Scientia et Technica Año XXVI, Vol. 26, No. 01, marzo de 2021. Universidad Tecnológica de Pereira. ISSN 0122-1701 y ISSN-e: 2344-7214

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 más 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

Scientia et Technica Año XXVI, Vol. 26, No. 01, marzo de 2021. Universidad Tecnológica de Pereira

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.

Scientia et Technica Año XXVI, Vol. 26, No. 01, marzo de 2021. Universidad Tecnológica de Pereira

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

Model type

Verification

Validation

Data model

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?

Conceptual

model

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

0.08

Crafts

0.7

Quality

review

0.13

Centrifuge

0.19

Washing

machines

0.8

Dryers

1.18

PT quality

review

Scientia et Technica Año XXVI, Vol. 26, No. 01, marzo de 2021. Universidad Tecnológica de Pereira

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

8625

6263

9108

8694

7959

10642

10313

12379

10442

9891

8788

11984

9604

8942

10460

10677

9840

9359

9432

6348

11048

12530

9543

11558

7150

12313

8111

6301

6641

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

Scientia et Technica Año XXVI, Vol. 26, No. 01, marzo de 2021. Universidad Tecnológica de Pereira

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

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.

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

$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

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.

REFERENCES

[1] P. Hadaya and L. Cassivi, “The role of joint collaboration planning

actions in a demand-driven supply chain,” Ind. Manag. Data Syst.,

vol. 107, no. 7, pp. 954–978, 2007, DOI:

10.1108/02635570710816694.

[2] K. Ma, S. Thomassey, and X. Zeng, “Development of a central order

processing system for optimizing demand-driven textile supply

chains: a real case based simulation study,” Ann. Oper. Res., vol.

291, no. 1–2, pp. 627–656, 2020, DOI: 10.1007/s10479-018-3000-

[3] T. M. Choi and S. Guo, “Responsive supply in fashion mass

customisation systems with consumer returns,” Int. J. Prod. Res.,

vol. 56, no. 10, pp. 3409–3422, 2018, DOI:

10.1080/00207543.2017.1292065.

[4] H. L. Chan, B. Shen, and Y. Cai, “Quick response strategy with

cleaner technology in a supply chain: coordination and win-win

situation analysis,” Int. J. Prod. Res., vol. 56, no. 10, pp. 3397–

3408, 2018, DOI: 10.1080/00207543.2016.1278283.

[5] A. Law and W. D. Kelton, Simulation Modeling and Analysis, Fifth

Edit. Tucson: McGraw-Hill, 2013.

[6] J. C. Puche Regaliza and J. Costas Gual, "The Favorable Effect of

the Lean Manufacturing Paradigm on Defect Reduction. Discrete

Simulation Techniques," An. That's it. Economics and Empres., vol.

XXI, pp. 75–103, 2011.

[7] T. L. Bermúdez Correa and D. A. Carreño Dueñas, "Towards the

use of simulation as a tool for the analysis of investment projects,"

Rev. Investig. Development and Innovation, vol.1, no. 2, pp. 40–52,

2011.

[8] A. Yacoub, M. E. A. Hamri, and C. Frydman, “DEv-PROMELA:

modeling, verification, and validation of a video game by combining

model-checking and simulation,” Simul. Trans. Soc. Model. Simul.

Int., pp. 1–30, 2020, DOI: 10.1177/0037549720946107.

[9] W. D. Kelton, Simulation With Arena, Sixth Edit. New York:

McGraw-Hill Education, 2015.

[10] R. G. Sargent, “Verification and validation of simulation models,”

J. Simul., vol. 7, no. 1, pp. 12–24, 2013, DOI: 10.1057/jos.2012.20.

[11] E. Acosta, F. Chiodi, M. Fernández, A. Tcach, and J. Francesconi,

"Stages of Validation and Verification in the Construction of

Models for the Simulation of Industrial Processes," Inst. Ind., vol.

43, no. 29, pp. 1–8, 2016, [Online]. Available:

http://www.edutecne.utn.edu.ar/coini_2016/trabajos/F003_COINI2

016.pdf.

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

End 1. % In System Less Than Time Limit

End 1. St Dev of

120

End 1. Maximum Time in System

510

End 1. Minimum Time in System

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

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