kbarbel640-del 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
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
min_factor: float # Multiplier for minimum
max_factor: float # Multiplier for maximum
most_likely_factor: float = 1.0
@dataclass
class MonteCarloResult:
"""Results of Monte Carlo simulation."""
iterations: int
mean: float
std_dev: float
p10: float # 10th percentile
p50: float # Median
p80: float # 80th percentile
p90: float # 90th percentile
min_value: float
max_value: float
values: List[float]
@dataclass
class RiskAnalysisResult:
"""Complete risk analysis result."""
base_estimate: float
risk_adjusted_mean: float
contingency_amount: float
contingency_percent: float
p50_estimate: float
p80_estimate: float
p90_estimate: float
high_risk_items: List[Dict[str, Any]]
item_risks: List[RiskParameters]
monte_carlo: Optional[MonteCarloResult] = None
# Default risk parameters by category
DEFAULT_RISK_PARAMS = {
'CONC': {'risk': RiskLevel.LOW, 'min': 0.95, 'max': 1.15},
'EXCV': {'risk': RiskLevel.MEDIUM, 'min': 0.85, 'max': 1.30},
'STRL': {'risk': RiskLevel.LOW, 'min': 0.95, 'max': 1.10},
'MECH': {'risk': RiskLevel.MEDIUM, 'min': 0.90, 'max': 1.25},
'ELEC': {'risk': RiskLevel.MEDIUM, 'min': 0.90, 'max': 1.20},
'FINI': {'risk': RiskLevel.HIGH, 'min': 0.85, 'max': 1.40},
'SITE': {'risk': RiskLevel.HIGH, 'min': 0.80, 'max': 1.50},
'DEFAULT': {'risk': RiskLevel.MEDIUM, 'min': 0.90, 'max': 1.25}
}
class CWICRRiskCalculator:
"""Calculate risk-adjusted estimates using CWICR data."""
def __init__(self, cwicr_data: pd.DataFrame):
self.work_items = cwicr_data
self._index_data()
def _index_data(self):
"""Index work items."""
if 'work_item_code' in self.work_items.columns:
self._code_index = self.work_items.set_index('work_item_code')
else:
self._code_index = None
def _get_risk_params(self, code: str) -> Dict[str, Any]:
"""Get default risk parameters for work item code."""
prefix = code.split('-')[0] if '-' in code else code[:4]
return DEFAULT_RISK_PARAMS.get(prefix, DEFAULT_RISK_PARAMS['DEFAULT'])
def define_item_risk(self,
code: str,
base_cost: float,
risk_level: RiskLevel = None,
distribution: DistributionType = DistributionType.TRIANGULAR,
min_factor: float = None,
max_factor: float = None) -> RiskParameters:
"""Define risk parameters for a work item."""
default_params = self._get_risk_params(code)
if risk_level is None:
risk_level = default_params['risk']
if min_factor is None:
min_factor = default_params['min']
if max_factor is None:
max_factor = default_params['max']
return RiskParameters(
work_item_code=code,
base_cost=base_cost,
risk_level=risk_level,
distribution=distribution,
min_factor=min_factor,
max_factor=max_factor,
most_likely_factor=1.0
)
def calculate_item_risk(self,
items: List[Dict[str, Any]]) -> List[RiskParameters]:
"""Calculate risk parameters for list of work items."""
risk_params = []
for item in items:
code = item.get('work_item_code', item.get('code'))
qty = item.get('quantity', 0)
# Get base cost
base_cost = 0
if self._code_index is not None and code in self._code_index.index:
wi = self._code_index.loc[code]
labor = float(wi.get('labor_cost', 0) or 0)
material = float(wi.get('material_cost', 0) or 0)
equipment = float(wi.get('equipment_cost', 0) or 0)
base_cost = (labor + material + equipment) * qty
# Get risk level from item or default
risk_level = item.get('risk_level')
if risk_level and isinstance(risk_level, str):
risk_level = RiskLevel[risk_level.upper()]
params = self.define_item_risk(
code=code,
base_cost=base_cost,
risk_level=risk_level,
min_factor=item.get('min_factor'),
max_factor=item.get('max_factor')
)
risk_params.append(params)
return risk_params
def _sample_distribution(self, params: RiskParameters) -> float:
"""Sample from probability distribution."""
