Data-driven maintenance: what it is and how it works

Data-driven maintenance is a modern practice in vehicle fleets, as it involves using vehicle management and monitoring technologies for more effective decision-making.

There is no doubt that data-driven management is becoming increasingly common across industries, and has begun growing in fleet management as well.

Maintenance, one of the fleet's main areas, should therefore also become a data-driven practice to ensure optimized, cost-effective operations.

See below:

• What does data-driven mean?
• What is data-driven maintenance?
• What are the benefits of data-based maintenance?
• How does data-driven maintenance work?
• How can you collect and store fleet maintenance data?

What does data-driven mean?

A data-driven approach refers to using quantifiable data to inform and guide business decisions.

For vehicle fleets, this means analyzing data collected from vehicles and operations to make more informed decisions about maintenance, operations and management.

The focus is on using concrete insights, rather than intuition or past experience, to drive real improvements and efficiency.

What is data-driven maintenance?

Data-driven maintenance in fleet management is a process in which data collected from vehicles is used to predict and prevent failures before they occur.

This differs from commonly used preventive maintenance, which works by scheduling services according to preset timetables.

With the data-driven approach, maintenance can be scheduled based on the vehicle's current condition and data trends, leading to advantages such as reduced fleet downtime.

Benefits of data-driven maintenance

Cost reduction

Data-based maintenance works like predictive maintenance, always predicting patterns and identifying the right time to make adjustments to vehicles.

This type of action can lower costs by preventing serious failures and extending vehicles' useful life.

Greater efficiency

With accurate data, maintenance operations are less susceptible to errors or redundancies.

In addition, anticipating problems and effectively scheduling vehicle corrections helps avoid fleet idle time and reduces vehicle unavailability rates. All of these factors increase a fleet's operational efficiency.

Improved safety

Data-driven maintenance helps ensure vehicles are always in ideal working condition, reducing the risk of unexpected failures and, consequently, road accidents.

Improved decision-making

With detailed, up-to-date information, managers can make more informed, strategic decisions by accessing and analyzing the data available in the operation's systems.

How does data-driven maintenance work?

1 – Data collection

Data-driven maintenance begins with continuous data collection from vehicles, which can include information about engine performance, vehicle usage patterns, road conditions and even environmental data.

2 – Use of technologies

Using advanced technologies such as artificial intelligence and machine learning, it is possible to process large volumes of data and generate important insights for decision-making.

4 – Protocols and team training

Team training should focus on the correct use of technologies implemented in the fleet, thus ensuring data is collected in the best possible way and managed correctly.

5 – Analysis and forecasts

All collected information is analyzed to identify patterns and predict when maintenance is needed. It also contributes to understanding fleet trends and patterns, whether positive or in need of improvement.

At this point, ROI in maintenance management should also be analyzed, along with how worthwhile the technology investment is in reducing maintenance costs and vehicle downtime.

How can you collect and store fleet maintenance data?

Data collection and storage in fleet management can use a variety of technologies, such as telemetry systems, IoT (Internet of Things) sensors and fleet management software.

These tools record various information in real time, from real-time location to engine condition on the road, and even drivers' driving patterns.

Collected data is stored in cloud systems or local databases, where it can be accessed for analysis and reports.

Many systems also generate reports automatically, requiring only the date, vehicle or operation filters for the desired analysis.

Data analysis in maintenance makes it possible to identify trends and potential problems before they become critical.

For example, an increase in fuel consumption can indicate the need for engine maintenance. Likewise, irregular braking patterns can signal problems in the braking systems.

By monitoring these indicators, maintenance can be performed promptly and effectively, generating all the expected benefits of data-driven maintenance management.

If you want to deepen your knowledge and skills in these aspects of fleet maintenance, do not miss the opportunity to take our maintenance management course.

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