Virtual Twin as a Service: How We Got Here

Virtual twins can trace their origins back as far as the simulators used by NASA during the 1960s space race. But it was the digital technology boom of the ‘70s, ‘80s and ‘90s (in fact Dassault Systèmes was formed in 1981) coupled with the development of emerging business methodologies like lean manufacturing, servitization, and Product Lifecycle Management (PLM – a term we actually coined back in 1999) that really laid the groundwork for virtual twins.

Virtual twins may have been conceptualized as early as 1991, in David Gelernter’s book Mirror Worlds, but it wasn’t until 2002 that the virtual twin concept – which tied PLM approaches to the real world – would be formalized by Dr. Michael Grieves of the University of Michigan. The term “digital twin” itself was first coined by NASA scientist John Vickers in 2010; and by that time the revolution was well underway.

By the 2010s, enabling technology had begun to catch up with the ambitions of virtual twin pioneers. Between 2004 and 2019, the average price of an IoT sensor fell from $1.30 to $0.44¹. This development, coupled with Nielsen’s Law of Internet bandwidth² – which stipulates that connection speed grows by 50% a year – made virtual twins more affordable and feasible than ever.

Beginning in the automotive and aerospace sectors, by the end of the decade, commercial applications had grown considerably. By 2017, digital twins were listed in Gartner’s top ten tech trends³, and in 2018 the government of Singapore was even able to announce that they had developed (with the assistance of Dassault Systèmes) a virtual twin of their entire city!⁴

[1] Supply Chain Dive, Declining price of IoT sensors means greater use in manufacturing
[2] NN Group, Nielsen's Law of Internet Bandwidth
[3] Gartner, Top 10 Strategic Technology Trends for 2018: Digital Twins
[4] Oodaloop, Speculative Design: “Virtual Singapore” is a Massive, Fully Functional Digital Twin of the Asian City-State

But what really took virtual twins into the mainstream was the COVID-19 pandemic, beginning in 2020, where the ability to test and prove new products in a high-speed, low-risk virtual environment became essential to managing a global crisis, whether that meant simulating treatment environments¹ or delivering new vaccines².

It’s no coincidence that 2020 also saw the foundation of key industry groups like the Digital Twin Consortium³ and the Industrial Digital Twin Association⁴, with the aim of shaping standards and practices around virtual twin technology.

[1] MPDI, Digital Twin of COVID-19 Mass Vaccination Centers
[2] MPDI, Digital Twin of mRNA-Based SARS-COVID-19 Vaccine Manufacturing towards Autonomous Operation for Improvements in Speed, Scale, Robustness, Flexibility and Real-Time Release Testing
[3] Tech Target, Digital twin consortium accelerates growth of the technology
[4] ZVEI, User organization "Industrial Digital Twin Association" founded

By 2023, virtual twins were a fully established part of the business landscape, helping drive innovation and operations management across sectors. Between 2021 and 2023, 75%¹ of companies in advanced industries had already adopted digital-twin technologies that have achieved at least medium levels of complexity. And by 2022, just for global manufacturing companies, 29% had either fully or partially implemented their digital twin strategies, compared to 20% in 2020².

As of 2024, interest in virtual twins is only increasing; McKinsey has predicted the market to grow to $73.5 billion by 2027³, at a compound annual growth rate of 37.8%. Twins are being deployed in everything from construction to consumer products to satellite programs.

[1] McKinsey, Digital twins: The key to smart product development
[2] EY, Digital twins: Creating intelligent industries
[3] McKinsey, Digital twins: The key to smart product development

Despite rapid growth, adoption is still dominated by larger, established firms. For virtual twins to become truly mainstream, and for more resource-limited or time-bound businesses to reap the benefits, a new paradigm of virtual twin delivery is needed. One that removes the cost and complexity of building and managing a whole virtual twin.

Virtual Twin as a Service has now emerged to address these issues. Based on established servitization models (like SaaS or EaaS) and enabled by ever more accessible networking and sensing solutions, as well as input from industry-leading execution partners like IBM, Virtual Twin as a Service gives customers an incredible new low-risk way of leveraging the value of virtual twins, faster and more affordably than ever before.