Lucas Santos Da Costa
IFSP - Câmpus Suzano
E-mail: lucascosta23@live.com
Willians Dos Santos Lúcio
IFSP - Câmpus Suzano
E-mail: willians.lhp1984@hotmail.com
Adriano Maniçoba Da Silva
IFSP - Câmpus Suzano
E-mail: adriano_m_s@hotmail.com
William De Paula Ferreira
IFSP - Câmpus Suzano
E-mail: eng.william.ferreira@gmail.com
Submission: 03/01/2017
Accept: 14/01/2017
ABSTRACT
The objective of this research is to demonstrate through
simulation techniques and analyses performed in production systems of a company
located in the city of Guarulhos, which produces an electronic component that
has plastic, acrylic and steel, the improvements that can be acchieved with the
use of a specialist software to assist the manager in decision making. For the
study, concepts of simulation, Monte Carlo method, queueing theory and the
software Arena were used. By simulating processes and evaluating performance,
the software offers reports that assist the manager to see more clearly
potential bottlenecks and points of improvement in process, thus effectively
contributing to the company's competitiveness on the market. With the study
presented in this article it was possible to verify the importance of the use
of this tool such as
barriers they currently face to grow faster, and to find evidences of how
collaboration between organizations could facilitate the process of acquiring
competitive advantage.
Keywords: Discrete simulation, production process, electronic components.
1. INTRODUCTION
Due to the need of organizations to compete with each
other, the use of differentiated resources can put their users at an advantage.
The simulation through the software Arena becomes a way for the organization to
evaluate its operational performance and diagnose possible points through the
analysis of reports made available by this program. Time and resource
optimization can elevate the organization's service level in order to reduce
costs and better serve its customers, thus making the in-depth study of this
subject a competitive advantage.
This study consists of demonstrating the efficiency of
a simulation software applied to an electronic components production process in
a company that initially used flow charts in spreadsheets to work with the
tasks performed in this assembly line.
It was verified that this process could be optimized
by using the computational tool of simulation, in order to seek a better
operational performance through reports that point out possible bottlenecks in
the process, helping the responsible manager in the decision-making.
According to Law and Mccomas (1999), the simulation is
commonly applied in productive systems. Among the benefits that the simulation
provides, these can be highlighted: analysis of machinery and its operators, evaluation
of performance and operational procedures.
The software Arena is an important computational tool
of simulation due to its ability to simulate a process, in a virtual and
assertive way, providing a broad visibility of the capacity of each process's
real capacity in a certain period of time and its bottleneck, with the
objective of strategically planning, thus avoiding negative unforeseen events,
giving an anticipated view of a given situation.
Considering that this study aimed to provide a better
method for the distribution of the activities related to the mentioned process
and taking into account that in the studies developed in Brazil still not
present the discrete simulation of the electronic components production
process, we observed this research gap.
Morabito
and Pureza (2010) define simulation as an essential planning tool that tries to
imitate, through logical relations, the operation of real systems, so that it
is possible to observe its behavior in different scenarios.
2. BIBLIOGRAPHIC REVIEW
The literature review will be composed of references
to the simulation, its models and basic elements, the Monte Carlo method and
the queuing theory and the software Arena, according to renowned authors in the
area, thus providing the adequate foundation for the study carried out.
2.1.
Simulation
According to Pritsker (1986), simulation or
computational simulation, as it is also known, is the process of projecting a
logical mathematical model of a real system and performing experiments of this
system on a computer. Pegden (1990) further complies that simulation is a
process that aims to design a computational model of an authentic system and
direct experiments with this model in order to understand its behavior and
evaluate strategies for its operation.
Simulation is a method used by professionals from a
variety of study fields, especially engineering, to provide a practical, lean,
focused and in-depth solution to the most complex problems faced daily. The use
of the simulation is due to its handling through sophisticated environments and
the development of computational models, becoming a tool used by large
companies in several countries that seek to minimize costs and maximize
profits.
In order to do this, it is important the use
simulation software that deals with tools that help the development of virtual
operations of the entire production process. There are no consequences in an
objective way, generating accurate reports that support the manager for decision-making.
