Evaluation of the Process of Attention using the Simulation of Processes

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ISSN No: 2456
International
Research
UGC Approved International Open Access Journal
Evaluation of the Process of Attention using
the Simulation of Processes
Muñoz Martiñon Rodolfo
Universidad de la República
Mexicana, Mexico
Universidad Insurgentes
ABSTRACT
The present work aims to perform an evaluation of the
customer service process in the service area, for
which a study using a reliability of 90% with the
process simulation technique was carried out. A
descriptive - analytical methodology was developed
with a sample of 68 time points for each area of the
care process. The results obtained helped to measure
the productivity of the service process, resulting
productivity of 89.31%.
Keywords— Process of attention, productivity and
simulation of processes
INTRODUCTION
Computer simulation developed at the same time as
the vertiginous progress of the computer took place.
Its first large-scale deployment was in the Manhattan
Project during World War II, to recreate a nuclear
detonation. The Monte Carlo method was used.
Computer simulations sometimes supplement or even
replace modeling systems for which it is not possible
to find analytical solutions in a closed fashion. There
are many types of computer simulation, but they all
share one common feature: they try to generate
sample of representative scenarios for a model in
which a complete relation of all possible states of this
would be very costly or impossible.
models were initially used as a supplement to other
parameters, but later their use was extended t
areas (Frank, 2011).
Aponte (2011) The simulation is to recreate a system
or imitate a reality that looks so much like it without
@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 1 | Issue – 5
ISSN No: 2456 - 6470 | www.ijtsrd.com | Volume
International Journal of Trend in Scientific
Research and Development (IJTSRD)
the Simulation of Processes
Robles Ramírez Diana P.
Universidad Insurgentes, Mexico
Vanessa Zamudio Hidalgo
Mexicana , México
The present work aims to perform an evaluation of the
customer service process in the service area, for
which a study using a reliability of 90% with the
process simulation technique was carried out. A
analytical methodology was developed
with a sample of 68 time points for each area of the
care process. The results obtained helped to measure
the productivity of the service process, resulting in a
Process of attention, productivity and
Computer simulation developed at the same time as
the vertiginous progress of the computer took place.
scale deployment was in the Manhattan
Project during World War II, to recreate a nuclear
detonation. The Monte Carlo method was used.
puter simulations sometimes supplement or even
replace modeling systems for which it is not possible
to find analytical solutions in a closed fashion. There
are many types of computer simulation, but they all
share one common feature: they try to generate a
sample of representative scenarios for a model in
which a complete relation of all possible states of this
would be very costly or impossible. Computerized
used as a supplement to other
their use was extended to other
Aponte (2011) The simulation is to recreate a system
or imitate a reality that looks so much like it without
needing to be it in order to estimate what their real
performance would be, this involves deception, trap
and in nature a case of deception is seen In the
chameleon since it is an animal that is characterized
by adopting different colors.
The simulation of a productive
the dynamic system of the process in a model,
objective is the obtaining of knowledge to transfer
them to the reality (EnpresaDigitala, 2003).
A simulation language is simulation software of a
general nature and has some special features for
certain applications, such as SLAM 11 and SIMAN
(simulation software) with its manufac
The model is developed using the appropriate
language instructions and allows the analyst a great
control for any kind of system (Salazar, s / f).
Vatic Group (2013) Simulation provides you with
excellent ways to communicate your ideas thro
 Provide a graphical representation of the system
studied
 Incorporating 3D animations that allow you to
visualize the process. This allows a greater
understanding of the challenges and
characteristics of the operation
 Animation that allows the directiv
the proposed changes and simplifies the work of
selling the internal projects to the directives
 Simulation also provides a tool to educate
operators and supervisors in the way the system
will operate. Aspects such as administration, shifts
and maintenance and operation strategies can be
Page: 74
6470 | www.ijtsrd.com | Volume - 1 | Issue – 5
Scientific
(IJTSRD)
Vanessa Zamudio Hidalgo
Mexicana , México
needing to be it in order to estimate what their real
performance would be, this involves deception, trap
nature a case of deception is seen In the
chameleon since it is an animal that is characterized
simulation of a productive process is the copy of
process in a model, the
of knowledge to transfer
reality (EnpresaDigitala, 2003).
simulation software of a
general nature and has some special features for
certain applications, such as SLAM 11 and SIMAN
(simulation software) with its manufacturing modules.
The model is developed using the appropriate
language instructions and allows the analyst a great
control for any kind of system (Salazar, s / f).
Vatic Group (2013) Simulation provides you with
excellent ways to communicate your ideas through:
Provide a graphical representation of the system
Incorporating 3D animations that allow you to
visualize the process. This allows a greater
understanding of the challenges and
characteristics of the operation
Animation that allows the directives to understand
the proposed changes and simplifies the work of
selling the internal projects to the directives
Simulation also provides a tool to educate
operators and supervisors in the way the system
will operate. Aspects such as administration, shifts
and maintenance and operation strategies can be

