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Demand Forecasting in Restaurants: How to Reduce Waste and Stockouts

8 March 2026 · 9 min

How to predict how many covers you will have and what they will order: practical methods, factors to consider and AI tools for restaurants.

T
Team BiteBase
BiteBase Editorial

Too much prep wastes money, too little disappoints guests

Every restaurant faces the same dilemma: prepare too much and throw food away, or prepare too little and tell customers "we are out." Demand forecasting is the science of finding the sweet spot. A restaurant that forecasts well wastes 2-3% of purchases. One that does not wastes 7-10%. The annual difference runs into thousands of euros.

Six factors that drive demand

  1. Day of the week — Saturday evening 80 covers, Tuesday 35. Historical data by day is the most reliable predictor.
  2. Weather — Rain means 10-20% fewer covers (especially with outdoor seating). Summer sun means 15-20% more.
  3. Local events — Football matches, concerts and fairs shift cover counts depending on your location.
  4. Seasonality — December full, January empty. August at the coast packed, August in the city quiet.
  5. Reservations — The most reliable data point. 40 reserved covers usually means 45-55 total (with walk-ins).
  6. Daily specials — If the daily special is fish, the standard fish main sells less.

A practical forecasting method

  1. Calculate average covers for each day of the week over the last 4 weeks.
  2. Adjust for known factors: confirmed reservations, weather forecast, local events.
  3. From predicted covers, estimate menu mix (40% first course, 30% main, etc.) based on history.
  4. Calculate ingredient quantities needed and compare with current stock.
  5. Order the difference.

BiteBase automates this with AI that analyzes sales history, reservations and seasonality to suggest quantities to prepare and order.

Common mistakes

  1. Preparing for the best case — do not prep for 80 covers when Tuesday averages 40.
  2. Ignoring seasonality — January is not December.
  3. Ignoring reservations — they are the most reliable predictor.
  4. Not tracking stockouts — every "we are out" is data telling you that dish needed more quantity.

FAQ

How accurate is AI forecasting? With 3+ months of history, 85-90% accuracy on cover counts and 75-80% on menu mix.

How do I handle unpredictable events? Keep a 10-15% safety stock on non-perishables. For perishables, order just in time.

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