Harrel and Tum (1997) define simulation as an activity
through which conclusions can be drawn regarding the behavior of a given system
by studying the behavior of the model corresponding to it in which the cause
and effect relationships are the same or similar to the real system.
The simulation uses not only the construction of
models, but also an experimental method where the system’s behavior is
verified, it builds theories and hypotheses predicting the future behavior,
that is, effects produced through changes in the system or analyzes used
according to the need for its operation. Through the simulation, it is possible
to identify the identification of lead-time, bottlenecks, size of stocks, use
of workers and machines and the production volume, so that it highlights the
decision making with greater clarity and efficiency.
According to Lobão and Porto (1996) and Ören and
Yilmaz (2012), the simulation has a succession of inferences about the most
varied activities in the production systems, for example: problem
identification; comparison with the other systems’ performance; studies
regarding the use of real capacity, inventory indexes, control logic,
sequencing, system problems, better layout and better level of operator
productivity; employees training; sensitivity analysis; planning and
acquisition assistance and prediction of behavior and performance.
2.2.
Basic
elements of the simulation
The simulation constitutes of basic elements that,
according to Banks and Carson II (2004), are defined as:
Entities: entities are the parts that “circulate” on
the model, affect and are affected by other entities, occupy resources and
queues, and interfere with the state of the system. Entities can be identified
through batches or units.
Attributes: they are proper characteristics of the
entity, but can assume values that differentiate each one. Attributes are
entity-only specifications.
Variables: these are information that reflect some
characteristics in the system, independently on the entities. They are variations,
which occur during the course of the system, according to the need to organize
the information.
Resources: They provide services to entities.
Resources that can be human or not and that provide some type of service to
entities and add value to that activity.
Queues: Locations occupied by entities while waiting
for a resource to be unoccupied by another activity or entity. They are
generated in the course of the process and generally require a balance in the
system so that they can be fluid.
2.3.
Systems
and models of simulation
For Seila (1995), a system is a set of components or
entities that interacts with each other. These systems may be discrete or
continuous, or a join of both. Some softwares have been developed to model
discrete or continuous systems or the combination of both.
The systems are said to be discrete as the variables
involved assume finite or infinite numerable values (for example, pieces
arriving at a machine) and continuous as the variables change continuously in
time (for example, kilometers driven by the trucks in the simulation of a
logistic system), in this case the languages of simulation must be able to solve
systems of differential equations.
In discrete event simulations the programs are
equipped with a clock, which is initialized with the event to which it is
linked and advances until the next event is programmed (PEREIRA, 2000).
There are two types of simulation models: the
deterministic, that according to Reis and Martins (2001, p.58), “it is assumed
that the data are obtained with certainty”. In these models of simulation there
are no random variables, the input data will have a single set of output results.
It is stochastic, according to Nascimento and Zucchi
(1997), which includes the probabilistic behavior in the internal relation of
the system, with the objective of capturing the probabilistic nature that
involves the variables surrounding the system, through the use of statistical
technique and the use of computers. In this case, there is one or more random
variables as input, which leads to random outputs.
The probabilistic models of simulation come from the
Monte Carlo method and focus on random phenomena, including risk analysis,
incorporating the environmental variables and, consequently, the elements of
uncertainty (NASCIMENTO; ZUCCHI, 1997).
The following table gives a brief summary of the
computational simulation concepts:
Table
1: Summary of simulation concepts
SYSTEM |
MODEL |
SIMULATION |
|
DISCRETE: variables involved assume
numerable finite or infinite values. |
DETERMINISTIC: variables assume certain
values. |
STATIC: studies the system without
taking into account its variability with time. |
TERMINANT: there is interest in studying
the system in a given time interval. |
CONTINUOUS: variables change constantly
over time. |
STOCHASTIC: variables assume different
values according to a given probability distribution. |
DYNAMIC: represents the system at any
time. |
NON TERMINANT: there is an interest in
studying the system from a certain stable state, and the study can continue
indefinitely. |
Source:
Pereira (2000)
2.4.
