auto-estimate-generator

Auto Estimate Generator

Business Case

Problem Statement

Manual estimate creation challenges:

• Time-consuming quantity mapping
• Inconsistent pricing rules
• Errors in calculations
• Difficulty updating estimates

Solution

Automated estimate generation from BIM/QTO data using configurable pricing rules and assembly mappings.

Technical Implementation

import pandas as pd
from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum


class ElementType(Enum):
    WALL = "wall"
    FLOOR = "floor"
    CEILING = "ceiling"
    DOOR = "door"
    WINDOW = "window"
    COLUMN = "column"
    BEAM = "beam"
    FOUNDATION = "foundation"
    ROOF = "roof"
    STAIR = "stair"
    MEP = "mep"


@dataclass
class QTOItem:
    element_id: str
    element_type: ElementType
    name: str
    quantity: float
    unit: str
    properties: Dict[str, Any] = field(default_factory=dict)


@dataclass
class PricingRule:
    rule_id: str
    name: str
    element_type: ElementType
    conditions: Dict[str, Any] = field(default_factory=dict)
    unit_cost: float = 0
    assembly_code: str = ""
    cost_breakdown: Dict[str, float] = field(default_factory=dict)


@dataclass
class EstimateItem:
    qto_element_id: str
    description: str
    quantity: float
    unit: str
    unit_cost: float
    total_cost: float
    rule_applied: str
    wbs_code: str = ""


class AutoEstimateGenerator:
    """Generate estimates from QTO data automatically."""

    def __init__(self, project_name: str):
        self.project_name = project_name
        self.pricing_rules: List[PricingRule] = []
        self.qto_items: List[QTOItem] = []
        self.estimate_items: List[EstimateItem] = []
        self.unmapped_items: List[QTOItem] = []

    def add_pricing_rule(self, rule: PricingRule):
        """Add pricing rule."""
        self.pricing_rules.append(rule)

    def load_pricing_rules_from_df(self, df: pd.DataFrame):
        """Load pricing rules from DataFrame."""

        for _, row in df.iterrows():
            conditions = {}
            if 'material' in row:
                conditions['material'] = row['material']
            if 'thickness_min' in row:
                conditions['thickness_min'] = row['thickness_min']
            if 'thickness_max' in row:
                conditions['thickness_max'] = row['thickness_max']

            rule = PricingRule(
                rule_id=row['rule_id'],
                name=row['name'],
                element_type=ElementType(row['element_type'].lower()),
                conditions=conditions,
                unit_cost=float(row['unit_cost']),
                assembly_code=row.get('assembly_code', ''),
                cost_breakdown={
                    'labor': float(row.get('labor_pct', 0.4)),
                    'material': float(row.get('material_pct', 0.5)),
                    'equipment': float(row.get('equipment_pct', 0.1))
                }
            )
            self.add_pricing_rule(rule)

    def load_qto_from_df(self, df: pd.DataFrame):
        """Load QTO items from DataFrame."""

        for _, row in df.iterrows():
            properties = {}
            for col in df.columns:
                if col not in ['element_id', 'element_type', 'name', 'quantity', 'unit']:
                    properties[col] = row[col]

            qto = QTOItem(
                element_id=str(row['element_id']),
                element_type=ElementType(row['element_type'].lower()),
                name=row['name'],
                quantity=float(row['quantity']),
                unit=row['unit'],
                properties=properties
            )
            self.qto_items.append(qto)

    def find_matching_rule(self, qto_item: QTOItem) -> Optional[PricingRule]:
        """Find pricing rule that matches QTO item."""

        matching_rules = []

        for rule in self.pricing_rules:
            if rule.element_type != qto_item.element_type:
                continue

            # Check conditions
            match = True
            for key, value in rule.conditions.items():
                if key.endswith('_min'):
                    prop_name = key[:-4]
                    if prop_name in qto_item.properties:
                        if qto_item.properties[prop_name] < value:
                            match = False
                elif key.endswith('_max'):
                    prop_name = key[:-4]
                    if prop_name in qto_item.properties:
                        if qto_item.properties[prop_name] > value:
                            match = False
                else:
                    if key in qto_item.properties:
                        if qto_item.properties[key] != value:
                            match = False

            if match:
                matching_rules.append(rule)

