Rethinking efficiency: Predictive route planning with AI

For companies, this means:

• Less uncertainty
• More speed
• A clear competitive advantage

‍
Especially in dynamic environments such as logistics, where time, costs and customer satisfaction are at stake every day, AI can be the decisive factor for sustainable success.

Challenge

A leading European logistics provider showed how great the potential actually is. Route planning — at the heart of every transport organization — was heavily manual and was based largely on individual experience. This led to complex scheduling processes, limited scalability and risks such as delays or inefficient fleet usage. Increasing costs, growing customer expectations and increasing pressure for efficiency made it clear that data-based and predictive planning was no longer just an advantage, but a business-critical necessity.

Our Approach

In order to exploit the full potential, we first analysed all relevant processes in the transport and dispatch environment and included the available data sources in detail. Together with our customer, the most promising use cases were prioritized — with a particular focus on ETA forecasting and predictive route planning.

By connecting existing systems and creating a central data pool, all order information, driver data and GPS signals could be brought together. On this basis, an AI-based forecasting model was developed, which is continuously trained and optimized. The forecasts and route suggestions obtained in this way were integrated directly into the daily schedule — seamlessly embedded into the existing TMS landscape.