[AL AWARDS Stories #1] Artificial intelligence and big data: GEFCO takes the plunge!

28 May 2018

We are delighted to present our first entry shortlisted for the Automotive Logistics Awards Europe 2018 in the “Inbound network optimisation” category: the Intercontinental Transport Plan.


Thanks to big data, our road freight engineers have identified new strategic corridors and improved the loading rate.

A European experiment that could determine the logistics of tomorrow

The data of 525 million of  lines, operated for 18 months by the road freight team in Europe, has been collected. The teams were searching to rely on this enormous amount of information to:

  • optimise flows
  • anticipate seasonal variations
  • adjust the transport corridors

However, how to use this big data? To transform this base, they turned to the machine learning. Thanks to its power of analysis, the artificial intelligence brings out observations and deductions escaping to the most experienced analysts.

To analyse such a big data, the GEFCO team had to look for a more powerful statistical analyses tool than the traditional ones that they use. Thus, they started a partnership with Microsoft analysts in June 2016.


A collaborative approach 

Indeed, their statistical analysis spirit combined with GEFCO’s operational approach proved to be truly fruitful. It allowed to look from a new angle at the database organisation to get a better overview of all flows. 
The key to this approach, which has mobilised a project team of up to 10 GEFCO employees, was based on the ability to standardise and communicate the different databases of GEFCO Overland division to feed the algorithm in a better way.


The results: strategic data for future development at GEFCO

Our engineers have identified the growing road tradelanes in Europe, which would have been impossible by relying on partial data. For instance, Artificial Intelligence has brought to the surface the customer need to create a direct corridor between Spain and Poland, without transit through France as it was before.

Once integrated into GEFCO's Transport Management System, this experience could also generate significant cost savings by increasing the loading rate of thegroupage lines. 

Thanks to the predictive capability of the machine learning and the data analysis collected, it allows to better understand the industries and to anticipate their requests. Artificial Intelligence could finally get its recognition!


of  lines analyzed

18 months

of data collection

Groupage​​​​​​​ filling rate