Tulio
Cremonini Entringer
Universidade
Estadual do Norte Fluminense Darcy Ribeiro, Brazil
E-mail: tulio_entringer@hotmail.com
Submission: 6/7/2019
Revision: 9/18/2019
Accept: 9/25/2019
ABSTRACT
An in-depth study of wait times in queues has become a factor of the banks' full interest for service improvements and reduced operating costs. When the demand is greater than the capacity offered the queue is formed, that is, queuing system is any process where a distinct group waits for a service. When there is adequate management, customer waiting time can be minimized, resulting in customer satisfaction and hence higher profitability. In view of this, the present work proposes a study of the flow of clients in an agency banking in the southern state of Rio de Janeiro. This work uses the qualitative and quantitative approach. The research is classified as descriptive, explanatory, bibliographical and case study. This work also presents the main concepts about simulation, modeling, specifically, in the Arena software, and describes the method of how the data were obtained for the construction of the model. The model simulates the capacity rate, number of customers in the queue, time of service and waiting time at which the customer can be subject at the agency banking. With such a study, we can analyze the behavior of queues in the system and thus propose improvements in order to guarantee the best scenario for both organization and customer satisfaction.
Keywords: Simulation; Queuing Theory; Banking service; Banking agency.
1.
INTRODUCTION
The service sector has great importance in the Brazilian
economy, being considered a promise for the development of the country, and one
of the greatest generators of jobs. According to Fiebig AND Freitas (2011), this
sector has some peculiarities that must be taken into account, which are the
intangibility: services can not be perceived by touch, nor smells and ears,
before the purchase can be made or the service requested; inseparability: it is
not possible to separate the service from the provider, and its production in
large quantities may not be easy; variability: the quality of the services
depends on the provider, without which the clients can intervene, participate
and help; and perishability: services can not be stored, resold or returned to
the provider. The same author reports that these factors represent one more
obstacle to follow in this sector.
Within the service sector, since the Middle Ages there
are reports of banking services, but it was in the seventeenth century that
began to act more formally with the issuance of paper money. Agencies banking generally (private or
public) are financial institutions that offer credit and payment services to
society, work with money deposited by their savers, providing credit to those
in need, being individuals or legal entities, charging interest on borrowers by
making the money circulate, thereby helping in the maintenance of the economy
(CAMARGO,
2009).
With increased competition in the banking sector due to
mergers and growth in the marketplace like credit shops, banks have begun to
get closer to their customers so they have no impact on profits. With the
advancement of technology there has been a contribution to changes in service
with a greater share of digital resources, where it is possible to do almost
all transactions from home, by telephone or computer applications. As a result,
more and more banks invest in these technologies to improve customer service in
order to increase loyalty and satisfaction with quality service, since keeping
a customer is more profitable than winning new ones (ZACHARIAS; FIGUEIREDO; ALMEIDA, 2008).
As in the banking sector the products are similar, what
counts are the quality of service and the services provided. As a result, the
service in the agencies has a level of demand with an increasingly high quality
service, taking into account customer satisfaction.
In this sense, the improvement of the processes of a
banking company is of extreme importance for the competitiveness, reliability
and profitability of the bank and the greater satisfaction of the clients. When
the management tools are used correctly, possible causes and solutions of the
problem can be perceived with some speed and become a factor of total interest
of the banks for improvements of their services (NUNES; NOGUEIRA, 2013).
When examining a problem to propose changes or
adaptations of the method already used, several ways of evaluating the
alternatives can be used. A simulation model fits perfectly in context. With
the use of simulation software, one can have an overview of the problem and
seek ways of improvement with a reduction of time and relevant costs (NUNES; NOGUEIRA, 2013).
In this context, this paper studies the behavior of the
queues of a branch office in the southern state of Rio de Janeiro, emphasizing
evaluating the operational performance of the system in operation and
efficiently dimensioning new systems, providing possible improvements.
1.1.
