Bid Analysis Comparator

Bid Analysis Comparator

Business Case

Bid evaluation requires systematic comparison across multiple criteria. This skill provides structured bid analysis and scoring.

Technical Implementation

import pandas as pd
from datetime import date
from typing import Dict, Any, List
from dataclasses import dataclass, field
from enum import Enum


class BidStatus(Enum):
    RECEIVED = "received"
    UNDER_REVIEW = "under_review"
    SHORTLISTED = "shortlisted"
    AWARDED = "awarded"
    REJECTED = "rejected"


@dataclass
class EvaluationCriteria:
    name: str
    weight: float  # 0-1
    max_score: int = 10


@dataclass
class BidScore:
    criteria: str
    score: int
    notes: str = ""


@dataclass
class Bid:
    bid_id: str
    bidder_name: str
    bid_package: str
    submitted_date: date
    base_bid: float
    alternates: Dict[str, float]
    status: BidStatus
    scores: List[BidScore] = field(default_factory=list)
    qualifications: List[str] = field(default_factory=list)
    exclusions: List[str] = field(default_factory=list)

    @property
    def total_weighted_score(self) -> float:
        return sum(s.score for s in self.scores)


class BidAnalysisComparator:
    def __init__(self, project_name: str, bid_package: str):
        self.project_name = project_name
        self.bid_package = bid_package
        self.bids: Dict[str, Bid] = {}
        self.criteria: List[EvaluationCriteria] = []
        self._setup_default_criteria()
        self._counter = 0

    def _setup_default_criteria(self):
        self.criteria = [
            EvaluationCriteria("Price", 0.35),
            EvaluationCriteria("Experience", 0.20),
            EvaluationCriteria("Schedule", 0.15),
            EvaluationCriteria("Safety Record", 0.10),
            EvaluationCriteria("References", 0.10),
            EvaluationCriteria("Capacity", 0.10)
        ]

    def add_bid(self, bidder_name: str, base_bid: float,
               submitted_date: date = None,
               alternates: Dict[str, float] = None) -> Bid:
        self._counter += 1
        bid_id = f"BID-{self._counter:03d}"

        bid = Bid(
            bid_id=bid_id,
            bidder_name=bidder_name,
            bid_package=self.bid_package,
            submitted_date=submitted_date or date.today(),
            base_bid=base_bid,
            alternates=alternates or {},
            status=BidStatus.RECEIVED
        )
        self.bids[bid_id] = bid
        return bid

    def score_bid(self, bid_id: str, scores: Dict[str, int]):
        """Score bid on criteria. scores = {'Price': 8, 'Experience': 7, ...}"""
        if bid_id not in self.bids:
            return
        bid = self.bids[bid_id]
        bid.scores = []
        for criteria, score in scores.items():
            bid.scores.append(BidScore(criteria, score))
        bid.status = BidStatus.UNDER_REVIEW

    def calculate_weighted_scores(self) -> pd.DataFrame:
        """Calculate weighted scores for all bids."""
        results = []
        criteria_weights = {c.name: c.weight for c in self.criteria}

        for bid in self.bids.values():
            row = {
                'Bidder': bid.bidder_name,
                'Base Bid': bid.base_bid,
                'Status': bid.status.value
            }
            total = 0
            for score in bid.scores:
                weight = criteria_weights.get(score.criteria, 0)
                weighted = score.score * weight * 10
                row[score.criteria] = score.score
                row[f'{score.criteria} (W)'] = round(weighted, 1)
                total += weighted
            row['Total Score'] = round(total, 1)
            results.append(row)

        return pd.DataFrame(results).sort_values('Total Score', ascending=False)

    def get_recommendation(self) -> Dict[str, Any]:
        """Get bid recommendation."""
        df = self.calculate_weighted_scores()
        if df.empty:
            return {'recommendation': 'No bids to evaluate'}

        top = df.iloc[0]
        lowest = df.sort_values('Base Bid').iloc[0]

        return {
            'highest_score': {
                'bidder': top['Bidder'],
                'score': top['Total Score'],
                'bid': top['Base Bid']
            },
            'lowest_price': {
                'bidder': lowest['Bidder'],
                'bid': lowest['Base Bid']
            },
            'total_bids': len(self.bids),
            'recommendation': top['Bidder']
        }

    def export_analysis(self, output_path: str):
        df = self.calculate_weighted_scores()
        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
            df.to_excel(writer, sheet_name='Comparison', index=False)

            # Bid details
            details = [{
                'Bidder': b.bidder_name,
                'Bid': b.base_bid,
                'Exclusions': '; '.join(b.exclusions),
                'Qualifications': '; '.join(b.qualifications)
            } for b in self.bids.values()]
            pd.DataFrame(details).to_excel(writer, sheet_name='Details', index=False)

Quick Start

comparator = BidAnalysisComparator("Office Tower", "Electrical")

bid1 = comparator.add_bid("ABC Electric", 850000)
bid2 = comparator.add_bid("XYZ Electric", 920000)

comparator.score_bid(bid1.bid_id, {'Price': 9, 'Experience': 7, 'Schedule': 8,
                                   'Safety Record': 8, 'References': 7, 'Capacity': 8})
comparator.score_bid(bid2.bid_id, {'Price': 7, 'Experience': 9, 'Schedule': 7,
                                   'Safety Record': 9, 'References': 9, 'Capacity': 9})

recommendation = comparator.get_recommendation()
print(f"Recommended: {recommendation['recommendation']}")

Resources

• DDC Book: Chapter 3.4 - Procurement