The True Cost of Unplanned Downtime
At $28,000 per hour, the manufacturer's 340 hours of annual unplanned downtime cost $9.5M per year in direct production losses. But the indirect costs were equally significant: emergency parts procurement at 3x normal cost, overtime labor for emergency repairs, customer delivery penalties, and the stress on a maintenance team that was perpetually in crisis mode.
Sensor Deployment Strategy
Rather than instrumenting all 400 machines at once, we used a criticality-based approach: rank machines by failure impact (production loss × failure frequency) and start with the top 80. This allowed us to demonstrate ROI within 6 months while building the team's confidence in the system before scaling.
Each critical machine received: a triaxial vibration sensor (detecting bearing wear, imbalance, and misalignment), a thermal sensor array (detecting insulation degradation and overheating), and a current transducer (detecting motor efficiency degradation). Data streams at 1kHz to edge computing nodes that perform initial signal processing before sending aggregated features to the cloud.
Machine-Specific Model Training
The key insight in predictive maintenance is that every machine has a unique "normal" — influenced by its age, load profile, maintenance history, and operating environment. We trained separate anomaly detection models for each machine type (CNC mills, stamping presses, conveyors, compressors) using 90 days of baseline data, then fine-tuned each individual machine's model over the following 60 days.
Integration with SAP PM
The system integrates directly with the plant's SAP Plant Maintenance module. When the AI predicts a failure with >75% confidence, it automatically creates a PM work order with the predicted failure mode, recommended parts, estimated repair time, and suggested maintenance window. Maintenance planners review and schedule — they don't have to create work orders from scratch or manually prioritize their queue.