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Asset Management

Case Study
Scenario

A transport vehicle maintenance operation that receives notifications on faults detected on transport assets that require resolution. Where a fault has been previously reported on a particular asset that was not resolved, they are treated as a “repeat fault”, and significant financial penalties are incurred.

Solution

Using a combination of eve insights and Robotics it was possible to identify if a fault raised was in fact a “repeat” of a previously identified issue that was not correctly resolved in real time. These faults were then flagged as a priority for the maintenance workers. In addition to this, machine learning techniques were used to predict when and where faults may occur and therefore perform preventative maintenance to avoid the fault occurring in the first place. This had a massive financial benefit for this operation in terms of avoiding penalties associated with SLA breaches.

Case Studies

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