cwicr-cost-calculator"
# CWICR Cost Calculator
## Business Case
### Problem Statement
Traditional cost estimation often produces "black box" estimates with hidden markups. Stakeholders need:
- Transparent cost breakdowns
- Traceable pricing logic
- Auditable calculations
- Resource-level detail
### Solution
Resource-based cost calculation using CWICR methodology that separates physical norms (labor hours, material quantities) from volatile prices, enabling transparent and auditable estimates.
### Business Value
- **Full transparency** - Every cost component visible
- **Auditable** - Traceable calculation logic
- **Flexible** - Update prices without changing norms
- **Accurate** - Based on 55,000+ validated work items
## Technical Implementation
### Prerequisites
```bash
pip install pandas numpy
```
### Python Implementation
```python
import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
class CostComponent(Enum):
"""Cost breakdown components."""
LABOR = "labor"
MATERIAL = "material"
EQUIPMENT = "equipment"
OVERHEAD = "overhead"
PROFIT = "profit"
TOTAL = "total"
class CostStatus(Enum):
"""Cost calculation status."""
CALCULATED = "calculated"
ESTIMATED = "estimated"
MISSING_DATA = "missing_data"
ERROR = "error"
@dataclass
class CostBreakdown:
"""Detailed cost breakdown for a work item."""
work_item_code: str
description: str
unit: str
quantity: float
labor_cost: float = 0.0
material_cost: float = 0.0
equipment_cost: float = 0.0
overhead_cost: float = 0.0
profit_cost: float = 0.0
unit_price: float = 0.0
total_cost: float = 0.0
labor_hours: float = 0.0
labor_rate: float = 0.0
resources: List[Dict[str, Any]] = field(default_factory=list)
status: CostStatus = CostStatus.CALCULATED
def to_dict(self) -> Dict[str, Any]:
return {
'work_item_code': self.work_item_code,
'description': self.description,
'unit': self.unit,
'quantity': self.quantity,
'labor_cost': self.labor_cost,
'material_cost': self.material_cost,
'equipment_cost': self.equipment_cost,
'overhead_cost': self.overhead_cost,
'profit_cost': self.profit_cost,
'total_cost': self.total_cost,
'status': self.status.value
}
@dataclass
class CostSummary:
"""Summary of cost estimate."""
total_cost: float
labor_total: float
material_total: float
equipment_total: float
overhead_total: float
profit_total: float
item_count: int
currency: str
calculated_at: datetime
breakdown_by_category: Dict[str, float] = field(default_factory=dict)
class CWICRCostCalculator:
"""Resource-based cost calculator using CWICR methodology."""
DEFAULT_OVERHEAD_RATE = 0.15 # 15% overhead
DEFAULT_PROFIT_RATE = 0.10 # 10% profit
def __init__(self, cwicr_data: pd.DataFrame,
overhead_rate: float = None,
profit_rate: float = None,
currency: str = "USD"):
"""Initialize calculator with CWICR data."""
self.data = cwicr_data
self.overhead_rate = overhead_rate or self.DEFAULT_OVERHEAD_RATE
self.profit_rate = profit_rate or self.DEFAULT_PROFIT_RATE
self.currency = currency
# Index data for fast lookup
self._index_data()
def _index_data(self):
"""Create index for fast work item lookup."""
if 'work_item_code' in self.data.columns:
self._code_index = self.data.set_index('work_item_code')
else:
self._code_index = None
def calculate_item_cost(self, work_item_code: str,
quantity: float,
price_overrides: Dict[str, float] = None) -> CostBreakdown:
"""Calculate cost for single work item."""
