Manufacturing · Vertex Manufacturing
Predictive maintenance saves manufacturer $3.1M in downtime
A precision-components manufacturer cut unplanned downtime 38% with IIoT telemetry and failure-prediction models across 3 plants.
38%
unplanned downtime reduction
$3.1M
annual downtime savings
27%
maintenance spares cost cut
14 days
average failure warning lead time
The Problem
Vertex's CNC and stamping lines suffered unplanned stoppages costing ~$40k per hour. Maintenance was calendar-based — over-servicing healthy machines while missing early failure signals on others.
The Solution
RippleCode deployed vibration, temperature and power sensors across 240 machines, streaming telemetry into an AWS IoT pipeline. Anomaly-detection and remaining-useful-life models trigger work orders in the existing CMMS, and plant dashboards give supervisors real-time asset health scores.
Architecture
- Edge gateways with local buffering across 3 plants
- AWS IoT Core + Kinesis streaming ingestion
- Time-series feature pipeline and anomaly detection (Prophet + autoencoders)
- Remaining-useful-life models per machine class
- CMMS integration for automated work-order creation
ROI
Full program cost recovered in 7 months; ongoing savings of $3M+ annually across downtime, spares and overtime.
Engagement
- Client
- Vertex Manufacturing
- Industry
- Manufacturing
- Service
- AI & IoT
- Timeline
- 10 weeks pilot (one line), 6 months to all 3 plants
Technologies
“We went from firefighting breakdowns to planning maintenance two weeks ahead. The ROI case made itself after the first prevented line-down event.”
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