Case study
Optimierung der Liefereffizienz durch Routenoptimierung
The Problem:
Our Solution:
Technology Stack:
Key Results & Value:
- 25% Fuel Cost Reduction: Significant decrease in operational overhead through minimized mileage and optimized pathing.
- 30% Faster Deliveries: Improved customer satisfaction by increasing the speed and daily volume of successful deliveries.
- 15% Maintenance Savings: Reduced fleet wear and tear by optimizing vehicle utilization and reducing unnecessary driving time.
- Automated Scalability: Enabled the seamless management of logistics across 20+ regions, removing the need for manual planning as the fleet grows.
Challenges:
- Real-time Variability: Traffic conditions and weather patterns are constantly changing, requiring a dynamic routing solution.
- Complex Scheduling: Integrating diverse delivery schedules and time constraints into route optimization was a complex task.
- Data Integration: Merging data from disparate sources (traffic APIs, weather APIs, client databases) required powerful data pipelines.
- Scalability: The solution needed to handle a growing number of deliveries and drivers without compromising performance.
Solution:
We created an AI system to find the best routes. It used machine learning to understand real-time information:
Predictive Modeling:
AI models predicted traffic congestion and weather impacts on delivery times.
Dynamic Route Calculation:
Algorithms dynamically adjust routes based on real-time conditions and delivery priorities.
User-Friendly Interface:
A mobile application provides drivers with optimized routes and real-time updates.
Data-Driven Insights:
The system provided analytics dashboards to track performance and identify areas for improvement.
Python
TensorFlow
PyTorch
Scikit-learn
AWS
Google Cloud AI
Google Maps Platform
OpenStreet
Map
Stack:
- Programming Languages: Python (for AI/ML algorithms and backend development)
- Machine Learning Libraries: TensorFlow/PyTorch (for model building and training), Scikit-learn (for data preprocessing)
- Data Sources: Real-time traffic APIs, weather data APIs, client’s delivery scheduling database
- Cloud Platform: AWS/Google Cloud (for scalable infrastructure and data storage)
- Mapping & Routing APIs: Google Maps Platform/OpenStreetMap (for route calculation and visualization)
Results the Client Got:
1. Significant Fuel Savings:
Optimised routes led to a 28% reduction in fuel consumption, resulting in substantial cost savings and a positive environmental impact.
2. Faster Delivery Times:
Delivery times were reduced by an average of 11 minutes per delivery, significantly improving customer satisfaction and on-time delivery rates.
3. Enhanced Operational Efficiency:
Automation of route planning and dispatching resulted in a 15.7% reduction in manual workload, streamlining operations and freeing up staff for other critical tasks.
4. Improved Driver Satisfaction:
Drivers benefited from clear, optimized routes, reducing stress and improving their overall experience.
5. Real-time Data Visualization and Improved Data Analytics:
The client gained access to a dynamic dashboard displaying real-time location data, route efficiency, and delivery performance. This allowed for immediate adjustments and informed decision-making, leading to significant operational improvements.
Mike Fliorko
Geschäftsführender Direktor, EMEA
Michael Babylon
Sales Director, Europe
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