Waiting Lines Situation
Queues represent a typical occurrence that individuals can experience within multiple areas associated with life (Anderson ainsi que al., 2016). With regard to example, people that visit banks are usually often confronted with the particular problem of getting to await in ranges to get assist from a lender employee. Therefore, lines are highly standard in banks and some other types of banking institutions, and they ought to be addressed along with the help associated with appropriate management plus analysis options.
According to Onoka, Babasola, Moyo, plus Ojiambo (2018), the application of queuing theory, which is a mathematical approach to waiting lines, can be applied to deal with the issue of queues at banking organizations. Using the example of Guarantee Trust Bank, the scholars considered the time that customers spend waiting in lines, the time necessary to spend on each client that is being serviced, the average cost that consumers lose when waiting in queues, as well as the average cost of each server necessary to achieve system optimization.
In the study, the researchers used the First Come First Serve (FCFS) multi-server queuing model that was used to develop a model for the queuing process. While the service rate followed the exponential distribution, the time spent waiting in lines was seen to follow a Poisson distribution. The researchers used a case study approach to analyzing queuing models through the random administration of questionnaires, interviews, as observations of participations.
Based on the results of the analysis, it could be concluded that queuing theory could offer innovative solutions at an optimal level of service. It was shown that hiring more personnel at a financial facility could enhance the capabilities of a bank to address the challenges of queues during busy times. Moreover, it was found that there was still more room for improvement at such departments as Bank cash deposit units, which also experience significant challenges with lines. Another important takeaway from the study is that software was essential to help the modeling associated with different banking models. These included customer service units, ATM money withdrawal units, as well as others.
In day-to-day life, clients experience severe disappointment when they possess to stand it ranges. The value associated with the study by Onoka et al. (2018) is associated along with offering banking institutions the solution to decrease the occurrence associated with lines through identifying how managers may address the requirements associated with facilities and the clients. Besides, the waiting around line optimization answer could be put on other contexts, like restaurants and telecommunication services. In the particular research, it has been shown that this issue of queues via applying the FCFS multi-server queuing design could help organizations determine that these people need to become more attentive to their own staffing solutions.
Hiring more staff might be a solution in order to address the task associated with long queues in many facilities. This particular points to the truth that organizations that cope with large volumes associated with clients should not really save costs upon personnel and their own training. Having several servers which could socialize with customers rapidly and efficiently implies that queuing problems are usually less complex as they may seem. Moreover, the costs that companies incur when there are customers who queue to be served have shown to be higher than the expenses necessary to hire and train new workers. It is recommended to further the research on waiting for line management and analysis due to the skewed perspective on the issue in scholarly literature.
Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., Cochran, J. L., Fry, M. J., & Ohlmann, J. W. (2016). Quantitative methods for business with CengageNOW (13th ed. ). Boston, MA: Cengage Learning.
Onoka, A., Babasola, O., Moyo, E., & Ojiambo, V. (2018). The application of queuing analysis in modeling optimal service level. International Journal associated with Scientific and Executive Research, 8(1), 184-194.