Predictive Maintenance & Quality Control

The Challenge

An industrial manufacturer was experiencing costly unplanned downtime and quality issues in their production line. Equipment failures were unpredictable, maintenance was reactive and expensive, quality defects were detected too late in the process, and overall equipment effectiveness (OEE) was below industry benchmarks.

Our Solution

Implemented IoT sensor data collection and ML models for predictive maintenance. Deployed sensors across 150+ pieces of equipment, built real-time anomaly detection models to predict equipment failures, created computer vision systems for automated quality inspection, and developed optimization algorithms for maintenance scheduling to minimize production impact.

Results & Outcomes

Technologies Used