An algorithmic application that dynamically improves fleet scheduling by optimizing existing flight schedules, while integrating live scheduling changes to reflect the best possible route for an operator. The end result is a significant reduction of "dead legs" and optimized resource allocation across the fleet. Of significance for flight planning departments, operators can perform manual checks on their schedules, run optimization on-demand, and lock in flight schedules, realizing immediate and auditable fleet savings.
A machine learning application that takes into account both historical flights, and external datasets (such as event calendars), combining them to accurately predict peak demand periods between clustered airports of high activity. When used in conjunction with Fleet Optimization, operators can ensure flight assets are available in high demand locations.
At present, Demand Prediction can accurately predict flights between city “airport clusters” with over 70% accuracy. As more datasets are incorporated, and input streams are added, prediction accuracy will only improve.
Flight Provisioning is a wholesale booking tool that works in concert with Optimization and Prediction. This tool equips operators with real time information as to the profitability of outsourcing any potential flight. Correspondingly, operators can determine whether it is best to satisfy a flight using their own fleet, or better serve the customer by outsourcing to a partner operator for a share of the revenue.
This powerful tool helps operators create custom rules governing the profitability of their own fleets, allowing operators to “live price” flights based on operating cost and real-time demand signals. Much like UBER’s™ “surge pricing", specific events and demand spikes will allow operators to maximize revenue when demand peaks.