- Most people look at an
Amazon Fulfillment Center
and imagine all the stuff inside.
When I look at it, I see data.
I'm Russell Allgor,
chief scientist with Amazon
Worldwide Operations.
There are one to four million
bins per fulfillment center,
and on the order of 10 million items.
We have computer vision
systems analyzing images
to help us securely keep
track of where everything is.
Since our fulfillment centers
are set up largely on
a Manhattan style grid,
the paths that the pods can follow
is relatively structured and organized.
So the first decision I have to make
is which orders I want
to pick at the same time,
in order to get the items in the same box
and that's a large combinatorial
optimization problem
that I have to solve and
I'm solving in real time.
Using that information,
we try to minimize the distance
the pods have to travel.
We have decision engines
and decision logic,
AI optimization that's running
to make those decisions
in real time on a constant basis
as the information underneath changes.
We may build predictions of an action
of how likely am I to
need to access this pod
in the next hour, two hours,
three hours, four hours.
Once we put a label on the box,
the transportation execution systems
and processes all have to take over.
So we'll use machine learning
to build information about
how long it takes to travel
from point A to point B.
To make the magic happen
and get Prime shipping in one day,
we need to better use
all of that information,
which machine learning
and optimization at
scale enables us to do.
(upbeat music)
