Productivity Analyzer
Business Case
Problem Statement
Understanding productivity requires:
-
Tracking actual output rates
-
Comparing to planned rates
-
Identifying problem areas
-
Forecasting project completion
Solution
Analyze labor productivity data to identify trends, compare to benchmarks, and provide actionable insights.
Technical Implementation
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
Quick Start
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}")
Common Use Cases
- Identify Problems
problems = analyzer.identify_problem_areas() for p in problems: print(f"{p['activity']}: {p['efficiency']}% - {p['priority']}")
- Forecast Completion
forecast = analyzer.forecast_completion("concrete_pour", remaining_quantity=500) print(f"Days needed: {forecast['days_needed']}") print(f"Completion: {forecast['estimated_completion']}")
- Compare to Benchmarks
comparison = analyzer.compare_to_benchmark("concrete_pour") print(f"vs Benchmark: {comparison['vs_benchmark_pct']}%")
Resources
- DDC Book: Chapter 4.1 - Productivity Management