of 6— Prepare your demand history data

What you'll accomplish

By the end of this guide, you'll have a working Python script that builds a statistical demand forecast (exponential smoothing) for your SKUs, calculates forecast error metrics, and outputs a CSV of forecasted demand — using ChatGPT to write and debug the code, no data science degree required.

What you'll need

Python installed (python.org) and a basic IDE (VS Code, or even Jupyter Notebook)
ChatGPT account (free) — for code generation and debugging
Historical demand data as a CSV (SKU, Date/Week/Month, Units Sold)
Time needed: 60 minutes initial build; run it in 5 minutes after that
Cost: Free (Python + ChatGPT free tier)

How-To Guide: Build a Statistical Demand Forecast Model in Python with ChatGPT

Step 1: Prepare your demand history data

Export demand history from your ERP as a CSV. You need at minimum:

A date column (weekly or monthly)
A SKU/product identifier column
A units sold column

Example columns: SKU_ID, Week_Ending, Units_Sold

Save it as demand_history.csv.