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. 2018 May 24;19:835–841. doi: 10.1016/j.dib.2018.05.101

Survey dataset on analysis of queues in some selected banks in Ogun State, Nigeria

Sheila A Bishop 1,⁎, Hilary I Okagbue 1, Pelumi E Oguntunde 1, Abiodun A Opanuga 1, Oluwole A Odetunmibi 1
PMCID: PMC5997939  PMID: 29900379

Abstract

Queuing theory is the mathematical study of waiting queues (or lines). The theory enables the mathematical analysis of several related processes such as arriving at the queue, waiting in line and being served by a server. This data article contains the analysis of queuing systems obtained from queues from the observed data of some selected banks in Ogun State. One of the gains expected from this survey, is to help review the efficiency of the models used by banks in such geographical locations in sub-Saharan countries. The Survey attempts to estimate the average waiting time and length of queue(s).

Keywords: Queues, Banks, Waiting time, Service, Length, Urban areas, Statistics

Specifications Table

Subject area Decision sciences
More specific subject area Queuing analysis, operations research, statistics
Type of data Tables
How data was acquired Field Survey and with the aid of stop watch and a recorder.
Data format Analyzed
Experimental factors Simple random sampling of some selected Banks in Urban areas of Ogun State, Nigeria.
Experimental features Analysis of the waiting and service times of selected customers.
Data source location Covenant University Ota, Ogun State, Nigeria
Data accessibility All the data are in this data article
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Value of the data

  • •

    The data could be useful in detecting the causes and proffering solutions to the problem of queues.

  • •

    Queues are necessary if order is to be maintained in the society, but most queues in sub-Saharan countries constitute a menace and sometimes end in riot and mob actions. Hence the data can be useful for security agents responsible for maintaining law and order [1], [2].

  • •

    The data could be used by banking regulatory bodies in Nigeria.

  • •

    The analysis of the data could be helpful in time management especially at peak periods [3].

  • •

    The data can also help the banks to improve on their services [4], [5], [6].

  • •

    The data can also help to rate the banks in terms of customers services satisfaction.

1. Data

The data was collected from three banks in three different urban areas of Ogun State. The Data was generated using a stop watch and a recorder to note the arrival time, the time spent on the queue (waiting time) before being attended to and the time used to serve a customer (Service time).

The notations used for the presentation of data are X1, X2, X3, and N1 for the first bank Y1, Y2, Y3, and N2 for the second bank and Z1, Z2, Z3, and N3 for the third bank respectively. They denote the following:

X1, Y1 and Z1 represents the time range when a customer arrives at the bank and the time his/her cheque or withdrawal booklet was collected for the first, second and third bank respectively.

X2, Y2 and Z2 represents the time used to process the cheque or withdrawal booklet in the first, second, and third banks respectively.

X3, Y3 and Z3 represents the total time in the system in the first, second, and third banks respectively.

N1, N2 and N3 represents the number of people who came to the first, second and third banks and were attended to.

The data taken covers only twelve weeks. Four weeks for each bank and the time is measured in minutes.

2. Experimental design, materials and methods

The study of queues is the study of waiting times which often results to models that predicts queue length and waiting time. The models are also used to make decisions on how to increase servers, optimize queue length and waiting time. Queue is often characterized by the following presented in Table 1.

Table 1.

Features of queue.

1 Queue is a linear data structure.
2 In queues insertion can take place at only one end called rear.
3 In queues deletions can takes place at the other end called front.
4 Queues are called FIFO (first in first out). The element first into the queue is the element deleted first from the queue.
5 Queues are also called LILO (last in last out).The element entered last into the queue is the element deleted last from the queue.
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Several operations can be done on queues which are listed as:

  • 1.

    Insertion: inserting a new element into the queue.

  • 2.

    Deletion: deleting a new element from the queue.

  • 3.

    Display: visit each node at least once.

  • •

    Queue is full- there is no room to insert a new element.

  • •

    Queue is empty- there is no element to delete from queue.

There are several methods of investigating phenomena that are modeled as queuing problems. Some are mentioned as follows:

  • i.

