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Kitchen forecasting basics: translate expected sales into tomorrow’s prep pars

Build a practical prep forecast from sales history, then adjust it for weather, events, bookings, and safety stock.

Forecast error should be visible in both directions. Preparing 42 portions against demand of 35 is a +20% error; preparing 30 against demand of 36 is a −16.7% error and may cause a sell-out. Track the sign rather than reporting a 20% ‘accuracy gap’ that treats shortage and excess alike. Over six weeks, repeated positive errors point to inflated pars, while alternating signs may indicate that the weather or booking adjustment is too crude.

Build a baseline from services that truly resemble tomorrow

  • History: select the same daypart and weekday across four to eight weeks. Use median covers and item units so one tour group or storm does not dominate the starting point.
  • Bookings and walk-ins: convert confirmed reservations using the restaurant’s own no-show rate, then add expected walk-ins without counting the same guest twice.
  • Weather and events: record adjustments as explicit percentages, such as −12% terrace covers for heavy rain or +15% pre-event mains, rather than hiding them inside a rounded guess.
  • Constraints: cap the forecast at available seats, oven throughput, delivery slots, or staffing capacity. Demand above a physical limit cannot become sensible prep.

Work backwards from covers to the amount on the prep list

Illustrative soup par for a rainy Wednesday

The median is 90 dinner covers and soup usually reaches 22% menu mix. Heavy rain is expected to raise soup mix by 25%, while bookings suggest total covers of 96. These are teaching figures, not a verified customer result.

Adjusted demand96 × 22% × 1.25 = 26.4 portionsRound operationally to 27 expected portions
Safety allowance27 × 8% = 2.16 portionsRound to 2 because soup can be replenished quickly
Usable opening stock−5 portionsCount only labelled stock within safe shelf life
Prep par27 + 2 − 5 = 24 portionsPrepare 24 new portions before service

The correct par is not 29. Existing safe stock reduces new prep, and the 8% buffer is applied to expected demand rather than added as an unexplained tray.

Create a forecast–actual loop the brigade will use

  1. 1Select the comparison set. Before the weekly ordering cut-off, pull covers and item units from four to eight matching services. Mark closures, promotions, holidays, and large parties; exclude only with a written reason.
  2. 2Layer known changes. Start with median covers, reconcile confirmed bookings using historical no-shows, then apply separate event, weather, and delivery adjustments. Write every percentage beside its source and cap at capacity.
  3. 3Calculate item pars. Multiply adjusted covers by recent item mix, add an item-specific safety percentage, and subtract usable opening stock. Use smaller buffers for products that can be cooked in 6 minutes and larger ones for a 3-hour braise.
  4. 4Close the learning loop. After service, enter actual units, stock-outs, substitutions, and edible leftovers. Review absolute and signed error weekly; change an adjustment rule only after at least three comparable misses point the same way.

FAQ

Should I forecast sales euros or covers first?
Forecast covers first for prep, then apply menu mix and portions. Sales euros help with labour and cash planning but can rise because prices changed without requiring more food. Delivery orders may need their own order-count forecast.
How much safety stock should I add?
Start around 5–10% for predictable items, then tune by shelf life and replenishment time. A salad component made in 10 minutes needs less protection than a braise that cannot be replaced during service.
What if weather forecasts keep changing?
Set a decision time, for example 15:00 the day before for ordering and 10:00 on the day for final prep. Record the weather information available at that moment; do not judge yesterday’s decision using today’s forecast.
When is there enough history for a new dish?
Use the closest predecessor or section mix for the first two weeks, set a conservative batch size, and log hourly demand. After 20–30 selling days, the item’s own weekday pattern becomes more useful.

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