Telematics has been the secret sauce of fleet management for years. Being able to provide service and change tires when needed, and to replace a fan belt that has overstayed its welcome, is key to running a successful, economical and cost-effective fleet.
But with the costs of running fleets going up, the question is whether traditional telematics, based on limited data and preconfigured error codes, is still sufficient – especially since more advanced technologies are available that help fleet managers better predict when problems will crop up and prevent them from happening in the first place.
Using machine learning, a system that can tap into the full array of data uploaded by a connected vehicle can do a better job of reading the true, current state of a vehicle’s systems, thus avoiding a situation where those problems put a vehicle out of commission.
Maintenance spending increased 3% to 5% in 2018 over year-earlier levels. At the end of 2018, it cost an average of $75.32 to keep a vehicle on the road, compared to $66.05 in 2017. Of course, you must maintain a vehicle if you want it to operate as needed, but the big problem isn’t maintenance costs, it’s downtime costs. According to industry experts, downed vehicles cost companies an average of $448 to $760 per vehicle per day.
Fleet managers have a lot to worry about: keeping vehicles on the road, ensuring safety and preventing accidents, making sure drivers follow the rules of the road, etc. Telematics, which is mostly based on basic vehicle information such as GPS and vehicle speed, can help them keep track of some of these issues.
But fleet managers want more. A report by Driscoll and Associates says advanced telematics systems installed in vehicles will, by 2022, expand the market to nearly $7 billion. Those advanced systems will include “video cameras, workforce management, data analytics and other functionality,” according to the report.
With connectivity now nearly ubiquitous in vehicles, data about nearly every system can be uploaded, and that data can be analyzed to ensure vehicle health, maximum safety, regulatory compliance, performance optimization and more.
A connected vehicle could upload data to a server equipped with big-data analytics and AI-based detection algorithms, which could analyze the performance of individual parts and systems in the vehicle, issuing an alert when a situation needs attention that, if not dealt with, could send a vehicle to the garage.
For example, the system could examine the response of brakes over time. Braking systems have an ideal braking distance and g-force. Usually, a vehicle is inspected for brake wear and tear on a regular schedule, depending on how many miles have been driven, etc. But a data analytics system could measure braking effectiveness in real time and upload that data for analysis, providing a timeline showing the ideal amount of time between maintenance checks.
The same system can be used to determine if drivers are observing speed limits and laws, which is important in protecting organizations from legal issues. Real-time data flowing in on speed, location, turning, braking force and other information is aligned with information about roads, including speed limits, geographical features and even potholes.
When analyzed together, the data paints a picture of how drivers should ideally be conducting themselves and provides fleet managers with information on whether that ideal situation is being met.
Advanced real-time data analysis systems are available right now, and with more connected vehicles on the road than ever before, the time is right for fleet managers to embrace this technology that can save them and their organizations time, effort and money. With this technology, managers can keep their vehicles on the road instead of in the garage.
This post was also published on WARDSAUTO