Autonomous Agents for Optimizing Business Operations

from sklearn.linear_model import LinearRegression
import pandas as pd
import matplotlib.pyplot as plt

# Load historical sales data
data = pd.read_csv('sales_data.csv')
X = data[['historical_sales', 'seasonality', 'economic_indicators']]
y = data['future_demand']

# Train the model
model = LinearRegression()
model.fit(X, y)

# Generate predictions
predictions = model.predict(X_test)

# Visualize the forecast
plt.plot(predictions)
plt.title('Predicted Demand for Next Quarter')
plt.xlabel('Time')
plt.ylabel('Units')
plt.show()
        

{
    "ProductID": "SKU123",
    "ForecastPeriod": "Q1 2025",
    "PredictedDemand": 10500,
    "ConfidenceInterval": [9800, 11200],
    "KeyFactors": ["Seasonality", "Market Growth"]
}
        

1. Collect historical sales data from internal databases and external sources.
2. Identify and gather external factors such as seasonality, promotions, and economic indicators.
3. Preprocess and clean the data to ensure accuracy and consistency.
4. Choose appropriate forecasting models (e.g., Linear Regression, ARIMA, Prophet).
5. Train the selected model using the historical and external data.
6. Validate the model's accuracy using statistical metrics like MAE, RMSE, or MAPE.
7. Generate demand forecasts and visualize the results for better interpretation.
8. Distribute the forecasted data to relevant stakeholders, including sales and marketing teams.
9. Continuously monitor forecast accuracy and adjust models as necessary.

import pandas as pd
from datetime import datetime

# Example production plan data
production_plan = pd.DataFrame({
    'Product': ['A', 'B'],
    'Quantity': [5000, 3000],
    'Start_Date': [datetime(2025, 3, 1), datetime(2025, 3, 15)],
    'Deadline': [datetime(2025, 3, 31), datetime(2025, 4, 15)]
})

# Resource allocation example
production_plan['Resource'] = ['Machine1', 'Machine2']

# Export to JSON
production_plan.to_json('production_plan.json', orient='records', date_format='iso')
        

{
    "Product": "A",
    "Quantity": 5000,
    "Start_Date": "2025-03-01T00:00:00",
    "Deadline": "2025-03-31T00:00:00",
    "Resource": "Machine1"
},
{
    "Product": "B",
    "Quantity": 3000,
    "Start_Date": "2025-03-15T00:00:00",
    "Deadline": "2025-04-15T00:00:00",
    "Resource": "Machine2"
}
        

1. Input demand forecasts from the Predict Demand agent to understand upcoming production needs.
2. Assess resource availability, including machinery, labor, and material inventories.
3. Optimize production schedules to maximize efficiency and meet deadlines using scheduling algorithms.
4. Validate the feasibility of the proposed schedule with production teams and adjust as necessary.
5. Allocate resources effectively to ensure that each production line has the necessary tools and personnel.
6. Generate detailed schedules and distribute them to relevant departments for implementation.
7. Continuously monitor production progress and make real-time adjustments to address any unforeseen delays or issues.
8. Maintain clear communication channels with all stakeholders to ensure alignment and transparency.
9. Review and analyze production outcomes to identify areas for improvement in future planning cycles.

import requests
import pandas as pd

# Function to place an order
def place_order(material_id, quantity):
    order_details = {
        "MaterialID": material_id,
        "Quantity": quantity,
        "OrderDate": "2025-02-01"
    }
    response = requests.post("https://api.supplier.com/orders", json=order_details)
    return response.status_code

# Function to check and trigger orders
def check_inventory(inventory_level, reorder_point, material_id, quantity):
    if inventory_level < reorder_point:
        status = place_order(material_id, quantity)
        if status == 201:
            print(f"Order placed: {quantity} units of {material_id}")
        else:
            print("Failed to place order")

# Example usage
current_inventory = 500
reorder_threshold = 1000
material = "MAT_X"
order_qty = 1500
check_inventory(current_inventory, reorder_threshold, material, order_qty)
        

{
    "MaterialID": "MAT_X",
    "Quantity": 1500,
    "OrderDate": "2025-02-01",
    "Status": "Order Placed"
}
        

1. Set reorder points based on lead times, usage rates, and safety stock levels for each material.
2. Integrate inventory management systems with real-time tracking to monitor current inventory levels continuously.
3. Implement automated alerts and triggers that activate when inventory falls below predefined thresholds.
4. Trigger automated order placements by interfacing with supplier APIs or ordering systems when reorder points are reached.
5. Confirm order placements and receipt acknowledgments from suppliers to ensure orders are successfully processed.
6. Update inventory records upon receipt of materials, adjusting stock levels accordingly.
7. Track delivery timelines and communicate with suppliers to manage any delays or issues proactively.
8. Analyze inventory trends and adjust reorder points and quantities as necessary to optimize stock levels.
9. Report inventory status and replenishment activities to relevant stakeholders to maintain transparency.

