# Predictive Maintenance & Asset Lifecycle Workflow
*Optimized for AI-Tadpole-OS*

## Objective
To eliminate unplanned downtime by predicting asset failures and scheduling proactive maintenance.

## Steps
1. **Sensor Ingestion**: Real-time telemetry (vibration, temperature, power) is streamed via the IoT MCP.
2. **Anomaly Detection**: `Maintenance Lead` identifies deviations from the baseline for critical machinery (e.g., CNC spindles, robotic arms).
3. **Risk Scoring**: Each asset is assigned a "Probability of Failure" (PoF) score.
4. **Maintenance Window**: 
   - If PoF > 80%, the `Maintenance Lead` requests a maintenance window from the `Plant Manager`.
   - The `Supply Chain Orchestrator` verifies that spare parts are available in stock.
5. **Work Order**: A digital work order is generated and assigned to a human technician.
6. **Post-Maintenance Validation**: The `Maintenance Lead` monitors the asset post-repair to ensure performance has returned to the expected baseline.

## Escalation
- If a critical failure is imminent and no maintenance window is granted within 4 hours, the `Maintenance Lead` alerts the `Plant Manager` to the potential for catastrophic downtime.