International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 1 | Issue – 5 Page: 75
explored with a better understanding of the
complex interactions that exist between system
processes. Simulation provides a method for
learning how to use problem solving techniques.
The origins of ProModel go back to when Harrell
worked as a Production Engineer at Ford Motor
Company in 1976. The challenge was to determine
the right balance between machine capabilities,
buffering, and resource allocation to meet production
levels Defined for each production line (Harrell, s / f).
GENERAL OBJECTIVE
Evaluate the care process using the process simulation
technique.
SPECIFIC OBJECTIVES
 Know the process of study attention.
 Understand the hierarchy of the care process.
 Apply the simulation technique for the care
process.
 Simulate the care process using specialized
software
 Analyze the productivity of the care process.
 Evaluate the results obtained from the analysis of
the productivity of the care process.
JUSTIFICACIÓN
Many service industries have a queuing system, where
products or customers arrive at a station and wait in a
row, get some kind of service, and then leave the
system (Kamlesh and Solow, 1996). Gross and Harris
(1998) mention that queuing theory is the study of
such waiting in its different forms, using queue
models represent queue systems that arise in practice.
The formulas for each model indicate what the
corresponding system should perform and indicate the
expected amount of time and people in a queue, in a
range of circumstances. A waiting line is constituted
by a client that requires a service that is provided by a
server in a certain period. Customers randomly enter
the system and form one or more queues to be
serviced. If the server is unoccupied, according to
certain rules pre-established with the service
discipline name, the service is provided to the queue
elements in a certain period of time, called service
time and then leave the system (Escudero,1972).
Vaughn (1993) mentions the need in organizations to
analyze, know and explore their care systems where
they generate waiting lines, in order to reduce the
impact they have on their productivity. Queue
networks and the broader view of systems, together
with the greater approximation of non-exponential
distributions and new solution methodologies, are
variations that make this study very useful to the
services sector, approaching more and more their
actual situation. Vaughn (1993) points out that the
study of the multidisciplinary approach of industrial
engineering has made technological, organizational,
commercial, economic, financial and operational
aspects indispensable to achieving profitability and
productivity, as the pillar of this area.
METHODOLOGY
The steps that were followed to develop the project
were those proposed by García, García and Cárdenas.
(2006).
Definition of the system. In this phase it is necessary
to know the system to be simulated. For this, random
visits were made to the care process. He tried to be a
very good observer.
Generation of the base simulation model. Once the
system to be studied is known, a simulation base
model must be developed. In this phase, the
preliminary model of the care process was carried out.
Figure 1 shows the sketch of the model.
Figure 1: Example of preliminary model
Collection and analysis of data. It is suggested to
start with the collection of the statistical information
of the variables of the model. Each process is
monitored to find the average time it takes each
activity. The formula proposed by Salas (s / f) was
used to know how many time samples should be
made.
 
2
2
1*
E
ppZ
n



Where:
N = is the number of times taken to develop.
Z = is the confidence interval to be used. For this
project 90% reliability was used.
P = is the probability of success. For this project was
used a 0.5 of success.
E = is the percentage of error allowed. For this project
10% was used.
For the present project, using 90% reliability, 10%
error, a probability of success of 0.5, 68 times were
made for each process.
Generation of the preliminary model. At this stage,
the information obtained from the analysis of the data,
the model assumptions and all the data required to
have a model that is closest to the reality of the
problem under study is integrated. Figure 2 shows an
example of the model already with the calculated
times.
Figure 2: Example of model with calculated.
Verification of the model. It is suggested to verify
the data collected in the previous stages, to prevent
errors in the model. For this stage, we re-reviewed the
calculated results obtained with the results. If any
errors are found, the changes are made at this stage.
Validation of the model. In this phase is to perform a
series of tests, using real input information to observe
their behavior and analyze their results. For this stage,
30 pilot tests were performed, in order to observe the
behavior of the model. For the study model, the pilot
tests were acceptable and nothing abnormal was
found in its operation.
Generation of the final model. Once the model has
been validated, the model is ready to be simulated and
studied. At this stage, we used the formula proposed
by Diaz (1995) who proposes a formula to know the
number of runs to simulate a process, in this case, the
process of attention.
The formula used was as follows:
2
2
2
)1(
A
Phpt
T
h 


