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In recent years the term ‘Digital Twin’ has been heard in NAV CANADA with little information about what it is and does or can do.  To date, CATCA members have had limited exposure to the concept, but they could be impacted greatly by its employment in the operation.

The first part of this article provides a high-level generic explanation of Digital Twin including how it works and benefits.  Following the generic explanation is some information on Digital Twin Technology in NAV CANADA.

What is Digital Twin Technology?

A digital twin is a digital representation of a physical object, process or service.  A digital twin can be a digital replica of an object in the physical world, such as a jet engine or wind farms, or even larger items such as buildings or even whole cities.

As well as physical assets, the digital twin technology can be used to replicate processes in order to collect data to predict how they will perform.

A digital twin is, in essence, a computer program that uses real world data to create simulations that can predict how a product or process will perform. These programs can integrate the internet of things, artificial intelligence and software analytics to enhance the output.

With the advancement of machine learning and factors such as big data, these virtual models have become a staple in modern engineering to drive innovation and improve performance.

In short, creating one can allow the enhancement of strategic technology trends, prevent costly failures in physical objects and, by using advanced analytical, monitoring and predictive capabilities, test processes and services.

How does it work?

The life of a digital twin begins with experts in applied mathematics or data science researching the physics and operational data of a physical object or system in order to develop a mathematical model that simulates the original.

The developers who create digital twins ensure that the virtual computer model can receive feedback from sensors that gather data from the real-world version.  This lets the digital version mimic and simulate what is happening with the original version in real time, creating opportunities to gather insights into performance and any potential problems.

A digital twin can be as complex or as simple as you require, with differing amounts of data determining how precisely the model simulates the real-world physical version.

The twin can be used with a prototype to offer feedback on the product as it is developed or can even act as a prototype in its own right to model what could occur with a physical version when built.

Why and How to Design Digital Twins?

As mentioned above, digital twins can be created for a wide range of applications, for example, to test a prototype or design, assess how a product or process will work under different conditions, and determine and monitor lifecycles.

A digital twin design is made by gathering data and creating computational models to test it.  This can include an interface between the digital model and an actual physical object to send and receive feedback and data in real time.


Once the data has been gathered it can be used to create computational analytical models to show operating effects, predict states such as fatigue, and determine behaviours. These models can prescribe actions based on engineering simulations, physics, chemistry, statistics, machine learning, artificial intelligence, business logic or objectives. These models can be displayed via 3D representations and augmented reality modelling in order to aid human understanding of the findings.


The benefit of digital twin differs depending on when and where it is used.  For example, using digital twin to monitor existing products, such as a wind turbine or oil pipeline, can reduce maintenance burdens and save many millions in associated costs. Digital twins can also be used for prototyping ahead of manufacture, reducing product defects and shortening time to market.  Other instances of digital twin use can include process improvements, whether that is monitoring of staffing levels against output or aligning a supply chain with manufacturing or maintenance requirements.

Common benefits include increased reliability and availability through monitoring and simulation to improve performance. They can also reduce risk of accidents and unplanned downtime through failure, lower maintenance costs through predicting failure before it occurs, and ensure production goals are not impacted by scheduling maintenance, repair and the ordering of replacement parts.  Digital twin can also offer continued improvements by analysing customisation models and ensure product quality through performance testing in real time.

However, for all the benefits, digital twin is not suitable in all instances as it can increase complexity.  Some business problems simply do not need a digital twin and can be solved without the associated investment in time and cost.

Digital Twin in NAV CANADA

The company has plans to leverage a Digital Twin system to improve “productivity”, loosely translating to staffing optimization and flexible sector boundaries.  Data will constantly be collected and continually run through simulations to make both strategic and tactical decisions about staff and airspace allocations (sector optimization).  Moreover, this process will also be used in managing the need to backfill leave, non-coverage duty (NCD) shifts, shortfalls, i.e., sick calls, and alternate usage of resources on days with staff overages.  The project also outlines that the system would be used to in the daily management of breaks and time in position to ensure alignment with fatigue guidelines.

The proof of concept (PoC) for the system was completed with Toronto FIR in December 2020 by Deloitte.  The NAV CANADA internal assessment of the PoC results showed that across the Toronto FIR specialties, the Digital Twin system would save up to 15% of control shifts as compared to the existing Optimal Staffing Strategy (OSS).  The project is expecting a conservative savings of 5% on overtime requirements almost immediately.  The project is currently conducting a complete Toronto FIR deployment with a national deployment to follow.

Ultimately, a Center Shift Manager (CSM) will use the system to make daily strategic and tactical staffing decisions, and those decisions will eventually involve reconfiguring sector boundaries as required.

Although there are many references to sector optimization as well as improved training success and qualification rates, there is no information about how it is achieved.  In addition, there are no mentions of licensing or Transport Canada approval(s) regarding any new licensing endorsements.

A benefit of the system that is not fully flushed out in documentation is the development of new methods and technology, such as Artificial Intelligence (AI).  The system can be used to model any sort of process or technology against significant amounts of data, both current and historic.  It can be expected that any idea from anyone, with or without any sort of ATC experience, which can potentially save staff, will be run through the system.  Results of those idea trials will drive future developments such as Trajectory Based Operations.

Prepared by Fred Cosgrove, CATCA Technology Branch and Co-Chair of the CATCA Technology Committee.  If there are any questions or desire to discuss the views of this article further, Fred can be reached at

All generic Digital Twin Technology in the above article was extracted from the TWI Global website.

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