gabrielmoreira CWICR Resource Analyzer
Business Case
Problem Statement
Construction projects require precise resource planning:
- How many labor hours are needed?
- What materials need to be procured?
- What equipment is required and for how long?
Traditional methods rely on experience-based estimates, leading to over/under allocation.
Solution
Data-driven resource analysis using CWICR's 27,672 resources with detailed breakdowns of labor norms, material requirements, and equipment usage.
Business Value
- Accurate planning - Based on validated resource norms
- Cost optimization - Identify resource inefficiencies
- Procurement support - Generate material lists
- Labor planning - Calculate crew requirements
Technical Implementation
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 collections import defaultdict
class ResourceType(Enum):
"""Types of construction resources."""
LABOR = "labor"
MATERIAL = "material"
EQUIPMENT = "equipment"
SUBCONTRACT = "subcontract"
class LaborCategory(Enum):
"""Labor skill categories."""
UNSKILLED = "unskilled"
SEMI_SKILLED = "semi_skilled"
SKILLED = "skilled"
FOREMAN = "foreman"
SUPERVISOR = "supervisor"
SPECIALIST = "specialist"
class EquipmentCategory(Enum):
"""Equipment categories."""
EARTHMOVING = "earthmoving"
LIFTING = "lifting"
CONCRETE = "concrete"
TRANSPORT = "transport"
COMPACTION = "compaction"
PUMPING = "pumping"
POWER_TOOLS = "power_tools"
SCAFFOLDING = "scaffolding"
@dataclass
class LaborResource:
"""Represents a labor resource."""
resource_code: str
description: str
category: LaborCategory
hourly_rate: float
skill_level: int
productivity_factor: float = 1.0
@dataclass
class MaterialResource:
"""Represents a material resource."""
resource_code: str
description: str
unit: str
unit_price: float
category: str
waste_factor: float = 0.05 # 5% default waste
@dataclass
class EquipmentResource:
"""Represents an equipment resource."""
resource_code: str
description: str
category: EquipmentCategory
hourly_rate: float
daily_rate: float
monthly_rate: float
fuel_consumption: float = 0.0 # liters per hour
operator_required: bool = True
@dataclass
class ResourceRequirement:
"""Calculated resource requirement."""
resource_code: str
description: str
resource_type: ResourceType
quantity: float
unit: str
unit_cost: float
total_cost: float
duration_hours: float = 0.0
@dataclass
class ResourceSummary:
"""Summary of all resource requirements."""
labor_hours: float
labor_cost: float
material_cost: float
equipment_cost: float
total_cost: float
labor_by_category: Dict[str, float] = field(default_factory=dict)
materials_list: List[Dict[str, Any]] = field(default_factory=list)
equipment_list: List[Dict[str, Any]] = field(default_factory=list)
class CWICRResourceAnalyzer:
"""Analyze resources from CWICR database."""
def __init__(self, cwicr_data: pd.DataFrame,
resources_data: Optional[pd.DataFrame] = None):
self.work_items = cwicr_data
self.resources = resources_data
# Create indexes
self._index_work_items()
if resources_data is not None:
self._index_resources()
def _index_work_items(self):
"""Index work items 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 _index_resources(self):
"""Index resources for fast lookup."""
if self.resources is not None and 'resource_code' in self.resources.columns:
self._resource_index = self.resources.set_index('resource_code')
else:
self._resource_index = None
def analyze_labor_requirements(self, items: List[Dict[str, Any]]) -> Dict[str, Any]:
"""Analyze labor requirements for work items."""
total_hours = 0.0
labor_by_category = defaultdict(float)
labor_by_skill = defaultdict(float)
labor_details = []
for item in items:
code = item.get('work_item_code', item.get('code'))
qty = item.get('quantity', 0)
if self._work_index is not None and code in self._work_index.index:
work_item = self._work_index.loc[code]
labor_norm = float(work_item.get('labor_norm', 0) or 0)
hours = labor_norm * qty
total_hours += hours
# Get category if available
category = str(work_item.get('category', 'General'))
labor_by_category[category] += hours
labor_details.append({
'work_item_code': code,
'description': work_item.get('description', ''),
'quantity': qty,
'labor_norm': labor_norm,
'total_hours': hours
})
return {
'total_labor_hours': round(total_hours, 2),
'labor_by_category': dict(labor_by_category),
'crew_days_8hr': round(total_hours / 8, 1),
'crew_weeks_40hr': round(total_hours / 40, 1),
'details': labor_details
}
def analyze_material_requirements(self, items: List[Dict[str, Any]],
include_waste: bool = True) -> Dict[str, Any]:
"""Analyze material requirements."""
