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&= nbsp;

Implem= entación de la simulación para la mejora de los procesos productivos

 

Implementation of = the simulation for the improvement of production processes

 <= /span>

 


= 1=

= Alexis Miguel Velásquez Jama

= https://orcid.org/0000-0002-2883-9022 

 

 <= /span>

= Universidad de Guayaquil

= alexis.velasquezj@ug.edu.ec  

= 2=

= Betsy Olvera Moran

= https//orcid.org/0000-0003-4644-8209

 

 <= /span>

= Universidad de Guayaquil.

= betsy.olveram@ug.edu.ec =

 

 =

 =

 

 

Artículo de Investigación Científica y Tecnológica

Enviado: 08/11/2022

Revisado: 23/11/2022

Aceptado: 06/12/2022

Publicado:05/01/2023

DOI: = https://doi.org/10.33262/concienciadigital= .v6i1.2441   

 

 <= /p>

 

 

Cítese:

 

 

Velásquez Jama, A. M., & Olvera Moran, B. (2023). Implementación de la simulación para la mejora de los procesos productivos. ConcienciaDigital, 6(1), 75-86. https://doi.org/10.33262/concienciadigital.v6i1.2441=

 

 

 

CONCIE= NCIA DIGITAL, es una revista multidisciplinar, trimestral, que se publicará en soporte electrónico tiene como misión contribuir a la   formación de profesionales competentes con visión humaníst= ica y crítica que sean capaces de exponer sus resultados investigativos y científicos en la misma medida que se promueva mediante su intervención cambios positivos en la sociedad. https://concienciadigital.org<= span style=3D'mso-bookmark:_Hlk92353975'>  =

la revista es editada por la editorial ciencia digital (editorial de prestig= io registrada en la cámara ecuatoriana de libro con no de afiliación 663) www.celibro.org.ec

 

 

 

Esta r= evista está protegida bajo una licencia Creative Commons Attribution Non Commerc= ial No Derivatives 4.0 International. Copia de la licencia: http= ://creativecommons.org/licenses/by-nc-nd/4.0/

 

Palabras claves:

Simulación, Lean Manufacturing, productividad, procesos, mejora de procesos

 =

 =

 

Resumen

Introducción: Las empresas de biene= s y servicios, con el objetivo de mantenerse competitivas necesitan adaptarse= y/o realizar cambios continuos en la filosofía de la gestión industrial, camb= ios que trae consigo incertidumbre por lo que la simulación de los procesos industriales permite analizar diferentes escenarios que permita mejorar la productividad y la toma de decisiones. Objetivo: evaluar la implementación de la simulac= ión para la mejora de los procesos productivos. Metodología: se plantea como una investigación teóric= a de tipo básica, con características que la sitúan como una investigación documental, descriptiva, no experimental, donde no existe manipulación de variables en base a el análisis de información de trabajo de carácter científico en el ámbito de la productividad, Resultados: Diferentes estudio= s demuestran que la simulación permite evaluar diferentes escenarios, factores, propue= sta, métodos Lean Manufacturing, a un coste bajo para la organización, obteniendo datos que permita mejorar la toma de decisiones, cambios en las directrices en los procesos productivos, así como la mejora producto de la optimización de los procesos. Conclusión: el efecto de la implementación de la simulación en la mejora de los procesos productivo desde una perspectiva teórica documental, la simulación tiene un efecto positivo y significativ= o al momento de desarrollar propuesta, escenarios, en la evaluación de la incertidumbre y la toma de decisiones, permitiendo mejorar los procesos productivos.

 

 

Keywords: 

Simulation, Lean Manufacturing, productivity, processes, process improvement

 

 =

Abstract

Introduction: Goods and services companies,= to stay competitive, need to adapt and/or make continuous changes in the philosophy of industrial management, changes that bring uncertainty, so t= he simulation of industrial processes allows analyzing different scenarios t= hat improve productivity and decision making. Objective: to evaluate t= he implementation of the simulation for the improvement of the productive processes. Methodology: it is proposed as a basic type of theoreti= cal research, with characteristics that place it as a documentary, descriptiv= e, non-experimental research, where there is no manipulation of variables ba= sed on the analysis of work information of a scientific nature in the field of productivity, Results: Different studies show that the simulation allows evaluating different scenarios, factors, proposal, Lean Manufactur= ing methods, at a low cost for the organization, obtaining data that allows improving decision making, changes in the guidelines in the productive processes, as well as the improvement product of the optimization of the processes. Conclusion: the effect of the implementation of the simulation in the improvement of the productive processes from a document= ary theoretical perspective, the simulation has a positive and significant ef= fect when developing a proposal, scenarios, in the evaluation of uncertainty a= nd decision making., allowing to improve production processes.

