Smarter maintenance planning at Fokker
Read moreThe client,
The challenge,
Maintenance was largely scheduled according to fixed intervals and engineers’ experience. Both were valuable, but sometimes maintenance happened too early or too late, and unexpected issues put asset availability under pressure.
Fokker wanted to make maintenance more predictable and coordinate staff, parts and hangar capacity more effectively.
Our solution,
We developed a planning tool that combines historical maintenance data, sensor data and operational data in a predictive model. The tool shows when maintenance is really needed and helps planners schedule work orders, parts and capacity in advance.
This shifts maintenance from reactive to proactive, with less unexpected downtime.
The approach,
On a Databricks platform in Azure, we brought together fragmented data sources and trained the predictive models. Working closely with the maintenance engineers, we translated their knowledge into features that make the model reliable.
The result is higher asset availability, less unplanned downtime and a schedule the whole organisation can rely on.
“Smarter maintenance planning for maximum asset availability”
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