Unplanned downtime reduced by 67%
How a US manufacturing plant saved $2.1M annually by predicting failures before they happen.
The Situation Before Sync4Tech
A US manufacturing facility running 24/7 production was experiencing 4–6 unplanned shutdowns per quarter, each costing an average of $85,000 in lost production, emergency labour, and expedited parts. Annual downtime costs exceeded $2M.
Maintenance was entirely reactive engineers responded to failures after they happened. The facility had sensors on critical equipment but no system to interpret the data they were generating. Thousands of data points went unread every hour.
Leadership wanted to move to a predictive maintenance model but had no data science capability in-house and needed a solution that could integrate with their existing SCADA systems without disrupting production.
How We Solved It
Sensor Data Pipeline
We built an AWS IoT pipeline ingesting real-time sensor data from 48 pieces of critical equipment into a central data lake.
Failure Pattern Analysis
We analysed 18 months of maintenance records and sensor data to identify the signatures that preceded each failure type.
Anomaly Detection Models
We trained equipment-specific anomaly detection models that flag degrading performance 48–72 hours before predicted failure.
Maintenance Workflow Automation
We automated maintenance work order creation in the CMMS when anomalies are detected, prioritised by failure probability and production impact.
Measurable Impact
Unplanned downtime costs reduced from $2.1M to under $500K in the first year.
From 4–6 unplanned shutdowns per quarter to fewer than 1 on average.
Full project cost recovered within six months of go-live.
"We have not had an unplanned shutdown since go-live. The ROI paid for the entire project in under 6 months. This has transformed how we think about maintenance."— Plant Director, US Manufacturing Company
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