# Find work item in database
if self._code_index is not None and work_item_code in self._code_index.index:
item = self._code_index.loc[work_item_code]
else:
# Try partial match
matches = self.data[
self.data['work_item_code'].str.contains(work_item_code, case=False, na=False)
]
if matches.empty:
return CostBreakdown(
work_item_code=work_item_code,
description="NOT FOUND",
unit="",
quantity=quantity,
status=CostStatus.MISSING_DATA
)
item = matches.iloc[0]
# Get base costs
labor_unit = float(item.get('labor_cost', 0) or 0)
material_unit = float(item.get('material_cost', 0) or 0)
equipment_unit = float(item.get('equipment_cost', 0) or 0)
# Apply price overrides if provided
if price_overrides:
if 'labor_rate' in price_overrides:
labor_norm = float(item.get('labor_norm', 0) or 0)
labor_unit = labor_norm * price_overrides['labor_rate']
if 'material_factor' in price_overrides:
material_unit *= price_overrides['material_factor']
if 'equipment_factor' in price_overrides:
equipment_unit *= price_overrides['equipment_factor']
# Calculate component costs
labor_cost = labor_unit * quantity
material_cost = material_unit * quantity
equipment_cost = equipment_unit * quantity
# Direct costs
direct_cost = labor_cost + material_cost + equipment_cost
# Overhead and profit
overhead_cost = direct_cost * self.overhead_rate
profit_cost = (direct_cost + overhead_cost) * self.profit_rate
# Total
total_cost = direct_cost + overhead_cost + profit_cost
# Unit price
unit_price = total_cost / quantity if quantity > 0 else 0
return CostBreakdown(
work_item_code=work_item_code,
description=str(item.get('description', '')),
unit=str(item.get('unit', '')),
quantity=quantity,
labor_cost=labor_cost,
material_cost=material_cost,
equipment_cost=equipment_cost,
overhead_cost=overhead_cost,
profit_cost=profit_cost,
unit_price=unit_price,
total_cost=total_cost,
labor_hours=float(item.get('labor_norm', 0) or 0) * quantity,
labor_rate=float(item.get('labor_rate', 0) or 0),
status=CostStatus.CALCULATED
)
def calculate_estimate(self, items: List[Dict[str, Any]],
group_by_category: bool = True) -> CostSummary:
"""Calculate cost estimate for multiple items."""
breakdowns = []
for item in items:
code = item.get('work_item_code') or item.get('code')
qty = item.get('quantity', 0)
overrides = item.get('price_overrides')
breakdown = self.calculate_item_cost(code, qty, overrides)
breakdowns.append(breakdown)
# Aggregate totals
labor_total = sum(b.labor_cost for b in breakdowns)
material_total = sum(b.material_cost for b in breakdowns)
equipment_total = sum(b.equipment_cost for b in breakdowns)
overhead_total = sum(b.overhead_cost for b in breakdowns)
profit_total = sum(b.profit_cost for b in breakdowns)
total_cost = sum(b.total_cost for b in breakdowns)
# Group by category if requested
breakdown_by_category = {}
if group_by_category:
for b in breakdowns:
# Extract category from work item code prefix
category = b.work_item_code.split('-')[0] if '-' in b.work_item_code else 'Other'
if category not in breakdown_by_category:
breakdown_by_category[category] = 0
breakdown_by_category[category] += b.total_cost
return CostSummary(
total_cost=total_cost,
labor_total=labor_total,
material_total=material_total,
equipment_total=equipment_total,
overhead_total=overhead_total,
profit_total=profit_total,
item_count=len(breakdowns),
currency=self.currency,
calculated_at=datetime.now(),
breakdown_by_category=breakdown_by_category
)
def calculate_from_qto(self, qto_df: pd.DataFrame,
code_column: str = 'work_item_code',
quantity_column: str = 'quantity') -> pd.DataFrame:
"""Calculate costs from Quantity Takeoff DataFrame."""
results = []
for _, row in qto_df.iterrows():
code = row[code_column]
qty = row[quantity_column]
breakdown = self.calculate_item_cost(code, qty)
result = breakdown.to_dict()
# Add original QTO columns
for col in qto_df.columns:
if col not in result:
result[f'qto_{col}'] = row[col]
results.append(result)
return pd.DataFrame(results)
def apply_regional_factors(self, base_costs: pd.DataFrame,
region_factors: Dict[str, float]) -> pd.DataFrame:
"""Apply regional adjustment factors."""
adjusted = base_costs.copy()
if 'labor_cost' in adjusted.columns and 'labor' in region_factors:
adjusted['labor_cost'] *= region_factors['labor']
if 'material_cost' in adjusted.columns and 'material' in region_factors:
adjusted['material_cost'] *= region_factors['material']
if 'equipment_cost' in adjusted.columns and 'equipment' in region_factors:
adjusted['equipment_cost'] *= region_factors['equipment']
# Recalculate totals
adjusted['direct_cost'] = (
adjusted.get('labor_cost', 0) +
adjusted.get('material_cost', 0) +
adjusted.get('equipment_cost', 0)
)
adjusted['total_cost'] = adjusted['direct_cost'] * (1 + self.overhead_rate) * (1 + self.profit_rate)
return adjusted
def compare_estimates(self, estimate1: CostSummary,
estimate2: CostSummary) -> Dict[str, Any]:
"""Compare two cost estimates."""
return {
'total_difference': estimate2.total_cost - estimate1.total_cost,
'total_percent_change': (
(estimate2.total_cost - estimate1.total_cost) /
estimate1.total_cost * 100 if estimate1.total_cost > 0 else 0
),
'labor_difference': estimate2.labor_total - estimate1.labor_total,
'material_difference': estimate2.material_total - estimate1.material_total,
'equipment_difference': estimate2.equipment_total - estimate1.equipment_total,
'item_count_difference': estimate2.item_count - estimate1.item_count
}
class CostReportGenerator:
"""Generate cost reports from calculations."""
