Laydyne

Convenience store lunch rush

Watch a konbini lunch rush: registers, coffee, eat-in seats and restocking at once.

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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.
Turn a comparison into a business decision →
Actual Laydyne 3D model: Convenience store lunch rush
Convenience store lunch rush1 level · 195 parts · synthetic layout

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.

Park → pick up items → queue → pay → coffee or eat-in → leave

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.

ScenarioCompletedMean waitMean cycleBlocked
Original scenario8928s5m 34s0

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

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