Only 2% of all machine generated data is used - what about the other 98%? With our predictive maintenance solution, we help you to avoid these unnecessary downtimes and expensive repairs. As we provide you information when to replace components most cost effective, you can optimize your maintenance schedule and increase the efficiency of your assets, optimized to your specific requirements.
You can save 30% of your annual ROI!
No matter of industrial manufacturing, electric bike, bus, truck - downtime is downtime. Doing maintenance after the fact puts your assets at risk of being permanently damaged and of repair cost above your annual returns.
But even regularily scheduled maintenance cycles bears risks:
The main advantage of predictive maintenance: it maximizes runtime, repairs are carried just-in-time. Estimates show that unplanned downtime cost industrial manufacturers over $50bn annually.
Predictions are only as strong as their accuracy. In our proof-of-concept we showed that a trucks CAN bus data is granular and detailed enough. With our approach we are able to deliver some impressive facts:
Vehicles are mobile computers with thousands of sensors creating gigabytes daily, so why waste this “treasure”? Using your telematics device already installed we collectall the relevant data and transfer it to the cloud where you can make use of it. And if you don’t have a telematics solution installed yet, you can use our end-to-end solution.
Minimize the time your assets spends in repairs and maintenance cycles and increase uptime. By knowing when and which components break significantly ahead of time and by enabling your maintenance crew with added insights into a components behaviour profile you get your assets running in the least amount of time possible.
We help you to avoid unnecessary downtimes and expensive repairs. As we provide you information when to replace components most cost effective, you can optimize your maintenance schedule and increase the efficiency of your fleet, optimized to your specific requirements.