Industry, innovation and infrastructure

We support our customers’ success by constantly further developing our own services. With digitalization projects and innovative approaches, we help shape more efficient and sustainable logistics while making our company fit for the future.

UN goal:

Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation.

Implementation at BLG

Helping to shape the sustainable logistics of tomorrow is one of our central projects for the coming years. There are a number of levers where a change toward more efficiency and lower environmental impact is possible. One of these is to reduce the number of empty runs and, hand in hand with this, optimized planning of utilization and capacities. This involves lots of data and forecasts – the perfect playing field for artificial intelligence. Together with a working group from the Fraunhofer Institute and other partners, we therefore launched the KITE project, which is funded by the Federal Ministry for Transport and Digital Infrastructure. KITE stands for Künstliche Intelligenz im Transport zur Emissionsreduktion, which translates as “Artificial intelligence for emission reductions in transportation”.

The goal: to reduce empty runs by up to 15 percent. To achieve this, a forecasting method is being developed to predict transport volumes at different levels – for example per customer, branch or company – and across different time horizons. These forecasts are then used in route planning to consolidate shipment volumes in a targeted manner. In a second step, a long-term forecast is generated, which is used as a basis for optimizing the network, for example by adding new interfaces. KITE runs until 2023. At the end of the project, the results are to be integrated into a software solution that can then also be used by other companies to reduce the number of empty runs.

Sabrina Schalk

Operational Team Leader MRP

Ms. Schalk, the KITE project is concerned with reducing the number of empty runs. What are the challenges in route and capacity planning and what benefit do you hope AI will bring?

Route planning for our trucks rarely starts with an exact pairing between unloading stations and new loading points. In addition, the trucks have to be made available again promptly at the normal loading points. This can result in empty runs, which we are at pains to avoid as far as possible. Fluctuating order volumes further complicate matters. The scheduler also has to manage the balancing act of meeting customer deadlines on the one hand and complying with EU regulations relating to driving and rest times on the other. AI allows us to collate all the parameters und thus lay the foundation for automated route planning. It should also be able to detect patterns in traffic flows. We hope this will enable us to suggest the best day for pickup to our customers and consequently optimize the entire network. This should also noticeably improve truck capacity utilization and with it the CO₂ balance in this area.

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