If you are a freight forwarder dealing with complex manual dispatching—this project developed an AI core that automates 80% of route planning. This can save 2-3 employees per hub and reduce planning time by 90%.
AI-Powered Automated Transport Planning Software to Reduce Logistics Costs and CO2
Imagine a super-smart digital brain that looks at thousands of possible delivery routes at once to find the absolute cheapest and fastest path. Instead of a person spending hours manually planning where trucks go, this software does it automatically in real-time. It's like having a GPS that doesn't just find one way, but optimizes the entire fleet's schedule to save fuel and time.
What needed solving
Logistics dispatching is often manual, slow, and fragmented across multiple software tools. This leads to high operational costs, wasted fuel, and inefficient use of human resources.
What was built
A self-learning, cloud-based AI dispatch software (LogisticsBrain) with a standardized API for TMS integration and a CO2 reporting module.
Who needs this
Who can put this to work
If you are a courier service dealing with 50+ hubs and multi-day routes—this project developed a cloud-based software that optimizes costs by 40%. It handles LTL, FTL, and excess pallets in one system.
If you are a logistics provider dealing with high carbon footprints—this project developed a CO2 reporting tool and optimization engine that reduces emissions and fine dust by 21%.
Quick answers
How much can this software reduce operational costs?
Based on available project data, the AI-based optimization engine can optimize costs by 40%.
Can this be scaled to large networks?
Yes, the AI core was extended to handle complex scenarios involving 50+ hubs and multi-day routes.
What is the licensing or IP model?
Based on available project data, the solution is offered as a Software as a Service (SaaS) model.
How does it integrate with existing IT systems?
The software uses standardized interfaces and APIs that have been successfully integrated with customer Transport Management Systems (TMS).
What is the timeline for implementation?
The project ran from June 2022 to May 2024, reaching a target of TRL 8 through pilot qualifications.
Who built it
The project was led by a single German SME, Smartlane GmbH, which acted as the sole coordinator and partner. This 100% industry-led structure allowed for a direct focus on commercialization and rapid integration with customer TMS systems, avoiding academic delays.
Contact Smartlane GmbH in Germany for SaaS licensing
Talk to the team behind this work.
Request a demo of the LogisticsBrain AI engine