Amazon: business, or machine?

In the end my interviewee got cold feet, not wanting to risk association with commentary about her new company - she had just joined Amazon at the time of interview.

Roughly week three, she’s currently a manager in what’s called the ‘middle mile network’ (“every mile has its challenge”), so she participates in all projects - “changing routes, adding new routes, launching new sort centres.”

“Packages go from warehouses to various distribution centres – there’s a very large network all over Europe. Holidays are quite the challenge when it comes to predicting volumes for each node! Other challenges are avoiding bottle necks, and contingency planning.”

She has a machine learning algorithm background, so even though applied ML is not her focus at Amazon, her mind is still tuned in with the various elements of logistics that could be improved by it.

Drawing on prior experience, she gave me a few examples of what machine learning can be used for in the industry.

“For example, forecasting volumes is a huge part of logistics: we try to predict which sort centres will be off or on, often based on political or natural disasters.”

“Volumes needs to be re-routed correctly and at low cost, as fast as possible. It’s a very broad challenge. To apply machine learning you need to gather data to understand variations and predict what’s likely to happen next. You incorporate everything, all data sources!”

“We would make forecasting as accurate as possible, connecting with various government websites – where there’s mismatch ML improves the process – and even anticipate natural disasters. We use RPA (robotic process automation) wherever possible to remove manual processes.“

When she mentioned natural disasters, my mind immediately jumped to climate change; I would love to be a fly on the wall of an Amazon climate prediction meeting, where bla