Fleet executives want technology that can solve their problems today — and in the future. They want to grow revenue while improving service levels and reducing cost, as doing so will produce better service for customers and generate better results. Anything that threatens these things is bad for business — especially unplanned downtime.
Think about this: The average minor break-down call is between two to two-and-a-half hours, and costs between $400 to $600 for a repair. A driver starts a day with 660 minutes of available driving time; wasting any of that time adds to the service cost and eats into the fleet’s bottom line. Add in the lost potential revenue from the driver’s next trip, and it adds up to a sizable amount of unnecessary, preventable costs.
Virtually every component manufacturer in trucking is now offering their customers data-collecting services that promises to help carriers drill down into their operations, locate problems and realize efficiencies. The core problem, though, is that all this data is often coming from multiple sources and is too complex, too costly and doesn’t add value. The challenge for fleets is how to understand and act on the available information without getting data overload.
Connected coupled technology provides a potential solution. When sitting alone, trucks and trailers must provide information needed for fleets to determine readiness for utilization goals. When coupled, the units must work cohesively as one to manage the system as a whole. This partnership between units allows for the different systems to collaborate and work together as a “team of information.” Cross-functional work between tractors and trailer technologies must be done in this manner for a complete coupled smart vehicle to happen.
With this type of connectivity, fleets can reduce maintenance issues by relying on data provided to address the problems. For example, with connected coupled technology, a trailer could notify the driver instantly of an issue, alert the maintenance team to the issue and even prevent a trailer from moving if a component does not meet Federal Motor Carrier Safety Administration standards.
This technology can leverage these instant alerts to help reduce lost time, which recovers potentially lost revenue. This recovered time equates to more miles for the driver, thus more pay. It cascades from there, providing more benefits that touch several line items on the fleet’s profit and loss statement.
An excellent option in this space is an open universal system with one plan. A plug-and-play technology that allows virtually any equipment supplier’s smart component to be plugged into the existing trailer’s network with no additional programming. This systems allows fleets to choose the equipment and component manufacturer that produces the best results for them, rather than letting the manufacturer choose what works best for their system.
This type of open system allows all the data to be in one place, especially for collecting and collating the data to produce viable reports that can drive results. This also allows for different supplier sensors to work together and produce collaborative alerts that when triggered alone could identify one issue but when taken together could catch a completely different problem. Separate data plans with separate reporting for each connected component will become untenable as the number of sensors on vehicles proliferate. To avoid data overload, one must govern the information on the edge to provide the building blocks needed for customers to leverage the data to produce the ROI they expect.
Besides the one system, an additional key in this mass data collection is to take the most complex data and break it down to fundamentals by providing only what’s needed or is a value-add.
And, because each fleet is different, a customizable user interface, or UI, is required. The customized UI will be the engine that determines what to do, despite the clutter of day-to-day events across the stream of data. The true tool is not the data or the components themselves, but it is the machine learning, over time, that drives you from a fix-as-fail to a fix-as-predicted or a predictive maintenance model.
This will save costly unplanned maintenance events and save a driver time, reduce the number of assets needed and improve the bottom line. Having this information sorted in the UI keeps a fleet from simply mining data to taking action, saving valuable time.
Gerry Mead has more than two decades of multifaceted experience in trucking. He has received several industry accolades, including recognition as the Tennessee Trucking Association’s Maintenance Professional of the Year for 2017. He is also an eight-year veteran of the U.S. Marine Corps.