We developed a custom fleet management SaaS platform with mobile apps for drivers and dispatchers, helping a Dutch logistics firm optimize 200+ vehicles across Europe. The AI-driven solution analyzed traffic patterns, weather, and delivery constraints to generate dynamic routes, reducing average delivery times by 18% and fuel consumption by 22%. Real-time tracking and predictive maintenance alerts cut vehicle downtime by 35%, while automated proof-of-delivery (ePOD) eliminated paperwork. The system integrated with existing ERP tools, providing live profitability analytics per route. Within 6 months, the client saw a 15% increase in daily deliveries without expanding their fleet.
Client Industry
Logistics & Transportation
Client Location
Rotterdam, Netherlands
Project Duration
7 months
Team Size
14 (Backend, Mobile, Data Scientists, GIS Specialists)
Technology Used
- Routing Engine: Python, OR-Tools, HERE Maps API
- Mobile Apps: Flutter (Driver & Dispatcher)
- Telematics: IoT OBD-II dongles for real-time diagnostics
- AI/ML: TensorFlow (ETA prediction, engine health)
- Cloud: Google Cloud (Multi-region deployment)
- Security: GDPR-compliant data encryption
Client Background
The client operated a mid-sized logistics fleet transporting temperature-sensitive goods across the Benelux region. Despite modern trucks, they struggled with:
- Inefficient routes (15% empty miles)
- Manual dispatch causing 2+ hour planning delays
- No real-time visibility into delays or vehicle issues
- Paper-based PODs leading to invoice disputes
Their legacy system couldn’t handle last-minute order changes or dynamic rerouting. With rising fuel costs and driver shortages, they needed an AI-driven solution to maximize asset utilization while meeting strict EU delivery windows.
Project Scope
- Dynamic Route Optimization
AI adjusted paths in real-time for traffic/weather, cutting 55K km/month in unnecessary mileage - Driver Mobile App
Turn-by-turn navigation with load-specific instructions (e.g., “Chilled goods → Priority unloading”) - Fleet Health Monitoring
Predictive maintenance alerts reduced breakdowns by 40% via OBD-II diagnostics - Automated ePOD
Digital signatures, photo capture, and auto-invoicing slashed admin work by 25 hours/week - Carbon Analytics
Tracked/optimized emissions per route to comply with EU sustainability mandates
Our Approach
- Data Pipeline Development
Aggregated traffic, weather, and historical delivery data to train routing models - Driver-Centric Design
Conducted workshops with 30+ drivers to optimize in-cab app usability. - Phased Rollout
Piloted with 20 trucks, then scaled fleet-wide after refining ETA algorithms. - ERP Integration
Connected with client’s SAP system for seamless order/dispatch sync.
Features Introduced
- AI Traffic Predictor
Learned regional congestion patterns to avoid delays (e.g., school zones at 3 PM) (30 words). - Multi-Stop Sequencing
Auto-reordered deliveries when customers rescheduled (40 words). - Load Balancing
Suggested optimal trailer configurations to reduce partial loads (30 words). - Driver Scorecards
Tracked fuel efficiency/safety metrics to incentivize best practices (30 words). - Dutch/French Language Support
Critical for cross-border operations.
Addressing Challenges
- Driver Resistance → Gamified training (badges for fuel savings) boosted adoption
- Data Silos → Unified API layer connected telematics/ERP/weather feeds
- Strict EU Regulations → Built-in tachograph compliance checks
- Urban Logistics → Added bike/cargo van routing options for city centers
Business Impact
- 22% Fuel Savings (€380K/year reduction)
- 18% Faster Deliveries (98% on-time rate vs. 82% previously)
- 35% Less Downtime via predictive maintenance
- 100% Digital PODs (Eliminated 7K paper sheets/month)
- 15% More Daily Jobs with same fleet size
