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Control estadístico de procesos y reducción del despilfarro

 

Statistical process control and waste reduction

 

= 1

Víctor Hugo Garofalo Largo

=

https://orcid.org/= 0000-0002-4259-6573

 

 

Universidad de Guayaquil, Facultad de Ingeniería Industrial. Guayaquil, Ecuador.

victor.garofalol@ug.edu.e c  

= 2

Mariuxi Viviana Ruiz Arana

 

https://orcid.org/0000-0002-6741-6890

 

 

Universidad de Guayaquil, Facultad de Ingeniería Industrial. Guayaquil, Ecuador.

 mariuxi.ruiza@ug.edu.e c

= 3

Lucia del Rocío Mendoza Macias=

 

https://orcid.org/0000-0003-0265-4449

 

 

Universidad de Guayaquil, Facultad de Ingeniería Química. Guayaquil, Ecuador.

lucia.mendozam@ug.edu.e c  

= 4

Sonia Melissa Rodríguez Mérchan

 

https://orcid.org/0000-0002-8895-4030

 

 

Universidad de Guayaquil, Facultad de Ingeniería Industrial. Guayaquil, Ecuador.

sonia.rodriguezm@ug.edu.ec

 

 

 

 

Artículo de Investigación Científica y Tecnológica

Enviado: 05/05/2022

Revisado: 10/06/2022

Aceptado: 13/07/2022

Publicado:03/08/2022

DOI:      https://doi.org/= 10.33262/ap.v4i3.1.235  

=  

 

 

Cítese:

 

 

Garofalo Largo, V. H., Ruiz Arana, M. V., Mendoza Macias, L. del R., & Rodríguez Mérchan, S. M. (2022). Control estadís= tico de procesos y reducción del despilfarro. AlfaPublicaciones, 4(3.1), 6–19.= https://doi.org/= 10.33262/ap.v4i3.1.235

 

 

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Esta revista está protegida bajo una licencia Creative Commons Attribution Non Commercial No Derivatives 4.0 International. Copia de la licencia: ht= tp://creativecommons.org/licenses/by-nc-nd/4.0/

 

Palabras claves:  cont= rol, estadístico, proceso, fracción, defectos

&= nbsp;

 

 

Resumen

Introducción. El control estadístico de procesos es un método para analizar el problema= de las variaciones de los procesos productivos. Objetivo. Verificar la influencia que tiene el control estadístico de procesos en la disminución del despilfarro, considerando p= ara el efecto, un caso de una empresa manufacturera que produce artículos plásticos, con base en materia prima reciclada. Metodología. Descriptiva, cuantitativa, de campo, con uso de = la observación directa y la carta de control p. Resultados. Los hallazgos obtenidos evidenciaron que, en una muestra de 8 lotes de producción, cada uno c= on 50 tuberías plásticas para instalaciones eléctricas, la fracción defectuosa = (p) fue igual a 5,25%, hallazgo que al verificarse en la norma internacional MIL-STD-105D, adaptada por el Institu= to Ecuatoriano de Normalización (INEN) para la aceptación o rechazo en muest= reo por lotes de producción, permitió declarar al lote como aceptado, porque = se garantiza la calidad del producto de plástico. Conclusión. Esta herramienta de g= estión de calidad facilita la toma de decisiones de acciones correctivas y preventivas para reducir el desperdicio y verifica la influencia q= ue tiene el control estadístico de procesos en la disminución del despilfarr= o.

