Supermarket checkout process simulation - Revision history

Rysc00: /* Results */

2025-01-10T22:41:41Z

‎Results

Rysc00: /* Results */

2025-01-10T22:40:00Z

‎Results

Rysc00: /* Simplified Environment */

2025-01-10T22:34:56Z

‎Simplified Environment

Rysc00: /* Simplified Environment */

2025-01-10T22:34:08Z

‎Simplified Environment

Rysc00: /* Dynamic Checkout Management */

2025-01-10T22:32:51Z

‎Dynamic Checkout Management

Rysc00: /* Checkout Management */

2025-01-10T22:30:25Z

‎Checkout Management

Rysc00: /* Customer Departure */

2025-01-10T22:29:56Z

‎Customer Departure

Rysc00: /* Customer Arrival */

2025-01-10T22:26:46Z

‎Customer Arrival

Rysc00 at 22:24, 10 January 2025

2025-01-10T22:24:07Z

Rysc00 at 22:20, 10 January 2025

2025-01-10T22:20:48Z

@@ Line 171: / Line 171: @@
 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 at checkouts exceeds 16 for 3 consecutive minutes.
 *Automatically trigger notifications for staff to open a new checkout.

@@ Line 168: / Line 168: @@
 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.
+The results also highlight the importance of adequate staffing during peak hours. During these periods (7:00–10:00 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:

@@ Line 146: / Line 146: @@
 ====Simplified Environment====
 * The simulation focuses exclusively on checkout processes, omitting other areas of the store (e.g., shopping aisles or interactions with staff).
-* The simulation does not consider staff availability when opening new checkouts.
+* The simulation does not consider staff availability when opening new checkouts and the delay between deciding to open a new checkout and actually opening it.
 * The simulation does not consider that self-service checkouts may be closed too.
 * The simulation does not cosider that proximity between customer and certain checkout also influences which checkout the customer chooses.

@@ Line 147: / Line 147: @@
 * The simulation focuses exclusively on checkout processes, omitting other areas of the store (e.g., shopping aisles or interactions with staff).
 * The simulation does not consider staff availability when opening new checkouts.
-* The simulation does not consider that self-service checkouts may closed too.
+* The simulation does not consider that self-service checkouts may be closed too.
 ====Static Rules for Opening and Closing Checkouts====

@@ Line 142: / Line 142: @@
 ====Dynamic Checkout Management====
-* New checkouts are opened if average satisfaction of the last 20 customers falls below a threshold. This assumes that such a metric is sufficient to represent overall satisfaction trends in real-time.
+* New checkouts are opened if average satisfaction of the last 20 customers falls below a 70 % threshold. This assumes that such a metric is sufficient to represent overall satisfaction trends in real-time.
 ====Simplified Environment====

@@ Line 117: / Line 117: @@
 ====Checkout Management====
-* Checkouts are dynamically opened and closed based on customer satisfaction and queue lengths:
+* Checkouts are dynamically opened and closed based on customer satisfaction:
 ** Opening New Checkouts: If the average satisfaction of the last 20 customers falls below the threshold (70%), a closed checkout is opened (if available).
 **Closing Checkouts: Regular checkouts with no customers are closed if more than one regular checkout remains open. Self-service checkouts remain open at all times.

@@ Line 112: / Line 112: @@
 * After completing service, customers leave the system, and their satisfaction is updated.
 ** Factors affecting satisfaction:
-*** Waiting time: Longer waiting times decrease satisfaction logarithmically.
+*** Waiting time: Longer waiting times decrease satisfaction superlinearly.
 *** Checkout type: Using a non-preferred type reduces satisfaction, while using a preferred type may increase it.
 * Departing customers contribute to the overall average satisfaction, which is monitored throughout the simulation.

@@ Line 84: / Line 84: @@
 ====Customer Arrival====
 * Customers are generated based on predefined arrival rates:
-** Peak Hours (8:00–10:00, 16:00–18:00): Arrivals follow a normal distribution with a mean of 4 customers per minute.
+** Peak Hours (7:00–10:00, 16:00–18:00): Arrivals follow a normal distribution with a mean of 4 customers per minute.
 ** Off-Peak Hours (10:00–16:00, 18:00–20:00): Arrivals follow a normal distribution with a mean of 2 customers per minute.
 * Each customer is initialized with attributes number of items, checkout preference, willingness to switch queues, and initial satisfaction.

@@ Line 19: / Line 19: @@
 ===Overview of the Model===
-The model is designed to replicate the customer checkout processes, focusing on interactions between customers and the store environment. It incorporates dynamic customer behaviors, such as queue selection, willingness to switch checkouts, and the impact of these decisions on their satisfaction.
+The model is designed to replicate the customer checkout processes, focusing on interactions between customers and the store environment. It incorporates dynamic customer behaviors, such as queue selection, willingness to switch checkouts, and the impact of these decisions on their satisfaction. The simulation integrates varying customer arrival rates throughout the day and models the performance differences between regular and self-service checkouts.
 ===Customers===

← Older revision		Revision as of 22:20, 10 January 2025
Line 1:		Line 1:
		+	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.
		+
	=Problem definition=		=Problem definition=