In this post we start off a series of blogs looking at Digital Twins to explore what they are and what they mean to different sectors.
In 2017 Gartner reported that Digital Twins were one of the Top 10 Strategic Technology Trends and recently estimated that 50% of all $billion companies would be looking into digital twins in some form by 2020.
The Gartner definition is one of many, but the unifying theme is that they replicate things either in the real world or have the potential to be in the real world in the future. This may be things such as:
The following table lists definitions as provided by many of the world’s largest companies:
|Sector||Digital Twin Definition|
|Boeing||Aerospace||An ultra-high-fidelity simulation that is a virtual working model of highly complex systems and components.|
|CDBB||Academia||A realistic digital representation of something physical.|
|Dassault Systémes||3D Simulation Software||A “Virtual Twin” is a virtual representation of what has been produced. We can compare a Virtual Twin to its engineering design to better understand what was produced versus what was designed, tightening the loop between design and execution.|
|Deloitte||Consulting||A near-real-time digital image of a physical object or process that helps optimise business performance.|
|General Electric||Multi-national conglomerate||A living model that drives a business outcome.|
|IBM||Software||A virtual representation of a physical object or system across its lifecycle, using real-time data to enable understanding, learning and reasoning.|
|Microsoft||Software||A virtual model of a process, product, production asset or service. Sensor-enabled and IoT connected machines and devices, combined with machine learning and advanced analytics, can be used to view the device’s state in real-time. When combined with both 2D and 3D design information, a digital twin can visualise the physical world and provide a method to simulate electronic, mechanical, and combined system outcomes|
|NASA||Government & Aerospace||A digital twin integrates ultra-high fidelity simulation with the vehicle’s on-board integrated vehicle health management system, maintenance history and all available historical and fleet data to mirror the life of its flying twin and enable unprecedented levels of safety and reliability.|
|Siemens||Multi-national conglomerate||A virtual representation of a physical product or process, used to understand and predict the physical counterpart’s performance characteristics|
Most of the above can be categorised as either modelling of:
They also generally fall into the traditional simulation category of 3D modelling whilst some would feature as dashboards using the Industrial Internet of Things to track assets, things, or people and their behaviours as a live model.
Overall, these digital twins must have a purpose to aid improving understanding of problems and adding value to businesses. This value-add can be gained from various routes including:
Digital Twins often bring together various technologies, such as IoT (for sensor data regarding the real world), Artificial Intelligence (AI) and data analytics to make use of and improve the understand of the data obtained.
The Gemini Principles, developed by CDBB as part of the UK’s Industrial Strategy planning, identified the following simple 9 guidelines to inform the creation of Digital Twins for buildings and infrastructure and specifically for the integration of these Digital Twins into a larger “National Digital Twin”.
Clearly with so many different definitions of Digital Twins there are multiple slants and perspectives as to what a user or stakeholder is trying to achieve. Outside of 3D modelling these broadly fall into the following areas:
Network world, described digital twins as virtual replicas of physical devices that data scientists can use to run simulations before actual devices are built and deployed. The focus in this case being simulation to understand what might be the case in the future.
Network World describe Digital Twins as a dynamic software model of a physical thing or system that relies on sensor data to understand its state, respond to changes, improve operations and add value. The key aspect of this is its integration with Internet of Things (IoT) based domains to gain more understanding of the real world. Nature describes the use of data collected from sensors being in real time, and that sophisticated computer models mirror almost every facet of a product, process or service.
Digital twins have been described in terms of their data as well, such as being a combination of metadata (e.g. classification, composition and structure), condition or state (e.g. location and temperature), event data (e.g. time series), and analytics (e.g. algorithms and rules).
Aside from performing what-if scenarios there is the prospect of forecasting, with a more corporate slant Forbes describes digital twins as “ A virtual model of a process, product or service.” It goes on to state that digital twins allow for analysis of data and monitoring of systems to head off problems before they even occur, preventing downtime and aiding planning for the future by using simulations.
Nature discussing digital twins from an industrial perspective describes Digital twins as “precise, virtual copies of machines or systems — that are revolutionising industry.” The benefits coming from the real time use of IoT and sensors in the context of digital twins to spot problems and increase efficiency. Many major companies already use digital twins to achieve this and that half of all corporations might be using them by 2021, one analyst predicts.
Digital twins were mooted as an idea in the book Mirror Worlds, in which mirror worlds were described as
They were first applied by Michael Grieves (Florida Institute of Technology) to the area of manufacturing, with the concept being introduced in 2002 by Grieves as the underlying model used for product lifecycle management (PLM), . The concept, which had a few different names, was subsequently called the “digital twin” by John Vickers of NASA in a 2010 Roadmap Report. In which its three distinct parts, where defined. The physical product, the digital/virtual product and the connections between the two products as dataflows, that link them together.
Some would say that the Digital Twin extends back further than this and has been around for more than 30 years, in various forms. Early forms of the concept might cover for example: product and process engineering teams using 3D renderings of computer-aided design (CAD) models, or to asset models and process simulations to ensure and validate manufacturability: NASA for example have run complex simulations of spacecraft for decades.
provides only a brief overview and a sneak peak into the world of digital twins
and a sample of how they can be interpreted. Over the rest of this blog series
we will be exploring in further detail some of these definitions from world
 Pettey, C. ‘Prepare for the impact of digital twins’ (2017). Gartner report available at https://go.nature.com/2krzbjd
 Gelernter, David Hillel (1991). Mirror Worlds: or the Day Software Puts the Universe in a Shoebox—How It Will Happen and What It Will Mean. Oxford; New York: Oxford University Press. ISBN 978-0195079067. OCLC 23868481.
 Grieves, Michael. (2015). Digital Twin: Manufacturing Excellence through Virtual Factory Replication.
 Grieves, Michael. (2011). Virtually Perfect: Driving Innovative and Lean Products through Product Lifecycle Management.
 Grieves, Michael. (2016). Origins of the Digital Twin Concept. 10.13140/RG.2.2.26367.61609.
 Technology Area 12: Materials, Structures, Mechanical Systems, and Manufacturing Road Map. 2010, NASA Office of Chief Technologist.