Scones at two in the afternoon
On a sunny Wednesday in the summer holidays, the café at your garden and play area runs out of sandwiches by 1pm and scones by 2pm. The queue for the till reaches the door. On the following Monday, it rains, advance bookings are low, and a tray of sandwiches and a batch of cakes go in the bin at closing.
The café manager orders from suppliers two or three days ahead and preps each morning. She checks the weather app and the diary on the wall for school groups. She does not see advance ticket sales, because they are in a different system that only the office uses.
Why café planning is guesswork
Café demand follows visitor numbers, but not simply. A school group brings its own lunch. A birthday party has pre-ordered food. A rainy day means more people in the café, but fewer people overall. Pass holders visit for shorter periods and may buy only a coffee.
The information that would help, such as advance bookings, groups, parties and the forecast, exists across ticketing, the learning team and events, but not in front of the café manager when she orders.
Supplier lead times mean decisions are made days before the visitors arrive, when the picture is still changing.
Till data from past days is also rarely used, because it sits in the EPOS back office and needs matching to what else was happening that day to mean anything.
What guesswork costs
| Pattern | Consequence |
|---|---|
| Running out at lunch | Queues, lost sales and poor reviews |
| Over-prepping on quiet days | Food thrown away |
| Staff rota'd without covers forecast | Too few or too many on the till |
| Groups and parties not visible | Surprise demand or wasted prep |
| No link to past sales | Same mistakes each season |
Café income is often a large part of an attraction's spend per visitor, and waste goes straight to the bottom line.
A cover forecast for the café
We build a café forecast that uses the data you already collect.
- We gather advance ticket sales and pass holder patterns from ticketing, school group and party bookings, events and the weather forecast.
- Past till data from your EPOS is matched to visitor numbers and conditions, so the model learns how café sales relate to visitors on different kinds of days.
- Each morning, and several days ahead, the café manager sees expected covers by time of day, with a range, plus the groups and parties with their own food arrangements.
- Suggested prep quantities for your main lines are shown against the forecast, based on your own recipes and past sales mix.
- The forecast updates as bookings and weather change, and alerts the manager when a day moves significantly.
- Actual sales and waste are compared with the forecast each day, so accuracy is visible and the model improves.
The café manager still decides what to order and prep. The forecast gives her the information she was missing.
Situations it accounts for
- Events with their own food stalls, which reduce café demand.
- Hot days that shift demand from hot food to ice cream and cold drinks.
- School groups that buy lunch rather than bringing it, as recorded in their booking.
- New menu items with no history, which start from your estimate.
A summer Wednesday afterwards
On Monday, the café forecast for Wednesday is high because advance sales jumped and the weather looks good. The manager orders more bread and fillings and adds a person to the till. On Wednesday, the sandwiches last until mid-afternoon. On the rainy Monday that follows, prep is reduced, and less goes in the bin.
- Café planning based on expected visitors
- Groups and parties visible to the café
- Suggested prep quantities for main lines
- Less waste and fewer lunchtime sell-outs
Is your café planned like this?
- Café orders are based on last week and instinct.
- The café cannot see advance ticket sales.
- You run out at lunch on busy days.
- Food is thrown away on quiet days.
- Groups and parties surprise the café team.