    Direct observation of practical situation

  • ii.

    The planned experiment under artificial conditions

  • iii.

    The simulation method

  • iv.

    The Mathematical Analysis method

  • v.

    Product-form solutions method

  • vi.

    Methods from complex-function theory

  • vii.

    Analytic-algorithmic methods

  • viii.

    Heavy and light traffic approximations

It is noteworthy that not all queuing problems can be investigated mathematically. Some investigators using (i) and (ii) above require a clear out study of the situation and therefore, necessary adjustments and manipulations are made.

2.1. Method of data collection

The investigators made use of (i) and (ii) mentioned above and with the aid of a stop watch and a recorder.

2.2. Data presentation

The data are presented in Table 2, Table 3, Table 4. It should be noted that the departure time was not captured because the customers often wait behind to count their money, wait for those that accompanied them or make non-transaction activities such as renewal of Automated teller machine (ATM) cards, registration of bank verification number, enquiries on new banking products and other complaints. The raw data containing the arrival times of the customers can be assessed as Supplementary Data.

Table 2.

The queuing data for the first bank.

Weeks Days X1 X2 X3 N1
1st MONDAY 12 26 38 880
TUESDAY 5 19 24 720
WEDNESDAY 6 8 14 1020
THURSDAY 11 20 31 802
FRIDAY 17 15 32 522
MONDAY 20 13 33 989
TUESDAY 22 18 40 684
2nd WEDNESDAY 24 19 43 548
THURSDAY 23 9 32 1021
FRIDAY 25 20 45 789
MONDAY 8 15 23 1000
TUESDAY 10 22 32 990
3rd WEDNESDAY 11 10 21 1001
THURSDAY 10 15 25 1051
FRIDAY 7 17 24 982
MONDAY 7 9 16 857
TUESDAY 10 9 19 981
4th WEDNESDAY 10 6 16 1057
THURSDAY 5 20 25 899
FRIDAY 10 12 22 996
Total 253 302 555 17,789
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Table 3.

The queuing data for the second bank.

Weeks Days Y1 Y2 Y3 N2
1st MONDAY 16 8 24 1034
TUESDAY 17 15 32 789
WEDNESDAY 18 8 26 1002
THURSDAY 13 15 28 910
FRIDAY 10 6 16 931
MONDAY 16 14 30 748
TUESDAY 14 9 23 924
2nd WEDNESDAY 9 17 26 872
THURSDAY 18 10 28 764
FRIDAY 15 10 25 890
MONDAY 15 19 34 971
TUESDAY 23 18 41 685
3rd WEDNESDAY 30 10 40 724
THURSDAY 28 9 37 873
FRIDAY 26 18 44 605
MONDAY 10 32 42 1017
TUESDAY 7 17 24 1009
4th WEDNESDAY 12 19 31 891
THURSDAY 11 26 37 948
FRIDAY 13 14 27 901
Total 321 294 615 17,488
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Table 4.

The queuing data for the third bank.

Weeks Days Z1 Z2 Z3 N3
1st MONDAY 10 12 22 767
TUESDAY 12 11 23 930
WEDNESDAY 7 7 14 921
THURSDAY 22 10 32 878
FRIDAY 11 12 23 790
MONDAY 11 18 29 876
TUESDAY 18 14 32 923
2nd WEDNESDAY 12 14 26 910
THURSDAY 10 18 28 1002
FRIDAY 9 8 17 949
MONDAY 16 10 26 934
TUESDAY 8 6 14 1011
3rd WEDNESDAY 12 7 19 874
THURSDAY 8 10 18 762
FRIDAY 6 9 15 631
MONDAY 13 12 25 989
TUESDAY 15 8 23 784
4th WEDNESDAY 16 14 30 648
THURSDAY 10 8 18 891
FRIDAY 11 15 26 752
Total 237 223 460 17,222
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2.3. Descriptive statistics

The descriptive statistics for the data are summarized as follows for the data of the first, second and the third banks respectively. These are shown in Table 5, Table 6, Table 7.

Table 5.

Description statistics for the queuing data of the first bank.

