For most of us, a Logistics decision means choosing between fast and expensive, or slow and cheap, delivery when we buy something online. Behind that choice is a trillion-dollar industry that is one of the most complex and dynamic of the modern world. Whoever you give your delivery money to has to decide how to move your purchase, perhaps across half the planet, while dealing with customs, regulators, multiple transport companies and multiple possible routes - all on time and all while turning a profit.
Thomas Larrieu, Chief Data & Research Officer at Upply
This would seem a natural fit for automated data analysis, the perfect place for a highly-computerized market that combines all the data about a particular delivery requirement and produces an optimized result. Indeed some operations have been improved over the decades, such as Transport Management Systems (TMS). However, it was only addressed at an enterprise level. There was no holistic solution that would truly change the market.
A data-focused logistics start-up in France is hell-bent on changing all that.
Big Market, Bigger Problems
As Thomas Larrieu, Chief Data & Research Officer at Upply, the aforementioned start up, says, the sheer complexity, size and footprint of the global Logistics industry has slowed down the evolution of an automated, efficient market where information is shared fairly and smartly to help every player succeed. “All the market data is out there but the industry’s only been seriously dealing with it for five years. Nobody’s been drawing it together,” he says. “Big companies are reluctant to do anything that might upset their position, small companies can’t afford to invest in the analytics and data gathering needed. So the idea of Upply was born around three years ago. To bring data science and automation where it’s most needed. We are convinced that it will improve the whole industry.”
That meant building the database, analytics package and API to deliver clean market data straight to users. It’s the first time this has been done.
#big data
