Maintenance represents one of the most substantial ongoing costs for any fleet, often leaving plenty of room for enhancement. In recent years, AI-powered predictive maintenance (PdM) has arisen as a seemingly unparalleled solution, yet one question lingers: how reliable is it in the practical, everyday operations of businesses?
The touted advantages of PdM are undeniably impressive, but the technology comes with a hefty price tag. Fleet managers must ensure they’re getting a solid return on investment before diving into such an expensive commitment.
The Inner Workings of Predictive Maintenance
Predictive maintenance reimagines preventive care, leveraging automated data analysis in place of traditional manual inspections. Instead of physically examining each truck, fleets can monitor performance via Internet of Things (IoT) sensors and telematics systems. Real-time data from these devices is analyzed by an AI model to predict when a vehicle might require repair.
Automating inspections allows businesses to cut down on planned downtime usually dedicated to routine checkups. Considering that approximately 70% of fleets conduct weekly tire inspections, this automation saves substantial time. Moreover, AI’s ability to detect subtle patterns that escape human notice means PdM could more effectively prevent costly breakdowns.
The crux of PdM’s reliability lies in real-time vehicle data. The AI’s accuracy hinges on the volume and quality of this data. As a result, some PdM services gather information from over 24 telematics providers, while in-house solutions necessitate multiple IoT sensors per vehicle.
Astute fleet owners will detect the potential issues arising here. Implementing and managing this many IoT devices is neither simple nor inexpensive. Yet, skimping on data can compromise PdM’s effectiveness, placing businesses in a delicate balance.
Is the Investment Justifiable?
Given PdM’s high costs and intricate implementation, it’s reasonable for fleet operators to question its value. However, success stories from its real-world application offer a hopeful outlook.
Take the city of Long Beach, California, for instance. After adopting predictive maintenance, the city realized annual savings of an astounding $809,500. These savings were primarily derived from preventing breakdowns and addressing minor vehicle issues before they escalated into larger problems. This substantial return easily offsets the initial investment, making a strong case for PdM.
However, it’s important to note that Long Beach’s solution spanned over 600 vehicles. Smaller fleets might not achieve comparable outcomes.
Smaller fleets have also seen PdM success. One PdM provider reported that a client saved $1 million in just four months across 80 trucks. However, this success story is somewhat of an outlier, stemming from uniformly changing NOx sensors every 200,000 miles, sometimes leading to unnecessary repairs. Not every fleet will find such drastic room for improvement.
The reliability of predictive maintenance varies based on the operation. Fleets with a larger number of trucks benefit more, due to their extensive data and economies of scale. Operations with high repair costs or significant planned downtime will also see considerable benefits, given the greater potential for optimization.
In contrast, smaller fleets may struggle to justify the investment. They might lack sufficient data to ensure PdM’s reliability. Similarly, PdM is most effective for fleets already employing telematics solutions, reducing upfront costs and providing richer datasets. Fleets with low annual repair costs won’t experience as much of a benefit either.
Not All Fleets Gain Equally from PdM
AI has matured to a point where PdM can reliably supplant traditional maintenance strategies. However, its effectiveness is contingent on the right circumstances.
Organizations need to evaluate their fleet size, data availability, and current maintenance expenditures to assess whether PdM is a worthy investment. While the technology will likely become more cost-effective over time, not all businesses will see significant immediate benefits. For now, the decision to adopt PdM must be carefully weighed, as its payoffs are not universally assured.