Real-Time Inventory Tracking for Grocery Stores Using MongoDB & Python

Automate Real-Time Stock Tracking and Updates

Grocery stores rely on real-time inventory management to ensure product availability and prevent stockouts or overstocking. By implementing MongoDB for centralized data management and Python for automation, grocery stores can efficiently track stock levels, process updates, and make data-driven restocking decisions in real time.

This system can automatically update inventory records whenever products are sold, restocked, or moved. Integrating MongoDB with Python scripts ensures that inventory data is always up-to-date and accessible from any location.

Code Example:

import pymongo
from datetime import datetime

# Connect to MongoDB
client = pymongo.MongoClient("mongodb://localhost:27017/")
db = client["grocery_store"]
inventory_collection = db["inventory"]

# Sample data: updating inventory after a sale
item_sold = {
    "item": "Apple",
    "quantity_sold": 10,
    "timestamp": datetime.now()
}

# Update inventory in MongoDB
def update_inventory(item_sold):
    item = inventory_collection.find_one({"item": item_sold["item"]})

    if item:
        new_stock = item["stock_quantity"] - item_sold["quantity_sold"]
        inventory_collection.update_one(
            {"item": item_sold["item"]},
            {"$set": {"stock_quantity": new_stock, "last_updated": item_sold["timestamp"]}}
        )
        print(f"Inventory updated for {item_sold['item']}. New stock: {new_stock}")
    else:
        print("Item not found in inventory.")

# Call function to update inventory
update_inventory(item_sold)

This Python code connects to a MongoDB database, fetches the current stock of a product (like an Apple), subtracts the sold quantity, and updates the stock in real-time.

Use MongoDB for Centralized Data Management

Using MongoDB to store inventory data ensures centralized, scalable, and flexible management of large datasets. This is especially beneficial for grocery stores that have many products with frequent stock changes.

With MongoDB:

• All inventory data is stored in one location.
• Updates are reflected across systems in real-time.
• Efficient queries can be run to track and manage inventory.

MongoDB’s flexibility with schema allows you to add or update product details (such as pricing, vendor information, etc.) without disrupting the system’s integrity.

Code Example:

# Sample function to add new products to the inventory
def add_product_to_inventory(product_name, initial_stock):
    product = {
        "item": product_name,
        "stock_quantity": initial_stock,
        "last_updated": datetime.now()
    }

    # Insert product into MongoDB
    inventory_collection.insert_one(product)
    print(f"Product {product_name} added with stock: {initial_stock}")

# Example of adding a new product
add_product_to_inventory("Banana", 100)

This Python script allows new items to be added to the MongoDB database, including their initial stock levels and timestamps. The system is scalable and can easily accommodate new products without system reconfiguration.

Avoid Overstocking and Waste by Automating Demand-Based Restocking

In grocery stores, overstocking or understocking can lead to waste or missed sales. By automating demand-based restocking using Python and MongoDB, grocery stores can optimize their stock levels, preventing both overstocking and waste.

Python can be used to calculate demand forecasts based on sales data, automatically triggering restocking alerts when items are running low. MongoDB stores this data and provides real-time visibility into stock levels, so the store can make informed decisions.

Code Example:

import pandas as pd
import numpy as np

# Simulate historical sales data for forecasting
sales_data = pd.DataFrame({
    'date': pd.date_range(start="2023-01-01", periods=30, freq='D'),
    'item': ['Apple']*30,
    'quantity_sold': np.random.randint(10, 50, size=30)
})

# Forecast future demand (simple moving average model)
sales_data['7_day_avg'] = sales_data['quantity_sold'].rolling(window=7).mean()

# Predict next day's demand
predicted_demand = sales_data['7_day_avg'].iloc[-1]
print(f"Predicted demand for the next day: {predicted_demand} units")

# Compare with current stock (using MongoDB data)
current_stock = inventory_collection.find_one({"item": "Apple"})["stock_quantity"]

# Restock alert if stock is lower than predicted demand
if current_stock < predicted_demand:
    print(f"Restock alert: Order {predicted_demand - current_stock} units.")
else:
    print("Stock is sufficient.")

This code uses a simple moving average model to predict future demand based on past sales data. The predicted demand is compared to the current stock in MongoDB, and a restock alert is triggered if the stock is lower than the forecast.

Why Choose Lillqvist Strat?

At Lillqvist Strat, we specialize in providing automated, data-driven solutions that transform grocery store operations:

• Automate real-time inventory tracking with MongoDB and Python to ensure stock accuracy.
• Centralize inventory management for easy access and monitoring.
• Optimize stock levels by leveraging demand-based restocking algorithms and predictive insights.

With Lillqvist Strat’s tailored solutions, grocery stores can:

• Save time by automating manual tasks.
• Reduce waste and improve stock efficiency.
• Make more informed purchasing and stocking decisions.

Let us help you streamline your grocery store’s inventory management, increase efficiency, and ultimately improve profitability. Choose Lillqvist Strat for your store’s future-ready automation needs.