        # Return most specific rule (most conditions)
        if matching_rules:
            return max(matching_rules, key=lambda r: len(r.conditions))
        return None

    def generate_estimate(self) -> Dict[str, Any]:
        """Generate estimate from QTO items."""

        self.estimate_items = []
        self.unmapped_items = []
        total_cost = 0

        for qto in self.qto_items:
            rule = self.find_matching_rule(qto)

            if rule:
                item_cost = qto.quantity * rule.unit_cost

                self.estimate_items.append(EstimateItem(
                    qto_element_id=qto.element_id,
                    description=f"{qto.name} ({rule.name})",
                    quantity=qto.quantity,
                    unit=qto.unit,
                    unit_cost=rule.unit_cost,
                    total_cost=round(item_cost, 2),
                    rule_applied=rule.rule_id,
                    wbs_code=rule.assembly_code
                ))
                total_cost += item_cost
            else:
                self.unmapped_items.append(qto)

        return {
            'project': self.project_name,
            'total_qto_items': len(self.qto_items),
            'mapped_items': len(self.estimate_items),
            'unmapped_items': len(self.unmapped_items),
            'mapping_rate': round(len(self.estimate_items) / len(self.qto_items) * 100, 1) if self.qto_items else 0,
            'total_cost': round(total_cost, 2),
            'items': self.estimate_items
        }

    def get_cost_by_element_type(self) -> Dict[str, float]:
        """Get cost breakdown by element type."""

        by_type = {}
        for qto in self.qto_items:
            for est_item in self.estimate_items:
                if est_item.qto_element_id == qto.element_id:
                    type_name = qto.element_type.value
                    by_type[type_name] = by_type.get(type_name, 0) + est_item.total_cost

        return {k: round(v, 2) for k, v in by_type.items()}

    def get_unmapped_summary(self) -> pd.DataFrame:
        """Get summary of unmapped items."""

        if not self.unmapped_items:
            return pd.DataFrame()

        data = []
        for item in self.unmapped_items:
            data.append({
                'Element ID': item.element_id,
                'Type': item.element_type.value,
                'Name': item.name,
                'Quantity': item.quantity,
                'Unit': item.unit,
                'Properties': str(item.properties)
            })

        return pd.DataFrame(data)

    def export_to_excel(self, output_path: str) -> str:
        """Export estimate to Excel."""

        result = self.generate_estimate()

        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
            # Summary
            summary_df = pd.DataFrame([{
                'Project': self.project_name,
                'Total QTO Items': result['total_qto_items'],
                'Mapped Items': result['mapped_items'],
                'Unmapped Items': result['unmapped_items'],
                'Mapping Rate %': result['mapping_rate'],
                'Total Cost': result['total_cost']
            }])
            summary_df.to_excel(writer, sheet_name='Summary', index=False)

            # Estimate items
            items_df = pd.DataFrame([{
                'Element ID': item.qto_element_id,
                'Description': item.description,
                'Quantity': item.quantity,
                'Unit': item.unit,
                'Unit Cost': item.unit_cost,
                'Total Cost': item.total_cost,
                'WBS': item.wbs_code,
                'Rule': item.rule_applied
            } for item in self.estimate_items])
            items_df.to_excel(writer, sheet_name='Estimate', index=False)

            # By element type
            by_type_df = pd.DataFrame([
                {'Element Type': k, 'Cost': v}
                for k, v in self.get_cost_by_element_type().items()
            ])
            by_type_df.to_excel(writer, sheet_name='By Type', index=False)

            # Unmapped items
            unmapped_df = self.get_unmapped_summary()
            if not unmapped_df.empty:
                unmapped_df.to_excel(writer, sheet_name='Unmapped', index=False)

        return output_path

    def suggest_missing_rules(self) -> List[Dict[str, Any]]:
        """Suggest pricing rules for unmapped items."""

        suggestions = []
        seen_types = set()

        for item in self.unmapped_items:
            key = (item.element_type.value, str(item.properties))
            if key not in seen_types:
                seen_types.add(key)
                suggestions.append({
                    'element_type': item.element_type.value,
                    'sample_name': item.name,
                    'properties': item.properties,
                    'count': sum(1 for i in self.unmapped_items
                                if i.element_type == item.element_type
                                and str(i.properties) == str(item.properties))
                })

        return sorted(suggestions, key=lambda x: x['count'], reverse=True)