Manufacturing Supply Chain Multi-Agent System
Build an intelligent supply chain management system with specialized agents for production planning, inventory optimization, logistics coordination, and quality control.
Overview
This example demonstrates how to build a multi-agent manufacturing system that can:
- Optimize production schedules and resource allocation
- Manage inventory levels and supply ordering
- Coordinate logistics and shipping operations
- Monitor quality control and compliance
- Forecast demand and adjust production
- Handle supplier relationships and procurement
- Track real-time manufacturing metrics
Perfect for: Manufacturing companies, factories, supply chain management firms, and industrial IoT platforms.
How It Works
Workflow Steps
- Demand Analysis: Manager agent analyzes orders and forecasts demand
- Production Planning: Creates optimal production schedules
- Inventory Management: Monitors stock levels and triggers reorders
- Quality Assurance: Ensures product quality standards
- Logistics Coordination: Manages shipping and delivery
- Supplier Management: Handles procurement and vendor relations
- Performance Monitoring: Tracks KPIs and optimizes processes
Agent Hierarchy
Install AgentRouter
# Install AgentRouter SDK
!pip install agentrouter -q
# Import required libraries
from agentrouter import ManagerAgent, WorkerAgent, tool
from agentrouter.visualization import PipelineInspector
import asyncio
import nest_asyncio
from typing import Dict, Any, List, Optional
from datetime import datetime, timedelta
import json
import random
nest_asyncio.apply()
Complete Code
# ============================================
# MANUFACTURING SUPPLY CHAIN MULTI-AGENT SYSTEM
# ============================================
# Configuration
API_KEY = "your-api-key-here" # Replace with your actual API key
# ============================================
# TOOL DEFINITIONS
# ============================================
@tool(
schema={
"type": "function",
"function": {
"name": "analyze_production_capacity",
"description": "Analyzes real-time production capacity, utilization rates, and identifies bottlenecks across manufacturing lines",
"parameters": {
"type": "object",
"properties": {
"facility_id": {
"type": "string",
"description": "Manufacturing facility identifier"
},
"product_line": {
"type": "string",
"description": "Product line to analyze"
},
"timeframe": {
"type": "string",
"enum": ["shift", "day", "week", "month"],
"description": "Analysis timeframe"
}
},
"required": ["facility_id"]
}
}
}
)
async def analyze_production_capacity(facility_id: str, product_line: str = "all", timeframe: str = "day") -> Dict[str, Any]:
"""Analyze production capacity and current utilization"""
# Simulated production data
capacity_data = {
"facility_id": facility_id,
"product_line": product_line,
"timeframe": timeframe,
"capacity_metrics": {
"max_capacity": 10000, # units per timeframe
"current_output": 8500,
"utilization_rate": 85, # percentage
"efficiency_score": 92,
"oee": 78.5, # Overall Equipment Effectiveness
"availability": 95,
"performance": 87,
"quality_rate": 99.2
},
"production_lines": [
{"line": "Assembly-A", "status": "operational", "output": 2800, "efficiency": 93},
{"line": "Assembly-B", "status": "operational", "output": 2700, "efficiency": 90},
{"line": "Assembly-C", "status": "maintenance", "output": 0, "efficiency": 0},
{"line": "Packaging", "status": "operational", "output": 3000, "efficiency": 95}
],
"bottlenecks": ["Assembly-C under maintenance", "Raw material supply delay"],
"recommendations": [
"Schedule maintenance during off-peak hours",
"Increase buffer stock for critical components",
"Consider adding third shift for high-demand periods"
],
"analysis_timestamp": datetime.now().isoformat()
}
return capacity_data
@tool(
schema={
"type": "function",
"function": {
"name": "check_inventory_levels",
"description": "Monitors inventory levels, tracks reorder points, and identifies critical stock shortages or overstock situations",
"parameters": {
"type": "object",
"properties": {
"warehouse_id": {
"type": "string",
"description": "Warehouse identifier"
},
"category": {
"type": "string",
"enum": ["raw_materials", "work_in_progress", "finished_goods", "all"],
