Queueing Theory
Advantages and Disadvantages of Queuing Theory
Queuing theory is a computational method used in analyzing the delay in long lines (Render, Stair, & Hanna, 2015). The theory is put into use in many businesses in areas such as the service process and arrival process. One of the advantages of the queuing theory in real life situation includes the improvement of the traffic flow, ensuring efficient customer service, and effective shipping of products from the business to the customers (Render et al., 2015). In the service industry, queuing is commonly used for organization and management of the services in the industries. It allows the industry to serve one person at a time without scrambling. It is difficult to accurately determine the arrival and departure time for customers since their needs vary and the type of facilities they want are unknown by the industry (János, 2012). However, the queuing theory helps the industry tell the number of customers and the specific type of facilities each one of them needs. Queuing techniques avoid chaos since customers are served in an orderly manner, one at a time (János, 2012). Service industries use different types of efficient queuing theory making their operations productive and more efficient.
Figure 1 Queuing Theory Model. Where λ = arrival rate. μ = rate of services providence. ŘI = average number of clients in line, S = number of service providers. Source: (Zai et al., 2009)
There are different techniques categorized under the queuing theory. Booking in advance is a good deal for any busy person. As such, specific time slots are assigned to each customer whereas other customers are not served during the reserved times. Other techniques are online queuing, waiting lobby and telephone technique (Jones & Kenward, 2014). The main advantages of queuing theory that is exhibited by the companies which incorporate the use of queues in their working operations include an increase in the purchases, increase efficiency in provision of services service, a decrease in the waiting time, and overall effectiveness of the company’s operations (Render et al., 2015). The increase in the efficiency of service providence is attributed to by possession of technology that ensures the best customer services. Good performance is because of a decrease in the wait time where the performance is changed positively. As such, the managers can monitor the short lines hence reallocate the resources which in turn increase the performance. Through the utilization of in-line merchandising, queueing theory can be advantageous in increasing the impulse purchases.
The increase in the performance measures of the business based on the queueing theory can be determined through the formulae below.
- Utilization of the server
.
- Utilization of the machines
Let U(i) denote the utilization of machine i. Then
,
Where Ti denotes the mean response time for machine I, that is the mean time while it is broken
Source: (János, 2012, p. 87).
Jones and Kenward (2014) explain that for a service industry that has employed an increased service efficiency technique, customers are more susceptible to making a mistake going to the wrong service desk. They are forced to queue g again in a different waiting line where he or she can be served which costs a lot of time. In other industries, issues are reported to the manager immediately to be solved reducing the waiting time of customers. This technique faces a challenge when the cashiers do not report the problem to the manager maybe due to absenteeism or incompetence making the customers wait for long to get a solution. This makes the customers upset because they are unaware of how long it will take for them to be served. Some people want to be served first before others hence at times causing scramble in the queue technique. In this case, service industries have put other systems to control the flow of their customers, which makes it expensive as they spend money paying a guard or printing tags with numbers specific to a customer.
Benefits Provided by Constant Service Time Model
The waiting line system does not always have an arrival rate that is not evenly distributed. As such, some systems require constant time for all the customers or the operations of the industry. The constant service time model is vital in such industries where some equipment or robots are used to deliver the services after some constant time intervals (Jones & Kenward, 2014). The constant service time models are widely used in different businesses, which involve servicing and manufacturing. Kindström and Kowalkowski (2014) explain that the constant service times are mainly associated with the automated equipment used in the services industry and the machinery used in manufacturing. Such models are of the essence in business sectors where the service times cannot be considered to be exponentially distributed. As such, it distributes the independent service times to the clients waiting to be served on the line.
The constant service model is established under the rule that all the services offered by the business take the same time to be completed (Kindström & Kowalkowski, 2014). When the companies use the same amount of time to offer the services, the amount of time used to take care of each customer is the same. This is beneficial in that it reduces time wastage and quality services can be offered since the number of customers to be catered for can be predicted hence the services are developed based on such numbers. Most distribution centers utilize this type of model in offering their services. When the customers place an order on the number of products they want, the company can pack them promptly which ensures the efficiency of the work operations (Jones & Kenward, 2014). All the customers get an equal amount of time, which shows the fairness exhibited by such businesses hence, in the long run, enticing more customers.
References
Jones, B., & Kenward, M. G. (2014). Design and analysis of cross-over trials. Boca Raton: CRC Press.
Kindström, D., & Kowalkowski, C. (2014). Service innovation in product-centric firms: A multidimensional business model perspective. Journal of Business & Industrial Marketing, 29(2), 96-111.
Render, B., Stair, R. M., Jr., & Hanna, M.E. (2015). Quantitative analysis for management (12th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
János S., (2012). Basic queueing theory. Debrecen: Debrecen University.
Zai, A., Farr, K., Grant, R., Mort, E., Ferris, T., & Chueh, H. (2009). Queuing Theory to Guide the Implementation of a Heart Failure Inpatient Registry Program. Journal of the American Medical Informatics Association, 16(4), 516-523. doi:10.1197/jamia.m2977
