Retail Sales Demand Forecasting for Inventory Planning
Forecast product demand per store for the next four weeks and recommend reorder quantities to avoid stock-outs and overstock.
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Problem Statement & Abstract
Shops lose money both when products run out and when unsold stock piles up. This project forecasts how much of each product each store will sell, and turns the forecast into a reorder plan.
Using a public multi-store retail dataset, the project creates time-series features such as lagged sales, rolling averages, day of week, month, holidays and promotions. A global XGBoost model learns patterns across all products and stores, and is compared with a seasonal naive baseline and Prophet using MAPE and WAPE.
Forecasts for the next four weeks feed an inventory module that calculates safety stock and reorder points from forecast uncertainty and supplier lead time, and recommends order quantities. A Streamlit dashboard shows forecasts per product and store, products at risk of running out and the expected value of overstock. Data is stored in PostgreSQL and forecasts refresh weekly.
Key Features
- Lag, rolling, calendar, holiday and promotion features
- Global XGBoost model across stores and products
- Comparison with seasonal naive and Prophet baselines
- Four-week forecasts with uncertainty bands
- Safety stock and reorder point calculation
- Recommended order quantities
- Stock-out and overstock risk dashboard
- Weekly automatic retraining
Suitable For
Everything included
+ GST • about $96 USD
Added to your cart
- Delivered in 10 to 12 working days
- Free revisions for 30 days while your guide reviews
- Report written to your university’s format