Practical Motivating Situation
For Labadessa and
Oliveira (2012), the customer is always in the first place, this is the basis
of the philosophy of quality. The quality care must be performed every day in
any company, regardless of the service or care provided by it,
thus
conserving its current customers and conquering new ones. Good service is
paramount for the growth of the company and its maintenance.
The first section presents the contextualization and the
objective and the practical motivating situation of this research. The second
section presents an approach to queuing theory, especially in banking agencies.
The third section presents the research methods, which included the description
of agency banking of this study and the simulation model. The following section
presents the analysis of results, which include the authors and journals that
published the most in the study area (publications analysis); the main
approaches of papers related to software quality (approach analysis); and
the most cited papers in the database searched, taking into account the
impact factor of the journal in which the work was published. Finally, the last
section presents the findings of the study.
2.
QUEUING THEORY
According to Hillier and Lieberman (2010), queuing training
is a phenomenon that occurs when demand becomes greater than the ability to
deliver a service over a given period. For example, if the number of servers is
less than necessary at a restaurant during lunch time, the customer may end up
waiting longer than he or she is willing, resulting in dissatisfaction or even
discontinuance of service usage.
However, the analysis of the opposite case is also
fundamental. If the number of servers allocated is greater than necessary, the
cost to maintain them becomes high. For these analyzes, it is necessary to take
into account the conditions of the establishment, such as office hours, number
of consumers seeking the service and peak hours.
In this context, the study of waiting in all its most
diverse forms arises. Queuing
theory, as it is known, uses queue models to represent different cases and aims
to balance the costs between offering services and the costs of
delays
suffered by users of the system (ARENALES et al., 2007). Moreira (2007) says
that this theory
is a body of mathematical knowledge, applied to the phenomena of
rows. Its
main objective is to develop mathematical models that allow predicting the
behavior of service delivery systems (MARINS, 2011).
For Guedes and
Araújo (2013), queuing theory aims to identify, through mathematical analysis,
the queuing measurement, providing for the organization of waiting, aiming at
customer satisfaction and profitability of the company, thus having a
satisfactory balance for both.
There is a diversity of applicability over queuing
theory, as shown in Table 1.
Table 1: Applicability of the queuing theory.
|
Author |
Applied Sector |
|
Bouzada
(2009) |
Call center |
|
Doile
(2010) |
Supermarket |
|
Camelo
(2010) |
Port terminal |
|
Pinto
(2011) |
Bank |
|
Santos and Lira (2017) |
Hospital |
|
Junior et al. (2017) |
Metallurgical industry |
A queuing system is identified as any and all processes
in which people arrive to receive a service they are waiting for (FOGLIATTI; MATTOS, 2007). It
basically consists of a source, where the customers arrive, the queue, where
they must wait and the service mechanism, where customers or products do what
is to be done and leave the system. It is important to remember that the
queuing system is formed by both who is being served and who is waiting for the
service. In this way, the basic process of queuing is: entering customers by a
source, waiting in queue, attending and leaving customers attended (Figure 1).
Figure 1: Elements
of a queue.
Source: Prado (2009).
Due to the possibilities of the arrival process of the
clients in a given establishment, there is a need to organize queues to improve
and facilitate the service, because if there is no organization, several
problems arise which make service difficult (SCHEMENNER, 1999).
For Martins el al. (2017), the sequence of the service is
what the next customer will be served. The queue can follow two models being
FIFO (First In - First Out) the first to arrive is the first to be attended,
that is, the first that enters the system and the first to leave it; LIFO (Last
In-First Out), the last one to arrive is the first to be attended, determining
priority, the customer with priority is the first to be attended.
According to Andrade (2009), the need to study queuing
theory would be to promote modifications that improve the performance of a
service, because when there is a great demand for a service or service system,
they cause large queues, resulting in customer dissatisfaction ; or when they
do not feel this demand, the system stays for a long period of time stopped,
bringing losses. In both situations there would be a recommendation for
experimental changes, almost always unfeasible for expenses.
With a system that can demonstrate the possible behavior
of the queue, it would be easier to choose the most viable changes, thus
demonstrating the form of attendance, customer arrival, system structure, and
queuing discipline.
