a5c-ai Maintenance Scheduler
Overview
The Maintenance Scheduler skill provides comprehensive capabilities for planning and scheduling maintenance activities. It supports preventive maintenance scheduling, autonomous maintenance checklists, predictive maintenance integration, and TPM pillar support.
Capabilities
- Preventive maintenance scheduling
- Autonomous maintenance checklists
- Predictive maintenance integration
- Spare parts planning
- Work order management
- MTBF/MTTR tracking
- Maintenance backlog management
- TPM pillar support
Used By Processes
- LEAN-005: Standard Work Documentation
- CAP-002: Production Scheduling Optimization
- QMS-001: ISO 9001 Implementation
Tools and Libraries
- CMMS systems (Maximo, SAP PM, Fiix)
- IoT sensors
- Predictive analytics platforms
- Mobile maintenance apps
Usage
skill: maintenance-scheduler
inputs:
equipment_list:
- equipment_id: "CNC-001"
name: "CNC Machine 1"
criticality: "high"
current_runtime: 4500 # hours
last_pm: "2025-12-15"
- equipment_id: "CONV-002"
name: "Conveyor System 2"
criticality: "medium"
current_runtime: 8000
last_pm: "2025-11-30"
maintenance_tasks:
- task_id: "PM-001"
description: "Lubrication"
frequency: "weekly"
duration: 30 # minutes
skills: ["mechanic"]
- task_id: "PM-002"
description: "Filter replacement"
frequency: "monthly"
duration: 60
skills: ["mechanic"]
production_schedule:
- date: "2026-01-25"
available_window: 2 # hours
technicians:
- name: "Tech A"
skills: ["mechanic", "electrical"]
availability: "day_shift"
outputs:
- maintenance_schedule
- work_orders
- parts_requirements
- resource_allocation
- backlog_report
- reliability_metrics
Maintenance Types
| Type | Description | Trigger |
|---|---|---|
| Reactive | Fix after failure | Breakdown |
| Preventive | Scheduled based on time/usage | Calendar/runtime |
| Predictive | Based on condition monitoring | Sensor data |
| Proactive | Eliminate failure modes | Root cause |
| Autonomous | Operator-performed | Daily/shift |
TPM Eight Pillars
| Pillar | Focus Area |
|---|---|
| Autonomous Maintenance | Operator ownership |
| Planned Maintenance | Scheduled PM |
| Quality Maintenance | Zero defects |
| Focused Improvement | Eliminate losses |
| Early Equipment Management | Design for reliability |
| Training | Skills development |
| Safety/Environment | Zero accidents |
| Office TPM | Administrative efficiency |
Reliability Metrics
MTBF (Mean Time Between Failures)
MTBF = Total Operating Time / Number of Failures
Example: 1000 hours / 5 failures = 200 hours
MTTR (Mean Time To Repair)
MTTR = Total Repair Time / Number of Repairs
Example: 25 hours / 5 repairs = 5 hours
Availability
Availability = MTBF / (MTBF + MTTR)
Example: 200 / (200 + 5) = 97.6%
Maintenance Scheduling Rules
| Priority | Criteria | Scheduling |
|---|---|---|
| Critical | Safety or production stop | Immediate |
| High | Affects quality or capacity | Next available window |
| Medium | Preventive maintenance | Scheduled window |
| Low | Nice to have | When convenient |
Predictive Maintenance Signals
| Technology | Monitors | Detects |
|---|---|---|
| Vibration | Rotating equipment | Bearing wear, imbalance |
| Thermography | All equipment | Hot spots, electrical |
| Oil Analysis | Lubricated systems | Wear particles, contamination |
| Ultrasound | All equipment | Leaks, electrical arcing |
Integration Points
- CMMS/EAM systems
- Production scheduling
- Spare parts inventory
- IoT/sensor platforms