Forecast-based prep
By default, prep list batch recommendations are computed from burn rate — historical consumption rate over a lookback window. Burn rate is a great signal for most kitchens, but it has a blind spot: it can't see ahead. If next Friday is a holiday, has unusually warm weather, or follows a marketing push, last month's average won't catch the spike.
Forecast-based prep flips the model: instead of "what did we use last month?", it asks "what will we sell tomorrow, and what ingredients does that require?"
How it works
When forecast mode is enabled on a prep list, Rinvy:
- Applies the stored demand forecast day by day over the prep window's actual dates (default: through the next prep date) — dates inside the forecast window use that exact day's predicted portions, dates beyond it use the item's per-operating-day average, and closed days contribute nothing.
- Multiplies those expected sales × the menu item's recipe ingredients × the portion multiplier.
- Rolls the ingredient demand up through any sub-recipes (recipe explosion).
- Subtracts current stock and divides by recipe yield to get recommended batches.
This is the same mechanism the order list's forecast mode uses: untouched items keep the forecast's exact per-day portions, and editing an item's expected sales rescales its day-to-day shape proportionally rather than flattening it (setting 0 removes the item).
The output is the same shape as burn-rate-based prep — batch counts per recipe — but the math is forward-looking.
Prerequisites
Forecast-based prep needs:
- Pro tier for both
prepListsanddemandForecastfeature flags. - Sales data uploaded — forecasts use sales history as the primary input. Without sales, the forecast falls back to burn-rate-derived estimates.
- Menu items linked to recipes (forecasts are per-menu-item; recipes are how they get converted to ingredients).
- A current forecast — see the demand forecast page to refresh.
Enabling forecast mode on a prep list
- Open the prep list while in DRAFT.
- Switch the recommendation source from Burn rate to Sales forecast.
Review the new recommendations. They'll typically differ from burn-rate numbers — sometimes meaningfully.
- Adjust as needed and approve.
The mode is set per prep list, not per recipe. You can switch back to burn rate any time before approving.
When to use which
| Situation | Use |
|---|---|
| Stable weekly cycle, no upcoming change | Burn rate |
| Holiday weekend or known event | Forecast |
| Recent menu change, no sales data yet | Burn rate (forecast won't have enough history) |
| Weather-sensitive menu (patio, cold drinks) | Forecast |
| First few weeks on Rinvy | Burn rate (forecasts need history to be useful) |
Why the numbers can move a lot
Burn rate and forecasts can disagree significantly. A few reasons:
- Burn rate averages across the lookback window; forecasts are date-specific.
- Forecasts can adjust for weather, day of week, operator-entered context, and day notes logged during the history period.
- Forecasts learn from their own track record — persistent over- or under-predictions (including per-weekday patterns) get corrected in the next generation.
- With a weather location set, forecasts also weigh observed weather history — how your kitchen actually traded on rainy vs. dry and warm vs. cold days — not just the upcoming forecast.
- A forecast for tomorrow says "we'll sell 40 burgers" — burn rate says "we sold 24 burgers/day on average over the last 30 days."
When they diverge, trust the source with more information about that specific day. For a normal Wednesday, burn rate is fine. On Mother's Day, the forecast captures the spike that the burn-rate average smooths away.
Common mistakes
Switching to forecast mode with an outdated forecast
The Outdated chip on the demand forecast page lists exactly why a forecast is behind — generated more than a day ago, changed business context, new sales data, changed menu items, or a window that has already passed. Regenerate before relying on forecast-based prep.
Expecting forecasts to be exact
A forecast is a prediction with uncertainty. Use it as a starting point, then adjust based on what you know about the day.
Forgetting to upload sales data
Without sales data, the forecast falls back to burn-rate-style estimates. The "switch" between modes becomes cosmetic. Upload at least 30 days of sales for forecasts to be meaningful.