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The implementation of the Waliot system has resulted in a significant increase in operational efficiency and cost reduction. The time it takes to refuel equipment has been reduced by 37%, which has minimized downtime and increased fleet productivity. The use of vehicle telematics and fuel analytics based on machine learning has reduced fuel losses by 92%, and the accuracy of accounting has reached 98%. Automation of reporting and real-time monitoring has ensured transparency of processes and the ability to make prompt decisions, which has improved control over resources and safety.
The main strategic result was the prospect of using modern data analysis methods. These methods allow for highly accurate tracking of changes in equipment operation and identification of potential risks. Predictive models will allow assessing the likelihood of malfunctions or deviations, for example, when equipment is used inappropriately or when malfunctions occur.
The project is a deep digitalization of the vehicle fleet using advanced technologies of transport telematics and fuel analytics based on machine learning. Using Waliot allows not only to monitor the movement of equipment in real time, but also to analyze data from various sensors and the CAN bus. The machine learning system identifies deviations in the operation of equipment and fuel operations, preventing theft and inefficient use of resources. The uniqueness lies in the integration of this data into a single solution, which significantly reduces downtime and increases the overall performance of the vehicle fleet. The telematics data accumulated by the system is used to generate BI dashboards and the work of the business analysts department.
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