cwicr-productivity-tracker

# CWICR Productivity Tracker ## Business Case ### Problem Statement Project performance tracking requires: - Comparing actual vs planned productivity - Identifying underperforming activities - Forecasting completion dates - Learning from historical data ### Solution Track productivity by comparing actual hours/quantities against CWICR norms, generating variance analysis and forecasts. ### Business Value - **Performance visibility** - Real-time productivity metrics - **Early warning** - Identify issues before escalation - **Continuous improvement** - Learn from variances - **Accurate forecasting** - Data-driven predictions ## Technical Implementation ```python import pandas as pd import numpy as np from typing import Dict, Any, List, Optional, Tuple from dataclasses import dataclass, field from datetime import datetime, timedelta from enum import Enum from collections import defaultdict class PerformanceStatus(Enum): """Performance status categories.""" EXCELLENT = "excellent" # >110% productivity ON_TARGET = "on_target" # 90-110% BELOW_TARGET = "below_target" # 70-90% CRITICAL = "critical" # <70% @dataclass class ProductivityRecord: """Single productivity record.""" work_item_code: str description: str date: datetime planned_hours: float actual_hours: float planned_quantity: float actual_quantity: float productivity_rate: float # Percentage status: PerformanceStatus variance_hours: fl...