// Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards.
| name | productivity-analyzer |
| description | Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards. |
| homepage | https://datadrivenconstruction.io |
| metadata | {"openclaw":{"emoji":"📊","os":["darwin","linux","win32"],"homepage":"https://datadrivenconstruction.io","requires":{"bins":["python3"]}}} |
Understanding productivity requires:
Analyze labor productivity data to identify trends, compare to benchmarks, and provide actionable insights.
import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional
from dataclasses import dataclass
from datetime import date, timedelta
from enum import Enum
class ProductivityStatus(Enum):
EXCELLENT = "excellent" # >110% of planned
ON_TARGET = "on_target" # 90-110%
BELOW = "below" # 70-90%
CRITICAL = "critical" # <70%
@dataclass
class ProductivityRecord:
date: date
activity_code: str
description: str
planned_output: float
actual_output: float
unit: str
manhours: float
crew_size: int
conditions: str # weather, access issues
@dataclass
class ProductivityAnalysis:
activity_code: str
description: str
total_planned: float
total_actual: float
total_manhours: float
planned_rate: float # unit per manhour
actual_rate: float
efficiency: float # percentage
status: ProductivityStatus
trend: str # improving, declining, stable
class ProductivityAnalyzer:
"""Analyze construction productivity data."""
# Industry benchmark rates (unit per manhour)
BENCHMARKS = {
'concrete_pour': 0.5, # m3/MH
'rebar_install': 15, # kg/MH
'formwork': 0.8, # m2/MH
'brick_laying': 35, # bricks/MH
'drywall': 1.5, # m2/MH
'painting': 3.0, # m2/MH
'conduit': 8, # m/MH
'pipe': 3, # m/MH
'excavation': 2.5, # m3/MH
'backfill': 3.0, # m3/MH
}
def __init__(self):
self.records: List[ProductivityRecord] = []
def add_record(self,
date: date,
activity_code: str,
description: str,
planned_output: float,
actual_output: float,
unit: str,
manhours: float,
crew_size: int,
conditions: str = "normal"):
"""Add productivity record."""
self.records.append(ProductivityRecord(
date=date,
activity_code=activity_code,
description=description,
planned_output=planned_output,
actual_output=actual_output,
unit=unit,
manhours=manhours,
crew_size=crew_size,
conditions=conditions
))
def import_from_dataframe(self, df: pd.DataFrame):
"""Import records from DataFrame."""
for _, row in df.iterrows():
self.add_record(
date=pd.to_datetime(row['date']).date(),
activity_code=row['activity_code'],
description=row.get('description', ''),
planned_output=float(row['planned_output']),
actual_output=float(row['actual_output']),
unit=row.get('unit', 'unit'),
manhours=float(row['manhours']),
crew_size=int(row.get('crew_size', 1)),
conditions=row.get('conditions', 'normal')
)
def _get_status(self, efficiency: float) -> ProductivityStatus:
"""Determine productivity status."""
if efficiency >= 110:
return ProductivityStatus.EXCELLENT
elif efficiency >= 90:
return ProductivityStatus.ON_TARGET
elif efficiency >= 70:
return ProductivityStatus.BELOW
else:
return ProductivityStatus.CRITICAL
def _calculate_trend(self, records: List[ProductivityRecord]) -> str:
"""Calculate productivity trend."""
if len(records) < 3:
return "insufficient_data"
# Sort by date
sorted_records = sorted(records, key=lambda x: x.date)
# Calculate efficiency for first and last third
n = len(sorted_records)
third = n // 3
early_efficiency = []
late_efficiency = []
for i, r in enumerate(sorted_records):
if r.manhours > 0:
eff = (r.actual_output / r.planned_output * 100) if r.planned_output > 0 else 0
if i < third:
early_efficiency.append(eff)
elif i >= n - third:
late_efficiency.append(eff)
if not early_efficiency or not late_efficiency:
return "stable"
early_avg = np.mean(early_efficiency)
late_avg = np.mean(late_efficiency)
if late_avg > early_avg * 1.05:
return "improving"
elif late_avg < early_avg * 0.95:
return "declining"
else:
return "stable"
def analyze_activity(self, activity_code: str) -> Optional[ProductivityAnalysis]:
"""Analyze productivity for specific activity."""
activity_records = [r for r in self.records if r.activity_code == activity_code]
if not activity_records:
return None
total_planned = sum(r.planned_output for r in activity_records)
total_actual = sum(r.actual_output for r in activity_records)
total_manhours = sum(r.manhours for r in activity_records)
planned_rate = total_planned / total_manhours if total_manhours > 0 else 0
actual_rate = total_actual / total_manhours if total_manhours > 0 else 0
efficiency = (total_actual / total_planned * 100) if total_planned > 0 else 0
return ProductivityAnalysis(
activity_code=activity_code,
description=activity_records[0].description,
total_planned=round(total_planned, 2),
total_actual=round(total_actual, 2),
total_manhours=round(total_manhours, 1),
planned_rate=round(planned_rate, 3),
actual_rate=round(actual_rate, 3),
efficiency=round(efficiency, 1),
status=self._get_status(efficiency),
trend=self._calculate_trend(activity_records)
)
def analyze_all_activities(self) -> List[ProductivityAnalysis]:
"""Analyze all activities."""