 

 

 

Introducción

En el ámbito industrial= las organizaciones, para mantenerse competitivas necesitan además de trabajar de forma eficiente, adaptar sus sistemas productivos, maquinarias, y habilidad= es de sus empleados, a los continuos cambios que las nuevas filosofías de gest= ión empresarial traen consigo, como los nuevos paradigmas en la que se conceptualiza la industria 4.0, y la modernización vinculada a ella (Uriarte et al., 2018).<= o:p>

Asimismo, gracias al desarrollo de los sistemas digitales en red, así como el uso de la tecnología de la información, la inteligencia artificial,= el internet de las cosas, big data, además del procesamiento de datos en la nu= be, las cuales forma parte de las herramientas y núcleo central de la industria 4.0, y su razón es lograr aumentar la eficiencia de los procesos industrial= es, comercialización de bienes y servicios (Buer et al., 2018).

Hay que considerar, que las empresas manufactureras tienen por delan= te grandes retos por la competencia cada vez más fuertes, por lo que deben plantearse como propósito principal, desarrollar estrategias coherentes, coordinadas con las actividades primordiales, que involucre todos los detal= les del proceso productivo, con el propósito de identificar el grado o nivel de correlación, entre las actividades empleadas por la organización.

Asimismo, el desarrollo de estrategias aunado al uso de las herramie= ntas tecnológicas se puede emplear en áreas tan diversas como concepción, desarrollo, diseño y producción de productos, así como, el control de flujo= s de materiales en las diferentes etapas del proceso, por lo que se puede aument= ar la competitividad, gracias a la transparencia, agilidad, adaptabilidad y flexibilidad (Zsifkovits & Woschank, 2019).

Del mismo modo, en sistemas avanzados de fabricación, el nivel de productividad se consigue optimizar mediante un apropiado diseño de los mecanismos y actividades a ejecutar, en las etapas iniciales del sistema de fabricación. Puesto que se puede modelar los parámetros de diseño con el propósito de conseguir los mejores rendimientos del proceso (Salawu et al., 2020).

En el mismo orden de idea, las teorías y/o metodologías que se emple= an en los entornos de manufacturas, como el Lean Manufacturing, utiliza= dos para mejorar la productividad, y la rentabilidad, se complementan y se optimizan, haciendo que sean más eficientes, acelerando las mejoras y reconfiguración del sistema, mediante un proceso de toma de decisiones mejo= rado y un aprendizaje organizacional sustentados en datos (Uriarte et al., 2018)= .

Se ha de considerar que, elegir, por ejemplo, la técnica o metodolog= ía en las que se basa el Lean Manufacturing tales como el VSM, las 5S, Kai= sen, SMED, por citar algunos, y a pesar de que existe referencia del éxito de su implementación, decidir cuál de ellos seria exitoso en el entorno de la organización es un proceso complejo. Es necesario considerar varios aspectos del proceso evaluado, donde de manera analítica y lógica se tome la mejor d= ecisión (Rodríguez-Cruz & Pinto, 2018).

Además, las decisiones que se consideran acertada son la que se toman basándose en la lógica a partir de un análisis coherente y sistemático dond= e se tienen en cuenta todos los datos y posibles alternativas. Es por ello por lo que la información es primordial en la toma de decisiones, puesto que ellas permiten notar los posibles cambios que se pueda generar en el entorno, así= como, innovar (Rodríguez-Cruz & Pinto, 2018).

Así mismo, considerar todas las variables, controlar y determinar sus efectos en un escenario industrial, es muy difícil, es por ello por lo que = se emplean modelos que permitan representar la realidad o una aproximación de ella, para estudiar y en función de la decisión tomada su resultado sea conveniente (Marulanda-Grisales & Múnera-Ramírez, 2019).

Una de las herramientas de apoyo en la toma de decisiones en el área= de la ingeniería de la producción ha sido el modelado y la simulación, las cua= les para el trabajo de (Abdulatif et al., 2020), se emplearon para examinar, analiza= r y recomendar mejoras en la línea de ensamblaje en la planta de fabricación considerada para el estudio, con el propósito de mejorar el rendimiento.

