Smarter maintenance planning at Fokker
Read moreThe client,
The challenge,
Maintenance was largely scheduled at fixed intervals and based on engineers’ experience. That experience was valuable, but it sometimes meant maintenance happened too early or too late, and unexpected issues put asset availability under pressure.
Fokker wanted to make maintenance more predictable and coordinate the use of people, parts and hangar capacity.
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 actually 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,
We brought together the scattered data sources on a Databricks platform in Azure and trained the predictive models. Working closely with maintenance engineers, we translated their knowledge into features that make the models 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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