Statistic X1 X2 X3 N1
Mean 12.65 15.1 27.75 889.45
Standard Error 1.483728 1.220224311 2.028578709 36.38272255
Median 10 15 25 981.5
Mode 10 20 32 #N/A
Standard Deviation 6.635431 5.457009013 9.072079782 162.7084816
Sample Variance 44.02895 29.77894737 82.30263158 26474.05
Kurtosis −0.78814 −0.85103544 −0.74558109 0.318384698
Skewness 0.800322 0.058384533 0.371099032 −1.13689222
Range 20 20 31 535
Minimum 5 6 14 522
Maximum 25 26 45 1057
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Table 6.

Description statistics for the queuing data of the second bank.

Statistic Y1 Y2 Y3 N2
Mean 16.05 14.4 30.75 874.4
Standard Error 1.418812 1.350244 1.669975 26.63826
Median 15 14.5 29 896
Mode 16 10 24 #N/A
Standard Deviation 6.345118 6.038473 7.468354 119.1299
Sample Variance 40.26053 36.46316 55.77632 14191.94
Kurtosis 0.177576 2.405767 −0.61616 −0.19879
Skewness 0.906584 1.132334 0.190103 −0.71182
Range 23 26 28 429
Minimum 7 6 16 605
Maximum 30 32 44 1034
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Table 7.

Description statistics for the queuing data of the third bank.

Statistic Z1 Z2 Z3 N3
Mean 11.85 11.15 23 861.1
Standard Error 0.880416 0.785644 1.289635 24.48328
Median 11 10.5 23 884.5
Mode 10 12 23 #N/A
Standard Deviation 3.937338 3.513508 5.767422 109.4926
Sample Variance 15.50263 12.34474 33.26316 11988.62
Kurtosis 0.97078 −0.48582 −1.07621 −0.24494
Skewness 0.940453 0.516912 −0.0823 −0.69166
Range 16 12 18 380
Minimum 6 6 14 631
Maximum 22 18 32 1011
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2.4. Analysis of variance

Analysis of variance (ANOVA) is done to investigate mean differences among the total time spent by the customers in the three banks. The result is presented in Table 8.

Table 8.

ANOVA result.

Source of Variation SS df MS F P-value F crit
Between Groups 610.8333 2 305.4167 5.347489 0.00744 3.158843
Within Groups 3255.5 57 57.11404
Total 3866.333 59
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There are significant mean differences among the total time spent by the customers in the three banks at 0.05 level of significance.

Further analysis of data can be carried out in the following areas using any of the statistical tools applied in Refs. [7], [8], [9], [10], [11].

  • i.

    The utilization factor or traffic intensity can be calculated using the arrival rate and the service time. This can used to determine average needed servers, number of automated banking machines (ATM). See Refs. [2], [4], [5], [6].

  • ii.

    The confidence intervals for average service rate and average arrival rate can be estimated assuming the service time and arrival time are independent and identically distributed.

  • iii.

    The data can be analyzed pictorially, that is using a Bar chat, Pie chat to show the traffic intensity and efficiency of the servers.

  • iv.

    The results from each bank can be compared to determine the level of service efficiency.

Acknowledgements

This research benefited from the sponsorship of the Statistics sub-cluster of The Industrial Mathematics Research Group (TIMREG) of Covenant University and Centre for Research, Innovation and Discovery (CUCRID), Covenant University, Ota, Nigeria.

The investigators will also like to appreciate the management of the three banks for giving them the opportunity to collect the data within the requested period.

Footnotes

Transparency document

Transparency data associated with this article can be found in the online version at doi:10.1016/j.dib.2018.05.101.

Appendix A

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.dib.2018.05.101.

Transparency document. Supplementary material

Supplementary material

mmc1.docx (42.7KB, docx)

Appendix A. Supplementary material

Supplementary material

mmc2.zip (3.6MB, zip)

References

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Associated Data

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Supplementary Materials

Supplementary material

mmc1.docx (42.7KB, docx)

Supplementary material

mmc2.zip (3.6MB, zip)

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