Saturday checkout
See where Saturday shoppers wait: the staffed lanes or the self-checkout pod.
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Does steering shoppers to self-checkout, or faster scanning, shorten the Saturday queue, or only move it?
- Who uses it
- Supermarket managers and front-end (checkout) supervisors
- How it can create value
- Compare self-checkout shares and scanning times before changing the cashier schedule or buying kiosks, and see where the queue goes. Weigh each wait reduction against staff hours and kiosk cost; shorter queues alone do not establish extra sales.
- Bring your site data
- Arrivals by time of day, measured scanning, payment and bagging times, the share of shoppers who use self-checkout and the lanes staffed by hour. How shoppers really choose lanes, balk or switch needs observation before trusting queue lengths.
A model you can inspect.
Shoppers arrive at parked cars (poisson, 300 per hour for 36 minutes), walk in, visit 1–2 fresh departments and 2–4 aisles, then either take a free one of eight staffed lanes (counting shoppers already walking to one; in turn when several are free, else the lane with the fewest ahead, judged from the front aisle) or join the single line of the six-kiosk self-checkout pod, and walk back to the car they came in with their bags; a stock clerk brings three pallets to the aisles. Lane 8's cashier takes a 15-minute break from 15:00, in the peak (no one is sent to the closed lane; those already in its line are served when it reopens). Shoppers keep out of the back of house. Walking, service times and FIFO queues only: no crowd physics, balking, lane switching or sales.
Change one thing
With the same shoppers, move the shares from 70/30 to 60/40 and 50/50, cut the mean scanning time from 90 to 60 s, or move lane 8's break out of the peak (breakStart 900 → 2400 s).
Read the consequence
The queue at each lane and at the pod, mean wait and visit time: watch whether the queue shrinks or only moves to the pod. Purchases, balking, lane switching and physical crowding are not modelled.
View the starting inputs
- Shoppers per hour
- 300
- Share choosing staffed lanes (%; the rest self-checkout)
- 70
- Mean scanning time at a staffed lane (s)
- 90
- Lane 8 cashier's 15-minute break starts (s)
- 900
Agent counts, routes and resource definitions are also saved in the project JSON.
A reproducible starting result.
One run, seed 11. Observation window 60 minutes.
| Scenario | Completed | Mean wait | Mean cycle | Blocked |
|---|---|---|---|---|
| Original scenario | 193 | 1m 34s | 14m 28s | 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
Supermarket and drugstore managers, checkout supervisors, and anyone improving how a shop runsHow many checkouts should be open?In a supermarket, your AI simulates how many staffed checkouts keep the mean wait under a minute at 120, 180 and 240 shoppers an hour, and what one shared line or self-checkout changes.Read the walkthrough 
Supermarket, drugstore and shopping centre managers, facility managers and fire safety managers, and the staff deciding where the AEDs goWhere do the AEDs go, and how many? A minute's walk in a supermarketWith one AED by a supermarket's service counter, its far corners are a 71.45 m and a 77.35 m walk away, over a minute at 60 m per minute. A second AED at the back keeps every point within 58.66 m.Read the walkthrough 
Managers deciding how many staff, checkouts and service counters to openHow many staff and counters do you need? A guide to queuesChoose staff and counter numbers from arrivals and service time. Use a queue calculator for steady demand, then compare simulations for peaks, walking, shared duties and people who leave.Read the walkthrough
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