Track material orders, deliveries, and inventory on construction sites. Monitor lead times, delivery status, and stock levels.
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。
Material management challenges:
Comprehensive material tracking system to monitor orders, deliveries, inventory, and alert on potential issues.
import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import date, timedelta
from enum import Enum
class OrderStatus(Enum):
DRAFT = "draft"
SUBMITTED = "submitted"
CONFIRMED = "confirmed"
IN_PRODUCTION = "in_production"
SHIPPED = "shipped"
DELIVERED = "delivered"
PARTIAL = "partial"
CANCELLED = "cancelled"
class PriorityLevel(Enum):
CRITICAL = "critical"
HIGH = "high"
NORMAL = "normal"
LOW = "low"
@dataclass
class MaterialOrder:
order_id: str
material_code: str
material_name: str
supplier: str
quantity: float
unit: str
unit_cost: float
total_cost: float
order_date: date
required_date: date
expected_delivery: date
actual_delivery: Optional[date]
status: OrderStatus
priority: PriorityLevel
delivered_qty: float = 0
notes: str = ""
@dataclass
class InventoryItem:
material_code: str
material_name: str
current_stock: float
unit: str
min_stock: float
max_stock: float
reorder_point: float
location: str
last_updated: date
@dataclass
class Delivery:
delivery_id: str
order_id: str
delivery_date: date
quantity: float
received_by: str
condition: str # good, damaged, partial
notes: str = ""
class MaterialTracker:
"""Track construction materials."""
def __init__(self, project_name: str):
self.project_name = project_name
self.orders: Dict[str, MaterialOrder] = {}
self.inventory: Dict[str, InventoryItem] = {}
self.deliveries: List[Delivery] = []
def create_order(self,
order_id: str,
material_code: str,
material_name: str,
supplier: str,
quantity: float,
unit: str,
unit_cost: float,
required_date: date,
lead_time_days: int = 14,
priority: PriorityLevel = PriorityLevel.NORMAL) -> MaterialOrder:
"""Create new material order."""
order = MaterialOrder(
order_id=order_id,
material_code=material_code,
material_name=material_name,
supplier=supplier,
quantity=quantity,
unit=unit,
unit_cost=unit_cost,
total_cost=round(quantity * unit_cost, 2),
order_date=date.today(),
required_date=required_date,
expected_delivery=date.today() + timedelta(days=lead_time_days),
actual_delivery=None,
status=OrderStatus.DRAFT,
priority=priority
)
self.orders[order_id] = order
return order
def update_order_status(self, order_id: str, status: OrderStatus):
"""Update order status."""
if order_id in self.orders:
self.orders[order_id].status = status
def record_delivery(self,
order_id: str,
quantity: float,
received_by: str,
condition: str = "good",
notes: str = "") -> Optional[Delivery]:
"""Record material delivery."""
if order_id not in self.orders:
return None
order = self.orders[order_id]
delivery = Delivery(
delivery_id=f"DEL-{len(self.deliveries)+1:04d}",
order_id=order_id,
delivery_date=date.today(),
quantity=quantity,
received_by=received_by,
condition=condition,
notes=notes
)
self.deliveries.append(delivery)
# Update order
order.delivered_qty += quantity
order.actual_delivery = date.today()
if order.delivered_qty >= order.quantity:
order.status = OrderStatus.DELIVERED
else:
order.status = OrderStatus.PARTIAL
# Update inventory
if order.material_code in self.inventory:
self.inventory[order.material_code].current_stock += quantity
self.inventory[order.material_code].last_updated = date.today()
return delivery
def add_inventory_item(self,
material_code: str,
material_name: str,
current_stock: float,
unit: str,
min_stock: float,
max_stock: float,
location: str):
"""Add item to inventory tracking."""
reorder_point = min_stock + (max_stock - min_stock) * 0.3
self.inventory[material_code] = InventoryItem(
material_code=material_code,
material_name=material_name,
current_stock=current_stock,
unit=unit,
min_stock=min_stock,
max_stock=max_stock,
reorder_point=reorder_point,
location=location,
last_updated=date.today()
)
def consume_material(self,
material_code: str,
quantity: float,
activity: str = "") -> bool:
"""Record material consumption."""
if material_code not in self.inventory:
return False
item = self.inventory[material_code]
if item.current_stock < quantity:
return False
item.current_stock -= quantity
item.last_updated = date.today()
return True
def get_pending_orders(self) -> List[MaterialOrder]:
"""Get all pending orders."""
return [
o for o in self.orders.values()
if o.status not in [OrderStatus.DELIVERED, OrderStatus.CANCELLED]
]
def get_late_orders(self) -> List[Dict[str, Any]]:
"""Get orders that are late or at risk."""
