AgentSkillsCN

Calculate Labor Productivity

计算劳动生产率

SKILL.md

Skill: Calculate Labor Productivity

Domain

advanced_manufacturing

Description

Calculates labor productivity metrics including output per hour, efficiency ratios, and identifies improvement opportunities.

Tags

productivity, labor, manufacturing, efficiency, workforce, OEE

Use Cases

  • •Productivity benchmarking
  • •Efficiency improvement
  • •Workforce planning
  • •Cost analysis

Proprietary Business Rules

Rule 1: Output Per Hour Calculation

Standard productivity metric computation.

Rule 2: Efficiency Ratio Analysis

Actual vs standard performance comparison.

Rule 3: Downtime Impact Assessment

Lost productivity from downtime events.

Rule 4: Improvement Opportunity Identification

Productivity gap analysis.

Input Parameters

  • •analysis_id (string): Analysis identifier
  • •production_data (list): Production output records
  • •labor_data (list): Labor hours and headcount
  • •standard_times (dict): Standard time allowances
  • •downtime_records (list): Downtime events
  • •benchmark_data (dict): Industry benchmarks

Output

  • •productivity_metrics (dict): Key productivity measures
  • •efficiency_analysis (dict): Efficiency breakdown
  • •downtime_impact (dict): Productivity losses
  • •trend_analysis (dict): Performance trends
  • •improvement_opportunities (list): Optimization areas

Implementation

The calculation logic is implemented in productivity_calculator.py and references data from CSV files:

  • •productivity_metrics.csv - Reference data
  • •industry_benchmarks.csv - Reference data
  • •efficiency_factors.csv - Reference data
  • •performance_ratings.csv - Reference data
  • •adjustment_factors.csv - Reference data
  • •cost_components.csv - Reference data
  • •parameters.csv - Reference data.

Usage Example

python
from productivity_calculator import calculate_productivity

result = calculate_productivity(
    analysis_id="PRD-001",
    production_data=[{"date": "2025-12-15", "units": 500, "product": "widget"}],
    labor_data=[{"date": "2025-12-15", "hours": 80, "headcount": 10}],
    standard_times={"widget": {"standard_minutes": 8}},
    downtime_records=[{"date": "2025-12-15", "minutes": 30, "reason": "setup"}],
    benchmark_data={"industry_avg_units_per_hour": 7}
)

print(f"Units Per Labor Hour: {result['productivity_metrics']['units_per_hour']}")

Test Execution

python
from productivity_calculator import calculate_productivity

result = calculate_productivity(
    analysis_id=input_data.get('analysis_id'),
    production_data=input_data.get('production_data', []),
    labor_data=input_data.get('labor_data', []),
    standard_times=input_data.get('standard_times', {}),
    downtime_records=input_data.get('downtime_records', []),
    benchmark_data=input_data.get('benchmark_data', {})
)