"description": "Inventory category"
},
"critical_only": {
"type": "boolean",
"description": "Show only critical items below reorder point"
}
},
"required": ["warehouse_id"]
}
}
}
)
async def check_inventory_levels(warehouse_id: str, category: str = "all", critical_only: bool = False) -> Dict[str, Any]:
"""Check inventory levels and identify reorder needs"""
inventory_data = {
"warehouse_id": warehouse_id,
"category": category,
"inventory_summary": {
"total_sku": 450,
"total_value": 2500000,
"critical_items": 12,
"overstock_items": 8
},
"items": [
{
"sku": "RM-001",
"name": "Steel Sheets",
"category": "raw_materials",
"current_stock": 500,
"unit": "tons",
"reorder_point": 800,
"max_stock": 2000,
"status": "critical",
"days_supply": 5,
"lead_time_days": 14
},
{
"sku": "RM-002",
"name": "Electronic Components",
"category": "raw_materials",
"current_stock": 15000,
"unit": "units",
"reorder_point": 10000,
"max_stock": 50000,
"status": "adequate",
"days_supply": 20,
"lead_time_days": 7
},
{
"sku": "FG-101",
"name": "Product Model A",
"category": "finished_goods",
"current_stock": 2500,
"unit": "units",
"reorder_point": 1000,
"max_stock": 5000,
"status": "optimal",
"days_supply": 15,
"lead_time_days": 2
}
],
"reorder_required": ["RM-001", "RM-045", "RM-078"],
"turnover_rate": 12.5,
"carrying_cost": 125000,
"stockout_risk": "moderate",
"last_updated": datetime.now().isoformat()
}
if critical_only:
inventory_data["items"] = [item for item in inventory_data["items"] if item["status"] == "critical"]
return inventory_data
@tool(
schema={
"type": "function",
"function": {
"name": "create_production_schedule",
"description": "Generates optimized production schedules balancing capacity, demand, and resource constraints",
"parameters": {
"type": "object",
"properties": {
"facility_id": {
"type": "string",
"description": "Facility identifier"
},
"orders": {
"type": "array",
"items": {"type": "object"},
"description": "List of production orders"
},
"start_date": {
"type": "string",
"description": "Schedule start date"
},
"optimization_goal": {
"type": "string",
"enum": ["minimize_time", "minimize_cost", "maximize_output", "balanced"],
"description": "Optimization objective"
}
},
"required": ["facility_id", "orders"]
}
}
}
)
async def create_production_schedule(facility_id: str, orders: List[Dict], start_date: str = None,
optimization_goal: str = "balanced") -> Dict[str, Any]:
"""Create optimized production schedule"""
if not start_date:
start_date = datetime.now().strftime("%Y-%m-%d")
schedule_id = f"SCHED{datetime.now().strftime('%Y%m%d%H%M%S')}"
# Simulated schedule generation
schedule = {
"schedule_id": schedule_id,
"facility_id": facility_id,
"start_date": start_date,
"end_date": (datetime.now() + timedelta(days=7)).strftime("%Y-%m-%d"),
"optimization_goal": optimization_goal,
"production_plan": [
{
"date": start_date,
"shift": "morning",
"line": "Assembly-A",
"product": "Model-X",
"quantity": 500,
"priority": "high",
"estimated_completion": "14:00"
},
{
"date": start_date,
"shift": "afternoon",
"line": "Assembly-B",
"product": "Model-Y",
"quantity": 400,
"priority": "medium",
"estimated_completion": "22:00"
}
],
"resource_allocation": {
"workers": 85,
"machines": 12,
"materials": "allocated",
"tools": "available"
},
"expected_output": 3500,
"efficiency_score": 88,
"cost_estimate": 175000,
"created_at": datetime.now().isoformat()
}
return schedule
@tool(
schema={
"type": "function",
"function": {
"name": "quality_inspection",
"description": "Executes quality control inspections, records defects, and generates compliance reports with Six Sigma metrics",
"parameters": {
"type": "object",
"properties": {
"batch_id": {
"type": "string",
"description": "Production batch identifier"
},
"inspection_type": {
"type": "string",
"enum": ["incoming", "in_process", "final", "random"],
"description": "Type of quality inspection"
},
"sample_size": {
"type": "integer",
"description": "Number of units to inspect"
}
},
"required": ["batch_id", "inspection_type"]
}
}
}
)
async def quality_inspection(batch_id: str, inspection_type: str, sample_size: int = 100) -> Dict[str, Any]:
"""Perform quality inspection"""
inspection_id = f"QC{datetime.now().strftime('%Y%m%d%H%M%S')}"