min_cost = params.base_cost * params.min_factor
max_cost = params.base_cost * params.max_factor
mode_cost = params.base_cost * params.most_likely_factor
if params.distribution == DistributionType.TRIANGULAR:
return np.random.triangular(min_cost, mode_cost, max_cost)
elif params.distribution == DistributionType.UNIFORM:
return np.random.uniform(min_cost, max_cost)
elif params.distribution == DistributionType.NORMAL:
mean = params.base_cost
std = (max_cost - min_cost) / 6 # 99.7% within range
return max(min_cost, min(max_cost, np.random.normal(mean, std)))
elif params.distribution == DistributionType.PERT:
# PERT/Beta distribution
mean = (min_cost + 4 * mode_cost + max_cost) / 6
std = (max_cost - min_cost) / 6
return max(min_cost, min(max_cost, np.random.normal(mean, std)))
elif params.distribution == DistributionType.LOGNORMAL:
# Lognormal for skewed risks
log_mean = np.log(params.base_cost)
log_std = 0.1 * (params.max_factor - params.min_factor)
return np.random.lognormal(log_mean, log_std)
return params.base_cost
def run_monte_carlo(self,
risk_params: List[RiskParameters],
iterations: int = 10000) -> MonteCarloResult:
"""Run Monte Carlo simulation."""
total_costs = []
for _ in range(iterations):
iteration_total = sum(
self._sample_distribution(params)
for params in risk_params
)
total_costs.append(iteration_total)
total_costs = np.array(total_costs)
return MonteCarloResult(
iterations=iterations,
mean=round(float(np.mean(total_costs)), 2),
std_dev=round(float(np.std(total_costs)), 2),
p10=round(float(np.percentile(total_costs, 10)), 2),
p50=round(float(np.percentile(total_costs, 50)), 2),
p80=round(float(np.percentile(total_costs, 80)), 2),
p90=round(float(np.percentile(total_costs, 90)), 2),
min_value=round(float(np.min(total_costs)), 2),
max_value=round(float(np.max(total_costs)), 2),
values=list(total_costs)
)
def analyze_risk(self,
items: List[Dict[str, Any]],
run_simulation: bool = True,
iterations: int = 10000) -> RiskAnalysisResult:
"""Complete risk analysis of estimate."""
risk_params = self.calculate_item_risk(items)
# Base estimate
base_estimate = sum(p.base_cost for p in risk_params)
# Run Monte Carlo if requested
monte_carlo = None
if run_simulation:
monte_carlo = self.run_monte_carlo(risk_params, iterations)
risk_adjusted_mean = monte_carlo.mean
p50 = monte_carlo.p50
p80 = monte_carlo.p80
p90 = monte_carlo.p90
else:
# Deterministic calculation
risk_adjusted_mean = sum(
p.base_cost * (p.min_factor + 4 * p.most_likely_factor + p.max_factor) / 6
for p in risk_params
)
p50 = risk_adjusted_mean
p80 = sum(
p.base_cost * (p.min_factor + p.max_factor * 3) / 4
for p in risk_params
)
p90 = sum(p.base_cost * p.max_factor * 0.9 for p in risk_params)
contingency = p80 - base_estimate
contingency_pct = (contingency / base_estimate * 100) if base_estimate > 0 else 0
# Identify high risk items
high_risk_items = [
{
'code': p.work_item_code,
'base_cost': p.base_cost,
'risk_level': p.risk_level.value,
'range': f"{p.min_factor:.0%} - {p.max_factor:.0%}",
'risk_exposure': p.base_cost * (p.max_factor - 1)
}
for p in risk_params
if p.risk_level in [RiskLevel.HIGH, RiskLevel.VERY_HIGH]
]
return RiskAnalysisResult(
base_estimate=round(base_estimate, 2),
risk_adjusted_mean=round(risk_adjusted_mean, 2),
contingency_amount=round(contingency, 2),
contingency_percent=round(contingency_pct, 1),
p50_estimate=round(p50, 2),
p80_estimate=round(p80, 2),
p90_estimate=round(p90, 2),
high_risk_items=sorted(high_risk_items, key=lambda x: x['risk_exposure'], reverse=True),
item_risks=risk_params,
monte_carlo=monte_carlo
)
def calculate_contingency(self,
base_estimate: float,
project_phase: str = 'detailed',
complexity: str = 'medium') -> Dict[str, Any]:
"""Calculate recommended contingency based on project phase."""
