Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
日本語の概要は準備中です。原文の説明を表示しています。
Track material procurement from requisition to delivery. Monitor lead times, vendors, and costs.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Long lead items and material procurement require careful tracking to avoid schedule delays.
import pandas as pd
from datetime import date, timedelta
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from enum import Enum
class ProcurementStatus(Enum):
REQUISITIONED = "requisitioned"
RFQ_SENT = "rfq_sent"
QUOTED = "quoted"
PO_ISSUED = "po_issued"
IN_PRODUCTION = "in_production"
SHIPPED = "shipped"
DELIVERED = "delivered"
class Priority(Enum):
CRITICAL = "critical"
HIGH = "high"
NORMAL = "normal"
LOW = "low"
@dataclass
class ProcurementItem:
item_id: str
description: str
spec_section: str
quantity: float
unit: str
required_date: date
lead_time_days: int
status: ProcurementStatus
priority: Priority
vendor: str = ""
po_number: str = ""
po_amount: float = 0.0
order_date: Optional[date] = None
expected_delivery: Optional[date] = None
actual_delivery: Optional[date] = None
@property
def must_order_by(self) -> date:
return self.required_date - timedelta(days=self.lead_time_days)
@property
def is_late_to_order(self) -> bool:
if self.status in [ProcurementStatus.PO_ISSUED, ProcurementStatus.IN_PRODUCTION,
ProcurementStatus.SHIPPED, ProcurementStatus.DELIVERED]:
return False
return date.today() > self.must_order_by
class MaterialProcurementTracker:
def __init__(self, project_name: str):
self.project_name = project_name
self.items: Dict[str, ProcurementItem] = {}
self._counter = 0
def add_item(self, description: str, spec_section: str, quantity: float,
unit: str, required_date: date, lead_time_days: int,
priority: Priority = Priority.NORMAL) -> ProcurementItem:
self._counter += 1
item_id = f"PROC-{self._counter:04d}"
item = ProcurementItem(
item_id=item_id,
description=description,
spec_section=spec_section,
quantity=quantity,
unit=unit,
required_date=required_date,
lead_time_days=lead_time_days,
status=ProcurementStatus.REQUISITIONED,
priority=priority
)
self.items[item_id] = item
return item
def issue_po(self, item_id: str, vendor: str, po_number: str,
amount: float, expected_delivery: date):
if item_id in self.items:
item = self.items[item_id]
item.status = ProcurementStatus.PO_ISSUED
item.vendor = vendor
item.po_number = po_number
item.po_amount = amount
item.order_date = date.today()
item.expected_delivery = expected_delivery
def update_status(self, item_id: str, status: ProcurementStatus):
if item_id in self.items:
self.items[item_id].status = status
if status == ProcurementStatus.DELIVERED:
self.items[item_id].actual_delivery = date.today()
def get_items_to_order(self) -> List[ProcurementItem]:
"""Get items that need to be ordered soon."""
cutoff = date.today() + timedelta(days=14)
return [i for i in self.items.values()
if i.status in [ProcurementStatus.REQUISITIONED, ProcurementStatus.RFQ_SENT,
ProcurementStatus.QUOTED]
and i.must_order_by <= cutoff]
def get_late_items(self) -> List[ProcurementItem]:
return [i for i in self.items.values() if i.is_late_to_order]
def get_summary(self) -> Dict[str, Any]:
by_status = {}
total_value = 0
for item in self.items.values():
status = item.status.value
by_status[status] = by_status.get(status, 0) + 1
total_value += item.po_amount
return {
'total_items': len(self.items),
'by_status': by_status,
'total_po_value': total_value,
'items_to_order': len(self.get_items_to_order()),
'late_items': len(self.get_late_items())
}
def export_log(self, output_path: str):
data = [{
'ID': i.item_id,
'Description': i.description,
'Spec': i.spec_section,
'Qty': i.quantity,
'Unit': i.unit,
'Required': i.required_date,
'Lead Time': i.lead_time_days,
'Must Order By': i.must_order_by,
'Status': i.status.value,
'Vendor': i.vendor,
'PO': i.po_number,
'Amount': i.po_amount
} for i in self.items.values()]
pd.DataFrame(data).to_excel(output_path, index=False)
tracker = MaterialProcurementTracker("Office Tower")
item = tracker.add_item(
description="Structural Steel W14x90",
spec_section="05 12 00",
quantity=500,
unit="TON",
required_date=date(2024, 6, 1),
lead_time_days=90,
priority=Priority.CRITICAL
)
tracker.issue_po(item.item_id, "ABC Steel", "PO-2024-001", 450000,
date(2024, 5, 15))
urgent = tracker.get_items_to_order()
print(f"Items needing order: {len(urgent)}")
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概要と使いどころ
Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.
日本語の概要は準備中です。原文の説明を表示しています。
Check construction AI systems against the EU AI Act (Regulation 2024/1689): classify risk of estimation, scheduling, CV and agent tools, document transparency, keep human oversight. Use when deploying or auditing AI in construction.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrate multiple AI agents for construction workflows: estimator, scheduler, document, QA and safety agents coordinated by a supervisor agent, with human checkpoints. Use when automating end-to-end project processes with agentic AI.
日本語の概要は準備中です。原文の説明を表示しています。
Create Apache Airflow DAGs for construction data pipelines. Orchestrate ETL, validation, and reporting workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Automate as-built documentation and digital handover for construction. Compile project records, generate O&M manuals, create asset databases, and ensure complete project closeout.
日本語の概要は準備中です。原文の説明を表示しています。