CWICR Material Procurement
Business Case
Problem Statement
Material procurement needs accurate quantity lists:
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What materials are needed?
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How much of each with waste allowance?
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When are they needed on site?
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How to group for suppliers?
Solution
Generate procurement lists from CWICR material data with waste factors, delivery scheduling, and supplier grouping.
Business Value
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Accurate quantities - Based on validated norms
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Waste included - Industry-standard waste factors
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Timely delivery - Aligned with schedule
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Cost optimization - Bulk ordering opportunities
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 MaterialCategory(Enum): """Material categories for procurement.""" CONCRETE = "concrete" STEEL = "steel" TIMBER = "timber" MASONRY = "masonry" FINISHES = "finishes" MEP = "mep" INSULATION = "insulation" ROOFING = "roofing" EARTHWORK = "earthwork" OTHER = "other"
class ProcurementPriority(Enum): """Procurement priority levels.""" CRITICAL = 1 HIGH = 2 MEDIUM = 3 LOW = 4
@dataclass class MaterialItem: """Single material item for procurement.""" material_code: str description: str category: MaterialCategory unit: str net_quantity: float waste_factor: float gross_quantity: float unit_price: float total_cost: float lead_time_days: int required_date: datetime order_date: datetime supplier: str = "" work_item_codes: List[str] = field(default_factory=list)
@dataclass class ProcurementList: """Complete procurement list.""" project_name: str generated_date: datetime total_items: int total_cost: float items: List[MaterialItem] by_category: Dict[str, float] by_supplier: Dict[str, List[MaterialItem]]
Standard waste factors by material type
WASTE_FACTORS = { 'concrete': 0.05, # 5% 'reinforcement': 0.03, # 3% 'formwork': 0.10, # 10% 'masonry': 0.05, # 5% 'timber': 0.08, # 8% 'drywall': 0.10, # 10% 'tiles': 0.10, # 10% 'paint': 0.05, # 5% 'insulation': 0.05, # 5% 'pipes': 0.03, # 3% 'cables': 0.05, # 5% 'default': 0.05 # 5% }
Standard lead times by category (days)
LEAD_TIMES = { 'concrete': 1, # Ready-mix 'reinforcement': 7, # Steel delivery 'formwork': 3, # Standard forms 'masonry': 5, # Block delivery 'timber': 5, # Lumber 'structural_steel': 21, # Fabrication 'windows': 28, # Manufacturing 'doors': 14, # Standard doors 'mep': 14, # MEP equipment 'finishes': 7, # Standard finishes 'default': 7 }
class CWICRMaterialProcurement: """Generate procurement lists from CWICR data."""
def __init__(self, cwicr_data: pd.DataFrame,
resources_data: pd.DataFrame = None):
self.work_items = cwicr_data
self.resources = resources_data
self._index_data()
def _index_data(self):
"""Index data for fast lookup."""
if 'work_item_code' in self.work_items.columns:
self._work_index = self.work_items.set_index('work_item_code')
else:
self._work_index = None
def get_waste_factor(self, material_type: str) -> float:
"""Get waste factor for material type."""
material_lower = str(material_type).lower()
for key, factor in WASTE_FACTORS.items():
if key in material_lower:
return factor
return WASTE_FACTORS['default']
def get_lead_time(self, material_type: str) -> int:
"""Get lead time for material type."""
material_lower = str(material_type).lower()
for key, days in LEAD_TIMES.items():
if key in material_lower:
return days
return LEAD_TIMES['default']
def get_category(self, material_type: str) -> MaterialCategory:
"""Determine material category."""
material_lower = str(material_type).lower()
category_mapping = {
'concrete': MaterialCategory.CONCRETE,
'cement': MaterialCategory.CONCRETE,
'steel': MaterialCategory.STEEL,
'rebar': MaterialCategory.STEEL,
'reinforcement': MaterialCategory.STEEL,
'timber': MaterialCategory.TIMBER,
'wood': MaterialCategory.TIMBER,
'lumber': MaterialCategory.TIMBER,
'masonry': MaterialCategory.MASONRY,
'block': MaterialCategory.MASONRY,
'brick': MaterialCategory.MASONRY,
'paint': MaterialCategory.FINISHES,
'tile': MaterialCategory.FINISHES,
'floor': MaterialCategory.FINISHES,
'electrical': MaterialCategory.MEP,
'plumbing': MaterialCategory.MEP,
'hvac': MaterialCategory.MEP,
'insulation': MaterialCategory.INSULATION,
'roof': MaterialCategory.ROOFING
}
for key, cat in category_mapping.items():
if key in material_lower:
return cat
return MaterialCategory.OTHER
def extract_materials(self,
items: List[Dict[str, Any]],
schedule: Dict[str, datetime] = None) -> List[MaterialItem]:
"""Extract material requirements from work items."""