import pandas as pd
import matplotlib.pyplot as plt

def identify_waste(production_data):
    waste_data = production_data[production_data['defect_rate'] > 0.05]
    return waste_data

# Load production data
production_data = pd.read_csv('production_data.csv')

# Identify waste
waste = identify_waste(production_data)

# Visualize waste sources
waste.groupby('waste_type').size().plot(kind='bar')
plt.title('Waste by Type')
plt.xlabel('Waste Type')
plt.ylabel('Quantity')
plt.show()
        

{
    "WasteType": "Scrap Metal",
    "DefectRate": "5%",
    "Quantity": 200
}
        

1. Collect comprehensive production data, including output volumes, defect rates, and waste types.
2. Analyze data to identify instances where waste exceeds acceptable thresholds.
3. Segment waste data by type, source, and severity to understand specific areas of concern.
4. Use statistical analysis and visualization tools to uncover patterns and trends in waste generation.
5. Determine root causes of identified waste through further investigation and data correlation.
6. Generate detailed reports and visualizations to present findings to relevant stakeholders.
7. Collaborate with quality assurance and production teams to develop strategies for waste reduction.
8. Implement corrective actions and monitor their effectiveness in reducing waste.
9. Continuously review waste data to ensure ongoing improvement and adherence to waste reduction goals.

import pandas as pd
import matplotlib.pyplot as plt

def log_waste(date, waste_type, quantity):
    waste_log = pd.DataFrame({
        'Date': [date],
        'Waste_Type': [waste_type],
        'Quantity': [quantity]
    })
    waste_log.to_csv('waste_log.csv', mode='a', header=False, index=False)

def visualize_waste_trends():
    waste_data = pd.read_csv('waste_log.csv', names=['Date', 'Waste_Type', 'Quantity'])
    waste_data['Date'] = pd.to_datetime(waste_data['Date'])
    trends = waste_data.groupby(['Date', 'Waste_Type']).sum().unstack().fillna(0)
    trends.plot(kind='line')
    plt.title('Waste Trends Over Time')
    plt.xlabel('Date')
    plt.ylabel('Quantity')
    plt.show()

# Example usage
log_waste('2025-01-19', 'Scrap Metal', 200)
visualize_waste_trends()
        

{
    "Date": "2025-01-19",
    "Waste_Type": "Scrap Metal",
    "Quantity": 200
}
        

1. Set up a waste tracking system integrated with production processes to capture real-time waste data.
2. Define standardized categories for different types of waste and scrap.
3. Log waste data daily, including date, type, and quantity, into a centralized database.
4. Implement automated data entry mechanisms to reduce manual errors and ensure consistency.
5. Generate regular reports and dashboards to visualize waste trends and identify patterns.
6. Analyze data to evaluate the effectiveness of waste reduction initiatives and identify areas for improvement.
7. Share insights with relevant departments to foster a culture of continuous improvement.
8. Adjust waste management strategies based on data-driven insights to achieve waste reduction goals.
9. Conduct periodic reviews to ensure that tracking mechanisms remain effective and aligned with organizational objectives.

def schedule_recycling(waste_id):
    # Placeholder for recycling scheduling logic
    print(f"Recycling scheduled for Waste ID: {waste_id}")

def schedule_disposal(waste_id):
    # Placeholder for disposal scheduling logic
    print(f"Disposal scheduled for Waste ID: {waste_id}")

def manage_waste(waste_type, waste_id):
    if waste_type.lower() in ['scrap metal', 'plastic', 'paper']:
        schedule_recycling(waste_id)
    else:
        schedule_disposal(waste_id)

# Example usage
manage_waste('Scrap Metal', 'WASTE123')
        

{
    "WasteID": "WASTE123",
    "Action": "Recycling",
    "ScheduledDate": "2025-01-20",
    "Status": "Scheduled"
}
        

1. Classify waste types based on material properties and regulatory requirements.
2. Determine the appropriate handling method for each waste type (recycling or disposal).
3. Coordinate with certified recycling vendors and disposal services to schedule waste handling.
4. Implement tracking systems to monitor the movement and handling of waste materials.
5. Ensure that recycling and disposal processes comply with all relevant environmental and safety regulations.
6. Document all waste handling activities to maintain compliance records.
7. Optimize waste handling workflows to improve efficiency and reduce handling times.
8. Provide training to staff on proper waste classification and handling procedures.
9. Regularly review and update waste management protocols to incorporate best practices and regulatory changes.

class CustomProductionAgent:
    def __init__(self, product, quantity, steps):
        self.product = product
        self.quantity = quantity
        self.steps = steps

    def execute_step(self, step):
        # Placeholder for step execution logic
        print(f"Executing step: {step}")

    def process_order(self):
        for step in self.steps:
            self.execute_step(step)
        return {
            "Product": self.product,
            "Quantity": self.quantity,
            "Status": "Processed"
        }

