Scientia et Technica Año XXVI, Vol. 26, No. 01, marzo de 2021. Universidad Tecnológica de Pereira. ISSN 0122-1701 y ISSN-e: 2344-7214
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Abstract The textile industry in Colombia is a source of
employment for more than 200.000 people and more than 50% of
this production is undertaken in Medellin. Modeling and
improving textile processes allow this economic line to be
competitive internationally. In this paper, we make a description
about the use of discrete event simulation in a textile finishing
company through the presentation of the results of four scenarios,
which finally shows the potential of discrete simulations in
productive environments and its high impact when modelling part
of reality without the necessity of experimenting with the real
system. The method used in this paper is summarized in three
major stages: the first one is the simulation methodology, the
second one is the data to support the simulation, and the final stage
is an analysis of the results with the comparison of the four
scenarios. The simulation was statistically validated and verified
with the real behaviors of the company and it is executed by using
software tools such as EasyFit®, Microsoft Excel® and Simul8®.
Index Terms Discrete event simulation, textile finishes,
verification model, validation model.
Resumen La industria textil en Colombia es fuente de empleo
para s de 200000 personas y en la ciudad de Medellín se
encuentra más del 50% de esta producción. La necesidad de
modelar y mejorar procesos textiles permite a este renglón de la
economía ser competitivo a nivel internacional. En este documento
se realiza una descripción acerca del uso de la simulación discreta
en una empresa de servicios de acabados textiles, a través de la
interpretación de cuatro escenarios; lo que se traduce en
demostrar el potencial de la simulación discreta en entornos
productivos de servicios y su alto impacto al modelar sin necesidad
de experimentar con el sistema real. El método utilizado en el
presente escrito se resume en tres etapas, la primera comprende la
metodología de simulación, la segunda los datos que soportan la
simulación y por último un análisis de resultados con la
comparativa de los escenarios. La simulación fue validada
This manuscript was sent on October 21, 2020 and accepted on February 16,
2021.
J.A. Marín is an Industrial Engineer and a student in master’s engineering
program of Universidad de Antioquia Colombia (e-mail:
jalexander.marin@udea.edu.co).
estadísticamente y verificada con los comportamientos reales de la
empresa y se ejecuta por medio de herramientas de software como
EasyFit®, Microsoft Excel® y Simul8®.
Palabras claves Simulación discreta, acabados textiles,
verificación y validación.
I. INTRODUCTION
HE increase of service companies worldwide, and therefore
in Colombia, represents for the national industry the key
to economic strengthening based on the fact that this sector does
not always have dedicated and specialized companies in all
clusters and which are neglected by investors; this is how they
become a great opportunity for micro and medium-sized
enterprises. Large textile companies transfer part of their
processes to other small textile companies according to
customer demand [1], because if they did not support each
other, it would be more costly.
Additionally, textile companies pay high costs for semi-final
product waste [2]. The production of small series, mass
customization and rapid responses have become increasingly
prevalent in the future textile supply chain [3]. Moreover,
textile research has been emphasized, for instance, supply chain
coordination under energy consumption constraints [4], among
other increasingly relevant factors.
In line with the above, the company under study has been
providing the textile finishing or manual finishing service, and
as its services increase in relation to the cycles of its demands,
low and high seasons are generated. Because of the latter, it has
become crucial to improve the processes, this behavior requires
meeting customers' needs in a timely manner.
This article provides an analysis for different scenarios of the
company by using discrete simulation; it considers variations in
C.C. Mosquera is an Electrical Engineer and a student in master’s
engineering program of Universidad de Antioquia Colombia (e-mail:
cristhian.mosquera@udea.edu.co).
Y.F. Ceballos is a Ph.D. in engineering of Universidad Nacional de
Colombia and Full-time professor at the industrial department, engineering
faculty of Universidad de Antioquia Colombia (e-mail:
yony.ceballos@udea.edu.co).
