Shopfloor Copilot is an AI-powered predictive maintenance software that calculates real-time equipment health scores (0–100), predicts failure probability, and generates 48-hour maintenance forecasts — all running on-premise from your OPC UA machine data.
Predictive maintenance (PdM) is a data-driven maintenance strategy that monitors real-time equipment health indicators to predict failures before they occur. Unlike preventive maintenance (fixed calendar schedules) or reactive maintenance (respond to breakdowns), predictive maintenance analyses equipment behaviour trends — cycle time degradation, error rate increases, temperature deviations — to schedule maintenance at the optimal moment.
Studies show that predictive maintenance programs reduce maintenance costs by 15–25% and unplanned downtime by 30–50% compared to preventive programs. For a production line operating 2,000 hours per year with 5% unplanned downtime, a 50% reduction translates to 50 additional production hours per year.
Shopfloor Copilot calculates equipment health from multiple data sources collected via OPC UA:
Composite health score per machine, calculated from OEE trends, unplanned stop frequency, alert severity history, and signal anomaly rates. Updated every cycle.
48-hour failure probability using Facebook Prophet time-series forecasting. Predicts when health score will breach critical thresholds.
Extrapolates the degradation trend to estimate the date and time at which equipment will reach a maintenance-required threshold.
Four severity levels: Critical, High, Medium, Low. Each alert type has configurable acknowledgment SLAs and escalation rules.
Per-asset health score trend over the last 30 days. Identify gradual degradation patterns that single-point OEE readings miss.
Integrate predicted failure dates with your maintenance calendar. Share upcoming maintenance tasks via digital shift handover.
Shopfloor Copilot's equipment health score combines four weighted factors:
A score of 80–100 indicates healthy equipment. 50–79 indicates degrading performance requiring attention within the next maintenance window. Below 50 signals high failure risk and should trigger priority maintenance.
The business case for predictive maintenance in discrete manufacturing is well-established:
Predictive maintenance (PdM) uses data analysis and machine learning to predict equipment failures before they occur. Unlike preventive maintenance (fixed schedules) or reactive maintenance (responding to breakdowns), predictive maintenance monitors real-time equipment health indicators to identify degradation trends and trigger maintenance at the optimal moment.
Equipment health scores (0–100) combine OEE trend data, unplanned stop frequency, active alert severity, and signal anomaly rates from OPC UA data. A score above 80 indicates healthy equipment; 50–79 indicates degrading performance; below 50 signals high failure risk requiring immediate attention.
Preventive maintenance runs on a fixed schedule regardless of equipment condition — leading to over-maintenance (wasting money on healthy equipment) or under-maintenance (missing early degradation). Predictive maintenance schedules work based on actual equipment health data, reducing costs by 15–25% and unplanned downtime by 30–50% in typical implementations.
Explore the prototype's real-time health scores across 24 stations, 48-hour failure forecasting charts, and maintenance alert management — running from simulated OPC UA data.
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