Case Study: a major European aircraft and airline supplier improves predictive maintenance with Capgemini

Aircraft component manufacturer introduces predictive asset maintenance

The customer, a major European aircraft component supplier, faced significant operational disruptions and high maintenance costs due to unexpected machine failures. After a prolonged spindle issue caused a two-month outage, they sought to move from preventative to condition-based maintenance and connect their diverse milling machines for Industry 4.0 data availability. To address this, they partnered with vendor Capgemini to implement its Predictive Asset Maintenance solution, powered by its XIoT platform and Intel-based edge computing technology.

Capgemini's solution deployed edge analytics and sensors to monitor machine data in near real-time, using predictive algorithms to detect deviations and anticipate failures. This provided actionable alerts to field personnel, reducing unscheduled stops and enabling quicker interventions. The implementation resulted in substantial savings for the customer, including a reduction in maintenance costs by up to 40% in some cases, improved machine availability, and the creation of a valuable historical data source for continuous process improvement.