There are more electronic parts and sensors in trucks and semis than ever before. In many ways, trucks now operate a lot more like computers, generating rich data and information that tell us how they are operating at any given second. More than 200 truck components are sensored today, from the diesel particulate filter and selective catalytic reduction to the alternator. These sensors can be immensely valuable to operators — but only if the operators capture and use the data.
Artificial intelligence and machine learning enable trucking fleets to do that. This is not some next-generation technology that will be available in 20 years. It’s being used now, across our economy, and the potential economic benefit for fleet operators is immense.
A Different Approach to Fleet Maintenance
Answer this question: How much money could be saved if you knew vehicles across your fleet were running sub-optimally or on the verge of a breakdown? You already have the answers to questions like this – they are hiding in your data.
The standard practice in the industry is to diagnose trucks while they're in the shop and follow a preventive maintenance schedule. Today, given the data we have from vehicles and technology we have to analyze it, we can do far better. We can actually predict failure, rather than hoping mechanics and a maintenance routine keeps the fleet on the road.
Using real-time data allows fleet companies to capture untapped value that is vital to staying competitive.
Problems AI Can Solve in the Fleet Industry
Until about five years ago, IoT sensors that capture detailed truck data were cost prohibitive. But with advances in technology, dramatic cost reductions, and increasing pressure to make drivers’ trips run smoothly, companies are now able to cost-effectively predict and prevent breakdowns in real-time.
MINOR REPAIRS WITH COSTLY DOMINO EFFECTS
Sometimes issues or failures that are less severe can have a domino effect. Catching upstream issues — like turbo failure — can prevent costly downstream issues — like aftertreatment failure. Waiting until the next PM visit may be too late to catch a catastrophic downstream failure that could have been caught by real-time condition monitoring.
