User:Rysc00 - Revision history

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← Older revision		Revision as of 22:20, 10 January 2025
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		+	Supermarket checkout operations represent a critical touchpoint in the customer journey, directly influencing overall satisfaction and loyalty. Inefficiencies at checkouts, particularly during peak hours, can lead to long queues, dissatisfaction, and potential loss of customers. This project addresses these challenges by developing a simulation model based on agent-based simulation (ABS) methodology in NetLogo, with the goal of formulating actionable, data-driven rule for opening new checkouts. The primary objective is to maintain customer satisfaction above 70% through observable and practical strategy for opening new checkouts. The simulated store is Tesco Vodňany.
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	=Problem definition=		=Problem definition=

← Older revision		Revision as of 22:14, 10 January 2025
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	=Code=		=Code=
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		+	https://www.simulace.info/images/Checkouts.nlogo

	=Resources=		=Resources=

← Older revision		Revision as of 22:01, 10 January 2025
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	=Conclusion=		=Conclusion=
		+
		+	The simulation results demonstrate how a structured approach can improve checkout management in supermarkets. By analyzing customer behavior and satisfaction, the study showed that dynamically opening new checkouts based on observable metrics—such as a sustained queue of 16 or more customers for 3 consecutive minutes—is an effective way to address operational inefficiencies. While real-time satisfaction monitoring is impractical, this alternative rule corelates with the satisfaction-based decision making and offers a realistic solution.
		+
		+	Beyond reaching the goal of the simulation, the model proved useful for broader operational insights. It can be applied to calculate staffing requirements for maintaining higher customer satisfaction than the current target, test the effects of adding additional checkouts, or simulate scenarios with increased customer flow during peak hours or special events.
		+
		+	Implementing the proposed rule requires careful planning, especially to ensure sufficient staffing during busy periods. Additionally, integrating AI and camera systems for real-time queue monitoring could automate the process, enabling quick and effective responses to changing conditions.

	=Code=		=Code=

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 To operationalize the proposed observable rule, leveraging camera systems and AI technologies is recommended. By monitoring the number of customers at checkouts in real-time, the system can:
+*Detect situations where the number of customers exceeds 16 for 3 consecutive minutes.
-Detect situations where the number of customers exceeds 16 for 3 consecutive minutes.
+*Automatically trigger notifications for staff to open a new checkout.
 Such an AI-driven solution would ensure quick reactions to changing conditions and reduce the reliance on manual monitoring. This technological integration would provide a scalable and efficient method to uphold customer satisfaction consistently.

← Older revision		Revision as of 21:54, 10 January 2025
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	The results also highlight the importance of adequate staffing during peak hours. During these periods (8:00–10:00 and 16:00–18:00), the simulation confirmed that one regular checkout and the self-service terminals are insufficient to handle the increased customer flow efficiently. To maintain smooth operations and high satisfaction levels during peak times, additional staff should be available to manage extra checkouts when needed. Conversely, during off-peak hours, the standard configuration of one regular checkout and self-service terminals suffices.		The results also highlight the importance of adequate staffing during peak hours. During these periods (8:00–10:00 and 16:00–18:00), the simulation confirmed that one regular checkout and the self-service terminals are insufficient to handle the increased customer flow efficiently. To maintain smooth operations and high satisfaction levels during peak times, additional staff should be available to manage extra checkouts when needed. Conversely, during off-peak hours, the standard configuration of one regular checkout and self-service terminals suffices.
		+
		+	To operationalize the proposed observable rule, leveraging camera systems and AI technologies is recommended. By monitoring the number of customers at checkouts in real-time, the system can:
		+
		+	Detect situations where the number of customers exceeds 16 for 3 consecutive minutes.
		+
		+	Automatically trigger notifications for staff to open a new checkout.
		+
		+	Such an AI-driven solution would ensure quick reactions to changing conditions and reduce the reliance on manual monitoring. This technological integration would provide a scalable and efficient method to uphold customer satisfaction consistently.