J.A. Marín- ;C.C. Mosquera-Zapata ;Y.F. Ceballos
DOI: https://doi.org/10.22517/23447214.24540
Artículo de investigación científica y tecnológica
Proposal of Improvement for a Textile Finishing
Company in the Medellin city Through of
Discrete Simulation
Propuesta de Mejoramiento para una Empresa de Acabados Textiles de la Ciudad
de Medellín por medio de Simulación Discreta
T
Scientia et Technica Año XXVI, Vol. 26, No. 01, marzo de 2021. Universidad Tecnológica de Pereira
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labor, and the increase of installed capacity or production for
new references are contemplated without implying difficulties
or productive shutdowns when conducting the experiments; and
finally, it is intended to optimize economic resources.
By understanding simulation as a tool of experimentation on
a model that imitates reality, and with the aim of gaining an
understanding of production systems in the textile finishing
company, this article applies the simulation of discrete events,
making reference to the modeling of a system as it evolves over
time by a representation in which state variables change
instantly at separate times [5].
A simulation of the finishing process is introduced for a
reference type with Simul8. This tool is implemented with the
purpose of determining the maximum level of productivity and
the effect that changes can generate, mainly from the
perspective of process improvement. Simultaneously, the
proposed simulation model will enable to identify possible
bottlenecks, or whether there is an over or underutilization of
resources. Finally, the improvement proposals are presented,
being these consistent and possible as a support for
administrative decision making.
II. THEORETICAL FRAMEWORK
Textile production requires a systemic analysis where
discrete simulation provides two ways for carrying out the
study of the system: on the one hand, one can experiment with
the system itself, and on the other hand, experiment with a
system model. There will be no alteration in the real system
since the possibility to test any idea in the model one can arise
attractive alternatives that would not have been possible to
launch directly in the real system. Consequently, simulation
is a technique which involves “the structuring of a model that
represents a real situation (system), and then conducting
experiments on the model". Hence, it is possible not only to
describe how the textile system behaves, but also to analyze
what the impact of a decision would be on the system and on
each of the actors involved.
The simulated system imitates the operations of the real
system over time and thus achieves an appropriate
interpretation of the variables which describe the behavior of
the problem in reality [5]. Additionally, tracking system state
changes as a result of the occurrence of events, or events is
another strength to employ such modeling techniques [6].
The current challenge is having the necessary skills to
develop such models, which are fundamentally synthesized in
the ability to study systems as a whole and in a mind that
understands the cause-effect relationships that occur between
the different actors that make part of a value network [7].
The qualities of simulation-based verification emerge from
the fact that a simulation is like an empirical experiment. In
other words, simulation is efficient because the scenarios
raised are properly configured, therefore forcing designers to
gain more knowledge about the system under study.
The simulation-based verification greatly depends on the
proposed scenarios. Similarly, the simulation is performed
under a specific set of conditions called the experimental
framework [8].
The simulation environment uses a flowchart-based
modeling methodology that facilitates the description of
discrete event systems. Systems are described using the point
of view of the entities that flow through them and the
available resources. The models are structured in a
hierarchical and modular way. They are defined by a
flowchart and static data [9].
In the process of validation and verification (V&V) of the
models, it is possible to perform techniques and tests that can
be used both objectively or subjectively, making reference in
an objective manner to all types of mathematical procedures
or statistical tests such as hypothesis tests or confidence
intervals [10].
III. METHODOLOGY
The methodology implemented to carry out the simulation
in the textile finishing company is described below:
Search and selection of the company: It was decided to
carry out this work in the textile finishing company because
of the arrangement and closeness to provide relevant
information that enables modelling the system and presenting
alternative solutions.
Model construction: The study of the necessary variables
was made to represent the model, relationships, and process
flows.
Assumptions of the simulation model: For this model, it is
necessary to highlight the following assumptions, since they
represent reality in a computerized way and exclude factors
that are not controllable within the simulated environment (all
of them have been validated with experts in the company):
The company works 24 hours non-stop.
A year's work is simulated.
The machines do not suffer breakdowns during the
testing time and are also not enlisted, but their
efficiency is 80% by the loading time.
60% of the garments that arrive in the system must be
changed sideways in the process.
The service time of each workstation is standard and
represents the average time observed by the
engineering team, plus the supplement.
Distances are not evaluated so transport times are
omitted from the analysis.
