A digital twin (DT) is a simulation or virtual model of a physical object that accurately mirrors the structure of that object. The concept was first introduced in 2002, but the technologies needed to make the concept widely accessible are finally here. Key enablers such as cloud, data storage, computing power, wireless networks, ML, AI, IoT, and sensors are making DTs highly desirable for enterprise management.
Plenty of sectors and application areas already have mature DT practices for building products and processes as they become more detailed and predictive. For instance, cloud computing has made the computing power required to run simulations and forecasts widely available. Moreover, the falling price of network-connected IoT sensors is making it practical to monitor large objects in granular detail.
DT is not just a niche technological breakthrough, rather a key focus area for the world’s largest technology firms:
Though major US technology companies are leading the simulation race, industry collaborations and consortiums are helping standardize the architecture, security, and interoperability of DTs:
Digital Twin Consortium: Object Management Group’s (OMG) Digital Twin Consortium is a collaborative organization driving DT innovation through consistent approaches and open-source development. Some of the most prestigious organizations are members, including Dell, GE, Ansys, Microsoft, Bentley, etc.
In Dec 2021, the Digital Twin Consortium and Industrial Digital Twin Association (IDTA) entered into a liaison agreement to create and develop DT enabling technologies, with an aim to propel the monetization and adoption of DTs
IDTA: Since IDTA is a German consortium, the above agreement provides US technology leaders with a platform to have open discussions and share innovative ideas with international developers and companies in the DT space. Since IDTA went live in March 2021, the number of members has almost doubled to a total of 45 companies.
Companies across sectors and application areas utilize DT to improve planning and decision-making, including manufacturing, supply chain, automotive, infrastructure (smart cities), oil & gas, healthcare, among others. DTs are being used in product design, development, machine & equipment health monitoring, process support, and sales & services.
As cities continue connecting their urban environments, the concept of DT has entered the realm of smart cities and promises to enable city administrations and urban planners to make more accurate predictions and development strategies.
Another measure of success for technology is the level of investor traction and scale of growth presented by emerging start-ups. To explore this, a notable number of start-ups in the DT space have been identified as raising significant funding:
|Funding (MM USD)
|An AI-based tool being used to create a complete DT of US roads
|A DT model for the packaged goods market raising capital to establish a US headquarters
Capital being raised to help cities build DTs to better manage and monetize transit networks
|A provider of a DT of data infrastructure APIs
|A DT platform for electric cars raising capital for US expansion
The further adoption of DT will be directly correlated to the growth of AI, computing power, and IoT. As diverse sectors and innovators convert these technologies into empirical solutions from concept, DT will witness big growth as a byproduct. Growth will also be fueled by the constant surge in the complexity of future products and processes, wherein DT has the potential to simplify our understanding of complicated architecture and provide a virtual solution to a problem.
As digital twin opportunities become more accessible, finding the right use case can be daunting.
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