Within the queuing theory, it is inevitable not to think
about the long waits encountered in bank branches. Given this, the next topic
will portray this situation, where long queues generate customer
dissatisfaction, since the application of queuing theory can bring an
improvement in the management of customer service leading to customer
satisfaction and thus generating more business for the banking system .
2.1.
Queues in Agencies Banking
In one day of service, the concentration of clients does
not follow a continuous order, having moments with the flow greater or less at
certain different times. These queues are with oscillations to establish a
reasonable time of service, since maintaining the demand compatible with the
need is fundamental in service in the banking branches (MARQUES, 2012).
According to Fernandes and Santos (2008), with a poor
customer service, the client may have several attitudes, such as not going back
to the store, switching to a product or brand, complaining to salespeople or
even doing negative marketing, with people nearby.
With the highly competitive banking market, these
situations demand great attention, since having a loyal client over a long term
is fundamental (FERNANDES;
SANTOS,
2008). In this context, the poor quality of banking service is related to the
expectation of the service provided, the banks can not meet the clients' needs,
leaving them dissatisfied with the services offered (ZENOTE, 2007).
Second, Lovelock and
Wirtz (2006), given these concepts the result of a service provided depends on
who executes it. In the case of bank agencies, it is the employees who work in
the agency who are responsible for this. This group of employees performs the
service with personal characteristics influencing the final result of the
service activity, thus reflecting the image of the company.
For this study, the systematics proposed by Vergara
(2016), which qualifies the classification of the research in two aspects: as
to the ends and the means, was taken as basis.
This work uses the qualitative and quantitative approach.
The research is classified as regards the purposes as descriptive and
explanatory and as to the means such as bibliographical and case study.
Descriptive, because it presents the main concepts
regarding simulation, modeling, specifically, in the Arena software.
Explanatory because it describes the method of how the data were obtained for
the construction of the model.
As for the means, the research is classified as a
bibliographical of the virtual type, since the research was carried out through
literary revision, about modeling and simulation, and as a case study, since
the theoretical reference was applied in a company.
3.1.
Description of Agency Banking
This paper studies the behavior of
the queues of a branch office in the southern state of Rio de Janeiro.
The agency chosen for this study has
the services of hand boxes - conventional, preferential and other assistance,
such as management - and ATMs - self-service - with single independent queues,
characterizing, in this way, a typical agency with the services now in studied.
For analysis, only the behavior of conventional cartons will be studied.
The layout of the bank branch is
shown in Figure 2.
Figure 2: Layout of the bank branch.
The priority service works
separately, as shown in Figure 2 (zone 3), and is not the object of study of
this work. One can observe the entrance of the agency and soon after the ATMs.
After the revolving door are the conventional calls (zone 2), where the clients
take the passwords and wait for the service (zone 1). In the agency, the system
of queues is by password, that is, the client arrives takes the password, waits
in the chairs destined for that purpose, and is answered on a first-come,
first-served basis. And, finally, there is a specific area for other types of services
(zone 4), such as requesting credit cards, requesting loans, contacting
management, among others.
Regarding non-priority customer
service (conventional), it works from eleven o'clock until sixteen o'clock.
Being that it works with three employees in the first four hours and with four
employees in the last hour, following the policy adopted by the organization
during the lunch period of the employees, as shown in Table 2. In summary:
•
3 banking service desks working from 11:00 a.m. to
3:00 p.m.; and
•
4 banking service desks working from 3:00 p.m. to 4:00 p.m.
Table 2: Employee
schedule.
|
Schedule |
Service Desk |
|||
|
11:00 a.m. - 12:00 p.m. |
Employee A |
Employee B |
Employee C |
|
|
12:00 p.m. - 01:00
p.m. |
|
Employee B |
Employee C |
Employee D |
|
01:00 p.m. - 02:00
p.m. |
Employee A |
|
Employee C |
Employee D |
|
02:00 p.m. - 03:00
p.m. |
Employee A |
Employee B |
|
Employee D |
|
03:00 p.m. - 04:00
p.m. |
Employee A |
Employee B |
Employee C |
Employee D |
3.2.
