A Digital Twin is a digital model of a real-life object, process or system. The digital model can be informed by historical data and fed with live sensor data to make the digital model identical – a ‘twin’ – to the real-life subject. Digital Twins of discrete systems can be linked to create twins of larger, more complex systems such as a factory or a city.
The concept of Digital Twin was coined by Dr Michael Grieves and John Vickers at the University of Michigan in 2002:
"…the idea that a digital informational construct about a physical system could be created as an entity on its own. This digital information would be a “twin” of the information that was embedded within the physical system itself and be linked with that physical system through the entire lifecycle of the system. "
Deloitte: Industry 4.0 and the digital twin’ (2017)
Digital twins are being used in a range of industries and areas including building information management (BIM), infrastructure planning and management, construction, manufacturing and healthcare (2019). Digital twins can provide a safe virtual environment where users can simulate and test the impact of changes to assets, products, systems and processes based on real time data. Digital twins can also be used to proactively identify problems or emerging problems, and enable early intervention (see Sydney Water 2019). In some cases, the digital twin can be used to control its real-world counterpart. Gartner predicts that by 2021 approximately 50% of large industrial companies will have adopted digital twin technology (2019).
- Real time monitoring and feedback.
- Ability to model different scenarios and virtually test the feasibility and impact of changes with real time data.
- Support informed planning decisions, detect issues and intervene sooner, and make more accurate predictions.
- Measure performance.
- Share information with citizens and business.
- Accuracy: The accuracy of the twin compared to its physical counterpart could be limited, depending on what data is available and the quality of that data.
- Interoperability: There may be challenges in ensuring that a digital twin can/will work with existing assets, products, systems and processes.
- Unreadable data: Data formats have short lifecycles. For digital twins with long lifecycles, like buildings and infrastructure, the design software formats have a high risk of becoming unreadable by modern systems at some point in their service life.
- Data ownership: The data required for input into the twin and the data contained in the twin will be valuable. Ownership and data sharing arrangements should be agreed and clearly understood from the outset.
- Affordability: As an emerging technology involving high level of complexity, digital twins can be expensive to design and build.
Digital Twin NSW is creating a digital twin of the state to facilitate better planning, design and modelling for future needs. Department of Finance, Services and Innovation’s Spatial Services and the Commonwealth Scientific Industry Research Organisation’s Data61 have developed an interactive platform to capture and display real-time 3D and 4D spatial data in order to model the urban environment. This upgrade from traditionally held 2D spatial data is the NSW ‘Digital Twin’. The State Infrastructure Strategy 2018 recommended an upgrade to NSW’s spatial data from 2D to real-time 3D and 4D. The launch of this platform is the first step in making this recommendation a reality.
Phase one of the Digital Twin included digital visualisations of the local government areas that comprise the Western Sydney City Deal and Greater Parramatta to the Olympic Peninsula. Already the project has demonstrated the ability to upgrade NSW’s Spatial Data to 3D/4D and included the integration of live transport feeds as well as infrastructure building models.
Virtual Singapore - Dassault Systèmes created a digital twin for the entire city state of Singapore. The idea is that city planners can capture data about energy consumption to create more efficient options and test traffic light phases or airflow of skyscrapers. Other ideas include having shops adjust the hours they’re open based on how many people are walking past (Gartner 2019).
Links and further reading
- The Smart Cities Podcast Episode 17 - The Digital Twin: Why, What and How? (2019)
- This podcast episode includes a detailed discussion on how to approach scoping a digital twin project.
- Anylogic: How to Create a Digital Twin (2018)
- Lior Kitain: The Digital Twin: Powerful use cases for Industry 4.0 (2019)
- The Globe and Mail: Digital twins for personalised healthcare (2018)
- GE Global Research Centre: Minds + Machines: Meet a Digital Twin
- Takes a look at digital twin tech with the VP of Software Research for GE Global Research Centre (2016).
- Data Crunch podcast: Digital Twins, the Internet of Things, and Machine Learning (2018)