Skill: Calculate Capacity Utilization
Domain
advanced_manufacturing
Description
Calculates manufacturing capacity utilization metrics including OEE, bottleneck analysis, and capacity planning recommendations.
Tags
capacity, manufacturing, OEE, production, efficiency, planning
Use Cases
- •Capacity monitoring
- •OEE calculation
- •Bottleneck identification
- •Investment planning
Proprietary Business Rules
Rule 1: OEE Calculation
Overall equipment effectiveness computation.
Rule 2: Bottleneck Analysis
Production constraint identification.
Rule 3: Capacity Forecasting
Future capacity requirement projection.
Rule 4: Investment Trigger
Capacity expansion threshold analysis.
Input Parameters
- •
analysis_id(string): Analysis identifier - •
production_data(list): Production output records - •
equipment_data(list): Equipment specifications - •
downtime_records(list): Downtime events - •
quality_data(dict): Quality metrics - •
demand_forecast(dict): Future demand
Output
- •
utilization_rate(float): Capacity utilization - •
oee_metrics(dict): OEE breakdown - •
bottleneck_analysis(dict): Constraint identification - •
capacity_forecast(dict): Future capacity needs - •
recommendations(list): Optimization actions
Implementation
The calculation logic is implemented in capacity_calculator.py and references data from capacity_benchmarks.json.
Usage Example
python
from capacity_calculator import calculate_capacity_utilization
result = calculate_capacity_utilization(
analysis_id="CAP-001",
production_data=[{"date": "2025-12-15", "units": 950, "line": "Line_1"}],
equipment_data=[{"id": "Line_1", "max_capacity": 1000, "planned_hours": 16}],
downtime_records=[{"line": "Line_1", "minutes": 45, "reason": "changeover"}],
quality_data={"defect_rate": 0.02},
demand_forecast={"next_quarter": 85000}
)
print(f"Utilization Rate: {result['utilization_rate']:.1%}")
Test Execution
python
from capacity_calculator import calculate_capacity_utilization
result = calculate_capacity_utilization(
analysis_id=input_data.get('analysis_id'),
production_data=input_data.get('production_data', []),
equipment_data=input_data.get('equipment_data', []),
downtime_records=input_data.get('downtime_records', []),
quality_data=input_data.get('quality_data', {}),
demand_forecast=input_data.get('demand_forecast', {})
)