Leverages ML to Optimize Air Cargo
May 18, 2020
World’s largest airline uses
NVIDIA Quadro to better model cargo shipments, improve weight
distribution and save fuel.
If you think flying commercial is stressful, consider the air cargo
Unlike passenger flights, which are often booked and paid for months in
advance, cargo shipments are typically booked just 10 days before the
planned departure. And customers don’t have to pay until they drop off
However, even when customers create a booking to save a spot for their
shipment, some cargo never shows up at the warehouse. This wreaks havoc
with even the best-laid plans.
Along with its passenger business, American Airlines runs air cargo
services worldwide for consumers and businesses. These shipments play an
important role in keeping the world supplied with essentials, so the
company works hard to make sure goods are transported as efficiently as
That work requires the analysis of many variables, and the most
challenging one is determining if a cargo shipment will even show up for
Predicting the future isn’t easy, but data science can help. American
Airlines uses machine learning and Quadro-powered Z by HP data science
workstations to run models that assess how likely it is that a cargo
shipment will arrive, which allows them to better plan shipments ahead
The No-Show Package Problem
American Airlines receives thousands of shipments a day, and each needs
to be quickly managed by its cargo team. But the logistics of cargo
management are especially complex since some unknown number of bookings
will never turn up.
“American Airlines uses details from the bookings to plan the layout of
cargo holds and see where freight can be placed,” said Tassio Carvalho,
head of the Center for Machine Learning and Artificial Intelligence at
If a shipment doesn’t show up on the day of departure, there’s no time
to replan the layout or resell the space. This means the configuration
of the freight in the cargo hold is less optimal, which results in
increased fuel burn for the trip.
“No-shows cost us millions in lost revenue, and many times they can
result in us needlessly turning away other critical shipments when we
could have otherwise carried them,” said Chris Isaac, managing director
of American Airlines Cargo Revenue Management. “Being able to firm up a
flight’s bookings in advance allows us to recapture space that will go
unused and provide it to others who need it.”
Signed, Sealed and Delivered with Machine Learning
Using Z by HP data science workstations powered by Quadro GPUs, Carvalho
and his team created a machine learning model that takes data from the
customer’s booking and predicts the likelihood of whether the shipment
will arrive or not.
The team built the predictive model using H2O4GPU, an open source, GPU-accelerated
machine learning package, and loaded it with 500,000 booking records — a
whole year of data. Each record had about 20 features, and those were
segmented into about 100 derived features.
About three days before its scheduled flights, American Airlines will
run the details of each booking through the model. When the results show
a high prediction of a shipment not arriving, the team reaches out to
the customer and confirms whether or not they’ll show up for the
Using machine learning to flag at-risk shipments allows cargo agents to
focus on the bookings that have the lowest chance of materializing, and
spares them from having to call every customer.
“The model is valuable because it shows which shipments are likely to
become no-shows, and which bookings will have variations when they show
up at the airport,” said Carvalho. “With the data science workstations,
we’re able to get high accuracy with the models — at least 90 percent.
This helps us plan our cargo freights better than we ever could before.”
the Quadro GPUs, Carvalho and his team were able to perform computations
up to 10x faster than on CPUs. They get predictions and results much
quicker, leading to increased cargo space utilization and reduced fuel
American Airlines also announced they will be implementing a fair
booking policy that allows customers to cancel bookings for free with at
least 48 hours notice. The latest policy combined with the predictive
model enables American Airlines to maximize space on the aircrafts.
“The ability to use advanced analytics to solve one of our industry’s
biggest problems is a game changer for American Airlines,” said Isaac.
“We have the best data science team in the industry, and we couldn’t be
more excited to integrate the model into our business process.”