# Example usage
custom_order = CustomProductionAgent('Custom A', 100, ['Cut', 'Assemble', 'Test'])
order_status = custom_order.process_order()
print(order_status)
        

{
    "Product": "Custom A",
    "Quantity": 100,
    "Status": "Processed"
}
        

1. Define specific workflows tailored to unique components or custom configurations required for small production runs.
2. Allocate appropriate resources, including specialized machinery and skilled labor, to handle custom orders.
3. Implement flexible scheduling systems that can adapt to varying production timelines and order sizes.
4. Monitor production progress in real-time to ensure adherence to quality standards and timelines.
5. Utilize agile project management methodologies to allow for quick adjustments and iterations based on production feedback.
6. Integrate quality control checks at each step of the custom production process to maintain high standards.
7. Facilitate clear communication channels between production teams, suppliers, and customers to manage expectations and resolve issues promptly.
8. Optimize inventory management to ensure that unique components are available when needed without incurring excessive holding costs.
9. Analyze production data to identify bottlenecks and inefficiencies, implementing improvements to streamline future custom orders.
10. Ensure that all custom production activities comply with relevant industry standards and regulatory requirements.

def initiate_return(product_id):
    # Placeholder for return initiation logic
    print(f"Return initiated for Product ID: {product_id}")

def schedule_repair(product_id):
    # Placeholder for repair scheduling logic
    print(f"Repair scheduled for Product ID: {product_id}")

def process_return(defect_found, product_id):
    if defect_found:
        initiate_return(product_id)
        schedule_repair(product_id)
        return {
            "ProductID": product_id,
            "ReturnStatus": "Initiated",
            "RepairStatus": "Scheduled"
        }
    else:
        return {
            "ProductID": product_id,
            "ReturnStatus": "No Action Required",
            "RepairStatus": "N/A"
        }

# Example usage
result = process_return(True, 'PROD12345')
print(result)
        

{
    "ProductID": "PROD12345",
    "ReturnStatus": "Initiated",
    "RepairStatus": "Scheduled"
}
        

1. Receive and log returned goods into the returns management system.
2. Inspect returned products to identify and document defects or issues.
3. Determine the appropriate course of action based on the defect assessment (repair, rework, or disposal).
4. Initiate the return process by notifying logistics and arranging for the return shipment if necessary.
5. Schedule repairs or rework by coordinating with the maintenance or production teams.
6. Update inventory records to reflect the status of returned goods.
7. Communicate with customers regarding the status of their returns and any actions being taken.
8. Track the progress of repairs or rework to ensure timely resolution.
9. Update the system upon completion of repairs, ensuring that products are returned to inventory or dispatched back to customers as appropriate.
10. Analyze return data to identify trends and implement strategies to reduce future defects.
11. Maintain comprehensive records of all return, repair, and rework activities for accountability and continuous improvement.
12. Provide training to customer service teams to handle returns efficiently and empathetically.

def get_compliance_status(material_id):
    # Placeholder for compliance check logic
    # In a real scenario, this would query a database or API
    compliance_data = {
        "MAT_X": True,
        "MAT_Y": False
    }
    return compliance_data.get(material_id, False)

def flag_non_compliance(material_id):
    # Placeholder for non-compliance flagging logic
    print(f"Non-compliance flagged for Material ID: {material_id}")

def check_compliance(material_id):
    compliance = get_compliance_status(material_id)
    if not compliance:
        flag_non_compliance(material_id)
        return False
    return True

# Example usage
compliant = check_compliance('MAT_X')
print(f"Compliance Status: {compliant}")
        

{
    "MaterialID": "MAT_X",
    "Status": "Compliant",
    "ActionRequired": "None"
},
{
    "MaterialID": "MAT_Y",
    "Status": "Non-Compliant",
    "ActionRequired": "Immediate Review"
}
        

1. Review and stay updated with relevant local, state, and federal regulations pertaining to materials, production processes, and waste management.
2. Implement monitoring systems to track compliance across all operational processes in real-time.
3. Conduct regular audits and inspections to identify any deviations from compliance standards.
4. Utilize automated tools and software to flag non-compliant materials and processes.
5. Flag and document instances of non-compliance for corrective action and reporting.
6. Recommend and implement corrective measures to address non-compliance issues identified during audits.
7. Maintain comprehensive documentation of compliance efforts, including audit reports, corrective actions, and regulatory filings.
8. Provide training and resources to employees to ensure understanding and adherence to compliance requirements.
9. Collaborate with legal and compliance teams to interpret and apply regulatory changes effectively.
10. Develop and enforce standard operating procedures (SOPs) that incorporate compliance guidelines and best practices.
11. Leverage data analytics to identify trends in compliance issues and proactively address potential risks.
12. Ensure transparent communication with regulatory bodies during inspections and audits, providing necessary documentation and evidence of compliance.
13. Continuously evaluate and improve compliance monitoring systems to enhance accuracy and efficiency.
14. Integrate compliance checks into existing business workflows to ensure seamless adherence without disrupting operations.