def __init__(self, calculator: CWICRCostCalculator):
self.calculator = calculator
def generate_summary_report(self, items: List[Dict[str, Any]]) -> Dict[str, Any]:
"""Generate summary cost report."""
summary = self.calculator.calculate_estimate(items)
return {
'report_date': datetime.now().isoformat(),
'currency': summary.currency,
'total_cost': round(summary.total_cost, 2),
'breakdown': {
'labor': round(summary.labor_total, 2),
'material': round(summary.material_total, 2),
'equipment': round(summary.equipment_total, 2),
'overhead': round(summary.overhead_total, 2),
'profit': round(summary.profit_total, 2)
},
'percentages': {
'labor': round(summary.labor_total / summary.total_cost * 100, 1) if summary.total_cost > 0 else 0,
'material': round(summary.material_total / summary.total_cost * 100, 1) if summary.total_cost > 0 else 0,
'equipment': round(summary.equipment_total / summary.total_cost * 100, 1) if summary.total_cost > 0 else 0,
},
'item_count': summary.item_count,
'by_category': summary.breakdown_by_category
}
def generate_detailed_report(self, items: List[Dict[str, Any]]) -> pd.DataFrame:
"""Generate detailed line-item report."""
results = []
for item in items:
code = item.get('work_item_code') or item.get('code')
qty = item.get('quantity', 0)
breakdown = self.calculator.calculate_item_cost(code, qty)
results.append(breakdown.to_dict())
df = pd.DataFrame(results)
# Add totals row
totals = df[['labor_cost', 'material_cost', 'equipment_cost',
'overhead_cost', 'profit_cost', 'total_cost']].sum()
totals['description'] = 'TOTAL'
totals['work_item_code'] = ''
df = pd.concat([df, pd.DataFrame([totals])], ignore_index=True)
return df
# Convenience functions
def calculate_cost(cwicr_data: pd.DataFrame,
work_item_code: str,
quantity: float) -> float:
"""Quick cost calculation."""
calc = CWICRCostCalculator(cwicr_data)
breakdown = calc.calculate_item_cost(work_item_code, quantity)
return breakdown.total_cost
def estimate_project_cost(cwicr_data: pd.DataFrame,
items: List[Dict[str, Any]]) -> Dict[str, Any]:
"""Quick project cost estimate."""
calc = CWICRCostCalculator(cwicr_data)
report = CostReportGenerator(calc)
return report.generate_summary_report(items)
```
## Quick Start
```python
import pandas as pd
from cwicr_data_loader import CWICRDataLoader
# Load CWICR data
loader = CWICRDataLoader()
cwicr = loader.load("ddc_cwicr_en.parquet")
# Initialize calculator
calc = CWICRCostCalculator(cwicr)
# Calculate single item
breakdown = calc.calculate_item_cost("CONC-001", quantity=150)
print(f"Total: ${breakdown.total_cost:,.2f}")
print(f" Labor: ${breakdown.labor_cost:,.2f}")
print(f" Material: ${breakdown.material_cost:,.2f}")
print(f" Equipment: ${breakdown.equipment_cost:,.2f}")
```
## Common Use Cases
### 1. Project Estimate
```python
items = [
{'work_item_code': 'CONC-001', 'quantity': 150},
{'work_item_code': 'EXCV-002', 'quantity': 200},
{'work_item_code': 'REBAR-003', 'quantity': 15000} # kg
]
summary = calc.calculate_estimate(items)
print(f"Project Total: ${summary.total_cost:,.2f}")
```
### 2. QTO Integration
```python
# Load BIM quantities
qto = pd.read_excel("quantities.xlsx")
# Calculate costs
costs = calc.calculate_from_qto(qto,
code_column='work_item',
quantity_column='quantity'
)
print(costs[['description', 'quantity', 'total_cost']])
```
### 3. Regional Adjustment
```python
# Apply Berlin pricing
berlin_factors = {
'labor': 1.15, # 15% higher labor
'material': 0.95, # 5% lower materials
'equipment': 1.0
}
adjusted = calc.apply_regional_factors(costs, berlin_factors)
```
## Resources
- **GitHub**: [OpenConstructionEstimate-DDC-CWICR](https://github.com/datadrivenconstruction/OpenConstructionEstimate-DDC-CWICR)
- **DDC Book**: Chapter 3.1 - Construction Cost Estimation
标签
skill
ai