# Standard contingency ranges by phase
contingency_ranges = {
'concept': {'low': 0.25, 'medium': 0.35, 'high': 0.50},
'schematic': {'low': 0.15, 'medium': 0.25, 'high': 0.35},
'detailed': {'low': 0.08, 'medium': 0.12, 'high': 0.18},
'construction': {'low': 0.03, 'medium': 0.05, 'high': 0.08}
}
phase_range = contingency_ranges.get(project_phase, contingency_ranges['detailed'])
rate = phase_range.get(complexity, phase_range['medium'])
return {
'base_estimate': base_estimate,
'contingency_rate': f"{rate:.0%}",
'contingency_amount': round(base_estimate * rate, 2),
'total_with_contingency': round(base_estimate * (1 + rate), 2),
'project_phase': project_phase,
'complexity': complexity
}
def sensitivity_analysis(self,
risk_params: List[RiskParameters],
base_result: MonteCarloResult) -> pd.DataFrame:
"""Analyze sensitivity of total cost to each item."""
sensitivities = []
for param in risk_params:
# Calculate contribution to variance
item_variance = (param.base_cost * (param.max_factor - param.min_factor) / 6) ** 2
total_variance = base_result.std_dev ** 2
contribution_pct = (item_variance / total_variance * 100) if total_variance > 0 else 0
sensitivities.append({
'work_item_code': param.work_item_code,
'base_cost': param.base_cost,
'risk_level': param.risk_level.value,
'variance_contribution_pct': round(contribution_pct, 1),
'cost_range_low': round(param.base_cost * param.min_factor, 2),
'cost_range_high': round(param.base_cost * param.max_factor, 2)
})
return pd.DataFrame(sensitivities).sort_values('variance_contribution_pct', ascending=False)
def export_analysis(self,
result: RiskAnalysisResult,
output_path: str) -> str:
"""Export risk analysis to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary
summary_df = pd.DataFrame([{
'Base Estimate': result.base_estimate,
'Risk Adjusted Mean': result.risk_adjusted_mean,
'Contingency Amount': result.contingency_amount,
'Contingency %': result.contingency_percent,
'P50 Estimate': result.p50_estimate,
'P80 Estimate': result.p80_estimate,
'P90 Estimate': result.p90_estimate
}])
summary_df.to_excel(writer, sheet_name='Summary', index=False)
# Item Risks
items_df = pd.DataFrame([
{
'Work Item': p.work_item_code,
'Base Cost': p.base_cost,
'Risk Level': p.risk_level.value,
'Min Factor': p.min_factor,
'Max Factor': p.max_factor,
'Distribution': p.distribution.value
}
for p in result.item_risks
])
items_df.to_excel(writer, sheet_name='Item Risks', index=False)
# High Risk Items
if result.high_risk_items:
high_risk_df = pd.DataFrame(result.high_risk_items)
high_risk_df.to_excel(writer, sheet_name='High Risk', index=False)
# Monte Carlo distribution (sample)
if result.monte_carlo and result.monte_carlo.values:
mc_df = pd.DataFrame({
'Iteration': range(1, min(1001, len(result.monte_carlo.values) + 1)),
'Total Cost': result.monte_carlo.values[:1000]
})
mc_df.to_excel(writer, sheet_name='Monte Carlo', index=False)
return output_path
Quick Start
# Load CWICR data
cwicr = pd.read_parquet("ddc_cwicr_en.parquet")
# Initialize risk calculator
risk_calc = CWICRRiskCalculator(cwicr)
# Define work items
items = [
{'work_item_code': 'CONC-001', 'quantity': 150},
{'work_item_code': 'EXCV-002', 'quantity': 500, 'risk_level': 'high'},
{'work_item_code': 'STRL-003', 'quantity': 25}
]
# Run risk analysis
result = risk_calc.analyze_risk(items, iterations=10000)
print(f"Base Estimate: ${result.base_estimate:,.2f}")
print(f"P50: ${result.p50_estimate:,.2f}")
print(f"P80: ${result.p80_estimate:,.2f}")
print(f"P90: ${result.p90_estimate:,.2f}")
print(f"Recommended Contingency: {result.contingency_percent}%")
Common Use Cases
1. Phase-Based Contingency
contingency = risk_calc.calculate_contingency(
base_estimate=1000000,
project_phase='schematic',
complexity='high'
)
print(f"Contingency: ${contingency['contingency_amount']:,.2f}")
2. Sensitivity Analysis
sensitivity = risk_calc.sensitivity_analysis(
result.item_risks,
result.monte_carlo
)
print(sensitivity.head(5))
3. Export Report
risk_calc.export_analysis(result, "risk_analysis.xlsx")
Resources
- GitHub: OpenConstructionEstimate-DDC-CWICR
- DDC Book: Chapter 3.1 - Risk-Based Estimating