It is assumed that no defective products come out.
Employees are never absent.
When a batch arrives at a work center with multiple
machines, the one that is empty is occupied.
90% of garments pass the first quality review and the
remaining 10% is returned for crafting.
75% of garments pass the finished product review.
Collection of times, demands: the demands (lot size) and
arrival times of the orders were supplied by the company's
commercial staff and adjusted to standard probability
distributions using EasyFit®. As for the times of each
process, they are provided by process engineering,
specifically for the reference under study.
Simulation of the model in simul8 software: Once the
model is defined and the process flow is understood, its
respective construction is performed in the Simul8 software.
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To do so, it was necessary to adjust the demands and
service times to probability distribution standards, and then
use them in the model.
Model verification and validation: For the V&V of the data
and the computational models, the guiding questions of Table
I [11] are used.
IV. BUILDING THE MODEL
The field of study of this work was the textile finishing
company, a discrete simulation analysis is performed in the
most fixed dyeing area.
The analysis of the input data to the simulated model starts
with the construction of the flowchart of the process for the
reference in study, as shown in Figure 1, which allows the
beginning of the flow design of the process in Simul8 as well
as letting experts consider if the diagram represents
approximately the process. After this representation, service
times are associated with each workstation.
Statistical analyzes are carried out for the data of the demand
of the reference to determine the probability distributions under
which the behaviors of the variables associated with the study
case are modeled. Now of carrying out their respective
statistical check, they demonstrated an excellent behavior and
fit to theoretical distributions. At this point, it is paramount to
clarify that the demand data provided is a history of the
company's sales in units during a period of one month
approximately.
With the aid of Simul8, the data collected from the
frequencies and quantities in which batches arrive at the
company are captured and modeled by using the function
previously defined in the entities to be modeled. In this case, all
software tools can be used to validate the data statistically due
to a space which is presented with the purpose of validating the
model with different seeds, and hence, verifying through
confidence intervals whether the input data is viable or not. If
any error is detected in the validation of the model, it is
recommended to look for the causes of this fact and correct
them immediately before continuing its design since there is a
possibility that no reliable data could be analyzed.
After taking into consideration the aforementioned
conditions and including these data, the locations were entered
to the model where work centers will be modeled assigning the
operating time, and the route that the material will follow
throughout the process will be defined. It means that, for this
case, the material flow is established according to the agreed
restrictions initially, establishing that transport times between
locations is zero.
The number of machines, work centers, capacity, operators,
and processing time per unit in minutes can be observed in
Table II.
Global variables were also defined which were used as
accumulators at two specific points of interest, at the beginning
and at the end of the process to visualize how many units enter
and exit, aiming at having clarity about the performance of the
model. This fact complements the verification process since the
relation of inputs and outputs of the system should be based on
what normally happens for this reference.
The process undertaken on each of the entities is executed
using the Simul8 menu properties option, defining at each step
the operations and time data presented in the analytical course.
After entering each of the factors that make up the simulation
model in Simul8, it proceeds to perform the runs for the stated
time, which makes it possible to observe the results. In case of
some type of error, it is important to look for the causes of the
issue following the instructions generated in the alert messages.
Once it is possible to obtain results, it is validated again to
see if they represent reality by means of an analysis of
confidence intervals for arrivals and using the expected values
of the numbers of arrivals. Furthermore, this fact is determined
with the frequencies of occurrence in each event.
In the case of the company, it could be observed that the
reality vs the results is approximate, and the arrivals of the
model were statistically equal with a significance level of 95%,
according to the experts’ opinions. In this way, four
improvement proposals are introduced, making variations in the
TABLE I
GUIDING QUESTIONS FOR SIMULATION VERIFICATION AND VALIDATION
Verification
Validation
Are these discrete or
continuous events?
Does the model include
all the elements
considered in the
conceptual model?
Are the statistical measures
formulated correctly?
Are math and relationship
formulations correct?
Does it contain all the
relationships in the
conceptual model?
Are the events formulated
correctly?
Are the events rendered
correctly?
Is the model a valid
representation of the
actual system?
