8 Surprising Cost Savings of Pairing Truck Sensors with Data Science

Vehicle and engine sensors measure a wide range of operating conditions — from changes in sound to light, temperature, pressure, and motion. All the data can be used to evaluate the health of assets, but it takes more than data. Just like trained medical professionals have to interpret health data, so do fleet maintenance personnel.

Historically, data was accessible only when a truck pulled into the garage and technicians plugged in a code reader. Now, thanks to connections with telematics, sensor data is available anytime. Maintenance teams can receive immediate alerts for mechanical issues including tire pressure, brakes, electrical systems, lights, and engine performance.

That’s great for addressing component failures today. But what about tomorrow, the day after, or even five years down the road?

Predicting and preventing failure is the goal. Sensors make that possible with the help of data science. Pairing the two helps fleets see clearly into the future. By aggregating and analyzing data, predictive models can deliver insights that identify what components are likely to fail and when, along with the overall health of a truck. Armed with this information, fleet maintenance and operations personnel can stop breakdowns before they occur by repairing the right part at the right time.

Many of today’s fleets are reporting 6-10% out of service numbers due to truck maintenance needs. At a conservative cost estimate of around $750 per day, the price of downtime adds up. Simply improving uptime isn’t the only cost savings that predictive maintenance offers. Here’s eight more.

  1. Supports driver retention

    Surveys show the number one reason drivers leave fleets is because of the pay. For many drivers, having their truck in the shop impacts their paycheck. Plus, roadside breakdowns compromise driver safety in a profession already deemed one of America’s riskiest. Well-serviced trucks are easier to drive and generate more miles between maintenance visits.

  2. Helps manufacturers build better trucks

    Sensor data is valuable information for OEMs. Collecting the data through a predictive maintenance program and sharing the findings with manufacturers provides powerful insights on truck performance. The practice aids warranty collections and helps OEMs build better equipment in the future.

  3. Reduces fuel spend

    Uptime is the goal, but with all the pressure to repair malfunctioning trucks, finding underperforming assets often goes undone. With national diesel prices hovering above $5 a gallon, issues that impact fuel consumption have a high price tag. Predictive maintenance can also reduce fuel usage by preventing unscheduled maintenance events that require special routings.

  4. Develops technician skills

    Skilled labor able to work on today’s trucks is hard to come by. The ATA’s Technology Maintenance Council cited technician skills as a top-three area needing improvement to address maintenance bottlenecks. Sensor-driven predictive maintenance helps tackle this issue by giving fleets time to get in front of repairs. When the maintenance team knows a component is approaching its end of life across an entire model of trucks, they have time to train their technicians to address the issue. The practice eliminates backlogs created when only top mechanics have the skills for certain repairs and avoids sending trucks to over-the-road maintenance facilities or dealerships that typically cost more.

  5. Prevents unplanned maintenance events

    Most fleets engage in preventative maintenance for routine oil changes and brake inspections. Heading to the shop is predictable based on a truck’s mileage. Using these planned maintenance events for addressing repair recommendations generated through predictive analytics decreases downtime, lessens the impact to drivers pay, and eliminates unnecessary empty miles eating up fuel.

  6. Mitigates catastrophic failures

    Waiting to make a repair is a gamble. Using sensor data to anticipate component failures often means making a small repair to avoid a much bigger one. After all, replacing a cooling fan costs a couple hundred dollars whereas rebuilding a radiator can cost several thousand.

  7. Improves technician efficiency

    Shop technicians lose time making repairs by having to diagnose them. According to the ATA, many shops run with outdated diagnostic software. Predictive maintenance helps identify the problem before the truck arrives at the shop. Technicians know what requires their attention and can immediately get to work.

  8. Combats parts shortages

    Inventory stockouts represent a major problem for maintenance facilities. The parts simply aren’t available and getting them—especially from overseas—is taking upwards of six times longer than normal. Predictive analytics identifies what parts are needed, ahead of time, to prevent future failures. This gives maintenance teams time to source and order parts, which avoids premium prices and unnecessary downtime waiting for parts to arrive.

Uncover Additional Cost Savings with Uptake

Uptake’s predictive maintenance platform, Uptake Fleet, generates cost savings by analyzing sensor data. Our technology uses advanced data models powered by machine learning and artificial intelligence to deliver maintenance insights that mitigate risk, improve asset performance, and enhance safety. Your data paired with our science ensures you get every mile possible out of each asset.

Interested in how more than 200,000 trucks benefit from Uptake insights? Request more info on Uptake Fleet and discover how our predictive maintenance technology can transform your bottom line.