Once the domain of science fiction, Artificial Intelligence (AI) and machine learning (ML) are now science fact. AI is now a familiar concept across many industries, mainly for its ability to help businesses accelerate their digital transformation and reap benefits they previously hadn’t imagined. Designed to show you what you don’t know, AI and ML deliver valuable insights, streamline manual processes, and help businesses improve their productivity.
These two technologies have reached critical mass in their impact on businesses, serving as both a catalyst and an outcome by exceeding consumer expectations of better experiences. No industry has been untouched by the application of AI in the real world as it offers intelligent solutions that sense, learn, and interpret human behavior in complex situations. AI is improving human experience by assisting in decision-making and even acting on key decisions.
ML-based approaches can help automate analytical model building by analyzing data and enabling software applications to be more accurate in forecasting outcomes without being programmed to do so. AI and ML working together give you a team of automated eyes and ears that have the ability to analyze data and make predictions based on that analysis.
Fleet management machine learning can monitor data inputs from a fleet of vehicles and provide feedback on any changes. To understand how this benefits fleet management, it helps to know a little about how ML works.
Simply put, ML is precisely what it sounds like – give machines data, and they learn for themselves. This system offers the ability to consume a huge amount of information and build models around the patterns of behaviors. They can automatically learn and provide better outputs by analyzing known events without having to be programmed. As the system improves, it can be given new data and use it to predict the outcomes for events that haven’t happened yet.
Machine learning in fleet management improves the way data analytics systems handle big data. The system starts learning which data is most often checked during daily use and adjusts itself in real-time according to your habits. For instance, if you check your fuel expenses often, that is the first thing it will offer to you when you open the application.
Algorithms based on machine learning allow the development of dashboards with functionalities that make it easy to see different data points such as vehicle downtime and specific driver behavior that should be changed. These sophisticated neural networks can alert drivers if their vehicles require maintenance or are about to have technical difficulties.
Smart fleet management systems offer more diagnostic capabilities than traditional ones. When you have the ability to see the big picture of your fleet in one place, you can make better overall decisions. ML and AI systems can help decrease overall costs significantly.
Although we are not ready for self-driving cars on our roads, ML and AI in navigation allow fleet managers to automate tasks that can give them a competitive edge. A machine learning fleet management system with adaptive tracking of data points can give you crucial information on how to optimize your business and achieve a higher level of decision-making. Below are some important reasons why businesses should implement a fleet management machine learning solution.
These days it is not unusual for fleet managers to track their fleet’s activities with fleet management solutions and telematics. These systems collect and store a lot of data, so much that telematics systems generally offer data analytics that helps you review the data and make business decisions based on it.
Because the amount of data being collected from satellite tracking, onboard sensors, and Internet of Things (IoT) devices means that it can be difficult to find the data that they need. ML-enabled technologies help guide employees in real-time. For instance, information such as traffic conditions, weather, and road hazards can be used to anticipate risks and let back-office workers alert and guide drivers through these conditions as they occur.
By instantly sorting through all this data to reveal the most relevant pieces of information, machine learning in fleet management can help drivers prepare for unforeseen events while also helping the business improve its customer service. In addition, ML enables telematics to analyze huge amounts of data to detect trends in fuel use and speeding that can help your business improve or maintain its safety record and save on fuel costs.
The magic behind ML is how the system improves its interpretation of big data. As you employ your ML-enabled fleet management system, the system itself begins to learn your habits, what information is the most important to you, how much time it takes drivers to do a specific task, and more.
It won’t take long for a fleet management machine learning solution to automatically recognize anomalies in vehicle or driver behavior based on your previous behavior. It can then alert you about any unusual changes, such as sudden spikes in idle time or speeding violations.
These systems offer advanced dashboards that give you a visual display of the data they are collecting, letting you easily spot anomalies and drill further into the data. You can also refine these insights by adding additional parameters to find out more about what is going on, why specific changes are happening, and devise a possible solution.
You can also further explore these insights by adding more parameters into the dataset to determine what is happening and why these changes are occurring and view possible solutions.
AI and ML-based fleet management solutions continue to radically transform fleet management with sophisticated neural networks within fleet vehicles that automatically detect and alert drivers when something in the vehicles requires maintenance or if there is a technical error in the vehicle. They can also alert fleet managers of any potential issues with fleet vehicles.
This gives fleet managers and their mechanics time to diagnose and fix issues before becoming a serious liability and potentially putting drivers in danger. With vehicle performance and engine management data integrated in real-time and sent directly to the monitoring dashboard, fleet and operations managers can pull up information about each vehicle and get insight into its performance and condition right at their fingertips without the vehicle needing to be physically present.
