A regular maintenance schedule used to be a good solution for ensuring that your vehicle remained in good working order but things have changed markedly. Now that vehicles and the parts that go into them are X times better (i.e. last far longer) than they used to, it has brought a new challenge to the table through cost. The cost of maintenance has gone up considerably over the past five years, which means that over-servicing (i.e. replacing parts that still have life in them) or ignoring parts until they become a costly, emergency problem is and will continue to put the margins of any operator under severe pressure.
Why Calendar-Based Servicing Isn’t Precise Enough
Predictive maintenance solutions are enabling fleets to monitor the real-time health of critical systems like engines, gearboxes, and brakes. They take in data from a range of sensors placed across the vehicle to build a performance profile for each asset in your fleet. Over time, these profiles allow you to identify trends and predict when maintenance on a particular truck or component will be required.
Both types of system will save you time and money by avoiding breakdowns and unnecessary garage visits. But CBM takes the concept a significant step further. Because it’s based on actual data drawn from the real-world usage of your particular assets, you can be confident your maintenance schedule is perfectly tailored to the needs of your fleet. This will save you money and help your vehicles spend more time doing what they’re supposed to, out on the road, earning their keep.
The Data Pipeline That Makes it Work
Anticipatory maintenance is beneficial only when it is based on accurate data. This is where the role of the infrastructure becomes significant.
Predictive maintenance relies on the real-time data associated with the engine and other vehicle parts. Many commercial trucks produce data in real-time through their engine control units and CAN-bus network. The data is already there but the critical aspects are gathering, transmitting, and analyzing data, and taking necessary actions.
Real-time telematics identifies patterns by pulling the engine data through the vehicle and transferring it to the fleet management software through the internet. The pipeline here is the robust vehicle tracking system we are talking about. This system includes the necessary telemetry to monitor engine data, detect diagnostic trouble codes before they become visible on the dashboard, and share with the fleet manager the health of the asset of the entire fleet in real-time.
For “silent” failures, the early warning fails. For weeks a problem with a cooling system or fuel injector warning light may not be activated. It will simply reduce fuel efficiency and cause damage during the period. The real-time transfer of data would be an effective solution for this.
The ROI Case is Clear
Proactive maintenance leads to reduced costs and increased safety. Knowing exactly when to perform a repair keeps trucks out of the workshop for unnecessary work, and it means you don’t need to have a safety buffer included in your scheduling. Scrapping planned maintenance reduces downtime, which improves asset utilization and pays for itself in better fuel economy and longer component life.
Extending Asset Life into the Secondary Market
Many maintenance discussions within a fleet concern simply keeping the vehicle on the road. How often do we discuss what level of maintenance information we are capturing and cascading through the vehicle’s life, particularly its end-of-life? A vehicle with a complete, documented maintenance history, based on true condition data (not just the stamps in a service book), commands a higher price on the secondary market because the buyer knows exactly what they are getting.
Was an under-the-bonnet fire cleaned up and repaired, or did a sticky brake that required application pull the clutch never really get fixed? Was the vehicle overworked and under-maintained? Was a fault left for a service cycle to save money? Predictive maintenance produces this lifetime data trail automatically. The machine learning algorithms that develop as a result of analysing previous vehicle data present the registered service histories that will likely follow the vehicle through its entire life. For a five-year fleet with a five-year asset cycle, that resale value is already being materially included in the total cost calculation from day one.
The Economics Don’t Leave Much Room For the Old Approach
Operating a commercial fleet on reactive maintenance has always been costly. Scheduled maintenance was an improvement, but it’s essentially a series of best guesses. The tools are now available to make a safer, more productive guess. Fleets that apply condition-based monitoring are doing more than just adopting a new technology. They’re making a collective decision to protect their capital in an environment where parts are costly, downtime is overly expensive, and every vehicle needs to maintain tip-top shape. The question is no longer, is predictive maintenance worth it? The question is, can you afford not to use it?
