Cascade Manufacturing struggled with unplanned equipment failures that halted production lines and incurred six-figure emergency repair costs. We deployed an IoT-connected monitoring system that predicts failures 48 hours in advance and auto-generates maintenance work orders.
Three CNC machines accounted for 70% of unplanned downtime. Maintenance was reactive — a vibration sensor would trigger an alarm, but by then the spindle was already damaged. Average downtime per incident: 8 hours. Annual cost of lost production: $2.1M.
We installed IoT vibration and temperature sensors on critical machines, streaming data to a time-series database. An AI model (Minimax) predicts bearing failure 48 hours in advance with 94% accuracy. When a threshold is crossed, n8n automatically creates a work order in their CMMS, orders parts from inventory, and notifies the maintenance lead via Slack. RPA bots extract historical maintenance records from their legacy ERP (no API available) to enrich the prediction model. Change Management focused on reskilling maintenance staff from reactive repair to predictive analysis — 92% technician buy-in achieved.
“We went from firefighting to forecasting. The system caught a bearing failure two days before it would have destroyed a $80,000 spindle. That alone paid for the entire project in three months. The team reskilling program was essential — our technicians now trust the system.”
