How Fleets Can Use Predictive Analytics to Modernize Shops

As trucking companies evolve, one area generally stays the same: the maintenance facility. “The shop” is not often the first place to look for investments in modern interiors, furniture, and aesthetics. In fact, many look like they haven’t changed in years. Yet these maintenance operations are great sources of trucking innovation.

Maintenance facilities are full of highly skilled technicians using advanced diagnostics to work on complex machines. But one area remains largely untapped—the possibilities of predictive maintenance analytics. The data already exists. So much so that it could line the walls of any shop many times over. Yet much of it goes untouched. Not because the operation is complacent, but because making the information actionable is a challenge.

Thanks to technology, that is changing.

Maintenance is much more than screwdrivers and socket wrenches. The best new tool is data science. The following are hallmarks of a maintenance facility modernized by using predictive analytics.

1. Data-Driven Decision-Making

Predictive analytics apply to both aged and under-warranty equipment. The technology aggregates sensor and fault code data captured by onboard telematics, in addition to work order data. The analysis projects component failures down to the individual truck level. Maintenance operations gain valuable statistical insights into what parts are expected to fail and when.

By accurately forecasting malfunctions, maintenance can proactively prioritize repairs before they become breakdowns. Rather than addressing problems as they arise, the team can use the data to formulate a strategic plan for improving fleet uptime and reducing costs. The data also informs buying cycles by showing the highest performing trucks and indicating where a fleet can cost-effectively stretch the life of its equipment.

2. Conversion of Unplanned to Planned Maintenance Events

Deloitte reports that poor maintenance practices reduce an operation’s productivity by up to 20%. By contrast, predictive maintenance increases productivity by 25%. The most significant difference is affording a maintenance operation the ability to plan.

According to FleetNet America, the average roadside breakdown costs fleets $491 per incident. That number doesn’t include lost revenue or labor, the cost to reseat a driver, late delivery fees, or damage to the customer relationship. The true cost of downtime is much higher. By converting pending failures into planned events, fleets avoid or significantly reduce these expenses.

Most carriers engage in preventative maintenance for oil changes, tire rotations, and brake inspections. Layering predictive analytics onto the existing program allows maintenance to conduct routine service while also preemptively making repairs.

For example, fleets can pair a routine oil change with headlamp replacements shown to be approaching end of life. The driver avoids a safety violation, the fleet prevents roadside repair costs, and maintenance maximizes shop time to keep the truck running longer between service.

3. Effective Labor Usage

According to data from the Bureau of Labor Statistics, the U.S. will be short 163,000 diesel technicians by 2030. Maintenance operations try to prioritize what work hits the most skilled technicians. However, there simply are not enough to keep up with demand. Repairs stack up for certain mechanics while others go underutilized based on skill level. Even worse, fleets route repairs to over-the-road maintenance facilities and dealerships at a higher cost.

Predictive maintenance lessens this problem. By anticipating component malfunctions, fleets have time to train on the repair. They expand the capabilities of their existing labor force making more technicians available to address the issue. The added control of determining when the repair occurs improves workload allocations as well.

Unpredictable equipment failures and accidents are inevitable. But they aren’t as overwhelming when reducing the number of preventable breakdowns competing for resources. By leveraging predictive maintenance, fleets can maximize the use of every technician and each bay to get trucks back on the road generating revenue.

4. Automated Inventory Management

Modern maintenance shops integrate predictive analytics with their inventory management systems. The analytics helps set the survival thresholds for individual components, such as a part that has an 80% failure rate at the 48-month mark. With this data in hand, the team establishes a replacement timeline. As the part approaches the acceptable limit, the inventory management system is alerted and an order placed. When the truck enters the shop, the part is on hand and ready to go. No excess inventory or parts delays.

A $500 part ready and replaced today could save a fleet $5,000 in future repair costs by preventing downtime. Predictive analytics makes calculating these types of cost savings and managing inventory accordingly possible.

Modernize Maintenance with Uptake

Uptake Fleet helps carriers get more mileage out of their data and equipment. The technology generates best-in-class predictive maintenance insights and makes them actionable. Using raw data collected through Geotab telematics, Uptake applies data science models trained over billions of hours of analysis to enhance the lead time a fleet has to address failures.

With Uptake Fleet, maintenance operations can proactively schedule repairs, reduce over-the-road risks, and improve vehicle performance. The result is more uptime and a better bottom line.

Learn more about how to put data insights to work by downloading “The Ultimate Guide for Maximizing Vehicle Uptime.” Discover why fleets using Uptake and Geotab have the best tool for double-digit reductions in maintenance costs, breakdowns, and unscheduled repairs.

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