This past spring of 2020, we ripped out all of our phone infrastructure and rebuilt it from scratch. Little did we know that a month later, our entire company would be working remotely due to COVID-19. Had we not done that, the transition to remote work would have been virtually impossible. This transition turned out to be both well-timed and overdue because in rebuilding our telecommunications at Airspace, we completely redefined what we thought was possible.
Anyone familiar with time-critical shipping is familiar with the promise of a 99.9 on-time percentage. It is the number that almost every time critical forwarder boasts. I argue that not only is 99.9% a fabricated number that gives shippers a false sense of security, it is also the wrong number to set as a goal for the industry.
Machine learning is a hot topic, and for a good reason. Right at the top, I’d say, machine learning has evolved to the point where it can – and should – be used to help virtually all businesses increase efficiency while reducing costs.
There is a concept in computer science known as a black box function. A function is considered to be a black box when the developer using it does not have any information as to what is going on inside of it. This can be very useful for the developer because it is potentially many less things to worry about. But then something goes wrong with his program and all of the sudden he needs deep understanding of its inner workings. If he is unable to get that information, he may never be able to rectify the problem.
When you are in an emergency situation and dial 911, how soon the emergency responders start driving to your location is a big deal. However, a lot of things need to take place before that can happen. Should they send a police officer or an EMT? Where should they send them? How many should they send? To avoid making a critical mistake like sending the wrong personnel to the wrong location, emergency services trades time for correctness up front.
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