A Digital Twin is what it sounds like, a virtual representation of a physical object. One of the earliest examples of a Digital Twin was used by NASA engineers in the 1970s as part of its Apollo moon landing program to test strategies to fix a distressed Apollo 13 Spacecraft (called “the twin”). Digital Twin technology is now used where simulations are needed, besides outer space feats, for equipment maintenance, assembly line monitoring, building management and design, and commercial planning have emerged, mainly focused on cost savings. Since then, the application of Digital Twins has spread to other sectors, like automotive, construction, power, and utilities.

Though the Digital Twin technology is old, we’re seeing it in more mainstream spaces owing to its underlying technology having matured; advancements in IoT sensors, computing infrastructure, artificial intelligence (AI)/machine learning (ML), and analytics mean its use cases, too, have multiplied. 

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What can a Digital Twin do?

Digital Twins (DTs) can instantly reflect any changes in the physical twin in their digital copy. They differ from standard simulations as they can run simulations on multiple processes. A DT benefits from real-time data processing and creates an environment rather than a situation, whereas a DT differs from standard simulations as standard ones use static data and study a single process.

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What is a Digital Twin’s makeup?

The physical twin is fitted with IoT devices to collect data (e.g., indicators of asset health and temperature), which is processed and applied to its digital copy. Using a combination of ML models and data visualization, the DT can run simulations to understand how the physical twin performs and how it changes under several real-world scenarios, including its present and future performance. DTs are also similar in appearance to their physical counterparts (e.g., a wind turbine, a car, an off-shore oil rig, or factory equipment), replicating a near-exact environment.

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What are DTs used for?

As mentioned, since the days of the Apollo program, the application of DTs has spread to other sectors, including automotive, construction, real estate, and power and utilities. The main appeal of DTs is their ability to assist companies to enhance process efficiency to drive down operational costs and time to market and stay on top of evolving consumer needs. ABI Research projects that smart cities would achieve USD 280 billion in cost savings by 2030 through the use of DTs for urban planning. In the automotive sector, Rolls Royce used DTs to conduct safety evaluations of engine parts and reportedly halved its test analysis times. In addition, the Hong Kong International Airport developed DTs to aid in the design and implementation of new projects, passenger service, and maintenance.

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We have identified key DT use cases below:

• Maintenance and monitoring: Companies that facilitate the creation of digital twins to monitor and maintain assets, including buildings and network infrastructure.

• DT modeling: Platforms that facilitate digital twin modeling by either collectively or individually offering any combination of data capture and analytics solutions.

• Planning and management: Companies offering digital twins gain insights for use in commercial planning, logistics and distribution, and other supply chain processes.

• Predictive assessments: Companies that offer digital twins to assess risks and recommend solutions in areas such as disaster management, DNA testing, clinical trials (to simulate the effects of treatments), etc.

• Building and design: Used to design and build components contributing to smart cities, roads, airports, public transportation systems, etc.

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What’s bringing DTs to life?

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The advancements in IoT sensors: Enhancements in the underlying technology with the introduction of micro-electro-mechanical systems (MEMS), micro-sensor implants, and biodegradable sensors have made sensors more compact and dynamic to extract more detailed data in real time.

Reduced costs in sensors: At the same time, the use of DTs has become widespread due to the significant decrease in the cost of sensors (average cost of USD 0.38 in 2020 vs. USD 1.38 in 2004). This, too, has been a catalyst for IoT technology to emerge, especially in factory automation and industrial processes known as IIoT (for more details refer our Smart Factory industry hub). This boost is expected to continue according to Cisco’s Annual Internet Report, which forecasts the number of machine-to-machine (M2M) connections to reach 14.7 billion by 2023 from 7.4 billion in 2019. Moreover, 50% of all networked devices are projected to be IoT-based by 2023.

Evolution in computing infrastructure, AI/ML, and analytics streamline DT modeling: Since DTs handle vast amounts of data, gleaning meaningful insights can be a challenge. Data analytics and modern visualization tools, which incorporate interactive 3D, VR, and AR capabilities, are supported by advancements in AI/ML that assist in handling significant amounts of data, parsing data faster, and identifying appropriate data points for simulations. Access to superior computational power and storage as well as low latency networks contribute to the enhanced processing of DTs.

‍User-friendly technology: Adding on, startups such as CONXAI, which caters to the construction industry, have incorporated no-code capabilities, which could allow DTs to appeal to a wider, non-technical customer base.

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What are the challenges to growth?

Quality of data and interoperability of systems could compromise the accuracy and use of DTs

The accuracy of DTs largely depends on the quality of the data it is fed, so the data needs to be error-free and uninterrupted for DTs to function sustainably. Moreover, the interoperability of systems, which influences how data and models can be shared from one application to another, could hinder the mainstream adoption of DTs due to restrictions that can make it harder to scale them. 

Vulnerability to cybersecurity risks and privacy concerns

DT cybersecurity will be imperative as the links that DTs use to exchange data with their physical twin could be targeted by cybercriminals. This vulnerability could have detrimental consequences for the physical twin because sensitive data streams and physical assets in the hands of malevolent actors could deter enterprises from using DTs altogether. 

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Find out more about what investor interest is like, the startups urging this technology forward, and the DT ecosystem, as well as what’s next for this technology through our Digital Twin hub

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