Logistics Company
Route optimisation with AWS SageMaker
Starting point
- The Faber Group BV offers pallet and pooling services for a variety of industries
- Customers are provided with tailored solutions for their different pallet requirements
- The demand for shorter delivery times requires optimized routes
- Rising fuel prices and environmental regulations increase the pressure on the company
- The complexity of supply chains requires intelligent solutions
Role in the project
- Creation of a detailed requirements analysis
- Provision of a business solution design
- Technical design for the integration of SageMaker
- Development of the product-specific and customized customer solution
- Through the integration of Amazon SageMaker, a machine learning-based model was quickly and easily implemented
Features/Project outcome
- Development of a custom SageMaker model for machine learning
- Implementation of a Python algorithm for many-to-many route optimization
- Integration of the OpenRouteService API for geodata
- Saving the optimal routes and matching order routes with historical routes
- Limiting the results through additional inputs such as start and destination

Customer benefits
- Significant cost savings in the double-digit million range
- Optimisation of vehicle utilization and reduction of emissions
- Better planning and higher flexibility
- Increased customer satisfaction due to faster delivery times
- Expansion of details to narrow down search queries:
- Subsections < 200 km; total distance < 3,200 km
- Optimisation over n>1 trucks
- Customer number instead of invoice number for the search algorithm

Lars Ritter | Senior Manager