projects and even industries. LLMs can also help digital twins once they are already working, by processing and transferring the data that digital twins need to perform.
For more on the potential synergies between gen AI and digital twins, read “Digital twins and generative AI: A powerful pairing.”
Digital twins can be particularly helpful in improving sustainability efforts. Product digital twins can help organizations reduce the material used in a product’s design, as well as improve the traceability of a product to reduce environmental waste. Consumer electronics manufacturers have made significant improvements to sustainability by using digital twins, reducing scrap waste by roughly 20 percent. Supply chain digital twins can help organizations balance cost and speed with increased sustainability. And data twins can give organizations more visibility into upstream suppliers’ sustainability.
Digital twins require new ways of working, within R&D functions and beyond them. This means a successful digital-twin program is a change management effort, requiring senior management support and a strong project management team.
To overcome potential roadblocks, companies can adopt a phased approach to digital-twin adoption:
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This article was updated in August 2024; it was originally published in July 2023