Description of the simulation model
The modeling of a system depends on
the purpose and complexity of the system under investigation, and the model can
be of the mathematical, descriptive, statistical and input-output type (FREITAS
FILHO, 2008). Most simulation models are input-output type, that is, iterative
models where input data is provided and specific responses are obtained (FREITAS
FILHO, 2008). Thus, according to Chwif (2006), the input variables required to
execute the model are:
•
Service time;
•
Interval between successive arrivals.
The conceptual model was elaborated
using the logical elements proposed by (LEAL; ALMEIDA; MONTEVECHI,
2008). The translation of the conceptual model of the system to
the computer simulation model was performed using the software Arena®12,
student version (KELTON; SADOWSKI; STURROCK, 2007).
The data collected refer to the
first two weeks of August 2018 and are provided for academic purposes by the
agency's management.
Figures 3 to 9 show the arrival
times of the clients in the agency for the conventional attendances, during the
working day (11:00 a.m. - 04:00 p.m.) during the days under study.
As mentioned before, the software
used was Arena, student version, due to its ease of use, because the program
allows, besides the construction of the simulation model, analyze the input
data (through the Input Analyzer module), analyze the results (through the
Output Analyzer) and visualize the simulation (through the Arena Viewer) (PRADO,
1999).
Figure 3: Customer arrival time in minutes on
August 11, 2018.
Figure 4: Customer arrival time in minutes on
August 12, 2018.
Figure 5: Customer arrival time in minutes on
August 13, 2018.
Figure 6: Customer arrival time in minutes on
August 14, 2018.
Figure 7: Customer arrival time in minutes on
August 18, 2018.
Figure 8: Customer arrival time in minutes on
August 19, 2018.
Figure 9: Customer arrival time in minutes on
August 20, 2018.
These data were processed by the
Input Analyzer software, complementary to Arena®12, to determine the best
probability distribution that adheres to the set of values. In the same way, it
was carried out with the data referring to the time of service of the
conventional boxes, according to the attendants during working hours,
fragmented in periods of 1 hour. The F2 element in Table 3 shows the
theoretical distributions of probabilities that best adhere to the stochastic
behavior of the variables for each 1-hour interval of the agency's operation.
After data collection and
identification of the behavior of the variables that represent the customer
flow dynamics in the bank branch, the conceptual model of the system was shown,
shown in Figure 10.
Figure 10: Conceptual
model of customer flow in the bank branch.
Table 3: Description and parameters of the
elements of the conceptual model.
|
Description |
Parameters |
|
|
E1 |
Arrival of customers for conventional service |
Weibull (average: 1.46 e deviation: 0.747) min – 11:00 a.m. - 12:00
p.m. |
|
12 * Beta (average: 0.539 e deviation 3.19) min – 12:00 p.m. - 13:00
p.m. |
||
|
15 * Beta (average: 0.335 e deviation: 2.44) min – 13:00 p.m. - 14:00
p.m. |
||
|
Weibull (average: 1.44 e deviation: 0.814) min – 14:00 p.m.- 15:00
p.m. |
||
|
Lognormal (average: 1.71 e deviation: 3.19) min – 15:00 p.m.- 16:00
p.m. |
||
|
F1 |
Location for conventional service queue |
Infinite capacity |
|
F2 |
Conventional service |
-0.001 + Exponential (average: 3.67) min – 11:00 a.m. – 12:00 p.m. |
|
-0.001 + Exponential (average: 3.37) min – 12:00 p.m. - 13:00 p.m. |
||
|
15 * Beta (average: 0.62 e deviation: 2.85) min – 13:00 p.m. - 14:00
p.m. |
||
|
Gama (average: 4.41 e deviation: 0.614) min – 14:00 p.m. - 15:00 p.m. |
||
|
-0.001 + Exponential (average: 2.98) min – 15:00 p.m. - 16:00 p.m. |
||
|
F3 |
Location to exit |
Infinite capacity |
|
R1 |
Conventional service |
4 service desks |
Regarding the number of employees
working in the offices during the period of operation of the agency, it was
decided to insert these conditions in the computational model through the
Schedule resource. This resource establishes capacity rules that will be
effective in certain periods of time.
