← AI Assisted Solver / MRO case study

A worked example using synthetic data estimated from public information. It does not show any specific airline's actual plans or figures.

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Breakdown from the flight schedule

Engine planning starts from the flight schedule. Take the aircraft needed to fly each month's flights, the engines those aircraft carry on wing, and the spare engines held against unscheduled removals; subtract them from the engines owned, and what remains is the spare capacity for shop visits. In months where that capacity runs short, flights cannot be operated, and if demand grows, more engines are needed.

From the flight schedule to spare capacity for shop visits

Flight schedule (flights per day)

Varies with the season. This drives the number of aircraft and engines needed.

Spare capacity for shop visits (engines)

Serviceable engines − engines on wing − spares. In months below zero, some flights cannot be operated.

Spare capacity availableShort (some flights cannot be operated)

In reverse: converting engine spare capacity into seats

Spare capacity ÷ 2 = extra aircraft that can fly. × monthly flights × seats × stage length = additional ASK. In short months, ASK that cannot be flown × load factor × fare = revenue lost.

One layer up: if demand grows

Seasonal shape of demand vs. the plan's flight index

Market demand (government statistics)Plan flight index

Increase in engines on wing (engines)

Difference from the current plan when the same calculation is rerun with demand growth.

View monthly figures as a table

Inputs: flight schedule and fleet and engines. Results: a-breakdown.json · b-breakdown.json. Market demand: Ministry of Land, Infrastructure, Transport and Tourism (MLIT), Air Transport Statistics (e-Stat), domestic scheduled passengers.