One of the biggest costs associated with fleet management is maintenance. According to a summary of the most popular maintenance solutions on the market, Tech.co estimates an average of $35 per vehicle per month in costs, or around $5,000 per year for a fleet of 20 vehicles. These are basic maintenance costs, and breakdown and emergency repair will of course incur larger costs, or the increase of insurance premiums associated with making use of a policy. Having vehicles fit for purpose, that retain their maintenance assurance over a long time, is essential. Predictive maintenance, which uses a range of techniques to smartly preempt potential issues, is a way to vastly reduce overheads.
The Power of Predictive Maintenance in Fleet Management
Focusing on the last mile
Predictive maintenance is not an entirely new concept, of course. At its most simple, it’s a commitment to trying to stop issues before they arise. However, there are smart ways to put predictive maintenance into place, in a way that will help to make the best use of these tools and focus maintenance into the right areas of fleet management.
Increasingly, these efforts must focus around the last mile. Monitoring and management of the last mile is crucial in delivery, reducing handoffs and representing the best value for money for the customer. It’s only normal that maintenance focus should go into that area. What maintenance risks are present in the dropoff section of a delivery? Are there likely to be street hazards? What are the risks of accident and injury given the typically built up nature of final destinations? This monitoring can be used to inform maintenance as well as improve the last mile experience. As one ResearchGate published study found, using existing datasets from inside the business and looking at the highest value areas of service delivery will provide an informed picture of where to focus maintenance.
Better quality predictive tools
Proper structure will underpin an effective fleet management system. Taking it to the next level, however, is the use of next generation predictive tools. As always, artificial intelligence (AI) and machine learning (ML) are making waves in this sector.
According to industry magazine FleetMaintenance.com, the key change being made is in the way that real-time data is used. Real time data has always been crucial for preventing vehicles getting into crashes; even the simple traffic and accident warnings seen on modern route planning apps are the result of fine detailed live data. Where advanced data can be applied is in comparing travel conditions to understood historical precursors of crashes. AI has the ability to look at very specific vehicle conditions, compare them to environmental factors such as weather, and then produce informed decisions on the likelihood of an accident occurring, and provide suggestions on potential maintenance for that vehicle.
Predictive maintenance in action
These techniques are already being put into place with the advent of Consensus self-organized models approach (COSMO) - an internet of things (IoT) based system of fleet management that relies on the intercommunication of vehicles within the fleet, and other vehicles on the road, to ensure that safety is maintained on the road.
According to a study published by ScienceDirect, the system then advances to another level by producing broad and detailed datasets that can be used to, collectively, judge the potential for breakdown and repair across a wider fleet. Once again, the comparison with local conditions and driver ability is important; these provide clues as to why damage happens, and what can be done to prevent it in the future. The IoT approach, using mass connectivity, is an effective one, and will have a positive knock-on effect in enhancing daily maintenance of the fleet.
Predictive maintenance, then, is an iterative tool which can have hugely positive impacts on the business. Fleet maintenance is expensive, and overheads need to be reduced wherever possible. Taking the simple work of predictive maintenance, and then melding it with the advanced capabilities offered by AI and machine learning, is the crucial step up that will help to automatically and effectively manage vehicle maintenance. The key thing is that nothing different happens in the operation of the fleet; it’s just its data is used in that much more of a smart fashion.