With the proliferation of telematics through sensor technology, mobile-based data collection and, more recently, artificial intelligence (AI), there is an unprecedented amount of real-time information fleet managers can acquire from their connected vehicles. However, having all that data at your fingertips and knowing exactly what to do with it are two completely different propositions.
Think about your business objectives. Are you looking to reduce fuel costs, become a safer fleet, hold drivers and/or customers accountable, set smarter prices, map out the best routes — or all of the above? Knowing your business objectives will help set the course for use of a telematics data system.
The data from the telematics system will be an invaluable asset in reaching your business goals and can then help your fleet become more competitive, by winning and renewing more contracts, attracting and retaining the best drivers, and by making compliance a practically automated endeavor. So, how do you do it? The answer is all in the data, which will help you to expose inefficiencies, identify positive anomalies which can drive you to take knowledgeable actions in real-time.
Even with an abundance of raw information, it can be tough to know what the data means without something to compare it to or help ‘connect the dots.’ That’s where AI coupled with Machine Learning creates predictive analytics and historical data to highlight certain data points which can guide fleet managers as they review and make decisions.
Comparisons are critical for spotting harmful trends that can be improved upon, or a surprising positive trend that should become a standard process and replicated. Through AI, a base dataset is calculated to determine how your fleet currently performs. Through machine learning, that baseline is then assessed against new information from which opportunities can be recommended. Without much user intervention, some of the areas AI can expose include:
- Examining how long drivers take on similar types of jobs and routes
- Monitoring fuel spend and consumption
- Tracking idle time data coupled with location to see if you can avoid high idle zones
- Service companies can look at time on site data across the field to see if some of their technicians are more efficient than others
- Reviewing specific driving behaviors along with maintenance records to see if some drivers are causing more frequent repairs, or if some drivers have fewer repairs and figuring out why that might be
- Seeing which jobs take the longest or cost the most
- Identifying the correct equipment to use for different loads and/or different routes
- Becoming predictive with maintenance so that fleet managers can avoid safety issues, catch maintenance items before they get worse, ensure that vehicles are not pulled off the road due to unexpected maintenance items or vice versa are being serviced too frequently
- Analyzing driver records from deep knowledge of the vehicle telematics and onboard sensor information to coach desired driver behaviors
Look at Anomalies
Sometimes projects may go off the rails and fleets could out- or under-perform drastically compared to the expectation. These instances can be easily found and sorted through an advanced telematics platform and trigger a deeper dive into the numbers. Something off about a data set might require a closer look at other data points to get to the bottom of the issue.
Fleet managers can set up their platforms to automate this data-discovery process and get the answers they need in an instant. Deep machine learning technologies can discover anomalies based on historic data and associated real-time data. For example, this may show why fuel costs spike on a Friday on a given delivery, or why it takes less time to complete similar jobs for a specific client.
Through the use of smart telematics platforms powered by AI, fleet managers can effectively use the data to make actionable decisions with confidence. Often when there’s an inefficiency or a safety hazard, the course correction becomes apparent. The platform can be configured to provide fleet managers with important data patterns so that they can then identify trends and determine the best course of action in real-time. Some of the common data-based fleet decisions include:
- Educating and rewarding drivers
- Immediate route adjustments to routes or schedules
- Proactively identifying maintenance for in-service vehicles
- Purchasing equipment that has been proven to last longer
- Downsizing or adding to your fleet to increase production and revenue
- Immediately identifying and acting on trends that may have a better safety outcome
- Recognizing “hotspot” areas where increased idling or speeding events occur to explore how to avoid them
- Using the right vehicle and driver for each task
- Identifying customer issues before they arise and taking immediate counter measures to provide the best service
Fueling Decisions With Data
All of these points are based on the data you receive, but is there such a thing as too much data? Yes, sometimes you can have too much data, especially if you’re unable to draw important and actionable conclusions.
The key is to choose a telematics platform that is powerful enough and can secure the right data for your fleet. Through innovations in AI, machine learning, geofencing, data collection and camera/streaming technology, there has never been such a huge opportunity for fleet managers to have deep insights into what is going on with their fleet. A telematics system that uses next-gen technology like these also decreases the need for a data analyst and removes costly analysis periods where revenue may be lost waiting for those conclusions.
With these advancements in telematics, fleet and asset management platforms can now tell fleet managers what they don’t know when they need to know it. This further enables them to draw their own conclusions, apply their own best judgment backed by real-time data, and coach and enforce the best course of action. Ultimately, this updated technology allows fleet decision-makers to get ahead of potential issues rather than identifying them through historical data.