Where:
T = is the number of replications,
Ph = is the default imprecision level A
1 - = is the confidence level
For this investigation it was calculated with a
confidence interval of 99%, using a right tail of
probability of 0.05 and a level of imprecision of 0.05.
The theoretical probability of the interval according to
the author is in a range (0.045 - 0.055). The formula
will be as follows:
12644.125
0025.0
3136.0
)05.0(
)05.01(05.0)57.2(
)1(
2
2
2
2
2





A
Phpt
T
h
The result shows that for a reliability of 99% and an
imprecision of 0.05, the care process was simulated
126 times.
Determination of the scenarios for the analysis. It
is suggested to use possible solution scenarios for the
process studied. In this phase, possible solution
alternatives were observed so that the care process has
a competitive productivity.
Documentation of the model, suggestions and
conclusions. Once the analysis of the results has been
performed, it is necessary to carry out all the
documentation of the model. At this stage all the steps
of the project and their calculated calculations were
recorded.
  6865.67
01.0
6765.0
)10.0(
5.015.0*)645.1(
2
2


n

ANALYSIS OF RESULTS
At this stage the calculation of the productivity of the care process was performed, for which the results of
ProModel® specialized software were reviewed. Table 1 shows an example of the calculated values obtained.
Table 1: Example of results
Variable Total
ClientsServed 376
Clientsin queue 19
FailedCustomers 26
Total 421
Productivity 89.31
The table above represents the productivity of the care process, which is good, but has areas of opportunity,
since 45 clients are stopped, which represents 10.69% productivity.
Figure 3 represents an example of the percentage of utilization of the work areas
.
Figure 3: Example of percentage of use of work areas.
The previous figure shows how the work areas do not
work in a similar way, which means that productivity
is 89.31%.
CONCLUSIONS
The project was successfully concluded, obtaining the
evaluation of the care process. Productivity was
obtained, which is good but not competitive.
Likewise, it was possible to know the process of study
attention. It was also possible to understand the
hierarchy of the attention process. It was possible to
apply the simulation technique for the care process. It
was able to simulate the process of attention using
specialized software. In addition, it was possible to
analyze the productivity of the care process.
ACKNOWLEDGMENT
M.I.I. Arturo González Torres for his advice,
mentoring and project management.
REFERENCES
1) Aponte, D. (2011). Introducción al concepto de
simulación.
2) Díaz Emparanza, I. (1995). Selección del número
de replicaciones en un estudio de simulación.
Departamento de Econometría y Estadística.

Universidad del País Vasco -
EuskalHerrikoUnibertsitatea.
3) EnpresaDigitala. (2003). Simulación de Procesos
Productivos. EnpresaDigitala
4) Escudero, L. F. (1972). Aplicaciones de la
teoría de colas: análisis y solución de los
problemas de cuellos de botellas en almacén,
producción y ventas con referencias breves a
Business Games y teoría de juegos. Primera
edición. Bilbao: Deusto.
5) Frank. (2011). Breve Historia de la Simulación.
Simulalo.
6) García Dunna, E., García Reyes, H. y Cardenas
Barrón, L. E. (2006). Simulación y análisis de
sistemas con ProModel®. Pearson Prentice Hall.
7) Gross y Harris. Fundamentals of queueingtheory.
Tercera edición. New York; Chichester: John
Wiley, 1998. p. 20-34.
8) Harrell,C. (s/f). LosiniciosdeProModel. ProModel
Corporation.
9) Kamlesh y Solow. (1996). Investigación de
operaciones: el arte de la toma de decisiones.
Primera edición. México; New York: PrenticeHall
Hispanoamericana, 1996. p. 710 -711.
10) Harrell, C. (s/f). Los inicios de ProModel.
ProModel Corporation.
11) Salas Bacalla, J. (s/f). Muestreo de Trabajo.
Biblioteca Central Pedro Zulen. Universidad
Nacional Mayor de San Marcos.
12) Salazar Guerrero, A. (s/f). Simulación de eventos.
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13) Vaughn, R. C. (1993). Introducción a la
ingeniería industrial. Segunda edición.
Barcelona: revert. p. 30-32.
14) VaticGroup. (2013) ¿Por qué simular?- Cuándo y
qué ventajas tiene simular. VaticGroup.

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