 

Keywords:

 

Abstract

Introduction. Statistical process control is a method to analyze the problem of variations in production processes. Objective. To verify the influence that statistical process control has on the reduc= tion of waste, considering for this purpose, a case of a manufacturing company that produces plastic articles, based on recycled raw material. Methodology. Descriptive, quantitative, field, with the use of direct observation and the control c= hart p. Results. The findings obt= ained showed that, in a sample of 8 production batches, each with 50 plastic pi= pes for electrical installations, the defective fraction (p) was equal to 5.2= 5%, a finding that when verified in the international standard MIL-STD -105D, adapted by the Ecuadorian Institute for Standardization (INEN) for accept= ance or rejection in sampling by production batches, allowed the batch to be declared as accepted, because the quality of the plastic product is guaranteed. Conclusion. This quality management tool facilitates decision-making on corrective and preventive actions to reduce waste and verifies the influence that statistical process control has on reducing waste.

 

Introducción

El control estadístico de procesos es uno de los métodos aceptados por la comunidad internacional, para monitorear continuamente los procesos product= ivos y fortalecerlos, para lo cual, es necesario detectar las variaciones que se producen en la manufactura y en la producción de servicios, a través de herramientas que permitan medir cuantitativamente, el momento preciso en qu= e un lote se salió de control (Hurtado et al., 2020).

De esta manera, el propósito principal del control estadístico de procesos rad= ica en el mantenimiento de un proceso en un grado aceptable de conformidad, para asegurar la calidad de todo el lote fabricado o de los servicios prestados, para lo cual, surgieron como herramientas esenciales, las cartas de control= (Camero & Huamaní, 2016).

Una carta de control se concibe como una representación esquemática de la variabilidad de las características de calidad de los lotes de productos, c= on líneas de decisión agregadas, cuya medición estadística no considera el núm= ero total de artículos del lote, sino que toma una muestra para su evaluación y= el establecimiento de la distribución de los defectos en el tiempo, si los hub= iere (Restrepo, 2018)<= !--[if supportFields]>.

De acuerdo con la literatura teórica, la creación de las cartas de control fue= de autoría de Water Shewart, quien en 1924 introdujo este sistema de medición = de la calidad de los artículos, para reemplazar al método de inspección que subsistía hasta aquel tiempo y que tuvo lugar muchos años más, hasta que el mundo le otorgó la importancia que tiene actualmente en la industria y la economía (Ramírez & Ca= ntú, 2017).

Entonces, las variaciones en la conformidad de bienes o servicios, resulta ser la principal magnitud para medir, razón por la cual, las cartas de control fue= ron clasificadas en variación por causas asignables o causas comunes. Las prime= ras en mención se deben al material, máquinas, mano de obra, procesos, mientras= que las segundas, pueden ser desconocidas o insignificantes y no ser considerad= as en el control estadístico de procesos (Hurtado et al., 2020).

Asimismo, las cartas de control pueden ser por atributos o por variables. En el primer caso, miden las proporciones de las cantidades inconformes de los lotes, sin considerar magnitudes. Sin embargo, las segundas en mención, si consideran alguna magnitud, como distancia, peso, tiempo, entre otros, para medir la variación de los artículos en el proceso, determinado de este modo, el núme= ro de defectos por lote de producción, para probar o rechazar el mismo (Herre= ra & Herrera, 2019).

Además, existen las cartas de control multivariadas, las cuales además de monitorear los procesos por medio de alguna magnitud respectiva, también puede ejecutar correlaciones entre las mismas, para asegurarse de la cifra de su variabili= dad y si esta es realmente significativa (Camargo et al., 2018).

En efecto, se ha observado la problemática en los procesos productivos, la cua= l se centra en la variabilidad de los procesos, que suele alertar de la presenci= a de defectos y del incremento de los costos de la calidad, en los procesos productivos, que se encuentran ligados a un descenso de la eficiencia y de = la competitividad organizacional, requiriéndose de las cartas de control para = minimizar el impacto de esta situación conflictiva en las organizaciones manufacturer= as y de servicios.

