The Challenge
Our client, a leading Tier-1 auto parts supplier, was losing approximately $2.5M annually to unplanned machinery downtime. Their traditional preventative maintenance programs were inefficient, leading to parts being replaced while still fully functional, while random failures still paralyzed the assembly line.
They needed a way to listen to the machines and predict sub-system failures before production was impacted.
Our Solution
AdaptNXT deployed a comprehensive Edge-to-Cloud predictive maintenance solution:
- IoT Sensor Deployment: We retrofitted 40 critical CNC machines with high-frequency vibration and temperature sensors using MQTT over industrial Wi-Fi.
- Edge Computing: Instead of streaming terabytes of data to the cloud, we deployed edge gateways that process raw telemetry locally.
- Machine Learning Models: Using historical failure data combined with live streams, we trained custom deep learning models (LSTMs) that detect the subtle acoustic and vibrational anomalies preceding a spindle failure.
- Integration: We connected the predictive alerts directly into the client's existing ERP system, automatically generating work orders for the maintenance team 48 hours before estimated failure.
The Impact
The transformation was profound and immediate. Within the first six months of deployment, the system successfully predicted 14 impending machine failures, allowing maintenance to be scheduled during off-hours.
- 40% Reduction in overall unplanned downtime.
- 18% Decrease in spare parts consumption.
- Full ROI achieved in just 4.5 months.