Modernization and Customization of Legacy System
A leading Austria-based logistics company approached us to customize, modernize, and automate the existing inefficient legacy management system. The client was looking for an AI and ML-driven solution to address challenges like operational cost analysis, irregular revenue, on-time pickup irregularities, excessive fuel consumption, and vehicle downtime.
The project focused on modernizing and customizing the legacy fleet management system leveraging advanced AI and data-driven technologies. Facing issues with manual processes, outdated technologies, inefficient tracking mechanisms, and data-driven information the client wanted to elevate the fleet management system with real-time tracking, predictive maintenance, and accident risks, utilizing AI, ML, and data analytics.
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Location
Our client sought to achieve remarkable operational improvement with the modernization of its legacy system. The project’s key objectives include fuel consumption reduction, operational cost reduction, and enhanced profit margin by leveraging advanced analytics and reporting. The client wanted to make timely deliveries with real-time tracking to enhance customer satisfaction making it possible with real-time tracking. The client sought integration of the system with the existing ERP system for smoother data flow and more comprehensive reporting without disrupting the ongoing business operations.
The legacy fleet management system was unable to track and leverage real-time data lacking visibility which resulted in operational inefficiency as well as poor ROI. The absence of a predictive mechanism led to significant operational downtime and the lack of advanced data analytics limited the company’s ability to monitor various important aspects.
Implementation of an AI-powered platform combined with data analytics to make fleet management more advanced and efficient. The integration of advanced technologies, predictive analytics, custom dashboards, deep visibility with advanced analytics, and real-time driver monitoring and tracking, provided the company with a scalable and adaptive platform to meet future demands.
We followed agile methodologies to meet the client’s needs and expectations. Our team of experts provided end-to-end solutions followed by in-depth assessments utilizing cutting-edge solutions and advanced technologies.
Leveraged data to have detailed cost breakdown and operational cost, accurate revenue forecast, location-wise revenue details, and on-time pickup tracking.
ML models deployment to process data and predict maintenance, revenue, driver performance, utilization rate, profit margin trends, and accident risk.
Integrated the AI models into the existing fleet management platform, enabling seamless access to predictive insights and recommendations with anomaly detection in costs.
Detailed representation of operational cost, break-even analysis, multiple predictions, client insights, fleet insights, and other customized reports.
AI-driven route optimization and real-time traffic insights allowed the fleet to take more efficient routes, reducing fuel usage.
Dynamic routing and real-time tracking helped in avoiding delays facilitating more timely deliveries.
Predictive maintenance cut down on unplanned repairs and ensured vehicles were serviced at the most optimal times.
Advanced data analytics allowed for better vehicle scheduling, improving the overall utilization rate of the fleet and ROI.
Become partners for the long run