# Simulated inspection results
inspection_results = {
"inspection_id": inspection_id,
"batch_id": batch_id,
"inspection_type": inspection_type,
"sample_size": sample_size,
"results": {
"passed": sample_size - 3,
"failed": 3,
"pass_rate": 97.0,
"defects_found": [
{"type": "dimensional", "count": 1, "severity": "minor"},
{"type": "surface_finish", "count": 2, "severity": "minor"}
]
},
"quality_metrics": {
"sigma_level": 4.2,
"cpk": 1.45,
"ppm_defective": 3000,
"first_pass_yield": 97.0
},
"compliance_status": "compliant",
"standards_met": ["ISO 9001", "Six Sigma"],
"recommendations": [
"Investigate dimensional variance in Station 3",
"Recalibrate surface finishing equipment"
],
"inspector": "QC_Team_Alpha",
"inspection_date": datetime.now().isoformat()
}
return inspection_results
@tool(
schema={
"type": "function",
"function": {
"name": "manage_suppliers",
"description": "Manages supplier performance, processes purchase orders, and evaluates vendor compliance and risk",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": ["evaluate", "order", "negotiate", "audit"],
"description": "Supplier management action"
},
"supplier_id": {
"type": "string",
"description": "Supplier identifier"
},
"details": {
"type": "object",
"description": "Action-specific details"
}
},
"required": ["action"]
}
}
}
)
async def manage_suppliers(action: str, supplier_id: str = None, details: Dict = None) -> Dict[str, Any]:
"""Manage supplier relationships"""
if action == "evaluate":
return {
"action": "evaluate",
"supplier_id": supplier_id or "SUP-001",
"evaluation": {
"performance_score": 88,
"delivery_reliability": 95,
"quality_rating": 92,
"price_competitiveness": 85,
"compliance_status": "verified",
"risk_level": "low",
"contract_status": "active",
"annual_volume": 1500000
},
"recommendations": ["Negotiate volume discount", "Extend contract term"],
"evaluated_at": datetime.now().isoformat()
}
elif action == "order":
order_id = f"PO{datetime.now().strftime('%Y%m%d%H%M%S')}"
return {
"action": "order",
"order_id": order_id,
"supplier_id": supplier_id,
"status": "submitted",
"items_ordered": details.get("items", []),
"total_value": details.get("value", 50000),
"expected_delivery": (datetime.now() + timedelta(days=14)).strftime("%Y-%m-%d"),
"payment_terms": "Net 30",
"created_at": datetime.now().isoformat()
}
return {"action": action, "status": "completed"}
@tool(
schema={
"type": "function",
"function": {
"name": "coordinate_logistics",
"description": "Schedules shipments, tracks deliveries, optimizes routes, and manages carrier relationships",
"parameters": {
"type": "object",
"properties": {
"shipment_id": {
"type": "string",
"description": "Shipment identifier"
},
"action": {
"type": "string",
"enum": ["schedule", "track", "optimize_route", "update"],
"description": "Logistics action"
},
"destination": {
"type": "string",
"description": "Delivery destination"
},
"priority": {
"type": "string",
"enum": ["standard", "express", "urgent"],
"description": "Shipping priority"
}
},
"required": ["action"]
}
}
}
)
async def coordinate_logistics(action: str, shipment_id: str = None, destination: str = None,
priority: str = "standard") -> Dict[str, Any]:
"""Coordinate logistics and shipping"""
if action == "schedule":
shipment_id = f"SHIP{datetime.now().strftime('%Y%m%d%H%M%S')}"
return {
"action": "schedule",
"shipment_id": shipment_id,
"destination": destination,
"priority": priority,
"carrier": "Global Logistics Inc.",
"pickup_date": datetime.now().strftime("%Y-%m-%d"),
"estimated_delivery": (datetime.now() + timedelta(days=3 if priority == "express" else 5)).strftime("%Y-%m-%d"),
"tracking_number": f"TRK{random.randint(100000, 999999)}",
"cost": 1500 if priority == "standard" else 2500,
"status": "scheduled"
}
elif action == "track":
return {
"action": "track",
"shipment_id": shipment_id,
"current_location": "Distribution Center - Chicago",
"status": "in_transit",
"progress": 65,
"estimated_arrival": (datetime.now() + timedelta(days=2)).strftime("%Y-%m-%d %H:%M"),
"events": [
{"timestamp": "2025-01-10 08:00", "event": "Picked up from warehouse"},
{"timestamp": "2025-01-10 14:00", "event": "Arrived at sorting facility"},
{"timestamp": "2025-01-11 09:00", "event": "Departed for destination"}
]
}
