A Comparative Analysis of Waiting Lines in the Nigerian Banking Industry
Vol. 4 Issue 1
Waiting for services is part of our daily routine and it is a common experience in virtually every economic life. There is hardly any economic activity that waiting line is not essential. The major objective of the study is to compare the outcome of the application of queuing model/theory to Automated Teller Machine services of First bank of Nigeria, Access bank and United bank for Africa at Coker branch, Lagos, Nigeria. The specific objectives are to determine the mean number of arrivals per hour (λ) at the Automated teller machine facility of the three banks; examine the mean number of their customers served per hour (μ); analyse the relationship between the mean number of arrivals and the mean number of customers served per hour (λ and μ) in the three banks and evaluate the average time a customer spends waiting in the queue before being served by a facility. The study population comprised of 349, 530 and 431 customers, for First Bank, Access Bank and UBA Bank respectively and the basic data were collected using observational timing of customer entry and service over a period of two weeks. The chi-square goodness of fit test was used to test the arrival pattern to determine if it follows a Poisson distribution and also tested the service pattern to determine if it follows an exponential distribution. The results obtained from the chi-square test showed that the arrival pattern follows a Poisson distribution and that the service pattern follows an exponential distribution, hence it can be analyzed using Markovian process. The raw data were then analyzed using Excel template bearing the multichannel and two servers queue model equations. Findings revealed that Access bank have the highest arrival of customers with an arrival rate of 40 customers per hour compared to First bank and UBA with 36 and 32 customers per hour respectively. The service rate of the three banks for the first facility is 32, 34, and 33 customers per hour respectively, and the second facility is 34, 33 and 35 customers per hour respectively. This shows that for the first facility, Access bank serve more customers efficiently than first bank and UBA, and for the second facility, UBA serve more customers than First bank and Access bank. UBA with utilization factor of 47% of time can be considered efficient than First bank and Access bank with utilization factor of 54.2% and 59.5% of time respectively. Thus, it can be deduced from this analysis that queuing theory is a good measure of efficiency.
