There has been a lot of talk about the many ways connected passenger cars are transforming the driving experience for consumers. But there are equally profound changes happening on the commercial side of the business, too.
Improving vehicle usage transparency on a fleet-wide scale helps fleet operators make significant strides in addressing three major concerns: fuel and energy costs, vehicle downtime, and, of course, safety. Real solutions to these very real challenges are here, and each is made possible by applying the right data strategy to unleash the true power of commercial vehicle data.
Rethinking data, rethinking margins.Until now, conventional asset management has been pretty basic. Fleet managers are already using vehicle data, but in limited ways. Knowing the answers to simple questions like, “Where is my truck now?” or “Where was it for the past
24 hours?” is good information to have—but it’s not nearly enough.
The connected data solutions provided by Aptiv Connect allow fleet operators to combine technology with some basic psychology to create deeper, more meaningful insights, and to use those insights to take the actions that can breathe some much-needed
life into their margins.
It’s the technology part of the equation that helps operators monitor vehicle usage to measure progress against specific goals—and, according to research from the American Psychological Association, it’s the constant monitoring of
this progress that increases a fleet’s likelihood of achieving them.
After all, you can’t manage what you can’t measure, and connected vehicles help a fleet operator do both.
Taking action.Let’s say increasing safety is your fleet management goal. Using vehicle data to monitor vehicle performance—things like aggressive acceleration, harsh braking, and ABS activations—not only helps you measure a specific goal for your fleet (e.g., reduce harsh braking incidents by 70%), but for your drivers, too. It’s human nature to want to meet a clearly articulated goal, and, in this case, with the right training, drivers can achieve it—to the tune of reducing fleet-wide safety incidents by as much as 20%.
But it’s not just about safety. Better transparency can impact fuel and energy costs, too. Consider this: A typical fleet company works with an operating margin of 5-10%. Using fleet-wide connected vehicle data, an operator can accurately monitor and thus optimize routing for all its vehicles, plus improve overall driver behavior. Combined, these two improvements can dramatically reduce fuel energy costs. And with fuel costs taking up such a large portion of the operating margin, any savings made in this area have a significant ripple effect on an operator’s bottom line. How significant? Even a 2% increase in a fleet’s operating margin can equate to a 40% increase in income for the business.
The greater the uptime, the better the margin.Traditionally, the downtime created by regular maintenance can eat up whatever’s left of a fleet operator’s already tight margins. With the right connected vehicle data strategy, however, fleet operators can create predictive models with extremely high levels of accuracy and repeatability. We’ve seen greater than .98 regression levels. These models are related to critical vehicle consumables ranging from transmission fluid and coolants to air filters, brakes, and oil. The result: fleet managers have a clearer picture of when to schedule service.
Even better, these models can be correlated so that the fleet operator can bundle regularly scheduled maintenance. All needed service can be done in one visit, not the usual three to four it takes without this data-driven insight. This is a big convenience if you own one or two vehicles, but when you own hundreds, if not thousands, of them, it’s a game-changer that can reduce downtime by as much as 50%.
A brighter fleet future starts now.Reduced fuel costs. Reduced downtime. Better safety. Just three ways fleet operators can use connected vehicles to better navigate their rapidly changing business. With the right data strategy, fleet operators can dynamically configure data to measure the right signals, at the right sample rate, to address the right challenge, and do more with that data than ever before.
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