return {"action": action, "status": "completed"}
@tool(
schema={
"type": "function",
"function": {
"name": "forecast_demand",
"description": "Predicts future product demand using ML models, analyzing trends, seasonality, and market factors",
"parameters": {
"type": "object",
"properties": {
"product_id": {
"type": "string",
"description": "Product identifier"
},
"forecast_period": {
"type": "string",
"enum": ["week", "month", "quarter", "year"],
"description": "Forecast time period"
},
"method": {
"type": "string",
"enum": ["moving_average", "exponential_smoothing", "arima", "ml_model"],
"description": "Forecasting method"
}
},
"required": ["product_id", "forecast_period"]
}
}
}
)
async def forecast_demand(product_id: str, forecast_period: str, method: str = "ml_model") -> Dict[str, Any]:
"""Forecast product demand"""
# Simulated demand forecast
periods = {
"week": 7,
"month": 30,
"quarter": 90,
"year": 365
}
base_demand = 1000
forecast_data = []
for i in range(periods.get(forecast_period, 30) // 7):
week_demand = base_demand + random.randint(-200, 300)
forecast_data.append({
"week": i + 1,
"predicted_demand": week_demand,
"confidence_lower": week_demand - 150,
"confidence_upper": week_demand + 150
})
return {
"product_id": product_id,
"forecast_period": forecast_period,
"method": method,
"forecast": forecast_data,
"summary": {
"total_predicted": sum(f["predicted_demand"] for f in forecast_data),
"average_weekly": base_demand,
"peak_week": max(forecast_data, key=lambda x: x["predicted_demand"])["week"],
"confidence_level": 85
},
"factors_considered": ["seasonality", "historical_trends", "market_conditions", "promotions"],
"accuracy_metrics": {
"mape": 8.5, # Mean Absolute Percentage Error
"rmse": 125, # Root Mean Square Error
"r_squared": 0.89
},
"recommendations": [
"Increase production capacity for peak weeks",
"Build safety stock of 15% above forecast",
"Monitor competitor activities"
],
"generated_at": datetime.now().isoformat()
}
# ============================================
# AGENT SYSTEM SETUP
# ============================================
class ManufacturingSupplyChainSystem:
"""Complete manufacturing supply chain multi-agent system"""
def __init__(self, api_key: str):
self.api_key = api_key
self.manager = None
self.agents = {}
def setup(self):
"""Setup the multi-agent hierarchy"""
# Create Manager Agent - Operations Director
self.manager = ManagerAgent(
name="operations_director",
api_key=self.api_key,
model="usf-mini",
backstory="""I am an experienced Operations Director with 25 years in
manufacturing and supply chain management. I specialize in lean manufacturing,
Six Sigma, and digital transformation of industrial operations.""",
goal="Optimize manufacturing operations for efficiency, quality, and profitability",
instruction="""
1. Analyze production capacity and demand forecasts
2. Optimize production schedules and resource allocation
3. Manage inventory levels and supply chain operations
4. Ensure quality standards and compliance
5. Coordinate logistics and distribution
6. Monitor KPIs and implement continuous improvement
7. Manage supplier relationships and procurement
""",
knowledge_cutoff="15 January 2025",
temperature=0.5,
max_iterations=30
)
# Register manager tools
self.manager.register_tool(analyze_production_capacity)
self.manager.register_tool(forecast_demand)
# Create Level 1 Workers
# Production Planner
production_planner = self.manager.create_worker(
name="Production_Planner",
role="Creates and optimizes production schedules, allocates resources, and manages capacity planning",
backstory="Expert in production scheduling and capacity planning",
goal="Optimize production schedules for maximum efficiency",
instruction="Create and manage production schedules based on demand and capacity"
)
production_planner.register_tool(create_production_schedule)
production_planner.register_tool(analyze_production_capacity)
self.agents["production_planner"] = production_planner
# Supply Chain Manager
supply_chain_manager = self.manager.create_worker(
name="Supply_Chain_Manager",
role="Manages inventory levels, coordinates procurement, and optimizes supplier relationships",
backstory="Specialist in supply chain optimization and vendor management",
