Drivers Operations Analytics for a Construction Company
To enhance route efficiency and cultivate optimal driver behavior, a prominent US construction company needed a modern solution. TeamArt answered with an ML-based system that utilized real-time GPS data, ensuring impeccable route planning, anomaly detection, and optimal loading and dispatching operations.
Business Domain: Construction
Solution: Analytics, ML, Data Analytics
Engagement model: Staff augmentation
Key Technologies: Tableau, AWS React, AWS S3 buckets, AWS SNS Lambda, Python, Snowflake
The client, a leading US construction company, expressed a pressing need for an application that could comprehensively monitor drivers' routes, promptly detect anomalies, and analyze overall driving behavior. Given the nature of their business, ensuring that drivers adhered to designated routes and maintained expected driving standards was crucial for optimizing logistics, minimizing delays, and ensuring the safety and efficiency of operations.
Rising to the challenge, TeamArt engineered a Machine Learning (ML) based functionality tailored to study intricate driving patterns meticulously. This state-of-the-art system is adept at quickly identifying any anomalies, such as unexpected stops or route alterations. The solution's brilliance lies in its capability to utilize the GPS signal from trucks. This enables the system to accurately determine if a driver is situated in a loading or dispatching zone, offering deeper insights into their operational status and ensuring compliance with assigned tasks.
The client now benefits from real-time monitoring of their expansive truck fleet. This enhanced visibility allows for the instant assessment of each truck's current position, coupled with a detailed analysis of any anomalies or deviations from expected behavior. Moreover, the system is designed to send prompt notifications at the commencement of loading and dispatching operations. This seamless integration of technology has empowered the client with superior oversight, ensuring better logistics management, prompt anomaly detection, and overall improved operational efficiency.