Every quarter, our team at Helion 360 works with product-based clients who send over stacks of Excel files — purchase orders, stock counts, supplier lead times, sales velocity reports — and ask us to make sense of it all. For a long time, that meant hours of manual consolidation, pivot tables, and VLOOKUP gymnastics. Then I started using ChatGPT to help me write Python scripts that automate the entire inventory analysis workflow, and it changed everything.
This isn't a theoretical post. I'm going to walk you through exactly how I approached it, what the script does, and where ChatGPT genuinely saved me time versus where I still had to think critically.
The Problem: Inventory Data Is Messy and Repetitive
Most inventory Excel files aren't clean. Column headers shift between months. One file calls it "SKU ID," another says "Product Code." Stock quantities might be in units one sheet and cases another. When you're dealing with five files from three different warehouse locations, the normalization work alone can eat half a day.
The goal wasn't to replace analysis — it was to eliminate the manual grunt work so I could spend time on the insights, not the setup.
How I Used ChatGPT to Write the Script
I didn't just paste a vague prompt and hope for the best. I treated ChatGPT like a junior developer with strong Python knowledge and gave it structured context. Here's how the conversation evolved:
Step 1: Define the Problem Clearly in the Prompt
My first prompt looked like this:
"I have multiple Excel files in a folder. Each file represents a different warehouse location. They all have similar but not identical column headers for SKU, quantity on hand, reorder point, and supplier lead time. Write a Python script using pandas that reads all .xlsx files from a folder, normalizes the column headers, and merges them into a single master dataframe."
ChatGPT returned a solid starting script using os.listdir(), pd.read_excel(), and a header mapping dictionary. I gave it my actual column variations and it refined the mapping logic immediately.
Step 2: Add Analysis Logic
Once the data was consolidated, I asked ChatGPT to extend the script to flag SKUs that were below their reorder point, calculate days of inventory remaining based on average daily sales, and identify slow-moving stock (items with less than 10 units sold in the last 90 days).
It generated clean, readable logic — not overly complex — and I could follow every line. That matters because I needed to explain this to clients and modify it later myself.
Step 3: Output to a Formatted Excel Report
Raw dataframes aren't client-ready. I asked ChatGPT to use openpyxl to apply conditional formatting — red fill for below-reorder SKUs, yellow for slow movers, green for healthy stock. It wrote the styling logic without me needing to read through the openpyxl docs myself.
The final script produces a formatted Excel file with three tabs: Master Inventory, Reorder Alerts, and Slow Movers.
What the Final Script Actually Does
- Scans a designated folder for all .xlsx files automatically
- Normalizes inconsistent column headers using a configurable mapping dictionary
- Merges all location data with a source tag so you know which warehouse each row came from
- Calculates days of inventory remaining per SKU based on a rolling 30-day sales average
- Flags reorder alerts where quantity on hand falls below the reorder point threshold
- Identifies slow-moving inventory based on configurable sales velocity thresholds
- Outputs a color-coded Excel report with separate tabs for each analysis view
The whole script runs in under 30 seconds on files that used to take two hours to process manually.
Where ChatGPT Helped Most
I want to be honest about this because there's a lot of hype around AI automation. ChatGPT was genuinely excellent at:
- Boilerplate code — reading files, looping through directories, dataframe merges
- Library syntax — I didn't have to memorize openpyxl styling methods or pandas groupby arguments
- Debugging — when I got a KeyError or a dtype mismatch, I pasted the error back in and got a fix within seconds
- Iteration speed — what would've taken me three hours of Stack Overflow searching took 45 minutes of back-and-forth prompting
Where You Still Need Human Judgment
This is the part most AI content skips. ChatGPT doesn't know your client's business rules. It doesn't know that one warehouse reports quantities in cases of 12 while another uses individual units. It doesn't know that certain SKUs are seasonal and a "slow mover" flag is expected in Q1. You have to encode that context into your prompts and review the output critically.
The script is a tool. The analysis is still your job.
Practical Tips If You're Doing This Yourself
- Start with a sample dataset. Don't run untested scripts on your actual client files. Create a small representative sample and validate the output first.
- Use descriptive variable names in your prompts. Saying "column called reorder_point" gets better results than "the threshold column."
- Ask ChatGPT to add comments. Request that it annotate the code so you understand what each section does — this is essential if you need to modify it later.
- Version your scripts. Every time ChatGPT modifies the script, save a new version. You'll want to roll back if an update breaks something.
- Build in error handling. Ask ChatGPT to add try/except blocks so the script doesn't crash if a file is missing a column or is password protected.
The Business Impact
For one client — a mid-size distributor with four warehouse locations — this script cut our monthly inventory reporting time from roughly 6 hours to under 45 minutes. That's not just efficiency. It meant we could run the analysis weekly instead of monthly, giving them earlier visibility into reorder needs and reducing stockout incidents by about 30% over the following quarter.
That's the real argument for building these tools: not just speed, but frequency. When analysis is cheap to run, you run it more often, and you catch problems earlier.
Getting Started Today
You don't need to be a Python expert to do this. You need a basic understanding of what you want the script to do, the ability to install Python and a few libraries (pandas, openpyxl), and a willingness to iterate with ChatGPT until the output matches your needs. The barrier is lower than most people think, and the payoff compounds over time as you reuse and refine the script across multiple clients or use cases.
If you want to go further, consider scheduling the script to run automatically using Windows Task Scheduler or a cron job, and email the output report directly to stakeholders. That's a workflow I'm actively building out now — and yes, ChatGPT is helping me write that too.