Igualmente, es posible elaborar modelos de procesos donde se tomen en cuenta diferentes clases de situaciones que se pueden presentar en los proc= esos productivos, permitiendo hallar fallas, errores y tener una aproximación del comportamiento del sistema productivo cuando se realiza alguna modificación= a variable o factores de los mismo, esto sin comprometer gran cantidad de recursos (González et al., 2018).

De hecho, la simulación de procesos pretende realizar una representa= ción de un proceso real, permitiendo minimizar problemas gracias a las iteracion= es que se pueden realizar mediante la experimentación en el proceso de simulac= ión, asimismo, obtener datos que facilita entender y evaluar las características= reales del proceso (Vargas et al., 2020).

Recientemente, se ha encontrado que, las técnicas de optimización de= los procesos se utilizan con mayor frecuencia junto cola simulación, para encon= trar soluciones acertadas, o casi óptimas a los que analizan y esperan tomar de = decisiones, asimismo, reducir el tiempo de la fase de experimentación, crear más escena= rios hipotéticos diferentes, empleando solo la simulación. Este enfoque se ha definido como simulación inteligente (Uriarte et al., 2018).

Del mismo modo, una de las ventajas de los escenarios virtuales que = se logra en la simulación, las iteraciones cambiando los factores no cuesta co= mo si se desea realizar en la realidad. Lo que permite validar la decisión en función de la hipótesis planteada. El modelo de simulación facilita suprimi= r los errores provocados por la incertidumbre en un modelo analítico, que de ning= una manera se pueden manifestar en el momento que el sistema está operativo (Va= rgas et al., 2020).

Evidentemente, se constata que la simulación es una herramienta de a= poyo en la toma de decisiones. Se puede emplear la simulación por computadora de= un sistema de producción, en la concepción o antes de la puesta en marcha de u= na nueva línea de producción, o simplificar el control predictivo de la produc= ción. A su vez, al tener en consideración la aleatoriedad y los pormenores del sistema, mucho antes de construirlo, permite revelar problemas imprevistos y cambiar fácilmente los parámetros del sistema. (Abdulatif et al., 2020).

En consecuencia, este trabajo de investigación se plantea como propó= sito en base a una revisión sistemática de los marcos conceptuales de la simulac= ión, modelos matemáticos, manejo de la incertidumbre, y la toma de decisiones ap= licados los procesos productivos, permitiendo evaluar como la implementación de la simulación permite mejorar los procesos productivos, encontrar alternativas, innovar, en bases a procesos reales e hipótesis. Por lo que este trabajo es= de tipo documental descriptivo, y transversal.

Metodología

Para el desarrollo de esta investigación = se plantea que sea de tipo documental puesto que se centra en realizar un análisis teó= rico sobre los fundamentos acerca de la simulación en entornos industriales (Ber= nal, 2010), aunado a los objetivos de mostrar las generalidades y los basamentos teóricos englobados de la simulación de procesos productivos (Mejías, 2005), considera que este trabajo de investigación es teórica básica.

Se llevo a cabo una revisión sistemática = de la documentación encontrada y en base a ello se realizó un análisis metódico c= on el propósito de comprender el marco teórico y conceptual de la simulación y= su efecto en los procesos productivos, logrando simplificar el estudio de los datos más significativo tanto teóricos como los empíricos.

Por otro lado, el alcance de esta investi= gación está enfocada a una investigación descriptiva, a centrarse en recabar información de forma independiente, además, es una investigación no experimental, a no considerar manipular variables. De igual modo es transve= rsal puesto que los resultados se enmarcan en un el periodo actual (Hernández et al., 2014).

La información fue encontrada a partir de artículos científicos open Access en Google académico, Elsevier y Resear= chGate tanto en español como en inglés, empleando las palabras claves simulación, = Lean Manufacturing, productividad, procesos, mejora de procesos.<= /span>

Resultados y discusión

Teniendo como objetivo en este trabajo de investigación evaluar como la implementación de la simulación permite mejorar los procesos productivos en las empresas, de tal manera de aporta= r un basamento teórico, documental, y descriptivo, se ha encontrado que, La simulación seguirá siendo una herramienta importante entre las propuestas de las soluciones en la Industria 4.0, puesto que logra facilitar la evaluació= n de mejoras en un escenario altamente complejo y dinámico (Uriarte et al., 2018= ).