materials = defaultdict(lambda: {
'net_quantity': 0,
'work_items': [],
'required_date': None
})
for item in items:
code = item.get('work_item_code', item.get('code'))
qty = item.get('quantity', 0)
required_date = item.get('required_date')
if self._work_index is not None and code in self._work_index.index:
work_item = self._work_index.loc[code]
# Get material info from work item
material_desc = str(work_item.get('material_description',
work_item.get('description', '')))
material_unit = str(work_item.get('material_unit',
work_item.get('unit', '')))
material_norm = float(work_item.get('material_norm', 1) or 1)
material_cost = float(work_item.get('material_cost', 0) or 0)
# Calculate material quantity
material_qty = qty * material_norm
# Aggregate by material description
mat_key = f"{material_desc}|{material_unit}"
materials[mat_key]['net_quantity'] += material_qty
materials[mat_key]['work_items'].append(code)
materials[mat_key]['description'] = material_desc
materials[mat_key]['unit'] = material_unit
materials[mat_key]['unit_price'] = material_cost / material_norm if material_norm > 0 else 0
if required_date:
if materials[mat_key]['required_date'] is None:
materials[mat_key]['required_date'] = required_date
else:
materials[mat_key]['required_date'] = min(
materials[mat_key]['required_date'], required_date
)
# Convert to MaterialItem list
result = []
for mat_key, data in materials.items():
description = data['description']
waste_factor = self.get_waste_factor(description)
lead_time = self.get_lead_time(description)
net_qty = data['net_quantity']
gross_qty = net_qty * (1 + waste_factor)
unit_price = data.get('unit_price', 0)
required_date = data['required_date'] or datetime.now() + timedelta(days=30)
order_date = required_date - timedelta(days=lead_time)
result.append(MaterialItem(
material_code=mat_key.split('|')[0][:20],
description=description,
category=self.get_category(description),
unit=data['unit'],
net_quantity=round(net_qty, 2),
waste_factor=waste_factor,
gross_quantity=round(gross_qty, 2),
unit_price=round(unit_price, 2),
total_cost=round(gross_qty * unit_price, 2),
lead_time_days=lead_time,
required_date=required_date,
order_date=order_date,
work_item_codes=data['work_items']
))
return result
def generate_procurement_list(self,
items: List[Dict[str, Any]],
project_name: str = "Project") -> ProcurementList:
"""Generate complete procurement list."""
materials = self.extract_materials(items)
# Group by category
by_category = defaultdict(float)
for mat in materials:
by_category[mat.category.value] += mat.total_cost
# Group by supplier (placeholder - would use supplier mapping)
by_supplier = defaultdict(list)
for mat in materials:
supplier = self._suggest_supplier(mat)
mat.supplier = supplier
by_supplier[supplier].append(mat)
return ProcurementList(
project_name=project_name,
generated_date=datetime.now(),
total_items=len(materials),
total_cost=sum(m.total_cost for m in materials),
items=materials,
by_category=dict(by_category),
by_supplier=dict(by_supplier)
)
def _suggest_supplier(self, material: MaterialItem) -> str:
"""Suggest supplier based on material category."""
supplier_mapping = {
MaterialCategory.CONCRETE: "Ready-Mix Supplier",
MaterialCategory.STEEL: "Steel Fabricator",
MaterialCategory.TIMBER: "Lumber Yard",
MaterialCategory.MASONRY: "Masonry Supplier",
MaterialCategory.MEP: "MEP Distributor",
MaterialCategory.FINISHES: "Building Materials",
MaterialCategory.INSULATION: "Insulation Supplier",
MaterialCategory.ROOFING: "Roofing Supplier"
}
return supplier_mapping.get(material.category, "General Supplier")
def create_purchase_order(self,
materials: List[MaterialItem],
supplier: str,
po_number: str) -> Dict[str, Any]:
"""Create purchase order for supplier."""
