Welcome to trial and error. Without the error.
Optimal Reality is Deloitte’s digital twin capability based on simulation techniques pioneered in Formula One racing.
We help organisations run millions of permutations on a digital replica of their network to drive optimal decision making within seconds.
We believe in smarter cities, with connected and optimised transport, energy and people.
By harnessing Formula One simulation modelling and scaled cloud computing, Optimal Reality is tackling our built environment’s greatest challenges.
We're reimagining how people can live, move and work.
See how the Optimal Reality digital twin framework is redefining operational and strategic planning decisions:
This unique digital twin platform combines six technology disciplines to power millions of permutations of what-if scenarios, played out at Formula One speeds and with One accuracy.
We’re building solutions to wicked problems – problems once considered too difficult because of technology constraints, or because potential solutions were too risky.
The Optimal Reality digital twin lets organisations see, trial and solve their wicked problems differently.
We bring together disconnected problems to create a more complete view of how systems truly interact. Our model allows you to focus on time-critical problems and strategic planning challenges.
Combining historic, simulated and real-time data with the best simulation and machine learning technology, Optimal Reality allows businesses to understand the impacts of millions of what-if scenarios for both macro and micro decisions.
Optimal Reality enables businesses to make the right decision from millions of possible scenarios, based on the objectives they want to achieve. All applications are intuitively built to empower operations staff as well as technology teams.
How can we improve network planning to reduce disruption, enabling a more efficient aviation network?
Disruption to planned departures and arrivals at airports is a challenging and expensive event for airlines, airports, air traffic controllers and passengers. Today it is hard to predict and manage delays due to process and technology limitations.
The challenge: Reduce frequent disruptions that cost airlines and create a poor passenger experience.
Using the Optimal Reality Digital Twin, we created an accurate simulation of Australian airspace capable of modelling in the real time the location of each flight and all terminal congestion at major airports. We then created an application allowing Aviation Network Managers to understand, predict and visualise the impact of their decisions across the network.
The development of a suite of decision-making applications enables improved and collaborative decisions between Airservices, Airline partners and other external stakeholders.
More collaborative network planning and network management on the day of operations. Optimal Reality improved network performance, reduced cost to airlines and improved the passenger experience.
Forecasts show the application could deliver up to a 33% reduction in air delays.
How can we optimise airspace sectors to allow air traffic controllers to improve safety and operational efficiency?
Airservices manages an aviation network covering 11% of the world’s airspace. Currently this space is separated into fixed sectors. These sectors are monitored by certified Air Traffic Controllers (ATCs) who manage air traffic to ensure safe operation.
The challenge: allocate airspace sectors to ATCs in a way that ensures a manageable and safe taskload for each controller.
We built a taskload optimisation application using a simulation model of Australia’s airspace to dynamically assign sector configurations. Using predicted taskload, traffic behaviour and weather data for each sector, we configured sectors up to 24 hours in advance. This allows Airservices to better manage shift rosters for safe and efficient operations.
Preliminary results show an improvement in operational efficiency of up to 25% per shift and better management of mental workload in the Operations Room.
The airline industry is seeking to reduce costs and their environmental impact. We need to rethink airspace design to enable airlines to fly optimal routes that reduce fuel costs and CO2 emissions.
Airlines are investing heavily in capabilities that enable them to fly on an optimal route of their choice, taking advantage of weather patterns and other variables to reduce flight times, fuel costs and CO2 emissions.
Traditional airspace management approaches of “fixed” routes and sectors cannot support this approach and we must radically rethink how sectors are dynamically defined and managed to enable airlines to improve aviation efficiency by flying on ‘user preferred routes’.
Using the Optimal Reality Digital Twin, we are able to accurately simulate predicted traffic patterns and flows across Australian airspace for a given weather forecast and schedule.
Based on this predicted traffic flow, we created an application that dynamically calculates an optimal design of sectors across Australian airspace taking into account a number of complex constraints. For example, air traffic controller workload, controller certifications, workforce rostering rules, shift patterns, network regulations and time.
We have demonstrated an ability to dynamically redesign airspace on a shift by shift basis to enable airlines to fly their preferred routes, whilst maintaining a high standard of safety and efficiency across Air Traffic Control operations rooms.
How can we better plan for and respond to road incidents when they occur?
VicRoads plans, develops and manages the arterial road network as well as delivering road safety initiatives for the state of Victoria in Australia. In the face of increasing population growth and traffic volume, VicRoads is continuously trying to reduce congestion and improve responses to disruption.
The model serves as the base for applications focused on wicked problems: evaluating the risk of accidents in different parts of the network; evaluating the impacts of treatments like dynamic re-routing; even testing placement of emergency response services.
Using a combination of real-time data and simulated data feeds, we created a simulated model of the entire Victorian ground network. This model includes both individual vehicles and the city’s public transport systems.
The ability to simulate 50,000 ground vehicles in real-time with complex vehicle-to-vehicle and vehicle-to-environment interactions. Since this initial PoC the capability of the platform has been enhanced to handle far higher volumes of real-time interactions.