Monte
Carlo Method and the Queuing Theory
The term Monte Carlo was given by the researchers S.
Ulam and Nicholas Metropolis in tribute to Monte Carlo's, Monaco, most popular
activity, the games (GUJARATI, 2002). The Monte Carlo Method is defined
according to Hammersley and Handscomb (1964) as the part of experimental
mathematics that is focused on experiments with random numbers. This method is
based on relative numbers and statistical probability, a technique that
involves random values for problem solving.
Monte Carlo is used when the situation predicts the
use of random statistical data and look for probabilities, thus finding
variations that help in the arrival of conclusions of a certain system.
According to Moore and Weatherford (2006), the Monte Carlo method is one of
several methods to analyze the propagation of uncertainty, where its greatest advantage
is the determination of how a known random variation or error affects the
performance or the viability of the system being modeled.
According to Prado (2008), the analytical method that
approaches the subject through mathematical forms is called queuing theory.
According to Aurélio (2008), a queue is defined as a row of people positioned
in front of each other in the chronological order of arrival at a specific
boarding point, or a data organization structure in which they are retrieved in
the same order as they were entered.
These queues arise according to the demand and
capacity of an operation, the larger the demand, the larger the queue if the
capacity is not adequate to this demand, because of this, these are the big
factors causing bottlenecks in queues, resulting in delays in the process and
quality loss of the product or service.
Filho (2008) states that over time and in the face of
realities, methods of queuing analysis have emerged, such as the use of
computer systems and software and that simulation through the insertion of
collected data, to reach improvements and solutions of the problem with its
delimitations and the methodological proposal.
It can be said that the queue is formed with the
arrival of the client to the system, taking into account the waiting time in
the queue, the time of attendance and the time that the entity passes through
the system. Any processes in which people arrive to receive a service by which
they wait, may be called a queue (FOGLIATTI; MATTOS, 2007).
The queue discipline for Taha (2008) is a series of
rules which determine the order of customer service, and that this service can
be performed considering the coming order, being the first to come the first to
be serviced (FIFO - First In First Out), the last to come the first to be
serviced (LIFO - Last In First Out), random, that is, the calls are made
without considering the arrival order and, with priority, the calls are made
according to established priorities.
3. METHODOLOGY
In the beginning, the case study had the idea of
providing to researchers (students) real experiences in the field so that
they could develop experience for facing the real world, assuming
responsibilities, critical vision and self-independence, thus no longer needing
their teachers and mentors.
Yin (2001) conceptualizes case study as an empirical
investigation that investigates a contemporary phenomenon within its real-life
context, especially when the boundaries between phenomenon and context are not
clearly defined.
According to Yin (2001), it is interesting to carry
out a pilot case study before collecting data from the final cases of the
research. This can be chosen for several reasons that have nothing to do with
the criteria used to select the final cases in the case study project. Among
these reasons, it is possible to mention the ease of access to informants,
geographic convenience of the site, the existence of a large amount of data and
documents to be collected, or even the location represents the most complicated
of real cases.
The pilot case study assists researchers in improving
the plans for data collection, both in terms of data content and the procedures
to be followed. It is used in a formative way, helping the researcher to
develop the relevant alignment of the issues. In general, convenience, access
to data and geographical proximity may be the main criteria for selecting the
case or pilot cases (YIN, 2001).
4. RESULTS
4.1.
Current
situation descriptions
This case study was carried out in a large company
located in the city of Guarulhos, which performs in one of its processes the
machining of three different types of raw material, namely plastic, acrylic and
steel, used for the manufacture of an electronic component.
In order to machine these raw materials, three
different processes are used for each type of material, in which the “Tulip”
process is for plastic machining, the “Bell” process is for acrylic machining
and the “Axis” process is for the steel machining, each production process
receives at the beginning of its line one piece of raw material every 57
seconds in a constant way, in which each process has to machine about 500
pieces in an 8 hour shift. Since the “Tulip” process and the “Bell” process
work one shift while the “Axis” process works two shifts.
