Why airline capacity is the most mispriced resource in global commerce.

Every seat, pallet position, and departure slot is inventory with a hard deadline. Right up until the doors close it can still be sold at a price that reflects real demand; the instant they close, whatever wasn’t sold stops being worth anything at all.

Capacity does not lose a little value when it expires. It loses all of it, permanently.

The model

Set your network below, then run the year.

Aircraft type

Flights per year: 700

Capacity per flight (tonnes): 100

Average load factor: 78%

Price per kg: $1.20

Dynamic pricing recovery: 22%

Unsold tonnes this year

15,400

Destroyed — static pricing

$18.48M

Destroyed — dynamic pricing

$14.41M

Recovered by dynamic pricing

$4.07M

Simulation complete. $18.48M destroyed under static pricing; $14.41M under dynamic pricing, recovering $4.07M.

With JavaScript disabled the model can't run, but its logic is simple: a wide-body freighter network flying 700 flights a year at 100 tonnes each and a 78% load factor leaves roughly 15,400 tonnes unsold. At $1.20 a kilogram, that is about $18.5 million destroyed under static pricing a year — and roughly $4.1 million of that is recoverable under dynamic pricing at a 22% recovery rate.

Read the result

At the load factor and price you set, 15,400 tonnes of capacity go unsold across the year — space that existed, cost money to fly, and earned nothing. Under a static rate card, that destroys $18.48M in a single year.

Pricing that capacity dynamically against real-time demand, instead of a fixed rate set weeks in advance, recovers $4.07M of that — not by selling more seats, but by pricing the seats that do sell closer to what they were actually worth in the moment.

The gap between $18.48M and $14.41M is the size of the opportunity most networks leave on the table by treating capacity like a durable good instead of a perishable one.

The method

This model deliberately simplifies. It assumes a constant load factor and price across every flight in the year, rather than modelling day-to-day demand swings, seasonality, or route-level variation. It applies dynamic pricing as a single flat recovery rate on the value that would otherwise be destroyed, rather than simulating a real pricing algorithm.

Real networks are messier than this in both directions — some routes recover more, some less, and demand volatility changes the picture week to week. The model exists to make the size of the opportunity legible, not to replace a real pricing engine.

Embed this model

Copy this snippet into your own site. It links back here for attribution.

For the full argument behind this model — the perishability problem, the pricing shift, and the operating evidence — read the framework.

Read: Capacity Is the New Currency → Work with Lars on this →

FAQ

What does this model show?

It shows how much revenue a network destroys when cargo capacity goes unsold, using the flight volume, tonnage, load factor, and price you set, and compares that to the loss under dynamic pricing.

What assumptions does it make?

It assumes a constant load factor and price across all flights and applies dynamic pricing as a flat recovery rate on the value otherwise destroyed. It deliberately does not model day-to-day demand variance, seasonality, or route-level differences.

Can I embed it?

Yes — copy the iframe snippet on this page into your own site. It links back here for attribution.

What data would I need to run this on my own network?

Annual flight volume, average cargo capacity per departure in tonnes, average load factor, average price per kilogram, and an estimate of how much dynamic pricing or utilisation-based renegotiation could recover.