Are the math formulas and
relationships correct?
Are the statistical measures
formulated correctly?
Does the code contain all
aspects of the conceptual
model?
Can the model
duplicate the behavior
of the actual system?
Are the statistics and
formulas calculated
correctly?
Is the model credible
for system experts?
Does the model contain
coding errors?
TABLEII
WORK CENTER DATA
Work
center
Number of
machines
Number of
operators
Capacity
units
Processing
time per unit
(min)
Sucker
3
3
1
0.08
Crafts
0
6
1
0.7
Quality
review
0
1
1
0.13
Centrifuge
1
1
50
0.19
Washing
machines
5
2
20
0.8
Dryers
6
2
40
1.18
PT quality
review
0
1
1
1
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model, and executing new runs to review and analyze the
results, being these the ones of the application of the
experimental model. This process is carried out in Excel and
will be discussed later.
It is worth clarifying in this section that every day and real
models have a high degree of difficulty when modeling them
[8]. For this reason, this model presents some assumptions that
intend to foresee the aspects in which scope will not be
obtained, previously mentioned as assumptions of the
simulation model.
A. Model variables
1) Exogenous
Process time of each of the locations.
Daily demand from the entity.
Time between arrivals in the system.
2) Endogenous
Process time of each of the locations.
Daily demand from the entity.
Time between arrivals in the system.
3) State
Number of entities in the system at any time.
Number of inactive locations at any time.
Number of entities terminated.
Number of locations in use.
Number of units in the system.
B. Validation and verification of the model
In Table III these values are presented, the seeds were chosen
randomly, and no verification was used to guarantee their
properties.
A test is performed using confidence intervals for the total
variable arrivals of the Ref. 1 statistically and with a reliability
of 95% it can be assumed that the expected value is within the
limits of the interval, as seen in Table IV .
Table V and VII shows the responses of model validation,
which were answered by the company's experts.
The model described below use the ODD + D protocol [16].
For research purpose, the potentially recyclable material frames
containers and packaging of paper, cardboard, plastic, glass
and/or metal that can be recycled, reused, recovered and/or
reintegrated into the value chain. The proportion of this material
in all waste generated per person in a unit of time is presented
as a percentage, and it is assumed that the rest of the waste does
not meet those characteristics.
V. RESULTS AND ANALYSIS
The model results were compared in parallel with the four
proposals made. Finally, a financial analysis is proposed to
TABLE III
NUMBER OF ARRIVALS WITH DIFFERENT SEEDS
Test
Seed
Quantity
Test
Seed
Quantity
1
9
8625
16
50
6263
2
11
9108
17
96
8694
3
62
7959
18
19
10642
4
52
10313
19
37
12379
5
35
10442
20
84
9891
6
99
8788
21
85
11984
7
86
9604
22
36
8942
8
80
10460
23
43
10677
9
31
9840
24
13
9359
10
14
9432
25
89
6348
11
16
11048
26
28
12530
12
51
9543
27
69
11558
13
38
7150
28
54
12313
14
46
8111
29
40
6301
15
6
6641
30
27
13751
TABLE V
VERIFICATION GUIDING QUESTIONS
Model
type
Verification
Response
Data
model
Are these discrete or continuous
events?
Continuous
Are the statistical measures
formulated correctly?
He is in search of Engineer
and they accept the
calculations.
Are math and relationship
formulations correct?
Yes
Are the events formulated
correctly?
He is in search of Engineer
and they accept
Conceptual
model
Are the events rendered correctly?
The model fulfilled
Are the math formulas and
relationships correct?
The model fulfilled
Are the statistical measures
formulated correctly?
The model fulfilled
Does the code contain all aspects
of the conceptual model?
There is no evidence
Are the statistics and formulas
calculated correctly?
The models fulfilled
Does the model contain coding
errors?
Errors are presented, which
are corrected
TABLE VI
VALIDATION GUIDING QUESTIONS
Model type
Validation
Response
Data model
Does the model include all
the elements considered in
the conceptual model?
Yes
Does it contain all the
relationships in the
conceptual model?
Yes, for the model are
included assumptions
Conceptual
model
Is the model a valid
representation of the actual
system?
