Route optimization
Real-time Traffic Analysis: CrateDB can be used to analyze real-time data on traffic conditions, road closures, and congestion levels from various sources such as GPS devices, traffic cameras, and traffic sensors. By processing this data, CrateDB identifies optimal routes for vehicles, considering factors like travel time, distance, and road conditions.
Dynamic Route Adjustments: CrateDB enables transportation companies to make dynamic adjustments to routes based on changing conditions, such as accidents, construction, or weather events. By continuously monitoring and analyzing data, CrateDB provides up-to-date information to drivers and dispatchers, allowing them to reroute vehicles in real-time to avoid delays and optimize efficiency.
Vehicle tracking and fleet management
Real-time Location Monitoring: CrateDB collects and analyzes real-time data on the location and status of vehicles using GPS tracking devices and telematics systems. By processing this data, CrateDB provides transportation companies with visibility into the whereabouts of their vehicles, enabling them to track deliveries, monitor driver behavior, and optimize fleet operations.
Performance Analytics and Maintenance Scheduling: CrateDB analyzes data on vehicle performance metrics such as fuel consumption, engine health, and maintenance history. By identifying trends and patterns in this data, CrateDB helps transportation companies optimize vehicle maintenance schedules, predict potential issues before they occur, and ensure fleet reliability and efficiency.
Demand forecasting and supply chain management
Historical Data Analysis: CrateDB analyzes historical data on passenger or cargo demand, market trends, and supply chain logistics to identify patterns and trends. By understanding past demand patterns and market dynamics, CrateDB helps transportation companies forecast future demand more accurately.
Predictive Analytics: Leveraging advanced analytics techniques, such as machine learning algorithms, CrateDB predicts future transportation needs based on historical data and external factors such as economic indicators and seasonal trends. By incorporating predictive models into supply chain planning, CrateDB helps transportation companies optimize resource allocation, improve inventory management, and enhance service levels.
Case study: Thomas Concrete Group
Key objectives
- Display truck tracking information in an online portal and mobile app for construction managers to know the precise arrival time of concrete trucks at the construction site.
- Track concrete curing, a critical phase that requires careful monitoring.
Main technical challenges
- Possibility to deploy on the Edge (low connectivity sites).
- Real-time query to access information.
- Ability to consolidate and query data from multiple sites.
