cwicr-escalation について
建設見積もり向けの時間ベースのコスト調整スキルです。過去の指数、予測率、カテゴリ別係数を用いて、将来コストの予測や過去コストの日付換算を行います。
主な機能
- 01時間経過に応じたコスト調整
- 02過去の指数と予測率の利用
- 03カテゴリ別係数の適用
- 04将来コストの予測
- 05過去コストの換算
活用シーン
- 01施工時点のコスト見積もり
- 02インフレを考慮した予算計画
- 03契約価格調整の算出
- 04過去コストの基準日換算
インストール
コマンドをコピーしてターミナルで実行します。
$
npx skills add https:/ / github.com/ datadrivenconstruction/ ddc_skills_for_ai_agents_in_construction --skill cwicr-escalationインストール後、Claude Code で/cwicr-escalationを入力して読み込まれたか確認します。
同梱ファイルclaw.jsoninstructions.md
詳細
CWICR エスカレーション計算ツール
ビジネスケース
問題の説明
建設コストは時間の経過とともに変化します。
- インフレはすべてのコストに影響します
- 資材価格は変動します
- 労務費は毎年上昇します
- 工期の長いプロジェクトではエスカレーションが必要です
解決策
過去の指数、予測レート、カテゴリ別のエスカレーション係数を使用して、時間に基づくコストのエスカレーションを行います。
ビジネス上の価値
- 将来価格の算定 - 建設時点のコストを見積もります
- 予算計画 - インフレを考慮します
- 契約価格の設定 - エスカレーション条項を適用します
- 過去の分析 - 過去のコストを現在の価格に調整します
技術的な実装
import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass
from datetime import datetime, date
from dateutil.relativedelta import relativedelta
from enum import Enum
class EscalationType(Enum):
"""Types of escalation."""
LABOR = "labor"
MATERIAL = "material"
EQUIPMENT = "equipment"
GENERAL = "general"
@dataclass
class EscalationIndex:
"""Escalation index for a period."""
period: str # YYYY-MM
labor_index: float
material_index: float
equipment_index: float
general_index: float
@dataclass
class EscalationResult:
"""Result of escalation calculation."""
base_cost: float
base_date: date
target_date: date
months: int
escalation_rate: float
escalation_amount: float
escalated_cost: float
by_category: Dict[str, Dict[str, float]]
# Historical escalation rates (annual %)
HISTORICAL_RATES = {
2020: {'labor': 2.5, 'material': 1.8, 'equipment': 1.5, 'general': 2.0},
2021: {'labor': 3.2, 'material': 8.5, 'equipment': 2.0, 'general': 4.5},
2022: {'labor': 4.5, 'material': 12.0, 'equipment': 3.5, 'general': 7.0},
2023: {'labor': 4.0, 'material': 5.0, 'equipment': 3.0, 'general': 4.0},
2024: {'labor': 3.5, 'material': 3.0, 'equipment': 2.5, 'general': 3.0},
2025: {'labor': 3.0, 'material': 2.5, 'equipment': 2.0, 'general': 2.5},
}
# Material-specific escalation factors
MATERIAL_ESCALATION = {
'steel': 1.20, # Higher volatility
'lumber': 1.30, # High volatility
'concrete': 0.90, # Lower volatility
'copper': 1.25, # Commodity driven
'aluminum': 1.15,
'plastic': 1.10,
'glass': 0.95,
'default': 1.00
}
class CWICREscalation:
"""Calculate cost escalation over time."""
def __init__(self,
cwicr_data: pd.DataFrame = None,
custom_rates: Dict[int, Dict[str, float]] = None):
self.cost_data = cwicr_data
self.rates = custom_rates or HISTORICAL_RATES
if cwicr_data is not None:
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 get_rate(self,
year: int,
category: EscalationType = EscalationType.GENERAL) -> float:
"""Get escalation rate for year and category."""
year_rates = self.rates.get(year, self.rates.get(max(self.rates.keys())))
return year_rates.get(category.value, year_rates.get('general', 3.0))
def calculate_compound_factor(self,
base_date: date,
target_date: date,
category: EscalationType = EscalationType.GENERAL) -> float:
"""Calculate compound escalation factor between dates."""
if target_date <= base_date:
return 1.0
factor = 1.0
current = base_date
while current < target_date:
year = current.year
annual_rate = self.get_rate(year, category) / 100
# Calculate months in this year
year_end = date(year + 1, 1, 1)
if target_date < year_end:
months = (target_date.year - current.year) * 12 + target_date.month - current.month
else:
months = (year_end.year - current.year) * 12 + year_end.month - current.month
# Apply monthly compound rate
monthly_rate = (1 + annual_rate) ** (1/12) - 1
factor *= (1 + monthly_rate) ** months
current = year_end
return factor
def escalate_cost(self,
base_cost: float,
base_date: date,
target_date: date,
cost_breakdown: Dict[str, float] = None) -> EscalationResult:
"""Escalate cost from base date to target date."""