goal="Ensure smooth supply chain operations and optimal inventory",
instruction="Manage inventory, procurement, and supplier relationships"
)
supply_chain_manager.register_tool(check_inventory_levels)
supply_chain_manager.register_tool(manage_suppliers)
supply_chain_manager.register_tool(coordinate_logistics)
self.agents["supply_chain_manager"] = supply_chain_manager
# Quality Controller
quality_controller = self.manager.create_worker(
name="Quality_Controller",
role="Conducts quality inspections, ensures compliance, and drives continuous improvement initiatives",
backstory="Expert in quality management systems and continuous improvement",
goal="Maintain highest quality standards and compliance",
instruction="Ensure product quality and regulatory compliance"
)
quality_controller.register_tool(quality_inspection)
self.agents["quality_controller"] = quality_controller
# Create Level 2 Workers (Production Team)
# Assembly Line Supervisor
assembly_supervisor = production_planner.create_worker(
name="Assembly_Supervisor",
role="Monitors assembly line performance and coordinates production floor activities",
backstory="Experienced in managing assembly line operations",
goal="Ensure smooth assembly line operations"
)
assembly_supervisor.register_tool(analyze_production_capacity)
# Machine Operator
machine_operator = production_planner.create_worker(
name="Machine_Operator",
role="Operates manufacturing equipment and optimizes machine performance metrics",
backstory="Expert in operating and optimizing manufacturing equipment",
goal="Maximize machine efficiency and output"
)
# Maintenance Technician
maintenance_tech = production_planner.create_worker(
name="Maintenance_Technician",
role="Performs preventive maintenance and troubleshoots equipment failures",
backstory="Specialist in predictive maintenance and equipment reliability",
goal="Minimize downtime through proactive maintenance"
)
# Create Level 2 Workers (Supply Chain Team)
# Inventory Manager
inventory_manager = supply_chain_manager.create_worker(
name="Inventory_Manager",
role="Controls inventory levels, manages warehouse operations, and optimizes stock turnover",
backstory="Expert in inventory optimization and warehouse management",
goal="Optimize inventory levels and reduce carrying costs"
)
inventory_manager.register_tool(check_inventory_levels)
# Procurement Specialist
procurement_specialist = supply_chain_manager.create_worker(
name="Procurement_Specialist",
role="Sources materials, negotiates contracts, and evaluates supplier performance",
backstory="Expert in sourcing and vendor negotiations",
goal="Secure best value procurement and supplier relationships"
)
procurement_specialist.register_tool(manage_suppliers)
# Logistics Coordinator
logistics_coordinator = supply_chain_manager.create_worker(
name="Logistics_Coordinator",
role="Plans shipments, manages transportation, and optimizes delivery routes",
backstory="Specialist in transportation and distribution",
goal="Ensure timely and cost-effective product delivery"
)
logistics_coordinator.register_tool(coordinate_logistics)
# Shipping Manager (under Logistics)
shipping_manager = logistics_coordinator.create_worker(
name="Shipping_Manager",
role="Processes shipments, tracks deliveries, and manages carrier relationships",
backstory="Expert in shipping operations and carrier management",
goal="Optimize shipping operations and costs"
)
shipping_manager.register_tool(coordinate_logistics)
# Create Level 2 Workers (Quality Team)
# Quality Inspector
quality_inspector = quality_controller.create_worker(
name="Quality_Inspector",
role="Performs product inspections, records defects, and implements quality control measures",
backstory="Certified quality inspector with expertise in statistical process control",
goal="Identify and prevent quality issues"
)
quality_inspector.register_tool(quality_inspection)
# Compliance Officer
compliance_officer = quality_controller.create_worker(
name="Compliance_Officer",
role="Ensures regulatory compliance, maintains certifications, and manages audit processes",
backstory="Expert in manufacturing regulations and standards",
goal="Ensure full regulatory compliance"
)
print("✅ Manufacturing Supply Chain System initialized successfully!")
