Pathfinder can be used in different ways for varying situations:
- Creating fleet lines for your network department, with the objective of maximizing airline operational efficiency and reducing non-performance costs or to maximize fleet utilization.
- Allocating tails to flights with the objective of minimizing passenger delay minutes or delay cost.
- Different objective functions can be used depending on the use case to fully tailor to an airline’s strategic objective.
At the onset of Covid-19, Pathfinder was adapted to serve new use cases. It is now able to decide aircraft type and sub-type allocation (e.g., whether to use a 737-700, 800 or 900) with the objective of maximizing profit.
ALIGN is designed to facilitate the evaluation of resources needed to operate a given schedule. It is typically used by network departments in conjunction with the planning team for all operational resources (crew, ground resources, maintenance, etc.)t highlights the bottlenecks created by insufficient resources and provides data about likelihood and size of any potential delay.
Critically, it allows airlines to move from planning resources on a static schedule to a dynamic range of possible schedules, thanks to its simulation capabilities. Front-line staff to review forecasted KPIs of a given schedule and resource level and ensure that the airline is well set up to achieve its operational targets and strategy.
Finally, ALIGN is also used to create the business case for new equipment purchases or employee hires determining the operational benefit of those additional resources.
All airlines try to maximize their fleet utilization to achieve the most efficient turns and shortest ground times. With many ground resources to coordinate above and below the wing, executing a turn requires close collaboration and coordination across several departments and teams. Integral decision making is critical to the successful execution of a turn, with each department making decisions based on the same data.
Terra was designed to improve the resource allocation process for ground resources. It supports allocators of ground services departments to find the most optimal resource allocation on the day of operations (DoO)—taking into account resources available, equal distribution of workload for employees (employee satisfaction), delay probability, delay costs, and enhanced task times based on a number of factors, such as actual load.
To manage disruptions on the day of operations, Sentry can take into account all information (updated departure and arrival times, disruption caused by technical issues, crew and so on) and propose which flights to swap, delay, cancel, or use spare A/C on. It can be used with different (fully tailored) objective functions such as minimizing delay costs and minimizing the number of disrupted premium pasengers.
In the event that runway capacity is restricted due to bad weather or other factors, Runway can support airlines in determining which flights to cancel or delay. It develops a holistic view of the total cost and operational impact, including passenger impact, crew connections, maintenance requirements, etc.
CrewVision helps airlines navigate the challenges of long-term crew planning. It creates an optimal three-to-five-year crew plan, taking into account network and fleet changes, retirements, hiring needs, training requirements, both planned and unplanned crew absences, and relevant labour agreements. Built with an uncertain post-COVID environment in mind, it is capable of rapid scenario analysis, including multiple parallel runs, to ensure that crew planners can perform sensitivity analysis and find the optimal plan.
Harbinger is used on the day of operations to solve crew disruptions. It can re-allocate crew members to flights through reserve usage, deadheads, and crew reroutes. Its state-of-the-art algorithm will identify the optimal crew allocation, taking into account employee satisfaction, operational feasibility, costs and efficiency, labor agreements, and individual crew capabilities.
BagPro supports airlines in minimizing mishandled bag costs, incorporating cost drivers, the probability of mishandling, and expected throughput time. It respects all operational constraints such as driver capacity, baggage system capacity, unloading and waiting times, and driving times. The result is an automated optimization of baggage flows, reducing non-performance costs, increasing customer satisfaction, improving capacity utilization, and boosting employee satisfaction.