How AI transforms maintenance management

Technology in industry and transport continues to evolve rapidly, but the use of artificial intelligence in maintenance needs to be understood less as a novelty and more as a technical resource with its own applications, limits, and challenges.

Predictive maintenance, for example, uses technologies such as artificial intelligence and the Internet of Things to identify early signs of failure, interpret changes in operating conditions, and support decisions before an issue worsens. This is one case of AI application in fleet management.

Because the technology involves practical applications and points requiring attention, it is important to understand the role of artificial intelligence, where it can help, and which precautions are involved in implementation.

• How AI is applied to maintenance management;
• Advantages of AI in maintenance management;
• Challenges of implementing AI in maintenance management.

How is artificial intelligence applied to maintenance?

Artificial intelligence is used in maintenance largely through predictive maintenance. This approach uses AI technologies to identify warning signs at early stages, allowing interventions before they worsen.

It takes on an important role because some of these early symptoms may not be visible to the eye or detectable through conventional human inspections, and analysis of data collected by sensors and devices can indicate changes in operating conditions and performance.

In fleets and transport operations, where vehicle efficiency and reliability are crucial factors, predictive maintenance emerges as a solution to many issues. It corrects shortcomings in traditional maintenance processes and can also help reduce costs when the operation can act before an unexpected stop.

The use of artificial intelligence in maintenance, when discussing the predictive method, can involve sensors and devices connected to assets to gather data on vibration, temperature, fuel consumption, and other parameters detectable by the technology.

By collecting the large volume of data generated, systems connected to these sensors interpret the information and help identify changes in operating conditions and performance, generating insights and maintenance reports with analyses to support decision-making.

Goals and benefits of AI in maintenance management

Failure prediction

As mentioned earlier, failure prediction happens through technology and the use of artificial intelligence in maintenance.

Through collected data and precise analyses, AI systems can identify each vehicle's patterns and behavior, assessing when changes in operating conditions and performance occur, even small ones.

This allows proactive scheduling of corrective services, avoiding unexpected vehicle stops. It also makes correcting the issue more efficient and quicker, since the issue has not yet spread and can be corrected at its source.

Optimizing scheduled maintenance

Without artificial intelligence in maintenance, services followed regular schedules. Now, with AI identifying nonconformities in vehicles, preventive and corrective services become scheduled on demand.

In other words, the identified issue is assessed and its priority defined, allowing managers to find the most suitable time for intervention and schedule services more strategically.

In more mature predictive maintenance programs, it is also possible to automate work orders, inspection scheduling, and even parts of spare parts management, always according to the integration and data governance adopted by the operation.

This helps reduce fleet idle time and increase vehicle availability.

Greater durability of fleet assets

Early issue identification and scheduled maintenance contribute to extending a vehicle's useful life. Beyond ensuring longer use of the vehicle without issues, this has positive effects on fleet finances.

After all, the greater an asset's durability, the better the return on that investment tends to be.

Reduced operating costs

When unexpected stops are reduced, repair costs consequently end up falling too.

Over time, predictive maintenance tends to be more efficient at reducing costs, being more economical than a preventive maintenance routine and even more so than corrective maintenance, provided data and processes are well structured.

Therefore, using the artificial intelligence of predictive technologies can also bring a reduction in a fleet's operating costs.

Informed and strategic decision-making

With accurate data and advanced analyses, fleet managers can make informed and strategic decisions.

Artificial intelligence focuses on identifying trends and patterns, providing insights into fleet operation that can help uncover strategic opportunities for resource allocation and planning new investments.

In some cases, AI can provide detailed insights into vehicle performance, allowing managers to focus their role on assessing information, prioritizing what needs to be done, and making decisions best suited to the fleet's reality.

What are the challenges of using artificial intelligence in maintenance?

The main challenge of artificial intelligence in maintenance is implementing the technologies involved. This initial point creates other obstacles, including difficulty integrating AI with systems already in place and used in operations.

Although many systems have integration resources such as APIs, in some cases extra effort is needed to ensure full operation and the supply of correct, updated data to artificial intelligence.

Another important issue is the learning curve employees need to face.

Using AI systems and tools requires training for the operational team, which will be in direct contact with sensors and devices for data collection.

Managers must also receive training and skills development to interpret AI-generated insights, optimizing their decisions.

AI implementation can lead to a restructuring of the fleet's routine, establishing new and different processes that must be communicated to and understood by employees. This implies greater manager supervision to ensure everyone adapts to the new procedures and avoids errors.

Employees may face the challenge of resistance to this change in processes. Therefore, it is also worthwhile to begin building a culture of logistics technology and artificial intelligence adoption in maintenance before even implementing the new solutions.

Want to learn more about applying efficient maintenance management while making use of all available technological resources? Download our maintenance management manual for free (in Portuguese).