Yes
Can the model duplicate the
behavior of the actual
system?
If for the study reference
Is the model credible for
system experts?
Yes
TABLE IV
CONFIDENCE INTERVAL TEST RESULTS FOR THE EXPECTED VALUE OF TOTAL
ARRIVALS
Product
Arrivals
Lower limit
Upper limit.
REF 1
8.948
8.919
10.327
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justify the economic effects that the proposals provide on the
value generation for the company.
A. Proposal 1
Decrease 3 of the washing machines to maximize their use.
B. Proposal 2
Remove two dryer machines whose upper capacity is
subsequent than the point where bottlenecks and washing
machines are hypothetically presented.
C. Proposal 3
Hire 3 operators for the washing machines since this work
center is impacted with downtime in the machine caused by the
multiple displacements of the operators. The purpose would be
that new people make the trips, and whoever is in the work
center dedicates exclusively to loading and commissioning the
equipment.
D. Proposal 4
Combine the 3 previous proposals to review the sensitivity
of the system to parallel modifications when comparing the
results with the unmodified model.
The financial result performed is presented below by using
the results obtained from the simulation and performing
calculations with the variables of the number of units processed
that the model throws. The runs for each proposal were carried
out on an equal basis of simulation time conditions.
From a financial perspective, the result is not satisfactory for
Proposal 1, which is not important in Proposal 2, but very
representative in Proposal 3 and Proposal 4, being Proposal 3
the one that generates more profits for the company. This
situation is evident in Table VII, the results were tested with the
Dupont method
1
It is important to rescue the value that analysis discrete
simulation gives to the company. If there had been any changes
in the actual system, there would be no prior certainty of the
expected results, decision-making may be wrong or biased for
any of the improvements.
1
This model was invented by Donaldson Brown, an American electrical
engineer who joined the Treasury department of the dupont chemical company
in 1914. Years later, DuPont bought 23% of General Motors' shares and gave
Brown the task of straightening out General Motors' finances. Much of the
credit for GM's ascension is from Brown's planning and control systems. The
success that came in launched the DuPont model towards its pre-eminence in
Review of the variables of the simulation model and their
respective runs
1) Variable adjustments to theoretical distributions
The time distribution between arrivals conforms to a beta
distribution, the parameters and graphs of this setting made in
EasyFIT, can be seen in Fig. 1 and Fig. 2a.
For the number of arrivals per arrival the adjustments are
made in EasyFit, the data conforms to a Beta distribution, as
evidenced in Fig. 2b and Fig. 3.
The parameterization of service times is evident in the
courses presented above, considering that they are measured in
the study model and they are deterministic in nature.
Consequently, there was not enough information to give them
probabilistic character because it can somehow limit the results.
What is significant for the study is being able to make variations
to the model and determine the best option based on the fact
all major corporations in the U.S. and remained the dominant form of financial
analysis until the 1970s.
In simple terms, what is sought is to express the company's ROE based on three
components, namely one. Profit margin or operational efficiency; Two. Asset
rotation or efficiency in asset use; And three. The capital multiplier or the
degree of financial leverage.
TABLE VII
FINANCIAL RESULTS FOR MODELING THE FOUR SCENARIOS IN A MONTH OF
TESTING RUN USING THE DUPPON MODEL
Proposal
P 1
P 2
P 3
P 4
Additional revenue
from higher sales
volume per month
-$720.00
$0
$42.792.000
$41.784.000
a) b)
Fig. 2 a) Beta distribution parameters for the number of units per arrival.
b) Beta distribution parameters for time between arrivals
Fig. 1 Density distribution for time adjustment between arrivals
Fig. 3 Density distribution for adjusting number of arrivals per arrival
Scientia et Technica Año XXVI, Vol. 26, No. 01, marzo de 2021. Universidad Tecnológica de Pereira
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that, although the results are not exactly real, it Is behavior does
reflect the reality.
2) Simul8 Results
When simulating the model for one month with 7217 units
arrive to be processed by the line, this is contrasted with the
actual values of the company, according to the expert's criteria.
