productivity-analyzer

# 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 ```python 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 ...