Fleet management was conducted mainly with Excel reports that do not address all the needs of the different business actors involved in fleet decision making, and do not allow to track elements such as utilization, or dismissed vehicle age/km, planned/unplanned maintenance, among other KPIs.


Using custom developed models combined with traditional analytics, we developed a solution that consists in:

  1. Extract data from transactional systems
  2. Apply data anonymization and cleaning
  3. Apply data transformations to perform aggregations, time series analysis and additional features
  4. Developed custom AI/ML models to perform Planned and unplanned maintenance, Refueling forecast and deviation, Tolls validation, Tires exchange fraud detection, among others
  5. All data compiled in a “star schema” for better and easier analysis
  6. Deliver (update) a full fledge dashboard with all Fleet monitoring KPIs

Value Delivered

The development, from an analytical perspective of vehicle fleet intends to solve three core business necessities:

  1. Decision making: With the insights derived from this project, the business user is enabled to make informed decisions about, but not limited to:
    • Monthly car expenditure budgeting
    • Fleet electrification level
    • Adequate fleet size and most suitable procurement / dismissal strategy
  2. Reporting: The project is also intended to be used as a reporting tool where different business units and accounting departments can find a common ground to analyze and assess different dimensions of the fleet
  3. Outlier behavior identification: Erratic behaviors can be prompted and reported with the finest level of detail. Some examples of such behaviors that are capturable with the dashboard are the following:
    • Suspicious refueling and toll activities (weekend expenditures, excessive expenses…)
    • Recurrent repairs
    • Fast drivers on highways

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