late = []
today = date.today()
for order in self.orders.values():
if order.status in [OrderStatus.DELIVERED, OrderStatus.CANCELLED]:
continue
days_late = (today - order.expected_delivery).days
if days_late > 0 or (order.required_date - today).days < 3:
late.append({
'order_id': order.order_id,
'material': order.material_name,
'supplier': order.supplier,
'required_date': order.required_date,
'expected_delivery': order.expected_delivery,
'days_late': max(0, days_late),
'days_until_required': (order.required_date - today).days,
'status': order.status.value,
'priority': order.priority.value
})
return sorted(late, key=lambda x: x['days_until_required'])
def get_low_stock_items(self) -> List[Dict[str, Any]]:
"""Get items at or below reorder point."""
low_stock = []
for item in self.inventory.values():
if item.current_stock <= item.reorder_point:
low_stock.append({
'material_code': item.material_code,
'material_name': item.material_name,
'current_stock': item.current_stock,
'reorder_point': item.reorder_point,
'min_stock': item.min_stock,
'unit': item.unit,
'location': item.location,
'urgency': 'CRITICAL' if item.current_stock <= item.min_stock else 'REORDER'
})
return sorted(low_stock, key=lambda x: x['current_stock'])
def get_delivery_schedule(self, days_ahead: int = 14) -> pd.DataFrame:
"""Get expected deliveries for coming days."""
today = date.today()
end_date = today + timedelta(days=days_ahead)
scheduled = []
for order in self.orders.values():
if order.status in [OrderStatus.DELIVERED, OrderStatus.CANCELLED]:
continue
if today <= order.expected_delivery <= end_date:
scheduled.append({
'Date': order.expected_delivery,
'Order ID': order.order_id,
'Material': order.material_name,
'Quantity': order.quantity,
'Unit': order.unit,
'Supplier': order.supplier,
'Priority': order.priority.value
})
return pd.DataFrame(scheduled).sort_values('Date') if scheduled else pd.DataFrame()
def calculate_material_cost_summary(self) -> Dict[str, Any]:
"""Calculate material cost summary."""
total_ordered = sum(o.total_cost for o in self.orders.values())
total_delivered = sum(
o.delivered_qty * o.unit_cost
for o in self.orders.values()
)
total_pending = total_ordered - total_delivered
by_supplier = {}
for order in self.orders.values():
if order.supplier not in by_supplier:
by_supplier[order.supplier] = 0
by_supplier[order.supplier] += order.total_cost
return {
'total_ordered': round(total_ordered, 2),
'total_delivered': round(total_delivered, 2),
'total_pending': round(total_pending, 2),
'order_count': len(self.orders),
'by_supplier': by_supplier
}
def export_to_excel(self, output_path: str) -> str:
"""Export material tracking to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Orders
orders_df = pd.DataFrame([
{
'Order ID': o.order_id,
'Material': o.material_name,
'Supplier': o.supplier,
'Quantity': o.quantity,
'Unit': o.unit,
'Unit Cost': o.unit_cost,
'Total Cost': o.total_cost,
'Order Date': o.order_date,
'Required': o.required_date,
'Expected': o.expected_delivery,
'Status': o.status.value,
'Delivered': o.delivered_qty
}
for o in self.orders.values()
])
orders_df.to_excel(writer, sheet_name='Orders', index=False)
# Inventory
if self.inventory:
inv_df = pd.DataFrame([
{
'Code': i.material_code,
'Name': i.material_name,
'Stock': i.current_stock,
'Unit': i.unit,
'Min': i.min_stock,
'Max': i.max_stock,
'Reorder Point': i.reorder_point,
'Location': i.location
}
for i in self.inventory.values()
])
inv_df.to_excel(writer, sheet_name='Inventory', index=False)
# Late orders
late = self.get_late_orders()
if late:
late_df = pd.DataFrame(late)
late_df.to_excel(writer, sheet_name='Late Orders', index=False)
# Low stock
low = self.get_low_stock_items()
if low:
low_df = pd.DataFrame(low)
low_df.to_excel(writer, sheet_name='Low Stock', index=False)
return output_path
from datetime import date, timedelta
# Initialize tracker
tracker = MaterialTracker("Office Building A")
# Create order
order = tracker.create_order(
order_id="PO-001",
material_code="CONC-C30",
material_name="Concrete C30",
supplier="ABC Ready Mix",
quantity=200,
unit="m3",
unit_cost=150,
required_date=date.today() + timedelta(days=10),
lead_time_days=3,
priority=PriorityLevel.HIGH
)
# Update status
tracker.update_order_status("PO-001", OrderStatus.CONFIRMED)
# Record delivery
tracker.record_delivery("PO-001", quantity=200, received_by="John Smith")
late = tracker.get_late_orders()
for order in late:
print(f"{order['order_id']}: {order['days_late']} days late")
low_stock = tracker.get_low_stock_items()
for item in low_stock:
print(f"{item['material_name']}: {item['current_stock']} {item['unit']} - {item['urgency']}")
schedule = tracker.get_delivery_schedule(days_ahead=7)
print(schedule)
まだレビューはありません。使ってみた感想をお寄せください。
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。