In each process a machine is used to perform the piece
machining, in which all the machines need to adjust their parameters to each
step, this will only happen for the first piece that is machined in that step,
the others follow normally. The machines have different numbers of steps, as
needed for each raw material.
After the pieces go through the machining process,
they proceed to the assembly of the components containing a plastic piece, an
acrylic piece and a piece of steel, thus forming a single component, this
assembly is performed by a production assistant that spends around 3 minutes to
complete this assembly. After assembly a forklift operator, who spends around 7
minutes to perform this activity, took this component to the company stock.
Below, the current situation of the company and the
activities described above are represented in the form of a flowchart, as shown
in Figure 1.
Figure
1: Simulation flowchart
Source: Created by the
authors
This flowchart is currently used as the
basis for all planning of this process within the company.
4.2.
Modeling
in the Software Arena
According to the current situation of the company
presented above, it was verified that there was scope for improvement within
this activity. From this flowchart, a simulation of this process was mounted in
the software Arena. The study aimed to improve the performance of machines and
operators that are part of this operation.
Verifying the need for improvement in the process, the
following bottlenecks in the current system were diagnosed:
In this above analysis, it was observed that in
addition to the loss of time, there is a big problem with queues that form and
cause the company to have two shifts for the “Axis” process, component assembly
and dispatch to the component stock.
After the observation of these bottlenecks, the
necessary improvements were identified so that there is a production with
greater balance, which are:
·
Case 3 - In this case,
it was identified that the company has a structure for the installation of a
treadmill that would make this transition between production and stock in 15
seconds, reducing in 6 minutes and 45 seconds in time, and a forklift and an
operator in costs. According to the
improvements found, a simulation of the system in the software Arena was
performed based on the times that were questioned during the study, in order to
simulate if the improvements could actually occur and what would be the results
obtained through the analysis of the reports made available by the software
itself.
4.3.
Analysis
of the results
The tables and graphs below demonstrate the reports provided by the
software Arena, where present the relevant entities, queues and resources
(Entity, Queue and Resource) managed in this process:
Table
1: Report of entities regarding the input and output of pieces (Entity, Number
In / Number Out):
Entity |
||||
Number
of inputs |
||||
Average |
Minimum average |
Maximum average |
||
Steel
piece |
500 |
500 |
500 |
|
Acrylic
piece |
500 |
500 |
500 |
|
Plastic
piece |
500 |
500 |
500 |
Source: Created by the authors of the study
This table shows the
amount of pieces that made part of the process.
Graphic 1: Inputs
Source: Created by the authors of the study
Table 2: Number of Outputs
Number of outputs (Number Out) |
|||
Average |
Minimum average |
Maximum average |
|
Steel piece |
498 |
498 |
498 |
Acrylic piece |
498 |
498 |
498 |
Plastic piece |
498 |
498 |
498 |
Source: Created by the authors of the study
This table refers to
the amount of pieces that composes the process output.
Table 3: Work in Process
WIP |
|||||
|
Average |
Minimum average |
Maximum average |
Minimum Value |
Maximum Value |
Steel piece |
5.947 |
5.947 |
5.947 |
0 |
12 |
Acrylic piece |
5.428 |
5.428 |
5.428 |
0 |
11 |
Plastic piece |
4.909 |
4.909 |
4.909 |
0 |
10 |
Source: Created by the authors of the study
This table shows the
pieces that remained under process after the period studied.
Graphic 1 shows the number of raw material pieces that
entered the process and the Number Out field shows the quantity of raw material
that came out of the process, being that, in an 8 hour shift, the maximum
capacity of each process is 500 pieces, so it was observed that 2 pieces in
each process was kept in production at the end of the shift. In the WIP (Work
In Process) field, it Is possible to see the average number of pieces that were
in process during the shift and the maximum number of pieces that were in
process.