if cost_breakdown is None:
cost_breakdown = {
'labor': base_cost * 0.40,
'material': base_cost * 0.45,
'equipment': base_cost * 0.15
}
months = (target_date.year - base_date.year) * 12 + target_date.month - base_date.month
# Escalate each category
by_category = {}
total_escalated = 0
for category, amount in cost_breakdown.items():
esc_type = EscalationType.LABOR if category == 'labor' else \
EscalationType.MATERIAL if category == 'material' else \
EscalationType.EQUIPMENT if category == 'equipment' else \
EscalationType.GENERAL
factor = self.calculate_compound_factor(base_date, target_date, esc_type)
escalated = amount * factor
escalation = escalated - amount
by_category[category] = {
'base': round(amount, 2),
'factor': round(factor, 4),
'escalated': round(escalated, 2),
'escalation': round(escalation, 2)
}
total_escalated += escalated
total_escalation = total_escalated - base_cost
esc_rate = (total_escalation / base_cost * 100) if base_cost > 0 else 0
return EscalationResult(
base_cost=round(base_cost, 2),
base_date=base_date,
target_date=target_date,
months=months,
escalation_rate=round(esc_rate, 2),
escalation_amount=round(total_escalation, 2),
escalated_cost=round(total_escalated, 2),
by_category=by_category
)
def escalate_estimate(self,
items: List[Dict[str, Any]],
base_date: date,
target_date: date) -> Dict[str, Any]:
"""Escalate entire estimate."""
escalated_items = []
total_base = 0
total_escalated = 0
for item in items:
code = item.get('work_item_code', item.get('code'))
qty = item.get('quantity', 0)
# Get costs from CWICR
labor = 0
material = 0
equipment = 0
if self._code_index is not None and code in self._code_index.index:
wi = self._code_index.loc[code]
labor = float(wi.get('labor_cost', 0) or 0) * qty
material = float(wi.get('material_cost', 0) or 0) * qty
equipment = float(wi.get('equipment_cost', 0) or 0) * qty
base = labor + material + equipment
breakdown = {'labor': labor, 'material': material, 'equipment': equipment}
result = self.escalate_cost(base, base_date, target_date, breakdown)
escalated_items.append({
'code': code,
'base_cost': result.base_cost,
'escalated_cost': result.escalated_cost,
'escalation': result.escalation_amount
})
total_base += base
total_escalated += result.escalated_cost
return {
'items': escalated_items,
'total_base': round(total_base, 2),
'total_escalated': round(total_escalated, 2),
'total_escalation': round(total_escalated - total_base, 2),
'escalation_rate': round((total_escalated - total_base) / total_base * 100, 2) if total_base > 0 else 0,
'base_date': base_date,
'target_date': target_date
}
def project_future_costs(self,
base_cost: float,
base_date: date,
years_forward: int = 5,
annual_rate: float = None) -> pd.DataFrame:
"""Project costs for multiple future years."""
projections = []
current = base_cost
for i in range(years_forward + 1):
target = base_date + relativedelta(years=i)
year = target.year
if annual_rate is None:
rate = self.get_rate(year)
else:
rate = annual_rate
if i > 0:
current = current * (1 + rate / 100)
projections.append({
'Year': year,
'Date': target,
'Annual Rate': f"{rate}%",
'Projected Cost': round(current, 2),
'Cumulative Escalation': round(current - base_cost, 2),
'Cumulative %': round((current - base_cost) / base_cost * 100, 1)
})
return pd.DataFrame(projections)
def de_escalate_cost(self,
current_cost: float,
current_date: date,
base_date: date,
category: EscalationType = EscalationType.GENERAL) -> Dict[str, Any]:
"""De-escalate current cost back to base date."""
factor = self.calculate_compound_factor(base_date, current_date, category)
base_cost = current_cost / factor
return {
'current_cost': round(current_cost, 2),
'current_date': current_date,
'base_date': base_date,
'de_escalation_factor': round(1 / factor, 4),
'base_cost': round(base_cost, 2),
'category': category.value
}
def export_escalation(self,
result: EscalationResult,
output_path: str) -> str:
"""Export escalation to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary
summary_df = pd.DataFrame([{
'Base Cost': result.base_cost,
'Base Date': result.base_date,
'Target Date': result.target_date,
'Months': result.months,
'Escalation Rate': f"{result.escalation_rate}%",
'Escalation Amount': result.escalation_amount,
'Escalated Cost': result.escalated_cost
}])
summary_df.to_excel(writer, sheet_name='Summary', index=False)
# By Category
cat_df = pd.DataFrame([
{
'Category': cat,
'Base': data['base'],
'Factor': data['factor'],
'Escalated': data['escalated'],
'Escalation': data['escalation']
}
for cat, data in result.by_category.items()
])
cat_df.to_excel(writer, sheet_name='By Category', index=False)
return output_path
クイックスタート
from datetime import date
# Initialize escalation calculator
esc = CWICREscalation()
# Escalate single cost
result = esc.escalate_cost(
base_cost=1000000,
base_date=date(2024, 1, 1),
target_date=date(2026, 6, 1)
)
print(f"Base Cost: ${result.base_cost:,.2f}")
print(f"Escalated: ${result.escalated_cost:,.2f}")
print(f"Escalation: {result.escalation_rate}%")
よくあるユースケース
1. 将来のコストを予測する
projections = esc.project_future_costs(
base_cost=5000000,
base_date=date.today(),
years_forward=5
)
print(projections)
2. 見積もりをエスカレーションする
cwicr = pd.read_parquet("TR_workitems_costs_resources_DDC_CWICR.parquet")
esc = CWICREscalation(cwicr)
items = [
{'work_item_code': 'CONC-001', 'quantity': 150},
{'work_item_code': 'STRL-002', 'quantity': 25}
]
escalated = esc.escalate_estimate(
items,
base_date=date(2024, 1, 1),
target_date=date(2025, 12, 1)
)
3. 過去のコストをデエスカレーションする
base_cost = esc.de_escalate_cost(
current_cost=1200000,
current_date=date(2024, 6, 1),
base_date=date(2020, 1, 1)
)
print(f"2020 equivalent: ${base_cost['base_cost']:,.2f}")
リソース
- GitHub: OpenConstructionEstimate-DDC-CWICR
- DDC Book: 第3.1章 - コストエスカレーションの手法
コメント
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