Digital Twin

A digital replica of potential and actual physical assets, processes, people, places, systems and devices that can be used for various purposes.
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Low Risk
High Disruption Potential
Game Changer

A digital twin is a digital replica of a living or non-living physical entity. Digital twin refers to a digital replica of potential and actual physical assets (physical twin), processes, people, places, systems and devices that can be used for various purposes. The digital representation provides both the elements and the dynamics of how an Internet of things (IoT) device operates and lives throughout its life cycle.Definitions of digital twin technology used in prior research emphasise two important characteristics. Firstly, each definition emphasises the connection between the physical model and the corresponding virtual model or virtual counterpart. Secondly, this connection is established by generating real-time data using sensors. The concept of the digital twin can be compared to other concepts such as cross-reality environments or co-spaces and mirror models, which aim to, by and large, synchronise part of the physical world (e.g., an object or place) with its cyber representation (which can be an abstraction of some aspects of the physical world).

Digital twins integrate IoT, artificial intelligence, machine learning and software analytics with spatial network graphs to create living digital simulation models that update and change as their physical counterparts change. A digital twin continuously learns and updates itself from multiple sources to represent its near real-time status, working condition or position. This learning system learns from itself, using sensor data that conveys various aspects of its operating condition; from human experts, such as engineers with deep and relevant industry domain knowledge; from other similar machines; from other similar fleets of machines; and from the larger systems and environment of which it may be a part. A digital twin also integrates historical data from past machine usage to factor into its digital model.

In various industrial sectors, twins are being used to optimise the operation and maintenance of physical assets, systems and manufacturing processes. They are a formative technology for the Industrial internet of things (IIoT), where physical objects can live and interact with other machines and people virtually. In the context of the IoT, they are also referred to as "cyberobjects", or "digital avatars".

Healthcare is recognised as an industry being disrupted by the digital twin technology. With a digital twin, lives can be improved in terms of medical health, sports and education by taking a more data-driven approach to healthcare.

Trend Metrics

Trend Timeline (Last 4 weeks)

Based on web searches worldwide.

Disruption Breakdown

Success Factors

Cost Efficiency

Based on the cost of production and speciality needed in machinery and job roles.

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Ability to Mass Produce

Based on ease of access to all components and level of personalisation required.

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Speed to Produce

Time taken from manufacturing start point to consumer ready.

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Concept Realisation

Based on proven case studies of the technologies and concepts used.

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Consumer Needs Met

Based on consumer interest, needs and demand.

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Disruption Factors

Ecosystem Potential

Potential to integrate into existing consumer ecosystems (digital or lifestyle) and potential to create new ecosystems.

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Potential to Change Consumer Behavior

Potential to change behavior of the consumer if delivered successfully. Based on creating new interactivity, delivery systems, a unique service or through new knowledge delivered.

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Potential to Change Industry Behavior

Potential to change industry behavior if delivered successfully. Based on creating new technology, leveragable delivery systems or through new knowledge gained.

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Uniqueness of IP

Based on amount of existing consumer products and services leveraging this idea and examples of successful case studies.

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New Knowledge Gained

New technical, development, manufacturing or consumer knowledge gained if delivered successfully.

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Success Potential

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Disruption Potential

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Total Disruption Score

70

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If delivered successfully

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