Queues in Customer Service
Abstract
This research investigates the operational dynamics of a retail pet store, honing in on the challenges of a typical busy Saturday. Through conscientious data collection, the study establishes average arrival and service rates, confirming their adherence to the Poisson distribution. Subsequently, a simulation is executed to test the system’s response to increased customer arrivals due to growing demand.
The study initiates by characterizing the retail pet store’s operational position during peak hours, revealing key metrics of customer arrival and service rates on busy Saturdays. Statistical analysis validates the Poisson distribution pattern, setting the stage for a simulation model.
Utilizing means that were derived from the established distribution, the simulation mimics the dynamic store environment during peak customer activity. The primary objective is to assess the impact of increased customer arrivals on wait times and server resource demand. This test aims to uncover operational bottlenecks and vulnerabilities within the business.
Simulation results provide crucial insights into the system’s resilience and adaptability during surges in customer traffic. Analysis of wait times reveals the store’s operational coping mechanisms and identifies potential areas for improvement. Additionally, evaluating server demand highlights the delicate balance between customer service efficiency and resource allocation.
This study not only benefits the specific retail pet store but also extends insights for operational management in retail settings, dealing with fluctuating customer demands. By emphasizing statistical distribution analysis and simulation modeling, the research offers practical strategies for enhancing customer service and overall operational performance in dynamic retail landscapes. The findings aim to inform strategic decision-making, providing a roadmap for optimizing efficiency in similar settings.
Keywords
Queue, Queueing, Retail, Customer Service, Wait Time
Queues in Customer Service
CASB 105
This research investigates the operational dynamics of a retail pet store, honing in on the challenges of a typical busy Saturday. Through conscientious data collection, the study establishes average arrival and service rates, confirming their adherence to the Poisson distribution. Subsequently, a simulation is executed to test the system’s response to increased customer arrivals due to growing demand.
The study initiates by characterizing the retail pet store’s operational position during peak hours, revealing key metrics of customer arrival and service rates on busy Saturdays. Statistical analysis validates the Poisson distribution pattern, setting the stage for a simulation model.
Utilizing means that were derived from the established distribution, the simulation mimics the dynamic store environment during peak customer activity. The primary objective is to assess the impact of increased customer arrivals on wait times and server resource demand. This test aims to uncover operational bottlenecks and vulnerabilities within the business.
Simulation results provide crucial insights into the system’s resilience and adaptability during surges in customer traffic. Analysis of wait times reveals the store’s operational coping mechanisms and identifies potential areas for improvement. Additionally, evaluating server demand highlights the delicate balance between customer service efficiency and resource allocation.
This study not only benefits the specific retail pet store but also extends insights for operational management in retail settings, dealing with fluctuating customer demands. By emphasizing statistical distribution analysis and simulation modeling, the research offers practical strategies for enhancing customer service and overall operational performance in dynamic retail landscapes. The findings aim to inform strategic decision-making, providing a roadmap for optimizing efficiency in similar settings.