po_items = [m for m in materials if m.supplier == supplier]
return {
'po_number': po_number,
'supplier': supplier,
'date': datetime.now().isoformat(),
'delivery_date': min(m.required_date for m in po_items).isoformat() if po_items else None,
'items': [
{
'description': m.description,
'quantity': m.gross_quantity,
'unit': m.unit,
'unit_price': m.unit_price,
'total': m.total_cost
}
for m in po_items
],
'subtotal': sum(m.total_cost for m in po_items),
'item_count': len(po_items)
}
def export_to_excel(self,
procurement_list: ProcurementList,
output_path: str) -> str:
"""Export procurement list to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# All materials
items_df = pd.DataFrame([
{
'Description': m.description,
'Category': m.category.value,
'Unit': m.unit,
'Net Qty': m.net_quantity,
'Waste %': m.waste_factor * 100,
'Gross Qty': m.gross_quantity,
'Unit Price': m.unit_price,
'Total Cost': m.total_cost,
'Lead Time': m.lead_time_days,
'Order By': m.order_date.strftime('%Y-%m-%d'),
'Required': m.required_date.strftime('%Y-%m-%d'),
'Supplier': m.supplier
}
for m in procurement_list.items
])
items_df.to_excel(writer, sheet_name='Materials', index=False)
# By category
cat_df = pd.DataFrame([
{'Category': cat, 'Total Cost': cost}
for cat, cost in procurement_list.by_category.items()
])
cat_df.to_excel(writer, sheet_name='By Category', index=False)
# Summary
summary_df = pd.DataFrame([{
'Project': procurement_list.project_name,
'Generated': procurement_list.generated_date.strftime('%Y-%m-%d'),
'Total Items': procurement_list.total_items,
'Total Cost': procurement_list.total_cost
}])
summary_df.to_excel(writer, sheet_name='Summary', index=False)
return output_path
def get_critical_orders(self,
procurement_list: ProcurementList,
days_ahead: int = 14) -> List[MaterialItem]:
"""Get materials that need to be ordered soon."""
cutoff = datetime.now() + timedelta(days=days_ahead)
return [
m for m in procurement_list.items
if m.order_date <= cutoff
]
def aggregate_by_material(self,
items: List[Dict[str, Any]]) -> pd.DataFrame:
"""Aggregate materials across multiple work items."""
materials = self.extract_materials(items)
df = pd.DataFrame([
{
'Material': m.description,
'Category': m.category.value,
'Total Qty': m.gross_quantity,
'Unit': m.unit,
'Total Cost': m.total_cost,
'Work Items': len(m.work_item_codes)
}
for m in materials
])
return df.sort_values('Total Cost', ascending=False)
Quick Start
Load CWICR data
cwicr = pd.read_parquet("ddc_cwicr_en.parquet")
Initialize procurement generator
procurement = CWICRMaterialProcurement(cwicr)
Define work items
items = [ {'work_item_code': 'CONC-001', 'quantity': 150}, {'work_item_code': 'REBAR-002', 'quantity': 5000}, {'work_item_code': 'FORM-003', 'quantity': 300} ]
Generate procurement list
proc_list = procurement.generate_procurement_list(items, "Building A")
print(f"Total Items: {proc_list.total_items}") print(f"Total Cost: ${proc_list.total_cost:,.2f}")
Common Use Cases
- Get Critical Orders
critical = procurement.get_critical_orders(proc_list, days_ahead=7) print(f"Order immediately: {len(critical)} items")
- Create Purchase Order
po = procurement.create_purchase_order( proc_list.items, supplier="Steel Fabricator", po_number="PO-2024-001" )
- Export to Excel
procurement.export_to_excel(proc_list, "procurement_list.xlsx")
- Material Aggregation
materials_df = procurement.aggregate_by_material(items) print(materials_df.head(10))
Resources
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GitHub: OpenConstructionEstimate-DDC-CWICR
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DDC Book: Chapter 3.1 - Material Resource Planning