I led the discovery to find where AI could actually move the needle inside an airline's network-planning organization, then rebuilt the spill-and-recapture step that had become the bottleneck when the pandemic broke the network.
Confidentiality note. The client is anonymized and proprietary figures are withheld. Diagrams, screens and metrics shown here are sanitized recreations built for this portfolio, directionally accurate but not the client's actual numbers.
This is one fleet's rotations across one day. Multiply it by eleven fleets, two hundred stations, and a thousand legal and commercial constraints, then re-solve it every time demand moves. That's the problem network planning lives with.
An airline plans its network in two modes, a monthly re-plan that shapes the schedule, and a daily grind that keeps it flying. Either way it's a long chain of decisions: demand forecasting, cost analysis, route building, constrained demand, fleet assignment, and spill & recapture. The network was planned on a heavyweight commercial suite, Sabre's planning stack, and that solver was only one part of a much larger, human-driven process.
Every decision is boxed in by constraints that can't be bent: aircraft maintenance windows, crew legality, cabin configuration, airport curfews and other legal limits, and the connections that have to hold across the whole network. Then the pandemic hit, demand collapsed, the plan had to be redone constantly, and the existing process simply couldn't keep up.
I led the discovery across the network-planning organization: sitting with planners, tracing the process end to end through its commercial solver core, and pressure-testing each step for where modernization would earn its keep, and where it would only add risk. Some steps were already well-served. A handful were not.
We ran it as a structured prioritization with the planning teams. Every component of the incumbent suite that was a candidate to replace or modernize got mapped on one board, business value against delivery feasibility, and tagged business- or technical-facing, so the sequencing argument was made in the open rather than by whoever spoke loudest.
We also codified the network itself, every flight and airport, as a graph database, so the relationships became something a model could reason over. On top of it sat a demand forecast that reads context: when a planner overrides a flight by hand, say a storm shuts a hub, the forecast adapts to that decision instead of fighting it. But the step that kept surfacing as the real bottleneck was spill & recapture, high commercial value, computationally brutal, the single biggest reason a re-plan took days instead of hours. That is where I focused the team.
"Sensitivity analysis with parallel processing, quantifying passenger flow and optimizing revenue across a million decision points for city pairs globally."
Every itinerary competes for the same travelers. When a flight fills up or gets cut, some passengers spill onto another of the airline's flights, some are lost to a competitor, and the rest don't travel at all. Recapture estimates how many you keep, and that number is the demand truth the whole revenue plan is built on.
Getting it right means solving thousands of linked problems, one per schedule change. It's accurate and it's slow, which is exactly why it became the bottleneck the moment the network had to be re-planned every day.
I was the strategist and team lead on the rebuild. I led a team to build a new spill-and-recapture engine so the whole network could be evaluated in hours instead of days. The idea was simple to say and hard to do: solve the base problem once in the solver core, then reuse that work across the thousands of scenario tweaks a re-plan needs, running them in parallel instead of starting from scratch each time.
Crucially, none of the speed came at the cost of the constraints. Maintenance, crew legality, cabin configuration, curfews and connections were all preserved exactly. The win was letting planners re-plan as fast as demand was moving, the thing the pandemic made non-negotiable.
The model is operational today, and the approach was distinct enough from existing practice that the Federal Aviation Administration invited me to share how AI could improve their own safety operations.