Table 4: Report of queues in relation to the waiting
time (Queue / Waiting Time):
Queue |
||||
Waiting time |
||||
Average |
Minimum average |
Maximum average |
||
1 Stage Bell |
0.00005833 |
0.00 |
0.01166667 |
|
1 Stage Axis |
0.00018333 |
0.00 |
0.01194444 |
|
1 Stage Tulip |
0.00000167 |
0.00 |
0.00083333 |
|
2 Stage Bell |
0.00 |
0.00 |
0.00 |
|
2 Stage Axis |
0.00031778 |
0.00 |
0.01388889 |
|
2 Stage Tulip |
0.00 |
0.00 |
0.00 |
|
3 Stage Bell |
0.00325389 |
0.00 |
0.03694444 |
|
3 Stage Axis |
0.00053056 |
0.00 |
0.00 |
|
3 Stage Tulip |
0.00 |
0.00 |
0.00 |
|
4 Stage Axis |
0.00096944 |
0.00 |
0.01388889 |
|
Pieces to stock |
0.001388889 |
0.00 |
0.00277778 |
|
Assembling |
0.00277778 |
0.00 |
0.00555556 |
|
Set 1 assembling |
0.04650722 |
0.04250000 |
0.1150 |
|
Set 2 assembling |
0.02211889 |
0.00 |
0.05500000 |
|
Set 3 assembling |
0.00000611 |
0.00 |
0.01388889 |
Source: Created by the authors of the study
This table contains data referring to the waiting time
of the entities on queue.
Table 5: Report of queues in relation to the amount of
entities waiting (Queue / Number Waiting):
Queue |
||||
Waiting time |
||||
Average |
Minimum average |
Maximum average |
||
1 Stage Bell |
0.00364583 |
0 |
1 |
|
1 Stage Axis |
0.01145833 |
0 |
1 |
|
1 Stage Tulip |
0.00010417 |
0 |
1 |
|
2 Stage Bell |
0 |
0 |
0 |
|
2 Stage Axis |
0.01986111 |
0 |
1 |
|
2 Stage Tulip |
0 |
0 |
0 |
|
3 Stage Bell |
0.2034 |
0 |
3 |
|
3 Stage Axis |
0.03315972 |
0 |
1 |
|
3 Stage Tulip |
0 |
0 |
0 |
|
4 Stage Axis |
0.06059028 |
0 |
1 |
|
Pieces to stock |
0.2604 |
0 |
1 |
|
Assembling |
0.5208 |
0 |
2 |
|
Set 1 assembling |
29.067 |
0 |
8 |
|
Set 2 assembling |
13.824 |
0 |
4 |
|
Set 3 assembling |
0.00038194 |
0 |
1 |
Source: Created by the authors of the study
Table 4 shows the average waiting time (in minutes) in
the queue and Table 5 the average number of pieces waiting to be machined and
assembled respectively.
Table 6: Report of resources:
Resources |
||||
Occupation number |
||||
Average (%) |
Minimum average |
Maximum average |
||
Treadmill |
0,7813 |
0 |
1 |
|
Bell 1 Operator |
0,04166667 |
0 |
1 |
|
Bell 1.1 Operator |
0,7813 |
0 |
1 |
|
Bell 2 Operator |
0,01666667 |
0 |
1 |
|
Bell 2.2 Operator |
0,3472 |
0 |
1 |
|
Bell 3 Operator |
0,02708333 |
0 |
1 |
|
Bell 3.3 Operator |
0,9549 |
0 |
1 |
|
Axis 1 Operator |
0,0625 |
0 |
1 |
|
Axis 1.1 Operator |
0,8681 |
0 |
1 |
|
Axis 2 Operator |
0,02708333 |
0 |
1 |
|
Axis 2.2 Operator |
0,8681 |
0 |
1 |
|
Axis 3 Operator |
0,02083333 |
0 |
1 |
|
Axis 3.3 Operator |
0,8681 |
0 |
1 |
|
Axis 4 Operator |
0,02083333 |
0 |
1 |
|
Axis 4.4 Operator |
0,8681 |
0 |
1 |
|
Tulip 1 Operator |
0,02083333 |
0 |
1 |
|
Tulip 1.1 Operator |
0,5208 |
0 |
1 |
|
Tulip 2 Operator |
0,01458333 |
0 |
1 |
|
Tulip 2.2 Operator |
0,2951 |
0 |
1 |
|
Robot |
0,5208 |
0 |
1 |
Source: Created by the authors of the study
Table 6 shows the average utilization level of each
resource used in this activity in percentage (%) and its respective graphic
(Graphic 2) below. In the “Bell” process, there are 3 operators, where “Bell 1
Operator” makes the adjustment in the first weight and then follows as “Bell
Operator 1.1” for its respective line, and so on for all other operators (this
rule is stipulated for all operators).
