How we saved $18M annually with nearly zero maintenance

Challenge:

Unplanned equipment downtime costing $2M+ monthly. Reactive maintenance unable to prevent critical failures. 50+ production facilities globally.

Solution:

ML-powered predictive maintenance system using sensor data, maintenance logs, and operational metrics. Models predict failures 2-3 weeks in advance with 87% accuracy.

Results:

• 42% reduction in unplanned downtime

• $18M annual savings (maintenance costs + production recovery)

• 31% increase in equipment lifespan

• ROI achieved in 8 months

Technical approach:

• XGBoost for failure classification.

• LSTM networks for time-series forecasting.

• Real-time sensor data processing.

• AWS SageMaker deployment.

Key takeaway:

Production-first architecture with shadow mode deployment ensured safe rollout across 50+ facilities without operations.