Abstract
Retailing is an important part of the supply chain, the link at which money is transferred from the consumer and flows to upstream trading partners. However, retailing has received very little attention from researchers in terms of developing quantitative models that are used in the rest of the supply chain. This paper proposes analytical and computational models based on queuing theory and discrete-event simulation, respectively, for in-store shopper journeys. Specifically, based on empirical data from retail stores, we propose a \(M/G/\infty \) and \({M}_{t}/G/c\) queues in tandem to model the shopping process and the checkout process to characterize key aspects of shopper journeys. Such an analytical model is probably the very first of its kind proposed to mathematically characterize retailing and offers prospects for enabling better understanding and decision-making in this multi-trillion-dollar industry. A key advantage of such analytical models is that they can provide quick answers to questions such as average customer waiting times at checkout for different levels of staffing, which is an important issue in customer service vs staffing cost. A shortcoming of such analytical models is that they are based on assumptions therefore their output has limited accuracy, and these models cannot predict variance in the output. To overcome these shortcomings, this paper presents ShopperSim, which is a stand-alone discrete-event simulation software being developed using SimPy, a Python library, to simulate shopper journeys in a variety of store formats. To ensure practical relevance of these developments, ShopperSim has been programmed to statistically reproduce empirically observed shopping time, basket size, and store area covered – key attributes for shopper journeys. Tests indicate that ShopperSim can reproduce empirically observed statistics of key attributes for shopper journeys. Data generated from ShopperSim indicates that \(M/G/\infty \) and \(M/G/c\), a much simpler and more tractable tandem queuing model may be adequate for the tested case of a convenience store. In the future, analytical and computational models developed here can be leveraged in several ways for education, training, and operational decision-making in stores, including gamification of these decisions.
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References
Korgaonkar, P.A., Karson, E.J.: The influence of perceived product risk on consumers’e-tailer shopping preference. J. Bus. Psychol. 22, 55–64 (2007)
Mishra, R., Singh, R.K., Koles, B.: Consumer decision-making in Omnichannel retailing: literature review and future research agenda. Int. J. Consum. Stud. 45(2), 147–174 (2021)
Lilien, G.L., Rangaswamy, A.: Marketing engineering: computer-assisted marketing analysis and planning. DecisionPro (2004)
Grewal, D., Gauri, D.K., Roggeveen, A.L., Sethuraman, R.: Strategizing retailing in the new technology era. J. Retail. 97(1), 6–12 (2021)
Larson, J.S., Bradlow, E.T., Fader, P.S.: An exploratory look at supermarket shopping paths. Int. J. Res. Mark. 22(4), 395–414 (2005)
Sorensen, H., et al.: Fundamental patterns of in-store shopper behavior. J. Retail. Consum. Serv. 37, 182–194 (2017)
Dabholkar, P.A., Thorpe, D.I., Rentz, J.O.: A measure of service quality for retail stores: scale development and validation. J. Acad. Mark. Sci. 24, 3–16 (1996)
Kwak, J.K.: Analysis on the effect of express checkouts in retail stores. J. Appl. Bus. Res. (JABR) 33(4), 767–774 (2017)
Antczak, T., Weron, R., Zabawa, J.: Data-driven simulation modeling of the checkout process in supermarkets: insights for decision support in retail operations. IEEE Access 8, 228841–228852 (2020)
Miwa, K., Takakuwa, S.: Simulation modeling and analysis for in-store merchandizing of retail stores with enhanced information technology. In: 2008 Winter Simulation Conference, pp. 1702–1710. IEEE (2008)
Terano, T., Kishimoto, A., Takahashi, T., Yamada, T., Takahashi, M.: Agent-based in-store simulator for analyzing customer behaviors in a super-market. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds.) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol. 5712, pp. 244–251. Springer, Berlin, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04592-9_31
Williams, E. J., Karaki, M., Lammers, C., Verbraeck, A., Krug, W.: Use of simulation to determine cashier staffing policy at a retail checkout. In: Proceedings 14th European simulation symposium, pp. 172–176 (2002)
Mou, S., Robb, D.J.: Real-time labour allocation in grocery stores: a simulation-based approach. Decis. Support. Syst. 124, 113095 (2019)
Cho, S.I., Kang, S.J.: Real-time people counting system for customer movement analysis. IEEE Access 6, 55264–55272 (2018)
Nelsen, R.B.: An Introduction to Copulas. Springer (2006)
Dahm, M., Wentzel, D., Herzog, W., Wiecek, A.: Breathing down your neck!: the impact of queues on customers using a retail service. J. Retail. 94(2), 217–230 (2018)
Eick, S.G., Massey, W.A., Whitt, W.: The physics of the Mt/G/∞ queue. Oper. Res. 41(4), 731–742 (1993)
Allen, A.O.: Queueing models of computer systems. Computer 13(04), 13–24 (1980)
Matloff, N.: Introduction to discrete-event simulation and the simpy language. Davis, CA. Dept. Comput. Sci. Univ. Calif. Davis. 2, 1–33 (2008)
Mark, D.: Modular tactical influence maps. In: Game AI Pro 360: Guide to Tactics and Strategy, pp. 103–124. CRC Press (2019)
Ontanón, S., Synnaeve, G., Uriarte, A., Richoux, F., Churchill, D., Preuss, M.: A survey of real-time strategy game AI research and competition in StarCraft. IEEE Trans. Comput. Intell. AI Games 5(4), 293–311 (2013)
Fisher, M., Gallino, S., Netessine, S.: Setting retail staffing levels: a methodology validated with implementation. Manuf. Serv. Oper. Manag. 23(6), 1562–1579 (2021)
Chuang, H.H.C., Oliva, R., Perdikaki, O.: Traffic-based labor planning in retail stores. Prod. Oper. Manag. 25(1), 96–113 (2016)
Williams, E.J., Karaki, M., Lammers, C., Verbraeck, A., Krug, W.: Use of simulation to determine cashier staffing policy at a retail checkout. In Proceedings 14th European Simulation Symposium, pp. 172–176 (2002)
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Korrawee, H., Chakraborty, P., Prabhu, V. (2024). Analytical and Computational Models for In-Store Shopper Journeys. In: Thürer, M., Riedel, R., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments. APMS 2024. IFIP Advances in Information and Communication Technology, vol 729. Springer, Cham. https://doi.org/10.1007/978-3-031-65894-5_13
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