How we implemented a predictive maintenance system that reduced equipment downtime by 60% and increased operational efficiency for a heavy machinery manufacturer.
Downtime Reduction
Machines Monitored
Months Build Time
Predictive Maintenance faced frequent unplanned equipment failures causing costly production downtime. Their reactive maintenance approach was expensive and unpredictable, impacting customer operations globally.
We implemented a comprehensive predictive maintenance platform using IoT sensors and machine learning to monitor equipment health, predict failures, and optimize maintenance scheduling across their global fleet.
From reactive repairs to proactive predictive maintenance across industrial operations
We analyzed customer behavioral data, usage patterns, service history, and demographic information to identify churn indicators and patterns across different customer segments.
We built advanced ML models using ensemble methods to predict customer churn probability, integrated with automated retention campaign triggers and personalized offer generation systems.
The churn prediction system achieved high accuracy and reduced monthly churn. The company now proactively retains high-value customers with personalized offers before they consider switching.
The predictive maintenance platform transformed equipment reliability and operational efficiency
Significant reduction in unplanned equipment downtime
Comprehensive monitoring across global fleet
High accuracy in failure prediction and prevention
Cost savings from reduced downtime and optimized maintenance
Let's discuss how predictive maintenance AI can reduce downtime and maximize your equipment efficiency.