.If the cost is supposed to be correct, the best proposal would
be the 3 one, despite not being the one that maximizes
efficiencies , it keeps constant the costs vs units produced and
generates the best financial indicators according to the
evaluated model. Fig. 4 shows the Simul8 representation of the
model
Initially, as observed in the plant, it is assumed that the
bottlenecks, and long storage and waiting were generated by the
locations of simple capacity. However, it is observed with the
simulation that the hypothesis previously raised was wrong and
the bottleneck of the process was in the operators of the washing
machines. This fact that reaffirms the advantages of simulation
as support in decision-making.
In Table VIII it is possible to observe some of the results of
the simulation.
VI. CONCLUSIONS AND FUTURE RESEARCH
Concerning the information entered and analyzed in this
article for the case of application in the textile finishing
company, the simulation of discrete events was used. It allowed
in a structured manner to represent the model associated with
the process. Some assumptions were taken as 24 hours work
without stops and that the machines do not suffer breakdowns
in order to decrease the complexity of the system without
straying much from reality .It was tested by confidence
intervals of the arrivals variable with a reliability of 95%, the
results were in the expected ranges. The seeds were also
randomly assigned. With the model already proposed, the
Simul8 tool was validated and verified, of the four proposals
made, we proceed to select number three, therefore it is decided
to make changes to optimize the results. Furthermore, it was
found that the bottlenecks of the process were in the operators
section of the washing machines. The changes would be
beneficial to meet the commitments related with customers and
report economic gains for the company.
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Y.F. Ceballos was born in Guatape,
Antioquia, Colombia in 1979. He received
the Computer engineering (B.S.), M.S and
Ph.D. degrees in computer science from the
Universidad Nacional de Colombia,
Medellin, in 2004, 2007 and 2015,
respectively.
From 2005 to 2013, he was a professor with
the computer science Department. Since
2014, he has been an Assistant Professor with the Industrial
Engineering Department at Universidad de Antioquia. He is the
author of more than 20 articles in the past years. His research
interests include numerical methods, game theory, system
simulation, behavioral research, algorithms, and stochastic
processes.
TABLE VIII
METRICS OF ENTITIES IN THE SYSTEM
Base Run
Trial Average
End 1. Average Time in System
152
End 1. Number Completed
7716
End 1." In System Less Than" time
10
End 1. % In System Less Than Time Limit
4
End 1. St Dev of
120
End 1. Maximum Time in System
510
End 1. Minimum Time in System
3
Fig. 4 Graphical representation of the model in Simul8
Scientia et Technica Año XXVI, Vol. 26, No. 01, marzo de 2021. Universidad Tecnológica de Pereira
27
Dr. Ceballos is an actual member of Society for Industrial and
Applied Mathematics (SIAM) and the system dynamics society
(SDS).
ORCID: https://orcid.org/0000-0001-5787-8832
J.A. Marin was born in Sonsón, Antioquia,
Colombia in 1982. He received his degree in
Industrial Engineering (B.S.), from the
Universidad de Antioquia, Medellin, in
2014.
He is a master’s degree student (M.S.) at
University of Antioquia, Medellin. His
research interests include continuous
improvement, discrete-simulation, agent-
based modeling (ABM), process indicators, neural network,
dynamics system, Kaizen
From 2015, He has worked in different industries in the private
sector, including textile, agricultural, and construction
companies, and currently performs continuous improvement
functions for the financial vice-presidency of a renowned Latin-
American insurance company.
ORCID: https://orcid.org/0000-0001-6625-1783
C.C. Mosquera Zapata was born in Cali,
Valle del Cauca, Colombia in 1987. He
received his degree in Electrical
Engineering (B.S.), from the University of
Valle, Cali, in 2012. He is a master’s degree
student (M.S.) at University of Antioquia,
Medellin. His research interests include
agent-based modeling (ABM), efficient
energy use, neural network, electrical machines, electric
transportation technology and dynamics system. From 2013, He
has worked in different industries in the private and public
sector, including educational and construction companies, and
currently it is conducting activities independently in the
electricity sector.
ORCID: https://orcid.org/0000-0003-3143-7718