Convenience store lunch rush
Watch a konbini lunch rush: registers, coffee, eat-in seats and restocking at once.
No sign-in needed: open it in Studio, inspect the saved results and edit in 2D and 3D. Sign in to connect your own AI; project files remain free. Check Account for execution access. Opening asks before replacing your current space.
Should two cashiers stay on the registers through lunch, or would faster payment be enough?
- Who uses it
- Convenience-store owners, store managers and franchise operations staff
- How it can create value
- Compare a second cashier with shortening payment (cashless, self-checkout) before changing the shift plan. Weigh the modeled waits against the staff hours each option costs; shorter waits alone do not show extra sales.
- Bring your site data
- Arrivals per 15 minutes from POS data, measured payment and coffee times, eat-in seats, staff hours and their cost. Shoppers who leave because of the line need observed data and are not modeled.

A model you can inspect.
Lunch rush at the corner convenience store: about 150 shoppers an hour for 35 minutes come from the car park, pick up items at the shelves, drinks wall, bento chiller or magazines (a few use the ATM, copier or restroom), join the line and take the free register (or the one with fewer waiting), and some pour a coffee or take an eat-in seat; meanwhile a driver rolls three delivery cages into the back room and one clerk restocks the walk-in and the bento chiller. Cashiers are a staff count, not people on the plan: with one, a shopper at the second register waits for them; payment takes the fixed time you set. Queues are counts and waiting times, not crowd physics, and the run does not predict sales, basket size or shoppers who leave because of the line.
Change one thing
Compare 2 and 1 cashiers, then payment times of 35 s and 25 s, with the same shoppers and seed.
Read the consequence
Register queues and waits, eat-in occupancy and coffee-machine use. Shoppers take the free register or the shorter line; sales and crowd movement are not predicted.
View the starting inputs
- Shoppers per hour
- 150
- Payment time (s)
- 35
- Cashiers on the registers
- 2
- Eat-in seats
- 5
Agent counts, routes and resource definitions are also saved in the project JSON.
A reproducible starting result.
One run, seed 15. Observation window 60 minutes.
| Scenario | Completed | Mean wait | Mean cycle | Blocked |
|---|---|---|---|---|
| Original scenario | 89 | 28s | 5m 34s | 0 |
Waiting averages cover arrived jobs, including unfinished waits; cycle averages cover completed jobs only. These are synthetic calculations, not measured site performance, revenue or a safety assessment.
Engine, status and output notes
process-des-2.7.0. Status: Original scenario: completed. “Completed” is the engine run status, not success for every job. Check blocked jobs and warnings.
- No engine warnings.
Engine inputs and saved comparisons stay with the saved project. Any limited trace affects playback details, not aggregate metrics.
Articles on this example
Convenience store owners and managers, franchise store operations staff, and anyone drawing up a small shop’s rotaHow many people does a convenience store need at lunch?At a convenience store’s lunch peak of 150 shoppers an hour, two staff who both work the checkouts beat a cashier plus a restocker, and with semi-self checkouts one cashier keeps up.Read the walkthrough
Convenience store and small shop managers, those who plan the shelves and promotions, and store operations staff at head officeWhich shelves do shoppers see at a convenience store’s lunch rush?In a simulated lunch rush at a convenience store, the AI counts how long each shelf face is in view. The tobacco and lottery shelf behind the checkouts leads: all 85 shoppers face it as they queue.Read the walkthrough
Convenience and small store managers and owners, head-office staff planning store security, installers proposing security camerasHow many security cameras does a convenience store need?On a convenience store plan, six security cameras cover 91.9% of the sales floor, checkouts, entrance and ATM (weighted). With the lunch crowd in the way, 87% of people are seen, minute by minute.Read the walkthrough
Plan in 2D. Inspect in 3D. Keep the whole project.
- Open and adjust. Switch between 2D and 3D; edit a part, a floor or an input. Both views describe the same model.
- Connect your agent for free. Sign in, choose Connect AI and connect ChatGPT, Claude, Codex or Claude Code. Ask it to inspect the example, edit the layout and check a route.
- Save a checkpoint. Project → Project file, or ask the agent to export the complete project. Import it later to restore floors, scenarios, saved comparisons and the selected view.
- Compare options. Run new operational comparisons under the same demand. Check Account for execution access, built-in AI and manual cloud-saving limits.