How NovaFleet Reduced Delivery Times and Fuel Costs with Predictive Analytics
Overview
NovaFleet is a logistics company based in Melbourne, Australia, that manages a complex network of deliveries across the country. Due to high demand and fluctuating fuel prices, it was becoming increasingly challenging. They wanted a system to help them optimize routes, reduce fuel consumption, and increase delivery speed without compromising the fleet’s wear and tear.
Technical Stack
Industry
Logistics
Region
Australia
Project Size
Non- Disclosable
Highlights
Reduced delivery times by 30%
Cut fuel consumption costs by 20%
Improved route optimization accuracy by 40%
Reduced fleet downtime by 15%
Challenges & Solutions
The client wanted to predict fuel consumption under dynamic road conditions.
- Solution: We developed a predictive model using Python's Scikit-Learn. The model integrated historical and real-time traffic data, enabling NovaFleet to anticipate fuel needs with 87% accuracy. The model was seamlessly integrated with AWS Lambda for rapid scalability.
They also wanted to optimize complex routes with shifting demands.
- Solution: Our Python developers utilized Python’s NetworkX library to create an adaptive routing algorithm that recalibrates based on real-time updates. This allowed the system to reroute vehicles dynamically, responding to traffic, weather, and demand, reducing idle time and increasing delivery accuracy.
Another major challenge our client faced was unexpected breakdowns, which paused their business operations and led them to spend more.
- Solution: Our Python developers implemented a Django-based dashboard that captures real-time IoT data from fleet vehicles. They even use Pandas for data manipulation and TensorFlow for anomaly detection; the system now flags maintenance issues before they occur, reducing breakdowns by 22%.
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Years of Business
Experience
Happy
Customers
Countries with
Happy Customers
Agile enabled
employees
Core Features
- Real-Time Data Integration
- Adaptive Routing Algorithms
- Predictive Analytics for Fuel and Maintenance
- Automated Fleet Health Dashboard
No. of Developers
04
Time Frame
November 2023 - March 2024
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