Predictive Maintenance Cutting Unplanned Downtime by Half
An industrial manufacturer experiencing 14 hours of unplanned downtime per month across 8 production lines was losing $1.8M annually in production output. We deployed IoT sensor pipelines and anomaly detection models on edge infrastructure that generate maintenance alerts 48–72 hours before projected failure — at 91% precision, low enough to maintain maintenance team trust.
The manufacturer produces precision components for the automotive and aerospace sectors. Their 8 production lines run 24/7 with strict output commitments to OEM customers. Every hour of unplanned downtime triggers penalty clauses in customer contracts and disrupts downstream assembly schedules. The maintenance function had historically operated on time-based schedules — replacing components at fixed intervals regardless of actual condition.
Time-based maintenance was generating two problems simultaneously: over-maintenance (replacing components that had useful life remaining, wasting cost) and under-maintenance (missing failures that occurred between scheduled intervals). The engineering team had installed IoT sensors on critical equipment two years earlier but had no analytical capability to extract value from the data they were collecting.
14 hours of unplanned downtime per month — $1.8M in annual production loss
Over-maintenance: 34% of replaced components had >40% remaining useful life
2 terabytes of IoT sensor data collected and never analysed
No integration between sensor data and the maintenance work order system
8 different PLC/SCADA systems across production lines — fragmented data landscape
We built a streaming data pipeline that ingested sensor data from all 8 production lines in real time, normalised across the heterogeneous PLC/SCADA environment, and applied anomaly detection models (Isolation Forest for baseline, LSTM for temporal pattern detection) to generate failure probability scores. Alerts are generated at two thresholds: amber (48-hour warning, schedule maintenance) and red (12-hour warning, immediate action).
Sensor data audit: 2TB of historical data cleaned, normalised, and labelled against maintenance records
Edge deployment architecture — processing at the factory floor, not in the cloud, for sub-second latency
Isolation Forest baseline anomaly detection + LSTM for temporal failure pattern recognition
Two-tier alert system: amber (48–72hr predicted failure) and red (12hr predicted failure)
CMMS integration — alerts automatically generate work orders in the client's maintenance management system
Precision tuning: alert threshold calibrated to <10% false positive rate to maintain maintenance team trust
Six months post-deployment, unplanned downtime fell from 14 hours to 6.7 hours per month — a 52% reduction. Production loss savings of $1.8M annualised were realised in the first full year. The maintenance team responded positively: alert precision of 91% meant only 1 in 10 amber alerts was a false positive, high enough to build trust.
Unplanned downtime: 14 hrs/month → 6.7 hrs/month (52% reduction)
$1.8M annual production loss prevented
91% alert precision — false positive rate below the 10% trust threshold
Over-maintenance eliminated for 6 of 8 production lines
Average advance warning: 61 hours — sufficient for planned maintenance scheduling
“We've transformed from reactive fire-fighting to genuinely proactive maintenance. The team now looks at data before they look at the equipment. It's a different way of working.”
Dmitri Volkov
Head of Operations, Industrial Manufacturer
False positive rate is more important than sensitivity for maintenance use cases — a model that cries wolf loses adoption within weeks
Edge deployment (not cloud) was essential for real-time response and for data sovereignty requirements in the aerospace supply chain context
Labelling historical sensor data against maintenance records was the most time-consuming phase — and the most valuable investment
Key Results
Services Engaged
Technology Stack
Facing a Similar Challenge?
Let's discuss your specific context and what results are realistic for your organisation.
Get in touch