Queuing Analysis of Service Operations in Retail Business

This study examines service operations in retail business using queuing analysis, with a focus on a retail store in Mumbai, India. The research applies Little’s Law and the G/G/1 queuing model to analyze customer flow, waiting times, and queue dynamics. Data collected on March 30, 2023, includes 561 observations of arrival times, service start times, finish times, and queue lengths. The results highlight patterns in customer arrivals, queue sizes, and cycle times. The study finds that peak congestion occurs at specific intervals, particularly early in the morning and late in the evening, leading to prolonged wait times and inefficiencies in service operations. The findings suggest that the store’s current service capacity is insufficient to handle peak demand, resulting in an average customer wait time of 3.31 hours. To address these challenges, strategic interventions such as increasing checkout counters and opening additional stores in high-traffic areas are recommended. These insights provide valuable guidance for improving efficiency and customer satisfaction in retail service operations.