gabrielmoreira CWICR Subcontractor Analyzer
Business Case
Problem Statement
Evaluating subcontractor bids requires:
- Fair price benchmarks
- Bid comparison
- Outlier identification
- Negotiation support
Solution
Compare subcontractor bids against CWICR cost data to identify fair pricing, outliers, and negotiation opportunities.
Business Value
- Fair evaluation - Objective benchmarks
- Cost savings - Identify overpriced bids
- Risk detection - Flag unrealistic low bids
- Negotiation support - Data-driven discussions
Technical Implementation
import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional
from dataclasses import dataclass
from enum import Enum
from statistics import mean, stdev
class BidStatus(Enum):
"""Bid evaluation status."""
COMPETITIVE = "competitive"
HIGH = "high"
LOW = "low"
OUTLIER_HIGH = "outlier_high"
OUTLIER_LOW = "outlier_low"
@dataclass
class SubcontractorBid:
"""Subcontractor bid."""
subcontractor_name: str
trade: str
bid_amount: float
scope_items: List[Dict[str, Any]]
includes_material: bool
includes_labor: bool
includes_equipment: bool
duration_days: int
notes: str = ""
@dataclass
class BidEvaluation:
"""Bid evaluation result."""
subcontractor_name: str
bid_amount: float
benchmark_cost: float
variance: float
variance_percent: float
status: BidStatus
line_item_analysis: List[Dict[str, Any]]
recommendation: str
class CWICRSubcontractor:
"""Analyze subcontractor bids using CWICR data."""
OUTLIER_THRESHOLD = 0.30 # 30% from benchmark
HIGH_THRESHOLD = 0.15 # 15% above benchmark
LOW_THRESHOLD = -0.10 # 10% below benchmark
def __init__(self,
cwicr_data: pd.DataFrame,
overhead_rate: float = 0.12,
profit_rate: float = 0.10):
self.cost_data = cwicr_data
self.overhead_rate = overhead_rate
self.profit_rate = profit_rate
self._index_data()
def _index_data(self):
"""Index cost data."""
if 'work_item_code' in self.cost_data.columns:
self._code_index = self.cost_data.set_index('work_item_code')
else:
self._code_index = None
def calculate_benchmark(self,
scope_items: List[Dict[str, Any]],
include_overhead: bool = True,
include_profit: bool = True) -> Dict[str, Any]:
"""Calculate benchmark cost for scope."""
labor = 0
material = 0
equipment = 0
line_items = []
for item in scope_items:
code = item.get('work_item_code', item.get('code'))
qty = item.get('quantity', 0)
if self._code_index is not None and code in self._code_index.index:
wi = self._code_index.loc[code]
item_labor = float(wi.get('labor_cost', 0) or 0) * qty
item_material = float(wi.get('material_cost', 0) or 0) * qty
item_equipment = float(wi.get('equipment_cost', 0) or 0) * qty
labor += item_labor
material += item_material
equipment += item_equipment
line_items.append({
'code': code,
'quantity': qty,
'labor': round(item_labor, 2),
'material': round(item_material, 2),
'equipment': round(item_equipment, 2),
'total': round(item_labor + item_material + item_equipment, 2)
})
direct_cost = labor + material + equipment
overhead = direct_cost * self.overhead_rate if include_overhead else 0
profit = (direct_cost + overhead) * self.profit_rate if include_profit else 0
return {
'labor': round(labor, 2),
'material': round(material, 2),
'equipment': round(equipment, 2),
'direct_cost': round(direct_cost, 2),
'overhead': round(overhead, 2),
'profit': round(profit, 2),
'total': round(direct_cost + overhead + profit, 2),
'line_items': line_items
}
def evaluate_bid(self, bid: SubcontractorBid) -> BidEvaluation:
"""Evaluate single subcontractor bid."""
benchmark = self.calculate_benchmark(bid.scope_items)
benchmark_cost = benchmark['total']
variance = bid.bid_amount - benchmark_cost
variance_pct = (variance / benchmark_cost * 100) if benchmark_cost > 0 else 0
# Determine status
if variance_pct > self.OUTLIER_THRESHOLD * 100:
status = BidStatus.OUTLIER_HIGH
recommendation = "Bid significantly above benchmark. Request detailed breakdown or reject."
elif variance_pct < -self.OUTLIER_THRESHOLD * 100:
status = BidStatus.OUTLIER_LOW
recommendation = "Bid significantly below benchmark. Verify scope understanding and capacity."
elif variance_pct > self.HIGH_THRESHOLD * 100:
status = BidStatus.HIGH
recommendation = "Bid above benchmark. Consider negotiation or alternative bidders."
elif variance_pct < self.LOW_THRESHOLD * 100:
status = BidStatus.LOW
recommendation = "Bid below benchmark. Verify completeness and quality approach."
else:
status = BidStatus.COMPETITIVE
recommendation = "Bid within acceptable range. Proceed with standard evaluation."