4.
ANALYSIS OF RESULTS
4.1.
Current agency banking scenario
As is known, this study aimed at achieving the objectives
outlined at the beginning of work, seeks to quantify and analyze, in a bank of
the southern state of Rio de Janeiro, the following variables: number of
customers in the queue, queue waiting time,
service time and the percentage of occupancy of banking service
desks
(Table 4 and Table 5). Table 5 shows the customer time in the system (lead
time), in other words, the time the customer spent in the queue plus the
service time performed by the conventional service.
The results to be presented below are, in fact, mean
values obtained from all replications in each time period. The number of
replications was 22, referring to the number of business days in a month of
operation of the agency banking.
Table 4: Results regarding the current agency
banking scenario.
|
Current
agency banking scenario |
||||||
|
|
Number
of customers in the queue |
Queue
waiting time (min.) |
Occupancy
rate of banking service |
|||
|
Average |
Maximum |
Average |
Maximum |
Average |
Maximum |
|
|
11:00 a.m. - 12:00 p.m. |
0.3264 |
1.3997 |
2.8576 |
12.2899 |
50.07% |
60.78% |
|
12:00 p.m.
01:00 p.m. |
0.1875 |
0.7497 |
1.8244 |
7.2452 |
||
|
01:00 p.m. - 02:00 p.m. |
0.1025 |
0.4612 |
1.0465 |
4.3692 |
||
|
02:00 p.m. - 03:00 p.m. |
0.1069 |
0.5764 |
0.9170 |
4.3229 |
||
|
03:00 p.m. - 04:00 p.m. |
0.0836 |
0.6745 |
0.6873 |
4.8564 |
||
|
Average |
0.1614 |
0.7723 |
1.4666 |
6.6167 |
||
Table 5: Results of waiting time and service
time of the current agency banking scenario.
|
Current agency banking scenario |
||||
|
|
Waiting time (min.) |
Service time (min) |
||
|
Average |
Maximum |
Average |
Maximum |
|
|
11:00 a.m. - 12:00
p.m. |
7.1465 |
40.4608 |
4.2889 |
28.1709 |
|
12:00 p.m. 01:00 p.m. |
5.9155 |
32.6180 |
4.0911 |
25.3728 |
|
01:00 p.m. - 02:00
p.m. |
4.6425 |
37.8214 |
3.5960 |
33.4522 |
|
02:00 p.m. - 03:00
p.m. |
3.7207 |
32.9380 |
2.8037 |
28.6151 |
|
03:00 p.m. - 04:00
p.m. |
3.6276 |
20.8917 |
2.9403 |
16.0353 |
|
Average |
5.0106 |
32.9460 |
3.5440 |
26.3293 |
The results revealed that the agency
showed great performance and waiting time and number of customers in the queue.
However, it showed a relatively low rate of occupancy of the banking service
desks, that is, there was a significant percentage in which the banking service
desks became idle.
4.2.
New agency banking scenario
Based on the results obtained, a new scenario was
designed in order to allow a different situation to be assessed in relation to
the flow of clients that the agency may be subject to. This scenario is
described below and considers the number of attendants (banking service desks)
working during the working period.
The results of the simulations of the respective
scenarios are presented in Tables 6 and 7. The comparative results are shown in Table 8.
It was simulated to decrease the number of conventional
executive service from 3 (three) to 2 (two) banking service desks
during the first 3 (three) hours of
operation of the agency banking and from 4 (four) to 3 (three) banking service desks in the last hour of working.
