cwicr-risk-calculator

# CWICR Risk Calculator ## Business Case ### Problem Statement Cost estimates have inherent uncertainty: - What contingency to apply? - What is the confidence range? - Which items have highest risk? - How to quantify uncertainty? ### Solution Risk-adjusted cost calculations using contingency analysis, Monte Carlo simulation, and probability distributions based on CWICR cost data. ### Business Value - **Informed decisions** - Understand estimate uncertainty - **Appropriate contingency** - Data-driven risk allowance - **Confidence intervals** - P50, P80, P90 estimates - **Risk prioritization** - Focus on high-impact items ## 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 from enum import Enum import random class RiskLevel(Enum): """Risk level categories.""" LOW = "low" # Well-defined, standard work MEDIUM = "medium" # Some uncertainty HIGH = "high" # Significant uncertainty VERY_HIGH = "very_high" # Major unknowns class DistributionType(Enum): """Probability distribution types.""" NORMAL = "normal" TRIANGULAR = "triangular" UNIFORM = "uniform" PERT = "pert" LOGNORMAL = "lognormal" @dataclass class RiskParameters: """Risk parameters for a work item.""" work_item_code: str base_cost: float risk_level: RiskLevel distribution: DistributionType...