materials = defaultdict(lambda: {'quantity': 0, 'unit': '', 'cost': 0})
total_cost = 0.0
for item in items:
code = item.get('work_item_code', item.get('code'))
qty = item.get('quantity', 0)
if self._work_index is not None and code in self._work_index.index:
work_item = self._work_index.loc[code]
material_cost = float(work_item.get('material_cost', 0) or 0) * qty
if include_waste:
material_cost *= 1.05 # 5% waste factor
total_cost += material_cost
# Aggregate by category
category = str(work_item.get('category', 'General'))
materials[category]['cost'] += material_cost
return {
'total_material_cost': round(total_cost, 2),
'by_category': dict(materials),
'waste_included': include_waste,
'waste_factor': 0.05 if include_waste else 0
}
def analyze_equipment_requirements(self, items: List[Dict[str, Any]]) -> Dict[str, Any]:
"""Analyze equipment requirements."""
equipment_hours = defaultdict(float)
total_cost = 0.0
for item in items:
code = item.get('work_item_code', item.get('code'))
qty = item.get('quantity', 0)
if self._work_index is not None and code in self._work_index.index:
work_item = self._work_index.loc[code]
equipment_cost = float(work_item.get('equipment_cost', 0) or 0) * qty
equipment_norm = float(work_item.get('equipment_norm', 0) or 0) * qty
total_cost += equipment_cost
category = str(work_item.get('category', 'General'))
equipment_hours[category] += equipment_norm
return {
'total_equipment_cost': round(total_cost, 2),
'equipment_hours_by_category': dict(equipment_hours),
'total_equipment_hours': sum(equipment_hours.values())
}
def generate_resource_summary(self, items: List[Dict[str, Any]]) -> ResourceSummary:
"""Generate complete resource summary."""
labor = self.analyze_labor_requirements(items)
materials = self.analyze_material_requirements(items)
equipment = self.analyze_equipment_requirements(items)
# Calculate labor cost
avg_labor_rate = 35.0 # Default hourly rate
labor_cost = labor['total_labor_hours'] * avg_labor_rate
return ResourceSummary(
labor_hours=labor['total_labor_hours'],
labor_cost=labor_cost,
material_cost=materials['total_material_cost'],
equipment_cost=equipment['total_equipment_cost'],
total_cost=labor_cost + materials['total_material_cost'] + equipment['total_equipment_cost'],
labor_by_category=labor['labor_by_category']
)
def calculate_crew_requirements(self, labor_hours: float,
project_duration_days: int,
hours_per_day: int = 8) -> Dict[str, Any]:
"""Calculate crew size requirements."""
available_hours = project_duration_days * hours_per_day
min_crew_size = labor_hours / available_hours if available_hours > 0 else 0
return {
'total_labor_hours': labor_hours,
'project_duration_days': project_duration_days,
'hours_per_day': hours_per_day,
'minimum_crew_size': round(min_crew_size, 1),
'recommended_crew_size': int(np.ceil(min_crew_size * 1.15)), # 15% buffer
'utilization_at_recommended': round(min_crew_size / np.ceil(min_crew_size * 1.15) * 100, 1)
}
def identify_critical_resources(self, items: List[Dict[str, Any]],
top_n: int = 10) -> Dict[str, List[Dict]]:
"""Identify critical resources by cost impact."""
breakdowns = []
for item in items:
code = item.get('work_item_code', item.get('code'))
qty = item.get('quantity', 0)
if self._work_index is not None and code in self._work_index.index:
work_item = self._work_index.loc[code]
breakdowns.append({
'work_item_code': code,
'description': work_item.get('description', ''),
'quantity': qty,
'labor_cost': float(work_item.get('labor_cost', 0) or 0) * qty,
'material_cost': float(work_item.get('material_cost', 0) or 0) * qty,
'equipment_cost': float(work_item.get('equipment_cost', 0) or 0) * qty,
'total_cost': (
float(work_item.get('labor_cost', 0) or 0) +
float(work_item.get('material_cost', 0) or 0) +
float(work_item.get('equipment_cost', 0) or 0)
) * qty
})
df = pd.DataFrame(breakdowns)
if df.empty:
return {'labor': [], 'material': [], 'equipment': [], 'total': []}
return {
'labor': df.nlargest(top_n, 'labor_cost')[['work_item_code', 'description', 'labor_cost']].to_dict('records'),
'material': df.nlargest(top_n, 'material_cost')[['work_item_code', 'description', 'material_cost']].to_dict('records'),
'equipment': df.nlargest(top_n, 'equipment_cost')[['work_item_code', 'description', 'equipment_cost']].to_dict('records'),
'total': df.nlargest(top_n, 'total_cost')[['work_item_code', 'description', 'total_cost']].to_dict('records')
}
def analyze_productivity(self, items: List[Dict[str, Any]],
actual_hours: Optional[Dict[str, float]] = None) -> Dict[str, Any]:
"""Analyze productivity vs planned norms."""