# Line item analysis
line_analysis = []
for i, item in enumerate(bid.scope_items):
if i < len(benchmark['line_items']):
bench_item = benchmark['line_items'][i]
# Assume proportional pricing
expected = bench_item['total'] / benchmark['direct_cost'] * bid.bid_amount if benchmark['direct_cost'] > 0 else 0
line_analysis.append({
'code': item.get('work_item_code', item.get('code')),
'benchmark': bench_item['total'],
'expected_in_bid': round(expected, 2)
})
return BidEvaluation(
subcontractor_name=bid.subcontractor_name,
bid_amount=bid.bid_amount,
benchmark_cost=benchmark_cost,
variance=round(variance, 2),
variance_percent=round(variance_pct, 1),
status=status,
line_item_analysis=line_analysis,
recommendation=recommendation
)
def compare_bids(self,
bids: List[SubcontractorBid]) -> Dict[str, Any]:
"""Compare multiple bids."""
if not bids:
return {}
evaluations = [self.evaluate_bid(bid) for bid in bids]
# Statistics
amounts = [e.bid_amount for e in evaluations]
avg_bid = mean(amounts)
std_bid = stdev(amounts) if len(amounts) > 1 else 0
# Rank by variance from benchmark
ranked = sorted(evaluations, key=lambda x: abs(x.variance_percent))
# Find best value
competitive = [e for e in evaluations if e.status == BidStatus.COMPETITIVE]
if competitive:
best_value = min(competitive, key=lambda x: x.bid_amount)
else:
best_value = ranked[0]
# Identify outliers
outliers = [e for e in evaluations if e.status in [BidStatus.OUTLIER_HIGH, BidStatus.OUTLIER_LOW]]
return {
'bid_count': len(bids),
'average_bid': round(avg_bid, 2),
'std_deviation': round(std_bid, 2),
'spread': round(max(amounts) - min(amounts), 2),
'spread_percent': round((max(amounts) - min(amounts)) / avg_bid * 100, 1) if avg_bid > 0 else 0,
'benchmark': evaluations[0].benchmark_cost,
'best_value': {
'name': best_value.subcontractor_name,
'amount': best_value.bid_amount,
'variance_from_benchmark': best_value.variance_percent
},
'lowest_bid': {
'name': min(evaluations, key=lambda x: x.bid_amount).subcontractor_name,
'amount': min(amounts)
},
'outliers': [
{'name': e.subcontractor_name, 'status': e.status.value, 'variance': e.variance_percent}
for e in outliers
],
'evaluations': evaluations
}
def generate_negotiation_points(self,
evaluation: BidEvaluation) -> List[Dict[str, Any]]:
"""Generate negotiation points based on evaluation."""
points = []
if evaluation.status in [BidStatus.HIGH, BidStatus.OUTLIER_HIGH]:
points.append({
'topic': 'Overall Price',
'benchmark': evaluation.benchmark_cost,
'bid': evaluation.bid_amount,
'target': round(evaluation.benchmark_cost * 1.05, 2), # 5% above benchmark
'potential_savings': round(evaluation.bid_amount - evaluation.benchmark_cost * 1.05, 2)
})
# Suggest line item discussions
for item in evaluation.line_item_analysis:
points.append({
'topic': f"Line Item: {item['code']}",
'benchmark': item['benchmark'],
'suggestion': 'Request detailed breakdown'
})
return points
def export_bid_comparison(self,
comparison: Dict[str, Any],
output_path: str) -> str:
"""Export bid comparison to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary
summary_df = pd.DataFrame([{
'Number of Bids': comparison['bid_count'],
'Average Bid': comparison['average_bid'],
'Spread': comparison['spread'],
'Spread %': comparison['spread_percent'],
'Benchmark': comparison['benchmark'],
'Best Value Bidder': comparison['best_value']['name'],
'Lowest Bidder': comparison['lowest_bid']['name']
}])
summary_df.to_excel(writer, sheet_name='Summary', index=False)
# All evaluations
eval_df = pd.DataFrame([
{
'Subcontractor': e.subcontractor_name,
'Bid Amount': e.bid_amount,
'Benchmark': e.benchmark_cost,
'Variance': e.variance,
'Variance %': e.variance_percent,
'Status': e.status.value,
'Recommendation': e.recommendation
}
for e in comparison['evaluations']
])
eval_df.to_excel(writer, sheet_name='Evaluations', index=False)
return output_path
Quick Start
# Load CWICR data
cwicr = pd.read_parquet("TR_workitems_costs_resources_DDC_CWICR.parquet")
# Initialize analyzer
analyzer = CWICRSubcontractor(cwicr)
# Define scope
scope = [
{'work_item_code': 'ELEC-001', 'quantity': 100},
{'work_item_code': 'ELEC-002', 'quantity': 50}
]
# Create bid
bid = SubcontractorBid(
subcontractor_name="ABC Electric",
trade="Electrical",
bid_amount=75000,
scope_items=scope,
includes_material=True,
includes_labor=True,
includes_equipment=True,
duration_days=30
)
# Evaluate
evaluation = analyzer.evaluate_bid(bid)
print(f"Status: {evaluation.status.value}")
print(f"Variance: {evaluation.variance_percent}%")
print(f"Recommendation: {evaluation.recommendation}")
Common Use Cases
1. Compare Multiple Bids
bids = [
SubcontractorBid("ABC Electric", "Electrical", 75000, scope, True, True, True, 30),
SubcontractorBid("XYZ Power", "Electrical", 68000, scope, True, True, True, 35),
SubcontractorBid("Quick Elec", "Electrical", 82000, scope, True, True, True, 25)
]
comparison = analyzer.compare_bids(bids)
print(f"Best Value: {comparison['best_value']['name']}")
2. Negotiation Support
points = analyzer.generate_negotiation_points(evaluation)
for point in points:
print(f"{point['topic']}: Target ${point.get('target', 'N/A')}")
3. Export Report
analyzer.export_bid_comparison(comparison, "bid_comparison.xlsx")
Resources
- GitHub: OpenConstructionEstimate-DDC-CWICR
- DDC Book: Chapter 3.2 - Subcontractor Management