As queues in supermarkets seem to be inevitable, researchers try to find solutions that can improve and speed up the checkout process. This, however, requires access to real-world data for developing and validating models. With this objective in mind, we have prepared and made publicly available high-frequency datasets containing nearly six weeks of actual transactions and cashier operations from a grocery supermarket belonging to one of the major European retail chains. This dataset can provide insights on how the intensity and duration of checkout operations changes throughout the day and week.

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We build a realistic agent-based model for simulating customer decisions of picking lines in supermarkets that is calibrated to actual point of sale (POS) data from one of major European retail chains. It is implemented in the open-access NetLogo simulation platform and is freely available to academics and practitioners interested in testing how different checkout zone layouts, as well as queue management and feedback strategies impact the overall efficiency of the checkout process. In particular, we show that when customers pick a line by minimizing the expected waiting time, not only is this choice beneficial for the customers themselves, as it leads to shorter waiting times in queues, but also for the supermarket management, since it yields shorter working times of the cashiers. As such, we provide guidance as to the feedback that could be provided to customers entering the checkout zone.