Predictive AI Retail Demand Forecasting

A retail chain was losing money on both ends: overstocking slow-moving products and running out of bestsellers. Their ordering was based on gut feeling and basic spreadsheet averages, which couldn't account for seasonality, promotions, or market shifts.

I built a machine learning demand forecasting system using Prophet and Python that predicts product demand with high accuracy, weeks in advance.

Historical sales data is ingested from the POS system into Supabase

The Prophet model trains on each product category, accounting for seasonality, holidays, and promotional events

Weekly forecasts are generated automatically and pushed to a dashboard

Purchasing teams use the forecasts to optimize order quantities

Product-level demand forecasting with weekly granularity

Automatic detection of seasonal patterns and trend shifts

Promotion impact modeling (what happens to demand when you run a sale)

Dashboard with forecast vs. actual tracking

Alert system for anomalous demand spikes or drops

ML: Prophet, Python

Database: Supabase

Visualization: Custom dashboard

Overstock reduced by 35%

Stockouts reduced by 50%

Inventory carrying costs down 25%

Purchasing team saves 8+ hours per week on manual forecasting