IDEAS home Printed from https://ideas.repec.org/p/ahh/wpaper/worms2016.html
   My bibliography  Save this paper

Data-driven simulation modeling of the checkout process in supermarkets: Insights for decision support in retail operations

Author

Listed:
  • Tomasz Antczak
  • Rafal Weron
  • Jacek Zabawa

Abstract

We build a realistic agent-based model for simulating customer decisions of picking lines in supermarkets. The model is calibrated to actual point of sale (POS) data from three supermarkets of one of major European retail chains and is implemented in the open-access NetLogo simulation platform. The model can provide insights as to the impact of individual customer decisions of picking lines on 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.

Suggested Citation

  • Tomasz Antczak & Rafal Weron & Jacek Zabawa, 2020. "Data-driven simulation modeling of the checkout process in supermarkets: Insights for decision support in retail operations," WORking papers in Management Science (WORMS) WORMS/20/16, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    • Handle: RePEc:ahh:wpaper:worms2016
    as

    Download full text from publisher

    File URL: https://worms.pwr.edu.pl/RePEc/ahh/wpaper/WORMS_20_16.pdf
    File Function: Original version, 2020
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lee, Ju-Sung & Filatova, Tatiana & Ligmann-Zielinska, Arika & Hassani-Mahmooei, Behrooz & Stonedahl, Forrest & Lorscheid, Iris & Voinov, Alexey & Polhill, J. Gareth & Sun, Zhanli & Parker, Dawn C., 2015. "The complexities of agent-based modeling output analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 18(4).
    2. Sezer Ülkü & Chris Hydock & Shiliang Cui, 2020. "Making the Wait Worthwhile: Experiments on the Effect of Queueing on Consumption," Management Science, INFORMS, vol. 66(3), pages 1149-1171, March.
    3. Mou, Shandong & Robb, David J. & DeHoratius, Nicole, 2018. "Retail store operations: Literature review and research directions," European Journal of Operational Research, Elsevier, vol. 265(2), pages 399-422.
    4. Garaus, Marion & Wagner, Udo, 2019. "Let me entertain you – Increasing overall store satisfaction through digital signage in retail waiting areas," Journal of Retailing and Consumer Services, Elsevier, vol. 47(C), pages 331-338.
    5. Chun (Martin) Qiu & Wenqing Zhang, 2016. "Managing long queues for holiday sales shopping," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(1), pages 52-65, February.
    6. Yina Lu & Andrés Musalem & Marcelo Olivares & Ariel Schilkrut, 2013. "Measuring the Effect of Queues on Customer Purchases," Management Science, INFORMS, vol. 59(8), pages 1743-1763, August.
    7. H. A. Eiselt & C. -L. Sandblom, 2007. "Linear Programming and its Applications," Springer Books, Springer, number 978-3-540-73671-4, December.
    8. Jingqi Wang & Yong-Pin Zhou, 2018. "Impact of Queue Configuration on Service Time: Evidence from a Supermarket," Management Science, INFORMS, vol. 64(7), pages 3055-3075, July.
    9. Weiss, Elliott N. & Tucker, Chad, 2018. "Queue management: Elimination, expectation, and enhancement," Business Horizons, Elsevier, vol. 61(5), pages 671-678.
    10. Sorensen, Herb & Bogomolova, Svetlana & Anderson, Katherine & Trinh, Giang & Sharp, Anne & Kennedy, Rachel & Page, Bill & Wright, Malcolm, 2017. "Fundamental patterns of in-store shopper behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 37(C), pages 182-194.
    11. Aringhieri, Roberto & Duma, Davide & Fragnelli, Vito, 2018. "Modeling the rational behavior of individuals on an e-commerce system," Operations Research Perspectives, Elsevier, vol. 5(C), pages 22-31.
    12. Borges, Adilson & Herter, Márcia Maurer & Chebat, Jean-Charles, 2015. "“It was not that long!†: The effects of the in-store TV screen content and consumers emotions on consumer waiting perception," Journal of Retailing and Consumer Services, Elsevier, vol. 22(C), pages 96-106.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tomasz Antczak & Bartosz Skorupa & Mikolaj Szurlej & Rafal Weron & Jacek Zabawa, 2021. "Simulation modeling of epidemic risk in supermarkets: Investigating the impact of social distancing and checkout zone design," WORking papers in Management Science (WORMS) WORMS/21/05, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    2. Paul M. Torrens, 2023. "Agent models of customer journeys on retail high streets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(1), pages 87-128, January.
    3. Yong Li & Yu Sun & Chengcheng Zeng & Jinxing Li & Yanping Gao & Haisheng Li, 2022. "Research on the Influencing Factors for the Use of Green Building Materials through the Number Growth of Construction Enterprises Based on Agent-Based Modeling," Sustainability, MDPI, vol. 14(19), pages 1-13, October.