activities = set(r.activity_code for r in self.records)
return [self.analyze_activity(code) for code in activities if self.analyze_activity(code)]
def compare_to_benchmark(self, activity_code: str) -> Dict[str, Any]:
"""Compare activity to industry benchmark."""
analysis = self.analyze_activity(activity_code)
if not analysis:
return {}
# Find matching benchmark
benchmark = None
for key, value in self.BENCHMARKS.items():
if key in activity_code.lower():
benchmark = value
break
if benchmark is None:
return {
'activity': activity_code,
'actual_rate': analysis.actual_rate,
'benchmark': 'Not available',
'vs_benchmark': 'N/A'
}
vs_benchmark = (analysis.actual_rate / benchmark * 100) if benchmark > 0 else 0
return {
'activity': activity_code,
'actual_rate': analysis.actual_rate,
'benchmark_rate': benchmark,
'vs_benchmark_pct': round(vs_benchmark, 1),
'recommendation': 'Above benchmark' if vs_benchmark >= 100 else 'Below benchmark - investigate'
}
def identify_problem_areas(self) -> List[Dict[str, Any]]:
"""Identify activities with productivity issues."""
problems = []
for analysis in self.analyze_all_activities():
if analysis.status in [ProductivityStatus.BELOW, ProductivityStatus.CRITICAL]:
problems.append({
'activity': analysis.activity_code,
'efficiency': analysis.efficiency,
'status': analysis.status.value,
'trend': analysis.trend,
'manhours_impacted': analysis.total_manhours,
'priority': 'HIGH' if analysis.status == ProductivityStatus.CRITICAL else 'MEDIUM'
})
return sorted(problems, key=lambda x: x['efficiency'])
def forecast_completion(self,
activity_code: str,
remaining_quantity: float) -> Dict[str, Any]:
"""Forecast completion based on current productivity."""
analysis = self.analyze_activity(activity_code)
if not analysis or analysis.actual_rate == 0:
return {}
# Manhours needed at current rate
manhours_needed = remaining_quantity / analysis.actual_rate
# Average daily manhours
activity_records = [r for r in self.records if r.activity_code == activity_code]
avg_daily_mh = np.mean([r.manhours for r in activity_records]) if activity_records else 8
days_needed = manhours_needed / avg_daily_mh if avg_daily_mh > 0 else 0
return {
'activity': activity_code,
'remaining_qty': remaining_quantity,
'current_rate': analysis.actual_rate,
'manhours_needed': round(manhours_needed, 1),
'days_needed': round(days_needed, 1),
'estimated_completion': date.today() + timedelta(days=int(days_needed))
}
def export_analysis(self, output_path: str) -> str:
"""Export analysis to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary
analyses = self.analyze_all_activities()
summary_df = pd.DataFrame([
{
'Activity': a.activity_code,
'Description': a.description,
'Planned': a.total_planned,
'Actual': a.total_actual,
'Manhours': a.total_manhours,
'Efficiency %': a.efficiency,
'Status': a.status.value,
'Trend': a.trend
}
for a in analyses
])
summary_df.to_excel(writer, sheet_name='Summary', index=False)
# Problems
problems = self.identify_problem_areas()
if problems:
problems_df = pd.DataFrame(problems)
problems_df.to_excel(writer, sheet_name='Problem Areas', index=False)
# Raw data
records_df = pd.DataFrame([
{
'Date': r.date,
'Activity': r.activity_code,
'Planned': r.planned_output,
'Actual': r.actual_output,
'Unit': r.unit,
'Manhours': r.manhours,
'Crew': r.crew_size,
'Conditions': r.conditions
}
for r in self.records
])
records_df.to_excel(writer, sheet_name='Raw Data', index=False)
return output_path
from datetime import date, timedelta
# Initialize analyzer
analyzer = ProductivityAnalyzer()
# Add records
for i in range(10):
analyzer.add_record(
date=date.today() - timedelta(days=i),
activity_code="concrete_pour",
description="Slab pour Level 3",
planned_output=20,
actual_output=18 + (i * 0.3), # improving
unit="m3",
manhours=40,
crew_size=5
)
# Analyze
analysis = analyzer.analyze_activity("concrete_pour")
print(f"Efficiency: {analysis.efficiency}%")
print(f"Status: {analysis.status.value}")
print(f"Trend: {analysis.trend}")
problems = analyzer.identify_problem_areas()
for p in problems:
print(f"{p['activity']}: {p['efficiency']}% - {p['priority']}")
forecast = analyzer.forecast_completion("concrete_pour", remaining_quantity=500)
print(f"Days needed: {forecast['days_needed']}")
print(f"Completion: {forecast['estimated_completion']}")
comparison = analyzer.compare_to_benchmark("concrete_pour")
print(f"vs Benchmark: {comparison['vs_benchmark_pct']}%")
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