Fleet Analytics: Using Data to Drive Smarter Fleet Management Decisions

& Preeti Tirkey

Last Updated on March 5, 2024

Fleet business is huge and ever-expanding. The global fleet market will grow at a CAGR of 10.6% for the next 7 years, from 2023 to 2030 [1], reaching $52.5 billion. There will be more and more vehicles carrying cargo. The business owners need to upgrade the business and equip them with technology to run them better. Not only are they worried about the fleet’s proper functioning, but it must also be nerve-wracking to worry about these challenges every single day.

Fleet Acquisition
Fuel Management
Fleet Repair and Maintenance
Safety & Driver’s Health
Follow Regulatory Compliance

What is Fleet Analytics?

Fleet, in simple words, means the ‘vehicles’ that are used in the transportation business owned by a company, government, or any agency [2]. Fleet Analytics is the science of collecting data from each of the fleets, processing it using machine learning models, and deriving some useful insights from it. This is achieved with the help of IoT sensors, which are attached to each of the major components that collect information at regular time intervals.

The foundation stone of fleet analytics lies in the 3 components [3]:

Hardware: These are the automobile components that are enabled with IoT sensors. Examples: Accelerometer, Tyre Pressure sensor, Battery Ignition sensor, Engine Exhaust sensors, etc. They are linked with GPS to deliver the metadata to a cloud platform.
Communication Systems: Depending upon the cost and use case, any mode of communication like GPS, Bluetooth, mobile network, or direct satellite communication channel can be chosen.
Algorithms and Analytics: The data is stored on a cloud platform that has the capability to provide real-time analysis of a fleet. We can perform time series analysis, train models, and generate reports on business intelligence tools like Power BI and Tableau, which can aid in informed decision-making.

What do we do after collecting the data?

Once the data reaches the data centers, it is then cleansed, and patterns are analyzed based on collected data. There are two ways how a fleet is analyzed.

1) Fleet Management

Analyze the fleet’s current efficacy. A fleet’s current efficacy can be determined with the help of certain KPIs [4].

Analyze Cost of Ownership: With the performance of the fleet and the Operating Expenditure, identify vehicles that need to be remarketed.
Fleet Utility: Identify which vehicles are more occupied and which vehicles are underutilized based on engine rest time. This majorly helps in workload balancing and better asset utilization.
Engine Efficiency: Track the actual mileage vs the expected mileage to analyze if the fleet is operating as expected.
Energy Consumption: Analyze how much fuel is utilized by a vehicle to commute. If the consumption is higher, find workarounds to bring the fuel consumption numbers down.

2) Predictive Fleet Analytics

Fleet Analytics uses Machine Learning, exploratory data analysis, feature engineering, modeling, and data mining to predict the outcomes using statistical analysis to provide a swift and precise analysis of huge amounts of data. Predictive Fleet Analytics is widely applied to help fleet managers use recorded data to analyze fleet trends and improve future planning. [3]

Use cases of Predictive Fleet Analytics

Fleet Route Optimization

The business can track the most efficient and cost-effective routes for repetitive transport. Analytics leverages data from traffic monitoring and GPS sensors, which could help navigate the fleet more efficiently. Several factors, like knowing the number of traffic signals in a route (using front camera data), the number of stops, the number of lanes on the road, historical trip data, etc., help a lot to optimize the route. Optimizing the route not only saves transportation time but also helps save fuel costs, which is the main OpEx in a fleet business. This has a direct impact on the balance sheet for this industry.

Predictive Fleet Repair, Replace & Maintain

Fleets are machines running 24/7 that help transport goods or take people to places. Wear and tear are part and parcel of this business, which needs proper maintenance. Mandatory checks, repair and maintenance sheets, part replacement logs, warranty, and emissions reports can be stored digitally. Also, data from various sources like telematics data, data from various cloud or edge devices, GPS, vehicle cameras, traffic cameras, and driver monitoring applications are stored. Using the recorded data, analytics can identify its working conditions and help in predicting upcoming maintenance schedules so that the company can reduce downtime. The collected data can reveal the most common causes of accidents and give insight into how to avoid them. Identifying the problem before it occurs saves time and effort from accidental machinery failure.

Reduced Carbon Emissions

An ill-maintained fleet might require more liters of fuel and release more carbon than exhaust, which is detrimental to the environment. Using predictive analytics, a maintenance program can be run specifically to monitor the exhaust composition and its associated devices, which could help the fleet burn fuel efficiently; sensors can track the content of COx and SOx and raise an alarm if the percentage composition exceeds the permissible limits.

Enhance Driver Performance and Safety

Businesses invest a huge amount in the insurance of the fleet as well as the driver. It is their responsibility to explain new features in vehicles to the driver and train them so that the consignments can be delivered on time. The company can keep track of the driver’s health, driver’s past health record, his driving skills by storing data like the speed at which he operates, how he navigates on the road, his performance during the entire course of the journey, hard braking, rapid acceleration [4] and so on. Then, ML models can score the driver, and finally, we can classify whether a driver’s performance is good or not.

Conclusion

When actions are backed by identifying patterns, analyzing the trends, and supported by figures, the decision-making capability is increased enormously. This is the most favorable way to reduce financial loss, attain great fleet utilization, save lives, and keep the business running smoothly. Fleet Analytics is also helping remote engine diagnostics to its customers on the way. With the built-in portal, the drivers can visually see the problem on the screen and operate under the Standard Operating Procedure to rectify the problem. This simple amalgamation of technology and fleet devices has become a magic wand that tries to resolve most fleet problems. The Fleet Industry’s CAGR, crossing the $2.3 billion mark, is just a matter of time now.