Por esta razón, se justifica el desarrollo del presente artículo, porque busca = la aplicación práctica en un caso de una industria manufacturera de productos plásticos elaborados a base de materiales reciclados, para mejorar el contr= ol de los procesos, mediante la utilización de cartas de control apropiadas, debido a que según Uribe (2021)= , las mismas son herramientas que tienen como función esencial, el cumplimiento de las especificaciones de cualquier proceso productivo o de servicios. Además, Or= tiz & González (2018)= señalaron que = el control estadístico de procesos facilita el seguimiento de cualquier activi= dad productiva, identificando de manera óptima, las causas de variación, para reducir el tiempo de la toma de decisiones y aplicar acciones correctivas y preventivas, en cuestión de minutos, si no existen desviaciones mayores que requieran un tiempo mayor.

Al respecto, la importancia de este artículo radica en su aporte con la comuni= dad de Ingenieros Industriales, profesiones afines y expertos en los Sistemas de Gestión de la Calidad, que pueden tomar como fundamento referencial, los criterios indicados en este texto, para continuar exponiendo soluciones mejoradas al problema de la variabilidad de los procesos productivos y de servicios.

Además, el estado del arte del control estadístico de procesos, también justifica el desarrollo de este artículos, el cual es muy amplio, así por ejemplo, se re= visó un artículo realizado en Lima, cuyo objetivo fue medir la calidad de los procesos de una empresa de confección, en donde se pudo observar que los de= fectos del zurcido y las composturas por costura recta, representaron el 81% de los defectos y se encontraron fuera del límite de la carta de control escogida = para el efecto, que fue la carta p, sobre esta base se planearon las acciones correctivas para la mejora continua de ambos procesos (Pérez & Mont= oya, 2017).

Mientras que, en otro artículo realizado en Colombia, se pudo observar al aplicar la carta de control x al proceso de manufactura de poliestireno, los hallazgos evidenciaron que esta actividad no se encontraba marginalmente estable, por consiguiente, hubo un margen de reducción de variabilidad significativo, en= tre sus principales magnitudes, de presión, temperatura y resistencia a la difu= sión (Rahmer et al., 2020).

Se revisó también un artículo publicado a nivel nacional, cuya finalidad fue a= nalizar el cumplimiento de controles de calidad en el proceso productivo de varilla= s de acero, delimitado en una empresa siderúrgica, considerando la norma INEN 21= 67. Se aplicó la metodología cuantitativa, con control estadístico de procesos = basado en cartas de control univariantes, indagando las variables masa, fluencia y resistencia, evidenciándose cumplimiento con los límites establecidos en la norma anteriormente citada; pero también se observó incumplimiento de los límites de control sugeridos, causado por las limitaciones de talento human= o (Vélez, 2021).

Mientras que, en el ámbito local, una investigación desarrollada con el propósito de implementar el control estadístico de procesos de envases y tapas metálicas= en FADESA, utilizó la metodología cuantitativa, para evidenciar como principal= es resultados, un alto grado de defectos en tapas abrefácil, siendo los de may= or incidencia (69%), los filos de tapa con barniz y goma embarrada, además de = las tapas sin goma, pestaña incompleta y ojos de pez, estableciéndose como causa raíz, las limitaciones de control de calidad, que incrementó los costos de calidad en $86.554,12. La propuesta basada en el control estadístico de procesos bajo carta de control p, aportó para el fortalecimiento del contro= l y la reducción del desperdicio (Villegas, 2021)<= !--[if supportFields]>.

Se destaca entonces que, el amplio estado del arte sobre el control estadístic= o de procesos en las empresas manufactureras y de servicios, así como el uso de cartas de control que permitan verificar la variabilidad de los procesos y proponer acciones de mejora para garantizar su óptimo funcionamiento, constituyen también una justificación importante para el desarrollo del artículo.

El objetivo principal de este artículo consiste en verificar la influencia que tiene el control estadístico de procesos en la disminución del despilfarro, considerando para el efecto, un caso de una empresa manufacturera que produ= ce artículos plásticos, con base en materia prima reciclada.=

Para el efecto, se explican los aspectos metodológicos del estudio, previo a la exposición de los resultados de este, en donde se aplica una carta de contr= ol apropiada, para medir la variabilidad del proceso productivo, a lo que se a= ñade la discusión de estos hallazgos, finalizando con la emisión de conclusiones= y la presentación de la bibliografía utilizada para el desarrollo del artícul= o.

