Order for tomorrow's service, not last week's
Sales history by outlet, weekday and season becomes a forecast for each ingredient. Managers get suggested indents and purchase orders to approve, not a blank form.
What it replaces
The paper, calls and spreadsheets this takes off your team's plate.
How it works
Forecasts per outlet and item
Forecasts use each outlet's own history, so a weekday lunch outlet and a weekend family outlet get different plans.
- Weekday and seasonal patterns
- Holiday and event adjustments
- Accuracy tracked against actuals
Suggestions, not surprises
Forecasts become suggested indents and purchase orders. Managers adjust and approve, and the system learns from what they change.
- Lead time and pack sizes respected
- Minimum order quantities
- One tap approval
Prep plans for the kitchen
The same forecast drives prep quantities for the day, which reduces both over-prep wastage and mid-service shortages.
- Prep lists by station
- Central kitchen production quantities
- Wastage compared against forecast error
Getting started
The order we set it up in.
Learn
At least a few months of POS history.
Forecast
By outlet, day and item.
Suggest
Indents and POs drafted.
Approve
Managers adjust and approve.
What changes
What teams using Forecasting and Auto-ordering should expect to see.
- Less wastage from over-ordering
- Fewer emergency purchases during service
- Managers spend minutes on ordering, not an hour
Questions
How much history do we need?
Forecasts improve with more history. A few months of POS data gives a usable start; accuracy is shown so you know how far to trust it.
Does it order automatically?
It suggests. A person approves every order unless you choose to auto-approve specific low-risk items.
Does it account for promotions?
You can mark promotions and events so the forecast treats those days separately.
See DeepRestaurantAI with your own outlets
We'll connect a sample of your POS data and walk you through sales, purchasing, food cost and DeepObserve.ai camera AI for your format.