The owner of Eat Now Restaurant implemented an expanded menu last year. The menu

Question

The owner of Eat Now Restaurant implemented an expanded menu last year.  The menu was a success, drawing many more customers, who seemed to like the increased variety of the menu choices over the previous menu. But, good news soon became bad news as long waiting lines began to deter customers and business dropped off.  Because of the space and other limitations, there didn’t seem to be any viable options to consider.  Then a customer mentioned a technique called mass customization that was being used at his company.  He said it really streamlined processing and maybe it could work for the restaurant.  Describe how that approach could work at the restaurant and why it could be expected to reduce waiting lines.  What costs would be involved in transitioning to this system?  What other approaches could be used to reduce waiting line times?  Please include references.

Explanation / Answer

Waiting in lines is part of everyday life. Some estimates state that Americans spend 37 billion hours per year waiting in lines. Whether it is waiting in line at a grocery store to buy deli items (by taking a number) or checking out at the cash registers (finding the quickest line), waiting in line at the bank for a teller, or waiting at an amusement park to go on the newest ride, we spend a lot of time waiting. We wait in lines at the movies, campus dining rooms, the registrar’s office for class registration, at the Division of Motor Vehicles, and even at the end of the school term to sell books back. Think about the lines you have waited in just during the past week. How long you wait in line depends on a number of factors. Your wait is a result of the number of people served before you, the number of servers working, and the amount of time it takes to serve each individual customer. Wait time is affected by the design of the waiting line system. A waiting line system (or queuing system) is defined by two elements: the population source of its customers and the process or service system itself. In this supplement we examine the elements of waiting line systems and appropriate performance measures. Performance characteristics are calculated for different waiting line systems. We conclude with descriptions of managerial decisions related to waiting line system design and performance.

Arrival and Service Patterns Waiting line models require an arrival rate and a service rate. The arrival rate speci- fies the average number of customers per time period. For example, a system may have ten customers arrive on average each hour. The service rate specifies the average number of customers that can be serviced during a time period. The service rate is the capacity of the service system. If the number of customers you can serve per time period is less than the average number of customers arriving, the waiting line grows infinitely. You never catch up with the demand! It is the variability in arrival and service patterns that causes waiting lines. Lines form when several customers request service at approximately the same time. This surge of customers temporarily overloads the service system and a line develops. Waiting line models that assess the performance of service systems usually assume that customers arrive according to a Poisson probability distribution, and service times are described by an exponential distribution. The Poisson distribution specifies the probability that a certain number of customers will arrive in a given time period (such as per hour). The exponential distribution describes the service times as the probability that a particular service time will be less than or equal to a given amount of time

Waiting Line Priority Rules A waiting line priority rule determines which customer is served next. A frequently used priority rule is first-come, first-served. This priority rule selects customers based on who has been waiting the longest in line. Generally, customers consider first-come, first-served to be the fairest method for determining priority. However, it is not the only priority rule used. Other rules include best customers first, highest-profit customer first, quickest-service requirement first, largest-service requirement first, emergencies first, and so on. Although each priority rule has merit, it is important to use the priority rule that best supports the overall organizational strategy. For example, a firstcome, first-served rule doesn’t make sense in a hospital emergency room and in fact could cause unnecessary deaths. The priority rule used affects the performance of the waiting line system. As an example, first-come, first served is generally considered fair, yet it is biased against customers requiring short service times. When checking out at a store that is using first-come, firstserved as a priority rule, a customer waiting behind another customer with a large number of items waits longer than a customer waiting behind a second customer with only a few items. Although processing is sequential, the wait times vary because of the preceding customer. Also, priority rules besides first-come, firstserved may imply that some customers wait extremely long periods of time. For example, in a busy emergency room, someone not critically sick or injured could wait a significant period of time.

Performance measures are used to gain useful information about waiting line systems. These measures include: 1. The average number of customers waiting in line and in the system. The number of customers waiting in line can be interpreted in several ways. Short waiting lines can result from relatively constant customer arrivals (no major surges in demand) or from the organization’s having excess capacity (many cashiers open). On the other hand, long waiting lines can result from poor server effi- ciency, inadequate system capacity, and/or significant surges in demand. 2. The average time customers spend waiting, and the average time a customer spends in the system. Customers often link long waits to poor-quality service. When long waiting times occur, one option may be to change the demand pattern. That is, the company can offer discounts or better service at less busy times of the day or week. For example, a restaurant offers early-bird diners a discount so that demand is more level. The discount moves some demand from prime-time dining hours to the less desired dining hours. If too much time is spent in the system, customers might perceive the competency of the service provider as poor. For example, the amount of time customers spend in line and in the system at a retail checkout counter can be a result of a new employee not yet proficient at handling the transactions. 3. The system utilization rate. Measuring capacity utilization shows the percentage of time the servers are busy. Management’s goal is to have enough servers to assure that waiting is within allowable limits but not so many servers as to be costinefficient.

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