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The purpose of this analysis is to estimate the optimal number of cashiers that should be kept open in a retail store to minimize costs and, consequently, maximize profits. To conduct this study, we have chosen the largest retailer in the world; Wal-Mart. Everyone has experienced waiting in line, whether at a retail store, on the phone for technical help, at the doctor’s office or in the drive-through lane of a bank. Sometimes, it is a pleasant experience, but many times it can be extremely frustrating for both the customer and the store manager. Given the intensity of competition today, a customer waiting too long in line is potentially a lost customer. The problem in virtually every queuing situation is a trade-off decision. The manager must weigh the added cost of providing more rapid service (i.e., more checkout counters, more production staff) against the inherent cost of waiting. For example, if customers are walking away disgusted because of insufficient customer support personnel, the business could compare the cost of providing more staff to the value of potentially lost revenues resultant of decreased customer loyalty.
As part of our analysis, we combined different economic tools such as elasticity of customer loyalty based on line waiting times, queuing theory, and a cost-benefit analysis to determine the optimal number of open cashiers needed to provide a pre-determined level of customer satisfaction. To determine the elasticity of customer loyalty, we conducted an online survey (Appendix A) among Provo/Orem residents of various ages. To analyze Wal-Mart’s queuing system, we needed factors such as average line length, number of lines, customer arrival rate, cashiers’ service rate and average transaction amount and time per customer. Since store managers weren’t allowed to disclose this information, we collected our own data by observation (Appendix B). Once we knew how long customers were willing to wait and how to determine wait times based on the
As part of our analysis, we combined different economic tools such as elasticity of customer loyalty based on line waiting times, queuing theory, and a cost-benefit analysis to determine the optimal number of open cashiers needed to provide a pre-determined level of customer satisfaction. To determine the elasticity of customer loyalty, we conducted an online survey (Appendix A) among Provo/Orem residents of various ages. To analyze Wal-Mart’s queuing system, we needed factors such as average line length, number of lines, customer arrival rate, cashiers’ service rate and average transaction amount and time per customer. Since store managers weren’t allowed to disclose this information, we collected our own data by observation (Appendix B). Once we knew how long customers were willing to wait and how to determine wait times based on the