# Visualize hierarchy
inspector = PipelineInspector(self.manager)
print("\n📊 MANUFACTURING TEAM HIERARCHY:")
print(inspector.visualize(format='mermaid'))
print("\n🔧 MANUFACTURING TOOLS AVAILABLE:")
# Get all tools from inspector
for agent in inspector.pipeline_data['agents']:
if agent['tools']:
print(f" {agent['name']}:")
for tool_name in agent['tools']:
print(f" • {tool_name}")
async def optimize_operations(self, request: str, facility_id: str = "FAC001"):
"""Process manufacturing operations request"""
messages = [
{
"role": "system",
"content": f"Facility ID: {facility_id}\nDate: {datetime.now().strftime('%Y-%m-%d %H:%M')}\nShift: Current"
},
{
"role": "user",
"content": request
}
]
print(f"\n🏭 Factory ({facility_id}): {request}")
print("⚙️ Processing with manufacturing AI system...")
try:
response = await self.manager.execute(messages)
if response and "choices" in response:
result = response["choices"][0]["message"]["content"]
print(f"\n✅ Operations Recommendation:\n{result}")
return {
"success": True,
"facility_id": facility_id,
"request": request,
"recommendation": result,
"timestamp": datetime.now().isoformat()
}
except Exception as e:
print(f"❌ Error: {str(e)}")
return {
"success": False,
"facility_id": facility_id,
"request": request,
"error": str(e),
"timestamp": datetime.now().isoformat()
}
# ============================================
# EXAMPLE USAGE
# ============================================
async def run_manufacturing_example():
"""Run manufacturing supply chain scenarios"""
# Initialize system
manufacturing_system = ManufacturingSupplyChainSystem(API_KEY)
manufacturing_system.setup()
# Test scenarios
test_cases = [
{
"facility_id": "FAC001",
"request": "We have a large order of 5000 units due in 2 weeks. Our Assembly Line C is under maintenance. How should we optimize production to meet this deadline?"
},
{
"facility_id": "FAC002",
"request": "Raw material inventory for steel sheets is running low. What's our current stock situation and when should we reorder?"
}
]
print("\n" + "="*60)
print("🏭 RUNNING MANUFACTURING SCENARIOS")
print("="*60)
results = []
for test_case in test_cases[:1]: # Run first case for demo
result = await manufacturing_system.optimize_operations(
test_case["request"],
test_case["facility_id"]
)
results.append(result)
print("\n" + "-"*60)
return results
# ============================================
# MAIN EXECUTION
# ============================================
# Run the example
results = await run_manufacturing_example()
# Display results
print("\n📋 MANUFACTURING OPTIMIZATION RESULTS:")
for result in results:
print(json.dumps(result, indent=2))
Expected Output
✅ Manufacturing Supply Chain System initialized successfully!
📊 MANUFACTURING TEAM HIERARCHY:
graph TB
Operations_Director["Operations Director"]
Production_Planner["Production Planner"]
Supply_Chain_Manager["Supply Chain Manager"]
Quality_Controller["Quality Controller"]
Assembly_Supervisor["Assembly Supervisor"]
Machine_Operator["Machine Operator"]
Maintenance_Technician["Maintenance Technician"]
Inventory_Manager["Inventory Manager"]
Procurement_Specialist["Procurement Specialist"]
Logistics_Coordinator["Logistics Coordinator"]
Shipping_Manager["Shipping Manager"]
Quality_Inspector["Quality Inspector"]
Compliance_Officer["Compliance Officer"]
Operations_Director --> Production_Planner
Operations_Director --> Supply_Chain_Manager
Operations_Director --> Quality_Controller
Production_Planner --> Assembly_Supervisor
Production_Planner --> Machine_Operator
Production_Planner --> Maintenance_Technician
Supply_Chain_Manager --> Inventory_Manager
Supply_Chain_Manager --> Procurement_Specialist
Supply_Chain_Manager --> Logistics_Coordinator
Logistics_Coordinator --> Shipping_Manager
Quality_Controller --> Quality_Inspector
Quality_Controller --> Compliance_Officer
🔧 MANUFACTURING TOOLS AVAILABLE:
• analyze_production_capacity
• forecast_demand
• create_production_schedule
• check_inventory_levels
• manage_suppliers
• coordinate_logistics
• quality_inspection
============================================================
🏭 RUNNING MANUFACTURING SCENARIOS
============================================================
🏭 Factory (FAC001): We have a large order of 5000 units due in 2 weeks. Our Assembly Line C is under maintenance. How should we optimize production to meet this deadline?
