MAKE | Supply Chain Analytics
ML-powered equipment failure prevention
Predictive Maintenance (PdM) uses data analytics and machine learning to predict equipment failures before they occur.
Minimize unplanned equipment outages that cause costly production delays.
Optimize maintenance to extend operational lifespan of assets.
Lower maintenance costs by performing maintenance only when needed.
Fix when broken
Scheduled maintenance
Monitor and respond
Predict and prevent
The relationship between Potential Failure and Functional Failure:
Equipment begins showing signs of degradation - detectable through sensors and monitoring.
Equipment can no longer perform its intended function - production stops.
P-F Interval: The window for predictive maintenance to prevent failure
Z-score or Grubbs' test to identify outliers in performance
Random Forests, SVMs for pattern recognition and failure prediction
Time-series forecasting for equipment health trends
Estimating time until equipment failure
Mean Time Between Failures
Reduction in maintenance expenses
% of operational time