Building an Automated Inventory Replenishment System with Python

Managing inventory levels and ensuring timely stock replenishment is critical for maintaining smooth operations and meeting customer demand. By automating inventory replenishment with Python, MongoDB, and Pandas, businesses can optimize stock levels and reduce the risk of both overstocking and stockouts.

Lillqvist Strata offers cutting-edge solutions for automating inventory management, empowering businesses to streamline their operations and focus on growth.

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1. Replenishment Needs

Challenges in Inventory Replenishment

• Manual processes lead to human errors and inefficient stock management.
• Stockouts can cause delays in fulfilling customer orders, impacting satisfaction.
• Overstocking ties up capital and increases storage costs.

Why Automate Inventory Replenishment?

Automating the replenishment system:

• Ensures stock levels are replenished at optimal times.
• Minimizes human errors and administrative overhead.
• Helps businesses predict future stock needs based on sales trends.

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2. MongoDB Stock Data

Setting Up MongoDB for Stock Management

MongoDB can store product data such as sales trends, current stock levels, and reorder points. The flexibility of MongoDB allows for easy scaling as the number of products and transactions increases.

MongoDB Schema for Stock Data

from pymongo import MongoClient

# Connect to MongoDB
client = MongoClient("mongodb://localhost:27017/")
db = client["inventory"]
stock_data = db["stock"]

# Example stock data to insert
data = {
    "product_id": "1234",
    "product_name": "Widget A",
    "current_stock": 150,
    "reorder_point": 50,
    "sales_last_month": 200,
    "supplier": "Widget Supplier Inc."
}

# Insert stock data into MongoDB
stock_data.insert_one(data)

Querying Stock Data

# Query to find products with stock below the reorder point
low_stock_products = stock_data.find({"current_stock": {"$lt": "$reorder_point"}})
for product in low_stock_products:
    print(product)

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3. Python Reorder Logic

Determining Reorder Quantities

Using Python, we can automate the reorder logic by calculating how much stock to reorder based on sales data, current stock levels, and reorder points.

Reorder Logic Algorithm

def calculate_reorder_quantity(product_id, current_stock, sales_last_month, reorder_point):
    # Define reorder strategy: reorder to meet demand for the next month
    expected_sales_next_month = sales_last_month  # Adjust with seasonal factors if needed
    reorder_quantity = max(expected_sales_next_month - current_stock, reorder_point)
    return reorder_quantity

# Example: Calculate reorder quantity for a product
reorder_quantity = calculate_reorder_quantity("1234", 150, 200, 50)
print(f"Reorder Quantity: {reorder_quantity}")

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4. Pandas Demand Analysis

Analyzing Sales Trends for Demand Forecasting

Using Pandas, we can analyze past sales data to predict future demand and adjust the reorder logic accordingly. The analysis will help refine the reorder quantities and timing.

import pandas as pd

# Example sales data: Replace with actual sales data
sales_data = {
    "product_id": ["1234", "1235", "1236"],
    "sales_last_month": [200, 150, 250],
    "current_stock": [150, 200, 180],
    "reorder_point": [50, 60, 40]
}

df = pd.DataFrame(sales_data)

# Calculate reorder quantities based on sales data
df['reorder_quantity'] = df.apply(lambda row: calculate_reorder_quantity(row['product_id'], row['current_stock'], row['sales_last_month'], row['reorder_point']), axis=1)

# Visualize reorder quantities
print(df[['product_id', 'reorder_quantity']])

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5. Supplier Integration

Integrating with Suppliers for Automatic Ordering

The final step is to automate the interaction with suppliers. Once the reorder quantity is determined, Python can send automated orders to suppliers. This can be done by integrating with the supplier’s API or by generating orders that can be emailed.

Example: Generating an Order

import smtplib
from email.mime.text import MIMEText

def send_order_to_supplier(product_id, reorder_quantity, supplier_email):
    order_details = f"Product ID: {product_id}\nReorder Quantity: {reorder_quantity}"

    # Create the email message
    msg = MIMEText(order_details)
    msg['Subject'] = f"Automated Order for Product {product_id}"
    msg['From'] = "inventory@yourcompany.com"
    msg['To'] = supplier_email

    # Send the email
    server = smtplib.SMTP('smtp.yourcompany.com')
    server.login("your_username", "your_password")
    server.sendmail("inventory@yourcompany.com", supplier_email, msg.as_string())
    server.quit()

# Example: Send an order to a supplier
send_order_to_supplier("1234", reorder_quantity, "supplier@widget.com")

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Conclusion

Automating the inventory replenishment process with Python, MongoDB, and Pandas helps businesses:

• Ensure timely replenishment by analyzing sales data and stock levels.
• Minimize human errors by automating the reorder process.
• Optimize supply chain efficiency by integrating with suppliers for automatic ordering.

By leveraging Lillqvist Strat‘s solutions, you can streamline inventory management and ensure your business stays ahead in a competitive market.

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Key Benefits

• Automated stock tracking and replenishment based on demand.
• Data-driven decisions powered by sales trends and demand forecasting.
• Supplier integration for seamless ordering and timely deliveries.