Client

Our client is one of Australia’s leading international airport operators serving over 2 million passengers every year.

Purpose

Due to the rapidly changing nature of an airport environment, the client wanted to understand the impact to their operations when demand changed (e.g., COVID disruptions, or a flight schedule change), the cost/benefit trade-offs of reducing passenger queue lengths and wait times by increasing staffing, and to understand the impact of large capital expansions of the airport.

Forecast worked with the client to design and develop a digital simulator (or digital twin) of the airport that connects to the operational data and integrates with business logic to simulate the passenger movements, aircraft, and staff activities. This enabled the airport to analyse passenger movements, queue lengths, and wait times in detail across the short, medium and long term.

Approach

Working closely with the client we conducted a series of workshops to fully understand each part of the airports operations and plan out the scope of work. Due to the highly customised and highly dynamic nature of a digital twin, Forecast proposed to use the 3D design principle of Digital Twins, that is:

• Decentralised: there should be no single decision-making point of operating flow. The software components of Digital Twin should be able to evolve independently.

• Decoupled: the business logic, data and runtime parameters should be separated.

• Distributed: while the storage and processing of the Digital Twin could be distributed across multiple cloud computing resources, the behaviour of the Digital Twin should be consistent, like a single software entity

Forecast took the approach of building Minimum Variable Product (MVP) to verify the concept. The MVP allowed both Forecast and the client to understand the viability of the design within a short delivery time frame for minimal cost.

Outcome

Forecast delivered sophisticated passenger simulations with a web-front end to simulate 15 months of airport operations. By taking a phased MVP approach, Forecast was able to develop a workable solution for a minimal cost in a short time frame of 7 weeks. Forecast is currently working on developing the second phase of development to increase the network complexity, improve the UX/UI design, add additional dashboards, and improve the analytical workloads that underpin the digital twin.