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Predictive Maintenance on Commercial Vehicle Fleets

Digital supply chains have been a hot topic in recent years; this is also one of the core pillars of Industry 4.0 and IIoT.

When it comes to automotive fleet vehicles and trucks in the context of the digital supply chain, the critical factor is real-time or near real-time responsiveness to failure and being ahead of them to avoid disruptions in operations and unplanned maintenance.

This can be addressed in steps; the initial step is fleet preventive maintenance, and the next is automotive predictive maintenance.

Planned or proactive maintenance has been carried out by some commercial vehicle companies with varied success for some time now.

Importance of predictive maintenance

Can companies get to the next level where they can identify potential problems and fix them days before they occur, eventually improving their customer’s experience?

This is where predictive fleet maintenance is becoming increasingly popular in the commercial trucking industry.

It can help fleet managers minimize downtime and repair costs by identifying potential problems before they occur.

One common approach to predictive maintenance in commercial trucks is telematics data.

Telematics systems can collect data on the vehicle’s engine performance, fuel consumption, tire pressure, and other essential metrics by connecting to the trucks’ CAN (Controller Area Network) bus system.

This data can then be analyzed using machine learning algorithms to identify patterns indicating a potential problem.

For example, if the telematics system detects that the engine is running hotter than usual, this could be a sign that a part is starting to fail.

The system can alert the fleet manager to the potential issue, allowing them to take corrective action before a breakdown occurs.

In addition to telematics data, sensors, and cameras are used to monitor the condition of specific components, such as the brakes or transmission, or even driver behavior and road conditions.

This data can be used by companies to predict when maintenance will be needed.
preventative maintenance

Predictive maintenance benefits

  1. Cost Savings: Automotive Predictive maintenance helps fleet owners save money by reducing downtime, avoiding costly repairs, and minimizing the need for emergency maintenance. By addressing potential problems before they become major issues, fleet owners can prevent unexpected breakdowns and associated repair costs.
  2. Increased Reliability: By regularly monitoring and analyzing fleet truck data, predictive maintenance can help identify potential problems and address them before they lead to breakdowns or equipment failure. This can improve fleet reliability and reduce the risk of accidents and delays caused by equipment failures.
  3. Extended Equipment Life: Regular maintenance and timely repairs can extend the life of fleet trucks, which helps companies avoid the expense of prematurely replacing them. By catching issues early and addressing them promptly, predictive maintenance for vehicles can help ensure that fleet trucks are running efficiently and effectively.
  4. Improved Safety: Predictive vehicle maintenance can help identify potential safety hazards, such as worn brakes or tires, before accidents occur. By proactively addressing these issues, fleet owners can help ensure the safety of their drivers and other road users.
  5. Increased Efficiency: By monitoring and analyzing data on fleet trucks, automotive predictive maintenance can identify opportunities to improve fuel efficiency, optimize routes, and reduce idle time. This can help fleet owners save money on fuel costs and reduce their environmental impact.

How predictive maintenance can help customers

End customers can also experience several benefits as a result of automotive predictive maintenance on their commercial vehicle fleet, including:

  1. Improved Delivery Times: Predictive vehicle maintenance can help fleet owners keep their trucks in top condition, ensuring they run smoothly and are less likely to experience unexpected breakdowns or delays. This can help improve delivery times and ensure that end customers receive their products on time.
  2. Enhanced Product Quality: By keeping fleet trucks well-maintained, automotive predictive maintenance can help ensure that products are transported safely and in good condition. This can help improve product quality and increase customer satisfaction.
  3. Reduced Environmental Impact: Predictive maintenance for vehicles can help fleet owners optimize routes, reduce idle time, and improve fuel efficiency, which can lead to a reduction in greenhouse gas emissions and other environmental impacts. This can help end customers feel good about the environmental responsibility of the companies they are doing business with.
  4. Increased Safety: By proactively addressing potential safety hazards, automotive predictive maintenance can help reduce the risk of accidents and other safety incidents involving fleet trucks. This can help improve safety for both fleet drivers and other road users, including end customers.
  5. Consistent Service: Predictive vehicle maintenance can help ensure fleet trucks always operate at their best, providing consistent and reliable service to end customers. This can help build trust and loyalty among customers, who are likelier to do business with companies that consistently deliver high-quality service.

Role of AI-ML in predictive maintenance

Artificial Intelligence (AI) and Machine Learning (ML) are critical predictive maintenance technologies for fleet vehicles and trucks. The following graph shows how AI and ML can help with automotive predictive maintenance by improving the accuracy of maintenance predictions, reducing maintenance costs, and minimizing lost business.
Cost Comparison
Cost = maintenance cost + parts replacement cost + lost labor cost + unplanned downtime cost + tow cost + backup truck cost + delayed delivery time fines + business lost due to SLA delays

The graph’s x-axis represents time, with the leftmost point being the current time and the rightmost point being some point in the future. The y-axis represents the cost of maintenance.

The blue line represents the cost of maintenance without the use of AI and ML. This line shows that maintenance costs tend to be relatively low in the short term but can increase rapidly over time as equipment wears out and begins to require more frequent and expensive repairs.

The brown line represents the maintenance cost using AI and ML. This line shows that maintenance costs are lower overall and remain relatively flat over time. This is because AI and ML can help identify potential maintenance issues early, allowing for proactive repairs and avoiding costly breakdowns and emergency repairs.

The red dots represent the times at which maintenance is performed. With AI and ML, maintenance can be performed strategically, such as during planned downtime, rather than in response to a breakdown. This can help reduce the overall maintenance cost and extend the equipment’s life.

The above graph shows how AI and ML can help with predictive maintenance on fleet trucks by reducing maintenance costs and improving the accuracy of maintenance predictions.

By leveraging data and algorithms to predict potential issues and proactively address them, companies can ensure that their fleet trucks are running smoothly and efficiently, providing reliable and consistent service to their customers.


Fleet managers can reduce maintenance costs, minimize downtime, and improve overall efficiency by using automotive predictive maintenance in commercial trucks.

Predictive maintenance for vehicles like fleet trucks can significantly benefit fleet owners by reducing costs, improving reliability and safety, and increasing efficiency.

Lastly, end customers can benefit from predictive maintenance on fleet trucks through improved delivery times, product quality, environmental impact, safety, and consistent service.