	=Conclusion=		=Conclusion=

← Older revision		Revision as of 21:53, 10 January 2025
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	This observable rule was found to correlate well with maintaining sufficient customer satisfaction levels and is practical for real-world application. It ensures that staff can respond effectively to changes in customer flow without relying on unmeasurable metrics like satisfaction.		This observable rule was found to correlate well with maintaining sufficient customer satisfaction levels and is practical for real-world application. It ensures that staff can respond effectively to changes in customer flow without relying on unmeasurable metrics like satisfaction.
		+
		+	The results also highlight the importance of adequate staffing during peak hours. During these periods (8:00–10:00 and 16:00–18:00), the simulation confirmed that one regular checkout and the self-service terminals are insufficient to handle the increased customer flow efficiently. To maintain smooth operations and high satisfaction levels during peak times, additional staff should be available to manage extra checkouts when needed. Conversely, during off-peak hours, the standard configuration of one regular checkout and self-service terminals suffices.

	=Conclusion=		=Conclusion=

← Older revision		Revision as of 21:53, 10 January 2025
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	=Results=		=Results=
		+	The simulation results indicate that dynamically opening new checkouts successfully maintains customer satisfaction above the target level. Specifically, the average customer satisfaction achieved during the simulation was 75.3, with a standard deviation of 0.3. This demonstrates a consistently sufficient level of customer satisfaction.
		+
		+	It was verified that dynamically opening new checkouts based on the average satisfaction of the last 20 customers is an effective approach. However, this method relies on monitoring customer satisfaction in real-time, which is not feasible in a typical supermarket setting, as cashiers and staff cannot directly observe or measure customer satisfaction.
		+
		+	To address this limitation, the simulation results were used to derive an alternative, observable rule:
		+
		+	If there are 16 or more customers at the checkouts for 3 consecutive minutes, a new checkout should be opened.
		+
		+	This observable rule was found to correlate well with maintaining sufficient customer satisfaction levels and is practical for real-world application. It ensures that staff can respond effectively to changes in customer flow without relying on unmeasurable metrics like satisfaction.

	=Conclusion=		=Conclusion=

← Older revision		Revision as of 21:26, 10 January 2025
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	=Code=		=Code=
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		+	=Resources=
		+
		+	Bachárová, G. (2023, November 20). Pavol Halász, Tesco Stores ČR: Pro zákazníka je důležitá cena. Zboží a Prodej – zprávy z retailu. https://www.zboziaprodej.cz/2023/11/20/pavol-halasz-produktovy-reditel-tesco-stores-cr-pro-zakaznika-je-dulezita-cena-2/
		+
		+	Martin, J., Nenycz-Thiel, M., Dawes, J., Tanusondjaja, A., Cohen, J., McColl, B., & Trinh, G. (2020). Fundamental basket size patterns and their relation to retailer performance. Journal of Retailing and Consumer Services, 54, 102032. https://doi.org/10.1016/j.jretconser.2020.102032
		+
		+	Pallikkara, V., Pinto, P., Iqbal, T. H., & Commerce, B. (2024). Navigating waiting situations at retail checkouts: Associated emotional discomfort and its impact on shopping satisfaction. Management & Marketing, 12(2), 256–274. https://doi.org/10.2478/mmcks-2024-0012
		+
		+	Rajic, T., & Dado, J. (2013). Modelling the relationships among retail atmospherics, service quality, satisfaction and customer behavioural intentions in an emerging economy context. Total Quality Management & Business Excellence, 24(9–10), 1096–1110. https://doi.org/10.1080/14783363.2013.776759
		+
		+	STEM/MARK. (2024, February 13). Češi se k samoobslužným pokladnám staví pozitivně, reálně ale stále vítězí klasické pokladny s obsluhou. STEM/MARK. https://stemmark.cz/cesi-se-k-samoobsluznym-pokladnam-stavi-pozitivne-realne-ale-stale-vitezi-klasicke-pokladny-s-obsluhou/