Graphic 2: Use of resources
Source: Created by the authors of the study
In the graph it was observed that the “Robot” used for
the assembly has about 50% of its time occupied, a satisfactory number since
previously it was used an auxiliary that was supercharged in this function. The
same thing happens with the “Treadmill” resource, which used about 80% of its
time.
Table 7: Report of resources:
Resources |
||
Use |
||
Value (Quantity) |
||
Treadmill |
1500 |
|
Bell 1 Operator |
1 |
|
Bell 1.1 Operator |
500 |
|
Bell 2 Operator |
1 |
|
Bell 2.2 Operator |
500 |
|
Bell 3 Operator |
1 |
|
Bell 3.3 Operator |
500 |
|
Axis 1 Operator |
2 |
|
Axis 1.1 Operator |
500 |
|
Axis 2 Operator |
1 |
|
Axis 2.2 Operator |
500 |
|
Axis 3 Operator |
1 |
|
Axis 3.3 Operator |
500 |
|
Axis 4 Operator |
1 |
|
Axis 4.4 Operator |
500 |
|
Tulip 1 Operator |
1 |
|
Tulip 1.1 Operator |
500 |
|
Tulip 2 Operator |
1 |
|
Tulip 2.2 Operator |
500 |
|
Robot |
1500 |
Source: Created by the authors of the study
According to Table 7, it is possible to verify the
count made on the number of pieces that have passed through all the resources.
Graphic 3: Quantity
Source: Created by the authors of the study
The graphic shows data presented in the previous
table.
5. DISCUSSION OF THE RESULTS
According
to the results presented in the research it was possible to verify, the
improvement of the process through the data made available in the tables and
graphics related in the previous topic. Because of this, the use of resources
of technology like the software Arena can be taken into account now of a
strategic decision is made with the simulation being used as a parameter to
analyze and help the management.
The
obtained bibliographical references based the analysis conducted in this
process, in which through other studies related to the simulation functions
applied in this case became adequate and timely for the optimization of a
productive flow carried out in electronic spreadsheets for a simulation system
proposed in the software Arena that reduces costs.
The
concepts used in the dialogue with the research results demonstrate the
efficiency of the simulation knowledge in relation to the method that was
previously used by the company; the improvements indicated by the software
prove this and give opportunity for cost reduction in production, handling and
stocking, since it was seen a one-shift reduction in the company.
6. FINAL CONSIDERATIONS
According
to the studied situation, the application of simulation methods becomes
feasible to companies, since it brings a panoramic view of the process in which
initially there was only the use of flowcharts, which are limited. The software
Arena clearly and objectively demonstrates relevant information about outbound,
inbound, work in process and idleness, giving the manager greater control and
power in decision-making.
In
this study it was verified that in the company in which the simulation was
applied there was a great improvement in the process from the use of the
software Arena, there was a gain in several sectors in time and the reduction
of one shift in the “Axis” process, thus balancing all the company’s production
line. The reports presented during the research prove that the simulators bring
benefits so that the strategic planning of the company can be more and more
competitive, minimizing costs and maximizing profits. The objective of the
study was satisfactorily achieved from the perspective of the research that
sought to apply the simulation and software methods in the existing process in
the company and demonstrate improvements in the production of electronic
components, in addition to optimize time, resources and materials, as well as
minimizing costs of the process studied.
We
can assume, then, that the applications of these methods are recommended for
other productive processes within the company and because of the high
competitiveness in the market several organizations can adapt to this new
reality, since the simulation covers diverse study fields and is gaining more
and more power in the market.
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