Sensor Analytics Reduces Vehicle Breakdown and Delivers USD 6 Million Savings for a Logistics Company

Sep 23, 2016

Business Challenge

Proactive vehicle maintenance for better asset utilization

A freight and logistics company wanted to set up a statistical model for predictive vehicle maintenance and improve vehicle life.

Situation: Setting up predictive model for proactive vehicle maintenance

Client wanted to set up a predictive model for monitoring the utilization of its road fleet, and the health and lifecycle of the vehicles, in order to ensure on-time maintenance scheduling and improvement in performance, reduce vehicle breakdown chances and improve the asset lifecycle.

Contact our sensor data analytics experts to know more about our solution benefits.


Sensor data analytics, vehicle performance measurement and asset utilization analytics

We extracted the sensor data from all vehicles, utilized Fourier & SVM algorithm for data transformation, and further analyzed them through real-time monitoring, asset utilization analytics and predictive modeling, for alerts on the vehicle performance, upcoming maintenance, and servicing and scheduling preventive maintenance.

To know more about the benefits of vehicle performance and asset utilization analytics, request more information.


Reduction in vehicle breakdown by 70%

The client utilized our predictive monitoring system tracking of all its vehicles’ usage and received real-time alerts on any potential vehicle breakdown, which enabled it to schedule preventive processes for maintenance & repairs. The client reduced vehicle breakdown by 70% and realized USD 6 million in savings.

Recent Case Studies


Our advanced analytics expertise spans across industries, sectors, and functions, which enables us to deliver robust, agile solutions to all our clients. These are our core competencies, formed through years of experience.


Our free resources shed light on our extensive expertise and equip you with information to accelerate decision-making, growth, and innovation.

Talk to us
Talk to us