This client operates 3 manufacturing facilities with 180+ CNC machines. Unplanned equipment failures were costing an estimated $2.8M annually in downtime, rush repairs, and missed orders. Their maintenance was purely reactive—fix machines after they break, often waiting days for parts. Previous "smart factory" initiatives had failed because the data infrastructure wasn't there.
We partnered with their maintenance team to instrument 40 critical machines with vibration, temperature, and power consumption sensors. The first 3 months were just getting reliable data collection—sensors failed, network issues corrupted data, technicians accidentally unplugged things. Only after we had 4 months of clean baseline data did we start on the predictive models.
The platform ingests 1.2M+ sensor readings daily, using anomaly detection to identify machines drifting toward failure. Maintenance teams get 5-10 day advance warning of likely failures (we originally projected 7-14 days, but real-world accuracy required tightening the window). Dashboard shows real-time health scores for every instrumented machine.
Unplanned downtime reduced 62% on instrumented machines. We'd projected 74%, but some failure modes turned out not to have detectable precursor signals with our sensor setup. Maintenance parts inventory decreased 24% (less emergency stock needed). The system correctly predicts 78% of major failures with a false positive rate of 11%—higher than we wanted, but maintenance team says it's acceptable.
Unified patient data across 12 legacy systems. Reduced duplicate records by 73%—not the 89% we projected, but enough to materially reduce medication errors.
Built an ML-powered routing system that reduced fleet fuel costs by 18%. Took longer than planned because integrating with their dispatch system was harder than scoped.
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