⚙️ Processing with manufacturing AI system...
✅ Operations Recommendation:
Based on my analysis of your urgent order and current constraints, here's the optimized production plan:
📊 CURRENT SITUATION ANALYSIS:
• Order: 5000 units in 14 days (357 units/day required)
• Constraint: Assembly Line C under maintenance
• Available capacity: Lines A & B operational at 93% and 90% efficiency
• Current utilization: 85% overall
🎯 OPTIMIZED PRODUCTION STRATEGY:
1. IMMEDIATE ACTIONS (Days 1-3):
✓ Increase shifts on Assembly Lines A & B
✓ Current output: 5500 units/week → Target: 7000 units/week
✓ Add overtime shifts (4 hours/day on both lines)
✓ Reallocate 15 workers from Line C to A & B
2. PRODUCTION SCHEDULE:
Week 1 (Days 1-7):
• Line A: 2100 units (300/day @ 95% efficiency)
• Line B: 1750 units (250/day @ 92% efficiency)
• Packaging: Continuous operation
• Total Week 1: 3850 units
Week 2 (Days 8-14):
• Line A: 1400 units (200/day - normal pace)
• Line B: 750 units (107/day - reduced load)
• Buffer production for quality checks
• Total Week 2: 2150 units
3. RESOURCE OPTIMIZATION:
✓ Materials: Confirmed adequate raw materials (15-day supply)
✓ Labor: Deploy 85 workers across 2 shifts + overtime
✓ Quality: Maintain 99.2% quality rate with inline inspections
4. RISK MITIGATION:
• Buffer: Building 500 extra units (10% safety margin)
• Contingency: Partner facility on standby if needed
• Maintenance: Schedule Line C repairs for night shifts only
5. COST IMPACT:
• Overtime costs: +$18,000
• Efficiency gains: -$5,000 (reduced waste)
• Net additional cost: $13,000 (2.6% increase)
📅 DELIVERY CONFIDENCE: 95%
Expected completion: Day 13 (1 day buffer)
Quality assurance: All units will meet ISO 9001 standards
This plan ensures on-time delivery while maintaining quality standards and minimizing additional costs.
📋 MANUFACTURING OPTIMIZATION RESULTS:
{
"success": true,
"facility_id": "FAC001",
"request": "We have a large order of 5000 units due in 2 weeks...",
"recommendation": "Based on my analysis...",
"timestamp": "2025-01-12T13:45:30.123456"
}
Customization Options
1. Add IoT Integration
# Add IoT Monitoring Specialist
iot_specialist = production_planner.create_worker(
name="IoT_Specialist",
role="Industrial IoT Engineer",
backstory="Expert in sensor data analysis and predictive analytics",
goal="Monitor equipment health and predict failures"
)
2. Implement Predictive Maintenance
@tool(schema={...})
async def predict_equipment_failure(equipment_id: str, sensor_data: Dict):
"""Predict equipment failure using ML models"""
return {"failure_probability": 0.15, "estimated_time": "72 hours"}
3. Add Sustainability Tracking
@tool(schema={...})
async def track_sustainability_metrics(facility_id: str):
"""Track environmental and sustainability KPIs"""
return {"carbon_footprint": co2_emissions, "waste_reduction": percentage}
Production Considerations
- ERP Integration: Connect with SAP, Oracle, or other ERP systems
- Real-time Monitoring: IoT sensors for equipment and production tracking
- Safety Protocols: Automated safety compliance and incident reporting
- Quality Standards: ISO 9001, Six Sigma, and industry-specific compliance
- Supply Chain Visibility: End-to-end tracking from suppliers to customers
- Predictive Analytics: Machine learning for demand forecasting and maintenance
- Digital Twin: Virtual factory simulation for optimization
- Energy Management: Monitor and optimize energy consumption