Machine-learning modernizing rail freight

The back story

DB Cargo is the UK’s largest rail freight company, providing quality services and logistic solutions to a wide range of customers – from automotive to construction. DB Cargo’s mission is to be the first choice for rail freight in the UK.

Two people standing around a man on a computer pointing at his screen

The problem

As logistics is a complex, interconnected chain, disruptions at any link of the network can cause considerable delay and cost to the end customer. Several years ago, DB Cargo approached Jade to create two systems to help facilitate a big part of this chain: a customer-ordering system and a consolidated planning tool.

With their quest for continual improvement, DB Cargo challenged Jade to see how we could improve their order to cash processes (previously run through spreadsheets and manual processes) and raise their operational efficiencies to a new level. We accepted the challenge.


The solution

After a review of the work Jade had completed for DB Cargo over the years and recent experience with big data analytics, we discovered an opportunity to identify efficiencies through machine learning. This began through experience design workshops that involved stakeholders throughout the business as well as four of DB Cargo’s customers.

We created a mobile app that connects to existing software in the business, showing customers their train order and running information – with a highly accurate ETA feature that predicts train arrival times based on historical data.

"The collaboration through the design process has been very successful in producing a system that gives us access to the information we as a business need in real-time”, said a key external stakeholder about the project.

On top of this process, DB Cargo needed to vacate their data center, so they engaged Jade to migrate their servers to the cloud plus provide managed services to ensure minimal business disruption.


The outcome

Our ongoing partnership with DB Cargo has so far included:

  • Undertaking the UX, design, and delivery of the redesigned portal with improved usability and increased customer engagement.
  • A machine-learning engine to improve ETAs, providing insights for tasks such as scheduling rosters.
  • Full cloud migration of managed services reduced dependency on physical infrastructure.
  • The generation of fresh customer insights through UX techniques and machine learning, which help inform DB Cargo’s future decisions.