En el mismo orden de idea, se ha obtenido= que emplear diferentes metodologías para mejorar el rendimiento de una línea de producción, tales como la simulación discreta, métodos Lean y el diseño de experimento, permite determinar de manera clara los factores que pueden provocar demoras en los procesos, y a su vez, favorece la inclusión de nuev= os y mejores lineamientos de producción que incremente el rendimiento de la empr= esa (Fernández et al., 2019).

La simulación en general, y de manera sim= ple se puede clasificar en 3 clases de aplicaciones se puede hablar de tres tipos = de aplicaciones ver figura 1:

Figura 1

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Fuente: González et al. (2018)

La experimentación: su aplicación se real= iza cuando realizar una experimentación en el sistema real presenta un costo elevado, o cuando se desea diseñar un nuevo sistema donde al realizar las iteraciones se puede ir modificando continuamente hasta obtener el resultado deseado.

La predicción: se emplea cuando se desea evaluar el comportamiento del sistema real en un marco controlado, permitie= ndo evaluar la incertidumbre generada por los cambios realizados.

La enseñanza: permite el adiestramiento d= el personal que se hará a cargo del sistema, por ejemplo, los pilotos de aviac= ión,

Además de esta clasificación general de la simulación, dentro de ellas se revivan otras subcategorías o ramas en el control de la producción, logística, toma de decisiones entre otros (Gonzál= ez et al., 2018).

Igualmente, se ha logrado predecir de man= era adecuada las condiciones óptimas de operación en relación al ajuste de la velocidad y el tiempo, mediante la simulación de un sistema de fabricación,= partiendo el diseño de un modelo clásico donde se considera algunos parámetros de proceso, con el propósito de estudiar el efecto de variar la velocidad nomi= nal de un motor eléctrico en un rango por encima y debajo de dicha velocidad, de esta manera de los resultados obtenidos en la simulación se obtuvo los valo= res adecuado en los parámetros de diseño óptimos (Salawu et al., 2020).

Asimismo, la simulación en los procesos de recolección de residuos permite evaluar la interacción de todos los element= os que componen ese sistema, tales como los vehículos transportadores, puntos = de recolección, contenedores, distancia de los recorridos y tiempos de tránsit= o, entre otros. Permitiendo dimensionar su interacción en pro de conseguir la optimización de estas, lográndose desarrollar estrategias que ayuden a un desarrollo normal de proceso, así como facilitar la toma de decisiones (Diaz, 2022).

Se observa que, algunas herramientas del = Lean Manufacturing, como el mapeo de la cadena de valor VSM, mediante la simulación se puede analizar los escenarios propuestos o futuros que permit= an evaluar el efecto que tienen el cambio de factores como los tiempos de ejecución, tiempos de ciclo, y distancias recorridas, por nombrar algunas, con el obje= tivo de medir su viabilidad. Por lo que se facilita el desarrollo de planes de acción y justificación para la puesta en marcha en el sistema real de las mejoras propuestas (Bedoya-Díaz et al., 2020).

En efecto, se puede destacar que el uso combinado de herramientas Lean, simulación y optimización, presentan grandes ventajas en los entornos de la Industria 4.0. Esta combinación mejorará el proceso tradicional de toma de decisiones, acelerará las mejoras y reconfiguraciones del sistema y apoyará el aprendizaje organizacional (Uria= rte et al., 2018).

Igualmente, usando la simulación de han l= ogrado determinar la incidencia de la aplicación de la metodología del Design Thinking sobre el rediseño de procesos productivos sobre el desempeño operacional de la empresa considerada para el estudio mejoro de manera positiva, aumentando el rendimiento por hora, mejorando los tiempos de cicl= o y tiempo de respuesta de la dimensión de tiempo. Adicionalmente, se disminuye= ron índices como el costo de labor directa, costo asociado a las horas extra = (Ortiz, 2020).

Del mismo modo, la simulación al ser asis= tido con otras herramientas de manejo de datos, como las empleadas en el diseño = de experimento, se puede identificar factores con niveles de significancias o correlación a modelo planteado, así como, puntos críticos relevantes que se deben considerar en los escenarios planteados (Fernández et al., 2019).

