Glossary

Demand Forecasting

In one line: AI demand forecasting predicts how much of each product you’ll sell — by location, time period, and SKU — using your sales history plus external signals like weather, events, and seasonality.

What it actually does

Demand forecasting moves you from guessing (“order what we did last week, plus a bit more”) to knowing (“tomorrow you’ll sell 142 chicken bowls, 38 vegan wraps, 207 iced lattes”). The model learns patterns from your own data — weekly cycles, seasonal swings, payday effects — then layers in weather forecasts, local event calendars, and any marketing you’re running.

Why it matters for retail and F&B

Two problems disappear when forecasting works: overstock (waste, tied-up cash, markdown pressure) and stockouts (lost sales, walked-away customers). For a typical small retailer or restaurant, getting forecasting right is usually the single largest margin lever AI offers.

What you need to use it

• At least 12 months of sales history, ideally 18+
• Item-level data from your POS (not just totals)
• Daily granularity at minimum; hourly is better

Related terms

Inventory Optimization, SKU-Level Forecasting, Recommendation Engine