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

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%