Truck Fleets Can Better Leverage Data For Maintenance

Editor’s note: Written by Norman Thomas, is a fleet business consultant at Uptake, a Chicago-based provider of industrial intelligence. This is one in a series of periodic guest columns by industry thought leaders.

Fleet managers are facing two persistent, frustrating challenges: driver and technician retention.

The American Trucking Association, or ATA, estimates that the industry needs as many as 80,000 additional drivers at the present time. The American Transportation Research Institute, in its October 2021 report, found that driver and diesel technician retention and compensation were some of the top obstacles facing the industry.

A Costly Predicament

When these positions remain open, the cost starts to accelerate and the urgency to fill these positions becomes critical. The ATA also estimates the average cost to hire a new driver to be $8,000 per hire. Open technician positions can cost a shop up to $1,200 per day for lost productivity.

Compounding the technician cost is the fact that fleets are retaining older trucks and buying used equipment, which adds to the overall maintenance cost by extending the age of the truck fleet. A J.D. Power report indicates that in 2021, prices for four-year-old commercial trucks were 96.3 percent higher than the previous year — the highest on record.

Fewer technicians only compound the driver retention challenge. When vehicle maintenance is not addressed in a timely and cost-effective manner, maintenance departments are forced into a never-ending cycle of reactive work as trucks. The lack of mechanical upkeep results in less uptime and roadside breakdowns that undercut driver pay.

These issues come at a challenging time. Supply chain bottlenecks will be with us for an extended period, at least through the balance of 2022. The lack of goods of all kinds cascades through the system, raising prices for everything from cookies to the cost of shipping a sofa.

Predictive Maintenance Provides Answers

Fortunately, technology offers an answer. Fleets have made significant investments in technology, from telematics for vehicles to Computerized Maintenance Management Systems or CMMS, platforms for fleet maintenance. All these systems collect data by the terabyte, from fault codes and sensor data to fluid analysis readings, driver performance data, and dispatch information.

The issue for fleet managers isn’t collecting high-quality data—it’s making sense of it all. Fleets need a systematic way to leverage the information they collect to lower costs, improve repair scheduling, and give drivers more time on the road instead of in the shop or by the roadside. Predictive maintenance addresses the problem, turning data into actionable insight.

Predictive analytics solutions process data from various sources through pre-trained, predictive models and deliver insights, directing maintenance teams to take corrective action. These insights are integrated with the fleet’s work order management system, enabling fleet managers to make predictive maintenance decisions without the course of normal operations—a more efficient, proactive, and far more strategic solution.

Fleet-wide Benefits from Predictive Maintenance

Moving unplanned maintenance to planned maintenance will increase fleet uptime. It also reduces power derate and potential towing situations, both of which are costly. This applies to both private and for-hire fleets. With private operations, driver retention is less of a problem—but keeping drivers in-house can be a challenge. Predictive maintenance helps deliver more uptime, equating to more on-time deliveries and happier customers. On the other hand, for-hire fleets don’t want vehicles sitting idle in the lot. Greater uptime creates opportunities for more revenue miles and happier drivers.

Leverage Data

Three benefits stand out when in-house data is analyzed to improve uptime.

First, when vehicle conditions and impending repairs are known, managers improve asset utilization and availability. They can make better decisions about which vehicles are suited for a given route, and whether additional capacity is required, either within or outside their fleet. On the maintenance side, repairs can be scheduled for when particular technicians are available. Cylinder head failures on compressed natural gas trucks, for instance, require the attention of a seasoned technician.

Second, technicians can get better diagnostic and repair information. By taking full advantage of data coming out of vehicles through predictive maintenance software, technicians will know before the vehicle enters the shop what—and why—a problem exists, improving both wrench time and first-time fix rates. Furthermore, fleets can bundle repairs while performing regularly scheduled maintenance, reducing the time vehicles spend in the shop. Attention to vehicles with multiple critical issues helps prioritize the use of shop resources. And for technicians, that clarity allows them to simply do their job in a way that aligns with bottom-line results.

Anticipate Repairs

Third, managers can reduce first-come, first-serve repairs. Typically, critical repairs are reactive—a situation that results in lower fleet reliability due to last-minute rush jobs. Given the knowledge of the need for proactive maintenance, as well as customer commitments that must be prioritized, predictive maintenance allows managers to make the most of finite resources, improving labor utilization in the process.

The shortage of drivers and technicians may persist—but there is no shortage of data. It’s time to put this valuable digital asset to work to solve problems.

Editor’s note: welcomes divergent thoughts and opinions on transport technology and trucking industry issues. Use the comments section to cite yours. Qualified opinion leaders are welcome to offer suggestions for opinion columns. Contact [email protected]

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