Retail & venues / WORKED EXAMPLE
Convenience store lunch rush
Watch a konbini lunch rush: registers, coffee, eat-in seats and restocking at once.
Free to open, edit and save a file. Pro to run your changes. Opening a project 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.
01 / SETUP
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.
02 / ACTUAL ENGINE OUTPUT
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 store performance, revenue or a safety assessment.
Engine, status and output notes
process-des-2.2.0. Status: Original scenario: completed. “Completed” is the engine run status; check the blocked column for unreachable jobs.
- No engine warnings.
Engine inputs and saved comparisons stay with the downloaded project. Any limited trace affects playback details, not aggregate metrics.
03 / MAKE IT YOURS
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 Codex or Claude Code. Ask it to inspect the example, edit the layout and check a route.
- Save a checkpoint. Export → Project JSON, or ask the agent to export the complete project. Import it later to restore floors, scenarios, saved comparisons and the selected view.
- Compare with Pro. Run an edited scenario or compare frozen options under the same demand. Pro also adds built-in AI and manual cloud saving.