Case Study: Aviation Company improves predictive maintenance with Mu Sigma

Streamlining Airlines MRO Operations with Predictive Maintenance

Mu Sigma worked with a leading US Aviation Company that was struggling with more than 25,000 delays and 900+ cancellations in a year due to improper maintenance and repairs. The airline had already been using a predictive analytics platform, but it was not effectively reducing cancellations, out-of-service events, or delays and diversions, and the team could not pinpoint why the model was inaccurate.

Mu Sigma applied its Art of Problem Solving approach, using muPDNA and extensive exploratory data analysis to redefine the problem and clean up data issues. The team then built a rule-based engine to identify recurring error logs and introduced critical, medium, and low alert levels for maintenance triage; this helped predict and prevent recurring logs by an estimated 6–10% and doubled the accuracy of the initial CODD prediction model.