    4. Njomane, Linda & Telukdarie, Arnesh, 2022. "Impact of COVID-19 food supply chain: Comparing the use of IoT in three South African supermarkets," Technology in Society, Elsevier, vol. 71(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sezer Ülkü & Chris Hydock & Shiliang Cui, 2020. "Making the Wait Worthwhile: Experiments on the Effect of Queueing on Consumption," Management Science, INFORMS, vol. 66(3), pages 1149-1171, March.
    2. Pascucci, Federica & Nardi, Lorenzo & Marinelli, Luca & Paolanti, Marina & Frontoni, Emanuele & Gregori, Gian Luca, 2022. "Combining sell-out data with shopper behaviour data for category performance measurement: The role of category conversion power," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    3. Ladeira, Wagner Junior & de Oliveira Santini, Fernando & Pinto, Diego Costa, 2022. "Clockwise versus counterclockwise turning bias: Moderation effects of foot traffic and cognitive experience on visual attention," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    4. Jia Gao & Ying Rong & Xin Tian & Yuliang Yao, 2024. "Improving Convenience or Saving Face? An Empirical Analysis of the Use of Facial Recognition Payment Technology in Retail," Information Systems Research, INFORMS, vol. 35(1), pages 16-27, March.
    5. Liang, Jian & Zhao, Ya & Wang, Hai & Xiao, Zuopeng & Ke, Jintao, 2024. "Uncovering merchants’ willingness to wait in on-demand food delivery markets," Transport Policy, Elsevier, vol. 158(C), pages 14-28.
    6. Delasay, Mohammad & Ingolfsson, Armann & Kolfal, Bora & Schultz, Kenneth, 2019. "Load effect on service times," European Journal of Operational Research, Elsevier, vol. 279(3), pages 673-686.
    7. Naumov, Sergey & Oliva, Rogelio, 2025. "Structural feedback and behavioral decision making in queuing systems: A hybrid simulation framework," European Journal of Operational Research, Elsevier, vol. 324(3), pages 855-870.
    8. Trinh, Giang & Wright, Malcolm J., 2022. "Predicting future consumer purchases in grocery retailing with the condensed Poisson lognormal model," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    9. Zeynep Akşin & Baris Ata & Seyed Morteza Emadi & Che-Lin Su, 2017. "Impact of Delay Announcements in Call Centers: An Empirical Approach," Operations Research, INFORMS, vol. 65(1), pages 242-265, February.
    10. Brengman, Malaika & De Gauquier, Laurens & Willems, Kim & Vanderborght, Bram, 2021. "From stopping to shopping: An observational study comparing a humanoid service robot with a tablet service kiosk to attract and convert shoppers," Journal of Business Research, Elsevier, vol. 134(C), pages 263-274.
    11. Meherishi, Lavanya & Narayana, Sushmita A. & Ranjani, K.S., 2021. "Integrated product and packaging decisions with secondary packaging returns and protective packaging management," European Journal of Operational Research, Elsevier, vol. 292(3), pages 930-952.
    12. Kristiaan Kerstens & Ignace Van de Woestyne, 2018. "Enumeration algorithms for FDH directional distance functions under different returns to scale assumptions," Annals of Operations Research, Springer, vol. 271(2), pages 1067-1078, December.
    13. Christian Troost & Julia Parussis-Krech & Matías Mejaíl & Thomas Berger, 2023. "Boosting the Scalability of Farm-Level Models: Efficient Surrogate Modeling of Compositional Simulation Output," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 721-759, October.
    14. Mohammad Delasay & Aditya Jain & Subodha Kumar, 2022. "Impacts of the COVID‐19 pandemic on grocery retail operations: An analytical model," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2237-2255, May.
    15. John Sherwood & Anthony Ditta & Becky Haney & Loren Haarsma & Michael Carbajales-Dale, 2017. "Resource Criticality in Modern Economies: Agent-Based Model Demonstrates Vulnerabilities from Technological Interdependence," Biophysical Economics and Resource Quality, Springer, vol. 2(3), pages 1-22, September.
    16. Debjit Roy & Eirini Spiliotopoulou & Jelle de Vries, 2022. "Restaurant analytics: Emerging practice and research opportunities," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3687-3709, October.
    17. Gérard P. Cachon & Dawson Kaaua, 2022. "Serving Democracy: Evidence of Voting Resource Disparity in Florida," Management Science, INFORMS, vol. 68(9), pages 6687-6696, September.
    18. Lionel Nicod & Sylvie Llosa & David Bowen, 2020. "Customer proactive training vs customer reactive training in retail store settings: Effects on script proficiency, customer satisfaction, and sales volume," Post-Print hal-02490363, HAL.
    19. Thami, Prakriti, 2025. "Truth-Telling in a Priority Pricing Mechanism," Working Papers 2025:3, Lund University, Department of Economics.
    20. Ernesto Carrella & Richard M. Bailey & Jens Koed Madsen, 2018. "Indirect inference through prediction," Papers 1807.01579, arXiv.org.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ahh:wpaper:worms2016. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anna Kowalska-Pyzalska (email available below). General contact details of provider: https://edirc.repec.org/data/kbpwrpl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.