cwicr-cost-calculator
Scannednpx machina-cli add skill datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/cwicr-cost-calculator --openclawCWICR 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
pip install pandas numpy
Python Implementation
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
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
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
# 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
# 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
- DDC Book: Chapter 3.1 - Construction Cost Estimation
Source
git clone https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/blob/main/1_DDC_Toolkit/CWICR-Database/cwicr-cost-calculator/SKILL.mdView on GitHub Overview
CWICR Cost Calculator uses the CWICR resource-based methodology to produce transparent, auditable construction estimates. It breaks costs into labor, materials, and equipment, separating physical norms from volatile prices to keep pricing auditable. Built on 55,000+ validated work items to improve accuracy.
How This Skill Works
The tool models cost by applying resource norms (labor hours, material quantities) to a price dataset, then splits the totals into labor, material, and equipment costs with overhead and profit. It uses pandas/numpy data structures to assemble CostBreakdown and CostSummary objects, enabling item-level detail and auditable calculations. Prices can be updated without changing the underlying norms, preserving comparability across projects.
When to Use It
- When you need transparent, auditable estimates for bids and client reviews
- When you want to separate physical norms from price volatility to compare across markets
- When you have CWICR data ready and want itemized cost by labor, material, and equipment
- When you need to refresh prices globally without recomputing norms
- When benchmarking against a large repository of validated work items (55k+)
Quick Start
- Step 1: Install dependencies with 'pip install pandas numpy'
- Step 2: Load the CWICR dataset into a DataFrame and initialize CWICRCostCalculator with it
- Step 3: Request cost for a specific work item code and quantity, then review the breakdown and total
Best Practices
- Use the latest CWICR datasets to keep norms current
- Verify labor hours and material quantities before estimating costs
- Keep overhead and profit rates documented and adjustable (default 15% and 10%)
- Maintain separate norms and prices to enable scenario comparisons and audits
- Document the cost calculation steps to ensure full traceability
Example Use Cases
- Estimate a mid-size concrete slab by itemizing labor hours, material quantities, and equipment rental with a transparent total
- Cost an interior fit-out project, showing overhead and profit separately from base norms
- Run an earthworks project with equipment costs, validating against 55k+ work items
- Perform a price-update pass: refresh only price data while norms remain unchanged
- Audit an existing estimate by exporting the CostBreakdown and CostSummary for each work item