C3 IoT > Case Studies > Vehicle Fleet Analytics

Vehicle Fleet Analytics

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Technology Category
  • Analytics & Modeling - Big Data Analytics
  • Analytics & Modeling - Predictive Analytics
  • Functional Applications - Fleet Management Systems (FMS)
  • Infrastructure as a Service (IaaS)
Applicable Industries
  • Transportation
Applicable Functions
  • Logistics & Transportation
Use Cases
  • Fleet Management
The Challenge
Organizations frequently implement a maintenance strategy for their fleets of vehicles using a combination of time and usage based maintenance schedules. While effective as a whole, time and usage based schedules do not take into account driving patterns, environmental factors, and sensors currently deployed within the vehicle measuring crank voltage, ignition voltage, and acceleration, all of which have a significant influence on the overall health of the vehicle. In a typical fleet, a large percentage of road calls are related to electrical failure, with battery failure being a common cause. Battery failures result in unmet service agreement levels and costly re-adjustment of scheduled to provide replacement vehicles. To reduce the impact of unplanned maintenance, the transportation logistics company was interested in a trial of C3 Vehicle Fleet Analytics.
About The Customer
A global corporation in the transportation and logistics industry
The Solution
A global corporation in the transportation and logistics industry completed a trial of C3 Vehicle Fleet Analytics in less than one week, demonstrating the ability to rapidly develop big data predictive analytic applications on the C3 IoT Platform.

The trial was scoped to analyze 10,000 vehicles with historical maintenance records and 3 years of sensor data. C3 IoT first defined the data model to store vehicle and vehicle sensor data. Using the metadata based C3 Type System, C3 IoT rapidly defined the data and canonical object models and the transformations required to convert source objects to the C3 data model. This then enabled the C3 IoT team to rapidly ingest the sensor data for all 10,000 vehicles (approximately 1 TB) in under a day. Additionally, C3 IoT defined 26 time series analytics based on vehicle sensor data. These analytic were then fed into a machine learning classifier to predict battery failure.

With less than a week of work, the C3 IoT team:
• Developed a data and canonical objects model for vehicle operations
• Loaded a terabyte of vehicle and vehicle sensor data
• Developed 25 time series analytics
• Defined and executed a machine learning classifier across the entire data set to predict which vehicles would experience a battery failure
Quantitative Benefit
  • Fleet of 10,000 vehicles with 26 sensors per vehicle
  • One TB of data: 16 billion rows of raw data and 3 years of sensor data
  • More than 25 analytics created for failure prediction algorithms

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