Society 5.0: a simulation study of self checkout operations in a grocery store

  • Konstantinos Mykoniatis 
  • Samira Shirzaei , 
  • Michail Katsigiannis , 
  • Athanasios Aris Panagopoulos ,
  • Sahana Deb , 
  • Timothy Potter , 
  • Anastasia Angelopoulou  
  • a,b,c  Auburn University, 345 W Magnolia Ave, Auburn, AL, 36849, USA
  • California State University - Fresno, 5241 N Maple Ave, Fresno, CA, 93740, USA
  • e,f,g Columbus State University, 4225 University Ave, Columbus, GA, 31907, USA
Cite as
Mykoniatis K., Shirzaei S., Katsigiannis M., Panagopoulos A.A., Deb S., Potter T., Angelopoulou A. (2020). Society 5.0: a simulation study of self checkout operations in a grocery store. Proceedings of the 32nd European Modeling & Simulation Symposium (EMSS 2020), pp. 78-83. DOI: https://doi.org/10.46354/i3m.2020.emss.011

Abstract

Society 5.0 refers to a technology-based human-centered society that integrates cyber-physical systems and uses advanced technology to improve everyday life. Past and present queuing systems, such as grocery stores, will transition to Society 5.0. Queues in grocery stores are part of the shoppers' everyday routine. Changes in grocery stores' queues involve the replacement of servers by self-checkout machines. Nowadays, a continuously increasing number of grocery retailers have been adopting a self-service checkout approach as a waiting time-saving solution. This paper utilizes simulation to examine the queues in a grocery shop and compare the waiting time of shoppers and throughput during checkout in counter service and self-service. Point of Sales (POS) system transaction data from a grocery store were analyzed and used in the simulation model. Alternative scenarios were modeled and simulated to understand how a different number of cashiers and self-service machines will impact the throughput and customers' waiting time. The results provide insights on the flow efficiency and effectiveness of the checkout operations under different configurations.

References

  1. Antczak, T., & Weron, R. (2019). Point of Sale (POS) Data from a Supermarket: Transactions and
    Cashier Operations, Data, 4(2), 67. 
  2. Carson, I. I., & John, S. (2005, December). Introduction to modeling and simulation. In Proceedings of the 37th conference on Winter simulation (pp. 16-23). Winter Simulation Conference.
  3. Dharmawirya, M., & Adi, E. (2011). Case study for restaurant queuing model. In 2011 International Conference on Management and Artificial Intelligence.
  4. Gruber, J. W., Smiddy, R., Watson, J. M., & Williams, E. J. (2015, March). Simulation helps local grocery store compete effectively against large chains. In 2015 International Conference on Industrial Engineering and Operations Management (IEOM) (pp. 1-4). IEEE.
  5. Law, A and McComas, M. (1990). Secrets of successful simulation studies. Industrial Engineering, 22(5), 47-72.
  6. Melachrinoudis, E., & Olafsson, M. (1995). A microcomputer cashier scheduling system for
    supermarket stores. International Journal of Physical Distribution & Logistics Management,
    25(1), 34-50.Nosek Jr, R. A., & Wilson, J. P. (2001). Queuing theory and customer satisfaction: a
    Review of terminology, trends, and applications to pharmacy practice. Hospital pharmacy, 36(3), 275-279.
  7. Mykoniatis, K., & Angelopoulou, A. (2020). A modeling framework for the application of multi-paradigm simulation methods, SIMULATION, 96(1), 55-73.
  8. Williams, E. J., Karaki, M., Lammers, C., Verbraeck, A., & Krug, W. (2002, October). Use of simulation to determine cashier staffing policy at a retail checkout. In Proceedings 14th European simulation symposium (pp. 172-176).