Table 6: Results regarding the new agency
banking scenario.
|
New
agency banking scenario |
||||||
|
|
Number
of customers in the queue |
Queue
waiting time (min.) |
Occupancy
rate of banking service |
|||
|
Average |
Maximum |
Average |
Maximum |
Average |
Maximum |
|
|
11:00 a.m. - 12:00 p.m. |
0.9480 |
3.6402 |
8.5486 |
30.4762 |
71.10% |
93.82% |
|
12:00 p.m.
01:00 p.m. |
1.2872 |
4.0501 |
12.3475 |
37.3852 |
||
|
01:00 p.m. - 02:00 p.m. |
0.9577 |
4.5105 |
9.1531 |
36.9042 |
||
|
02:00 p.m. - 03:00 p.m. |
0.7563 |
3.1126 |
6.7289 |
20.0097 |
||
|
03:00 p.m. - 04:00 p.m. |
0.5350 |
3.1387 |
4.3665 |
20.5444 |
||
|
Average |
0.8968 |
3.6904 |
8.2289 |
29.0639 |
||
Table 7: Results of waiting time and service
time of the new agency banking scenario.
|
New agency banking scenario |
||||
|
|
Waiting time (min.) |
Service time (min) |
||
|
Average |
Maximum |
Average |
Maximum |
|
|
11:00 a.m. - 12:00
p.m. |
13.6301 |
55.3530 |
5.0815 |
24.8768 |
|
12:00 p.m. 01:00 p.m. |
16.1369 |
70.7904 |
3.7894 |
33.4052 |
|
01:00 p.m. - 02:00
p.m. |
13.3224 |
72.0594 |
4.1693 |
35.1552 |
|
02:00 p.m. - 03:00
p.m. |
9.8860 |
45.1307 |
3.1571 |
25.1210 |
|
03:00 p.m. - 04:00
p.m. |
7.6390 |
49.2781 |
3.2725 |
28.7337 |
|
Average |
12.1229 |
58.5223 |
3.8940 |
29.4584 |
Table 8: Comparison between the results of the current
scenario and the new simulated scenario.
|
|
Current scenario |
New scenario |
|
Average number of customers in the queue |
0,1614 |
0,8968 |
|
Average waiting time in queue (min.) |
1,4666 |
8,2289 |
|
Average service time (min.) |
3,5440 |
3,8940 |
|
Average system waiting time (min.) |
5,0106 |
12,1229 |
|
Average occupancy rate |
50,07% |
71,10% |
5.
CONCLUSION
The model adequately described the behavior of the system
under study and proved to be an important tool to assist banking managers in
making decisions about the operation of the resources and to better control the
behavior of the queues.
By means of the obtained results, that the number of
people in the queue in the current system studied is not significant, being
approximately, on average, 0.1614 people, which makes a change to a larger
number of attendants unnecessary.
Due to the absence of significant queues, the proposal
for a reduction in the number of attendants appeared. It was observed that the
queue that would be formed, with two attendants (and three in the last hour of
operation), would remain relatively small, with 0.8968 people waiting an
average time of 8.22289 minutes to be attended, average of 1.4666 minutes from
the previous scenario.
In addition, it was noticed that the average occupancy
rate of these attendants would be higher when compared to the current situation
of the bank
agency, that is, the employees would be 71.10% occupied during the interval of
operation. In the previous scenario, a low rate of employee occupancy (50.07%).
The model was consistent with the real system, clearly
showing that during the time period analyzed in the bank agency, it does not
have large queues and people wait a short time to be answered, which could be
confirmed with the data provided by the banking service
management.
In view of this, remaining in the proposed situation with
three attendants in total (two counters operating in the first four hours and
three counters in the last hour) is the best alternative, since there are no
significant queues for this situation and the employees are not overburdened,
or the occupancy rate may be considered acceptable although higher than the
previous scenario.
It is also worth mentioning the application of IDEF-SIM
in conceptual modeling. As indicated in the paper by Leal, Almeida and Montevechi (2008), the
creation of the conceptual model, using appropriate syntax and semantics, gives
more agility to the translation process of the computational model, improving
the development of the simulation project as a whole.
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