SENTIENT Case Study

Automotive service and repair provider achieves faster diagnosis and improved customer satisfaction

Customer

Automotive dealership that runs multiple service centers

Industry

Automotive Manufacturing

Function

Maintenance & Service

Problem

Modern automotive vehicles have thousands of sensors that track temperature, pressure, speed, rotations per minute, and many more such parameters. The data from these sensors enable technicians to diagnose maintenance issues in case of malfunctions or failures. For example, when a car comes in for repair service related to internal heating and cooling systems, heat sensor data can potentially offer useful insights for resolving the issue. The data gathered from these sensors (collectively called telemetry data) is often collected at a high rate of around millions of data points per second. The technicians in the service center wanted to use this data to provide better customer service while at the same time reducing the time taken to run a diagnosis on the vehicle’s condition, by analyzing and forecasting this data in real-time.

Solution

AI algorithm performs analytics on telemetry data in real-time and generates forecasts from the data that helped in predictive maintenance. Algorithm identifies potential anomalies in sensor data based on past history and prompt actions for maintenance. 

Result

The average time of running a diagnosis on a vehicle was reduced by 30 minutes. Increased customer satisfaction.

More Use Cases

For the Manufacturing Industry

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Electrical

Electrical equipment manufacturer saves $9.32 Million in Supply Chain Management

2263, KAUST Tech Park Headquarters, 

4700 KAUST, Thuwal, Saudi Arabia 22935

Contact

Call : +966 54 312 3734

Email : info@unitx.tech

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