Automating Warehouse Management with Python and MongoDB

Managing a warehouse efficiently requires real-time inventory tracking, automated stock updates, and data-driven decision-making. Python, MongoDB, and Pandas can streamline operations by automating stock management, reducing errors, and optimizing warehouse space.

Lillqvist Strat provides automation solutions that increase accuracy and improve warehouse efficiency.

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1. Warehouse Needs

An automated warehouse management system should:

• Track inventory in real time using MongoDB.
• Automate stock level updates and reorder alerts.
• Use Pandas to analyze stock trends and improve efficiency.
• Provide real-time dashboards for warehouse managers.

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2. MongoDB Inventory Schema

We use MongoDB to store warehouse inventory and track stock levels dynamically.

Setting Up the Database

from pymongo import MongoClient

client = MongoClient("mongodb://localhost:27017/")
db = client["warehouse_management"]
inventory_collection = db["inventory"]

Defining the Inventory Schema

product = {
    "product_id": "SKU12345",
    "name": "Wireless Keyboard",
    "category": "Electronics",
    "quantity": 100,
    "reorder_level": 20,
    "last_updated": "2025-02-23"
}

inventory_collection.insert_one(product)

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3. Python Automation for Stock Updates

To prevent stockouts, we automate reorder alerts when stock falls below the reorder level.

def check_reorder_levels():
    low_stock_items = inventory_collection.find({"quantity": {"$lt": 20}})
    for item in low_stock_items:
        print(f"⚠️ Low stock alert: {item['name']} (Stock: {item['quantity']})")

check_reorder_levels()

Automating Stock Adjustments

def update_stock(product_id, sold_quantity):
    inventory_collection.update_one(
        {"product_id": product_id},
        {"$inc": {"quantity": -sold_quantity}}
    )
    print(f"Stock updated for {product_id}: -{sold_quantity} units.")

update_stock("SKU12345", 5)

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4. Pandas Stock Analysis

To analyze stock movement and optimize ordering, we use Pandas.

import pandas as pd

# Load inventory data into Pandas
inventory_data = list(inventory_collection.find({}, {"_id": 0}))
df = pd.DataFrame(inventory_data)

# Analyze stock levels
stock_summary = df.groupby("category")["quantity"].sum()
print(stock_summary)

# Export stock report
df.to_excel("warehouse_report.xlsx", index=False)

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5. Real-Time Inventory Updates

To keep warehouse managers informed, we integrate real-time updates with MongoDB’s change streams.

from pymongo import MongoClient

def watch_inventory_changes():
    with inventory_collection.watch() as stream:
        for change in stream:
            print(f"🔄 Inventory Update: {change}")

watch_inventory_changes()

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Conclusion

Automating warehouse management with Python, MongoDB, and Pandas reduces manual work, prevents stockouts, and optimizes inventory management.

Lillqvist Strat provides end-to-end automation solutions for warehouses, helping businesses maximize efficiency and profitability. Let’s automate your warehouse today!