Analysing Queuing Systems in Convenience Store Using Queuing Models

Abstract

Long customer waiting periods during busy hours are a frequent concern in convenience stores, especially at popular locations like FamilyMart and 7-Eleven. This research utilizes queuing theory to examine customer waiting times and service effectiveness by contrasting single-server (M/M/1) and multi-server (M/M/s) queue models. Data were gathered through direct observation over five days, from Sunday to Thursday, encompassing both peak (12:00 p.m. – 2:00 p.m.) and non-peak periods. The primary variables documented included customer arrival times, service durations, waiting durations, and server utilization. Microsoft Excel is used for data organization and initial computations, while Python programming is employed to calculate queue performance metrics such as average waiting time, average service time, queue length, and server utilization. The findings suggest that waiting times and server utilization rise considerably during peak hours, particularly in single-server environments, whereas multi-server systems exhibit improved performance in managing increased customer arrivals. The findings underscore the significance of proper counter allocation and effective queue management in minimizing customer wait times. This study offers valuable insights for enhancing service efficiency and customer satisfaction within convenience store operations.