Most importantly, AI fleet management can save you time diagnosing issues and give you a clear picture of your entire fleet. In addition, it can offer cost-effective and efficient solutions to potential maintenance issues for fleet vehicles. Using the predictive maintenance capabilities of this technology can help fleets significantly reduce their overall costs.
Because of the significant savings AI fleet management offers, let’s compare preventative vs. predictive maintenance. Both preventive and predictive maintenance are strategies used to increase the reliability of fleet vehicles and reduce the number of failures that can keep them off the road. With each strategy, maintenance work orders are scheduled in advance of when the maintenance is actually performed. They are both a type of scheduled maintenance.
The difference between the two is that preventive maintenance is regularly scheduled based on criteria such as time between oil changes or miles driven, while predictive maintenance is based on the condition of the vehicle using metrics provided by a telematics solution. Because predictive maintenance is scheduled only when needed, it lowers costs incurred by parts and labor.
Preventive maintenance is scheduled and performed when vehicles are still running smoothly with the goal of preventing future breakdowns or emergency maintenance, keeping vehicles on the road. Preventing vehicle failures and extending their useful life helps keep your fleet moving and the business more profitable. The challenge with preventive maintenance is balancing the cost with returns. Fleet managers are required to make smart decisions on which the frequency of maintenance on each vehicle.
Although predictive maintenance has some of the same characteristics as preventive maintenance, there are some key differences. Predictive maintenance requires specific preset conditions. With AI fleet management, if a vehicle suddenly performs outside normal parameters, you can track measurements and data that help you make predictive maintenance decisions. This not only reduces unexpected vehicle failures, it also enables you to develop a robust maintenance program, minimize maintenance costs, reduce unneeded maintenance, and monitor the vehicles that keep your fleet running at optimized capacity.
Preventive and predictive maintenance both play an essential role in a strategic fleet management program.
ML is useful in all areas of fleet vehicle management, including efficiency and safety. Manual processes caused fleet management to be tedious and challenging, but ML helps streamline operational processes, making them straightforward and simple. And ML combined with machine vision can make fleet management even more efficient.
When considering how ML and AI in navigation work to improve fleet management, you may wonder what is machine vision? Machine vision AI can see and recognize objects and behavior through images. For example, using an in-cab sensor, machine vision can detect signs of fatigue and distraction in drivers.
Paired with AI and ML, machine vision helps fleets accurately and efficiently capture risks and alert drivers so they can address risks as they happen. Below are some of the main benefits of machine vision, machine learning, and AI.
A revolutionary technology, AI has innumerable impacts on fleet management, especially in the area of detecting unsafe driving behaviors. AI technology paired with dash cam technology can detect driving behaviors such as distracted driving, aggressive driving, texting, yawning, losing focus, and driver fatigue that may lead to accidents. Not only can these unsafe behaviors be dangerous to drivers and others on the road, but they can also damage your fleet’s productivity, damage its reputation, and cost the business money.
ML is fast becoming essential to the daily processes of an efficient fleet. It is no longer feasible for fleet managers to use paper and pen to keep track of fleet processes. With both complex and simple tasks automated, it frees up fleet managers so they can make key decisions that increase fleet efficiency. Key tasks including scheduling, route planning, and dispatching as well as managing jobs can be done by ML. The technology can use the data it collects to analyze drivers’ performance and schedule the best routes for them. ML can them assign and dispatch drivers to their destinations.
Gathering and analyzing data is one essential part of a fleet manager’s job. It helps determine issues and make decisions about what is best for the fleet. Analytics helps with everyday tasks such as route planning. It considers historical traffic data, job information, GPS location, and more to create the most cost-effective route to complete everything necessary for the day. It is also an excellent way to keep vehicles in good condition. Analytics offers ways to track vehicle status, maintenance history, and other details that help maintain vehicles’ good working order.
Maintenance is a crucial component of fleet management since vehicle breakdowns can cause significant delays and cost your business money. This means avoiding breakdowns is necessary to keeping a fleet efficient and productive. If fleet vehicles are not well maintained, breakdowns may not only keep fleet vehicles off the road but can cause accidents that put the reputation and safety of your fleet. As we’ve covered above, preventive and predictive maintenance can help you avoid these breakdowns. Machine learning can help identify issues before they cause breakdowns by alerting you when to schedule maintenance.
ML and AI will continue to evolve, and new ways for these technologies to benefit fleet managers will continue to emerge, making fleet management more straightforward and streamlined than ever. Fleet managers who adopt these innovative technologies will see reduced downtime, increased productivity, and lower administrative costs. All while identifying ways to improve and keeping you ahead of the competition.