Por otra parte, gracias a la evaluación d= e un proceso productivo mediante la simulación, se ha permitido reformar conceptos en su proceso, identificar que algunas actividades que se consideraban que genera= ban problemas, en realidad, de ninguna manera era causante de dichos problemas.= De igual manera, permite reubicar al personal de áreas donde no son necesitado= s, a áreas donde los datos obtenidos muestran la necesidad de apoyo. Permitiendo mejorar la eficiencia y productividad de la empresa (Correa et al., 2020).<= o:p>

Además, la simulación puede aportar datos estadísticos, que pueden ser utilizados para identificar parámetros que permitan suprimir los cuellos de botellas que provoquen un deficiente desenvolvimiento de los procesos, en este caso en la recolección de residuos sólidos (Diaz, 2022).

En este sentido, el empleo de la simulaci= ón junto con sistemas integrado de planificación y control, en conjunto de sis= tema de identificación de radio frecuencia, permite generar cronogramas de producción en base un sistema de adquisición de datos y monitoreo de producción. De igual forma, estos sistemas de identificación de radio frecuencia, se han empleado con el propósito de identificar y rastrear productos, y en los modelos desarrollados de simulación se emplean en la elaboración de estimaciones de tiempo de producción, utilización de recurso= s y análisis hipotéticos (Altaf et al., 2018).

En consecuencia, se ha demostrado la importancia de la simulación en diferentes aplicaciones en el ámbito industrial, teniendo como ventaja de evaluación de las respuestas a los cam= bios en los factores asociados a las actividades productivas, a un coste bajo, o= bteniendo datos que permita mejorar la toma de decisiones, cambios en las directrices= en los procesos productivos, así como la mejora producto de la optimización de= los procesos.

Conclusiones

·         En conclusión, este trabaj= o de investigación presenta el efecto de la implementación de la simulación en la mejora de los procesos productivo desde una perspectiva teórica documental,= por lo que se puede apreciar en la información analizada de las diferentes fuen= tes que la simulación tiene un efecto positivo y significativo al momento de desarrollar propuesta, escenarios, en la evaluación de la incertidumbre y la toma de decisiones, puesto que permite a un coste bajo evaluar como es el comportamiento del proceso productivo al momento de la implementación de las propuestas y/o escenarios planteados, así como modificar los lineamiento en= las políticas de las diferentes áreas de producción, implementación de nuevas máquinas, diseño entre otros.

Ref= erencias bibliográficas

Abdulatif, N., Yasser, S.,= Fahim, I., Emad, Y., Saleh, A., & Kassem, S. (2020). Decision Support Using Simulation to Improve Productivity: A Case Study. 2020 International Conference on Decision Aid Sciences and Application (DASA), 1120-1127. doi:10.1109/DASA51403.2020.9317043

Altaf, M., Bouferguene, A., Liu, H., Al-Hussein, M., & Yu, H. (2018). Integrated production planning and control system for a panelized = home prefabrication facility using simulation and RFID. Automation in Construction, 85, 369-383. doi: 10.1016/j.autcon.2017.0= 9.009.

Bedoya-Díaz, I., Chud-Pantoja, V., & Paredes-Rodríguez, A. (2020). Simulación de mejoras en el sistema productivo de una curtiembre basada en el mapeo de = su cadena de valor. Scientia et Technical, 25(3), 394–403. doi:10.225= 17/23447214.24231

Bernal, C. (2010). Metodología de la Investigación (Tercera ed.). <= /span>Colombia: Prentice Hall.

Buer, S., Strandhagen, J., & Chan, F. (2018). The link between Industry 4.0 and lean manufacturing: mapping current research and establi= shing a research agenda. International Journal of Production Research. doi:10.1080/00207543.2018.1442945

Correa, A., Castro, J., Garcés, C., & Ceballos, Y. F. (2020). = Simulación y evaluación de un proceso productivo de suelas termoplásticas en Colombi= a. Entre Ciencia e Ingeniería, 14(28), 10-15. doi:10.31908/19098367.1850<= /o:p>

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&= nbsp;

           

 

 

 

 <= /span>

 <= /span>

El artículo que se publica es de exclusiva responsabilidad de los autores y no necesariamente reflejan el pensamiento = de la Revista Conciencia Digital.


 

 

 

El artículo queda en propiedad de la revista y, por tanto, su publicación parcial y/o total en otro medio tiene que ser autoriz= ado por el director de la Revista Conc= iencia Digital.

 

 

 


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ISSN: 2600-5859

Vol. 6 No 1, pp. 75 – 86, enero-marzo 2023

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