4 Amazing Ways To Leverage AI In Fleet Management

By Emily Newton, Revolutionized

Artificial intelligence (AI) has reshaped how operations happen in various industries. The technology can often remove bottlenecks, prevent unwanted events, and help decision-makers feel more confident in what the future will bring while relying on less guesswork. Here’s a look at some of the exciting ways to deploy AI in fleet management.

1. Using AI In Fleet Management Can Streamline Maintenance Schedules

Keeping a fleet in top condition to operate safely can bring numerous challenges, and that’s especially true as the overall number of vehicles rises. Failing to stay on top of maintenance could mean a fleet manager faces rising costs stemming from emergency repairs. It could also disrupt time-sensitive schedules, particularly if too many vehicles are unusable at once due to maintenance issues.

Some AI tools for fleet management rely on predictive maintenance strategies. They typically use Internet of Things (IoT) sensors to detect things people may not immediately notice, such as excessive vibration or a higher-than-normal temperature. Then, fleet managers get alerts about those characteristics before breakdowns occur.

Researchers at Carnegie Mellon University recently received a multi-year, $10.5 million U.S. Army contract to expand the use of AI-based predictive maintenance. The plans involve applying the technology to ground vehicles and aircraft, but they may also extend to non-fleet equipment, such as generators. The hope is that this work will make artificial intelligence more accessible for use in a wide variety of public and private applications.

Using AI to keep more appropriate maintenance schedules for fleets should help managers lower overall costs by preventing costly situations due to unexpected failures. It could also keep everyone safer since proper maintenance makes dangerous issues, such as brake failures, less likely to occur when the vehicle is in use.

2. Tackling Routing Challenges With Artificial Intelligence

Getting goods to the right places is another ongoing challenge in this sector. However, applying AI in fleet management can help people cope with unforeseen obstacles and have higher on-time delivery rates.

Fleet operators can enhance their route planning with AI to help drivers avoid roadblocks, traffic accidents, and more.

When fleet managers get real-time driver location information, they can also rest assured that each employee is where they need to be at any time. Alternatively, if a driver is behind schedule, the fleet manager can reach out to get more details. Then, after learning the specifics, they can use AI to determine the best ways to help a driver save time and get back on track.

Researchers at MIT are working on an issue that could drastically enhance the applications of AI in fleet management. They found that many route-planning algorithms are only optimized for a few hundred cities. They become too slow when people try to use them with larger groups of cities.

The team wants to use machine learning to identify the most useful routing delivery subproblems to solve, rather than tasking an algorithm with addressing all the possible issues at once. This method could reportedly cause the algorithms to work 10 to 100 times faster than they can now while giving higher-quality results with less computing resources.

This application of AI in fleet management could also raise overall driver satisfaction levels. If employees believe there’ll be a lower likelihood of encountering problems during their routes, their feelings of frustration and negative stress should reduce.

3. Keeping Fleet Employees Safer

Platforms that use AI in fleet management can also help companies reduce accidents, injuries, and even associated lawsuits. Some high-tech systems can automatically detect dangerous driver behaviors, such as harsh braking and sharp turns. Options also exist that give drivers real-time feedback about risks.

One solution from Ctrack has both road- and driver-facing dashboard cameras to enhance safety. Steve Thomas, the company’s managing director, said the technology could prevent collisions by giving drivers the information they need to take corrective action.

The solution uses artificial intelligence and machine vision to identify risks both inside and outside the vehicle. It can detect things such as a driver with their eyes off the road or someone who’s eating while behind the wheel. This application of AI in fleet management can also upload the data to the cloud for later review by a manager.

Thomas also said that drivers appreciate the solution, too. “What we have seen during our initial vehicle trials of the AI dashcam is a positive response from not only the fleet manager, who is gaining a more complete picture of risk, but also the driver. When asked for feedback, the overwhelming response from drivers is that any initial reluctance has been replaced by an understanding that the cameras are having a positive effect on risky driving habits and improving their safety,” he noted.

A study from another fleet management provider indicated that the combination of AI dashcams and frequent coaching was a winning one for safety. The results showed that drivers with both of those things had 22% fewer accidents and a 56% reduction in unsafe driving incidents compared to those that did not use AI dashcams or coaching. This research occurred across two years on more than 5,000 fleets.

4. Reducing Labor Force Needs With AI In Fleet Management

The ongoing progress related to AI in fleet management has also created opportunities to gradually reduce the labor needed to keep companies running smoothly. Many of these options are still in their early stages, and they’ll likely become even more promising as adoption rates rise.

Some people are understandably wary about the prospect of huge trucks navigating themselves on busy roads with no driver in sight. However, AI tech advancements address that reality, too.

A startup called Kodiak Robotics Inc. recently introduced its Fallback feature that makes autonomous trucks pull over and stop operating when they detect a problem. More specifically, it tracks more than 1,000 metrics per second. The system can also alert a remote team for help after the truck reaches the side of the road. It analyzes the roadway 10 times every second, giving the vehicle the necessary data to locate the safest place for pulling over.

However, using AI for better fleet management could also mean employing humans to work in different ways.

Restaurant chain Chick-fil-A has partnered with a company called Refraction AI and ordered 10 self-driving vehicles to assist with deliveries. The vehicles have a fully autonomous mode that’s best suited for routes that are easiest for them to handle. However, there is also a teleoperated setting that involves having someone remotely monitor the vehicle and intervene to control it when needed.

These examples highlight how AI could minimize labor force needs while also affecting the training a person may need before working with a fleet company. Although an individual may get specialized, on-the-job education before interacting with a robotic vehicle, the requirements may be less stringent than those associated with being behind the wheel of a truck.

How Will You Use AI In Fleet Management?

The possibilities described here should give you valuable food for thought when considering how you might make AI a part of fleet operations. Moving forward with the technology will take time, effort, and financial resources, but it could pay off in the long run by making your company more resilient and competitive.

About The Author

Emily Newton is the Editor-in-Chief of Revolutionized. She regularly explores the impact technology has on the industrial sector.