if actual_hours is None:
return {'error': 'Actual hours required for productivity analysis'}
analysis = []
for item in items:
code = item.get('work_item_code', item.get('code'))
qty = item.get('quantity', 0)
if code in actual_hours and self._work_index is not None:
if code in self._work_index.index:
work_item = self._work_index.loc[code]
planned_hours = float(work_item.get('labor_norm', 0) or 0) * qty
actual = actual_hours[code]
productivity = planned_hours / actual * 100 if actual > 0 else 0
analysis.append({
'work_item_code': code,
'planned_hours': planned_hours,
'actual_hours': actual,
'productivity_percent': round(productivity, 1),
'variance_hours': planned_hours - actual
})
df = pd.DataFrame(analysis)
if df.empty:
return {'items': [], 'average_productivity': 0}
return {
'items': analysis,
'average_productivity': round(df['productivity_percent'].mean(), 1),
'total_variance': round(df['variance_hours'].sum(), 1),
'underperforming_items': len(df[df['productivity_percent'] < 90])
}
class ResourceOptimizer:
"""Optimize resource allocation."""
def __init__(self, analyzer: CWICRResourceAnalyzer):
self.analyzer = analyzer
def suggest_material_substitutions(self, items: List[Dict[str, Any]],
cost_threshold: float = 0.9) -> List[Dict]:
"""Suggest cheaper material substitutions."""
# Placeholder for substitution logic
return []
def optimize_crew_allocation(self, labor_by_category: Dict[str, float],
available_crew: Dict[str, int]) -> Dict[str, Any]:
"""Optimize crew allocation across categories."""
allocation = {}
unmet_demand = {}
for category, hours_needed in labor_by_category.items():
available = available_crew.get(category, 0)
days_needed = hours_needed / 8
if available > 0:
days_available = available * 1 # 1 day per person
if days_available >= days_needed:
allocation[category] = {
'assigned': int(np.ceil(days_needed)),
'remaining': available - int(np.ceil(days_needed))
}
else:
allocation[category] = {'assigned': available, 'remaining': 0}
unmet_demand[category] = days_needed - days_available
else:
unmet_demand[category] = days_needed
return {
'allocation': allocation,
'unmet_demand': unmet_demand,
'fully_staffed': len(unmet_demand) == 0
}
Quick Start
from cwicr_data_loader import CWICRDataLoader
# Load data
loader = CWICRDataLoader()
cwicr = loader.load("TR_workitems_costs_resources_DDC_CWICR.parquet")
# Initialize analyzer
analyzer = CWICRResourceAnalyzer(cwicr)
# Define project items
items = [
{'work_item_code': 'CONC-001', 'quantity': 150},
{'work_item_code': 'EXCV-002', 'quantity': 200},
{'work_item_code': 'REBAR-003', 'quantity': 15000}
]
# Analyze labor
labor = analyzer.analyze_labor_requirements(items)
print(f"Total Labor Hours: {labor['total_labor_hours']}")
print(f"Crew Days (8hr): {labor['crew_days_8hr']}")
Common Use Cases
1. Crew Planning
# Calculate required crew size
labor = analyzer.analyze_labor_requirements(items)
crew = analyzer.calculate_crew_requirements(
labor_hours=labor['total_labor_hours'],
project_duration_days=30
)
print(f"Minimum Crew: {crew['minimum_crew_size']}")
print(f"Recommended Crew: {crew['recommended_crew_size']}")
2. Material Procurement
materials = analyzer.analyze_material_requirements(items, include_waste=True)
print(f"Total Material Cost: ${materials['total_material_cost']:,.2f}")
3. Productivity Tracking
actual_hours = {
'CONC-001': 280,
'EXCV-002': 85,
'REBAR-003': 450
}
productivity = analyzer.analyze_productivity(items, actual_hours)
print(f"Average Productivity: {productivity['average_productivity']}%")
Resources
- GitHub: OpenConstructionEstimate-DDC-CWICR
- DDC Book: Chapter 3.1 - Resource-Based Cost Estimation