cwicr-historical-cost

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Track and analyze historical cost data using CWICR. Compare actual vs estimated costs, build project cost database, and improve future estimates.

AI & Automation 310 stars 79 forks Updated 2 weeks ago MIT

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Quality Score: 91/100

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Skill Content

# CWICR Historical Cost Tracker ## Business Case ### Problem Statement Improving estimates requires: - Actual cost feedback - Historical comparisons - Trend analysis - Lessons learned ### Solution Track actual costs against CWICR estimates, build historical database, and use data to improve future estimating accuracy. ### Business Value - **Accuracy improvement** - Learn from actuals - **Benchmarking** - Project comparisons - **Trend analysis** - Cost movement patterns - **Organizational knowledge** - Cost database ## Technical Implementation ```python import pandas as pd import numpy as np from typing import Dict, Any, List, Optional from dataclasses import dataclass, field from datetime import datetime, date from enum import Enum import json class ProjectStatus(Enum): """Project status.""" ESTIMATED = "estimated" IN_PROGRESS = "in_progress" COMPLETED = "completed" CANCELLED = "cancelled" @dataclass class CostRecord: """Historical cost record.""" project_id: str project_name: str work_item_code: str quantity: float estimated_cost: float actual_cost: float variance: float variance_percent: float completion_date: date notes: str = "" @dataclass class ProjectCostSummary: """Project cost summary.""" project_id: str project_name: str project_type: str location: str status: ProjectStatus estimated_total: float actual_total: float variance: float variance_percent: flo...

Details

Author
datadrivenconstruction
Repository
datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction
Created
7 months ago
Last Updated
2 weeks ago
Language
Python
License
MIT

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