How Retail Stores Can Automate Inventory Management with Protocols

Introduction

Retail businesses rely on accurate inventory management to avoid stockouts and overstocking. Manually updating inventory in Excel is time-consuming, prone to errors, and slows down decision-making. With Protocols, you can automate inventory tracking, ensuring you always know what’s in stock, what’s running low, and what needs restocking.

In this guide, I’ll show you how to use Protocols to update inventory automatically using Python and Pandas—even if you’ve never written a line of code before.

The Problem: Manual Inventory Updates Are Inefficient

Most retail stores track inventory in Excel, but updating stock levels manually after each sale is a nightmare. Mistakes lead to either lost sales due to stockouts or wasted money from over-ordering. A smarter approach is to automate the process with a script that:
✅ Reads sales data from an Excel file
✅ Updates inventory based on sales
✅ Generates a restock list when stock is low

The Solution: Automating Inventory Updates with Protocols

With Protocols, you can automatically process sales data and update inventory levels with just a few commands. Here’s how it works:

Step 1: Load Your Inventory and Sales Data

First, we need to import our inventory and sales data from Excel. We’ll use Pandas, a Python library that makes handling spreadsheets easy.

Example Inventory File (inventory.xlsx)

Product

SKU

Stock

Reorder Level

T-Shirt

1001

50

10

Jeans

1002

30

5

Shoes

1003

20

8

Example Sales File (sales.xlsx)

SKU

Quantity Sold

1001

5

1002

10

1003

15

Now, let’s load these files in Python.

import pandas as pd  

# Load inventory and sales data
inventory = pd.read_excel("inventory.xlsx")
sales = pd.read_excel("sales.xlsx")

# Display the first few rows
print(inventory.head())
print(sales.head())

Step 2: Update Inventory Based on Sales

We subtract the quantity sold from the current stock levels.

# Merge sales data with inventory
updated_inventory = inventory.merge(sales, on="SKU", how="left")

# If no sales, set Quantity Sold to 0
updated_inventory["Quantity Sold"].fillna(0, inplace=True)

# Subtract sales from stock
updated_inventory["Stock"] -= updated_inventory["Quantity Sold"]

# Remove the Quantity Sold column
updated_inventory.drop(columns=["Quantity Sold"], inplace=True)

print(updated_inventory)

Step 3: Generate a Restock List

If a product’s stock falls below the reorder level, it should be flagged for restocking.

# Filter products that need restocking
restock_list = updated_inventory[updated_inventory["Stock"] < updated_inventory["Reorder Level"]]

# Save to an Excel file
restock_list.to_excel("restock_list.xlsx", index=False)

print("Restock list saved as restock_list.xlsx")

Step 4: Save the Updated Inventory

Finally, we save the new stock levels back to Excel.

updated_inventory.to_excel("updated_inventory.xlsx", index=False)
print("Updated inventory saved as updated_inventory.xlsx")

The Result: Smarter, Faster Inventory Management

With this automation in place, every time you receive sales data, you just run the script, and it:
✅ Updates stock levels
✅ Identifies low-stock items
✅ Creates a restock list

Want This Without the Hassle? Let Me Handle It!

I specialize in automating business processes with Python, Pandas, and MongoDB. If you want an inventory automation system tailored to your store, I can build it for you. Contact me, and let’s make your retail operations seamless.

Ready to eliminate manual inventory tracking? Let’s talk!