Metodología

Debido a que el control estadístico de procesos (CEP) es una herramienta numérica,= se ha escogido el enfoque cuantitativo, el cual según Carhuancho et al. (2019), facilita la medición de las variables, permitiendo la aplicación de procedimientos estadísticos, como es el caso del CEP, bajo la aplicación de cartas de cont= rol, en el proceso de producción de artículos plásticos empleando materia prima reciclada.

Mientras tanto, se ha aplicado el diseño no experimental en la presente investigació= n, porque según Arispe et al. (2020= ), el mismo no requiere manipulación de variables, es decir, que al aplicarse la investiga= ción de campo, no se cambian los parámetros del control estadístico de procesos, obtenidos con base en el empleo de una carta de control apropiada para determinar los límites de control mínimo y máximo de los artículos de plást= ico elaborados con base en materia prima reciclada.

También se ha escogido la investigación descriptiva, en donde según Carhuancho et a= l. (2019), se detalla las características de las variables, tal como lo observa el investigador, de e= ste modo, se describirán los parámetros concernientes a los límites máximos y mínimos de defectos de los artículos plásticos manufacturados en base a materias primas recicladas, entre estos productos se citan, las tuberías eléctricas de diversas dimensiones.

Además, se ha escogido la investigación de campo, que según Cohen y Gómez se refier= e a aquella que se efectúa en el sitio donde ocurren los hechos, que, en este c= aso, se trata de la empresa manufacturera dedicada a la fabricación de artículos plásticos manufacturados en base a materias primas recicladas, cuyo nombre = se reserva debido a la confidencialidad de esta información.=

Dentro de la población que, en este caso, es la misma muestra, se ha considerado 8= muestras de la misma cantidad de lotes, cada muestra con 50 productos. Esto se expli= ca por lo que manifiesta Robles (2019= ), para quien, s= i el universo no supera los 100 elementos, no es indispensable la aplicación de muestre probabilístico, para este caso se considera a cada lote, como unidad elemental, por lo tanto, el universo ha sido reconocido con 8 lotes de 50 tuberías cada uno, que representan a las 8 muestras.

Mientras tanto, la técnica a utilizar para la recopilación de información consiste e= n la observación directa, que según Neill & Cortez (2018), se refiere a = la visualización directa de los hechos que conducen al registro de los datos, = por lo que, el registro de muestras y productos defectuosos es el instrumento p= ara considerar, el cual constituye un check list. En este caso, se tomar= án 10 muestras de lotes, con 50 unidades de tuberías eléctricas, cada lote de producto, en la empresa manufacturera de artículos de plástico, elaborados = con base en materia prima reciclada.

Procedimiento para el empleo de la carta de cont= rol p

Debido a que el caso citado se basó en los defectos de los artículos plásticos, se= gún el método por atributos, es decir, mencionando tipos de defectos como es el caso, de las ralladuras, ojos de pez, manchas, partes quebradas, las cuales= no son necesariamente medibles con dimensiones como la longitud ni el peso, entonces, solo se contó el número de productos defectuosos, por esta razón,= se utilizó la carta de control p, que mide la defectuosidad por atributos y no= por variables.

El primer paso consiste en revisar las 50 tuberías para conducto eléctrica, por cada lote, de manera que cuando se llegue al décimo lote, culmina la revisi= ón. Los defectos observados por lote se anotan en cantidad en un registro.=

Luego, se procede a efectuar la división entre el número de productos defectuosos = y el número total de artículos por lote, para obtener la fracción defectuosa (p)= .

Fracción defectuosa p =3D

No. de unidades defectuosas

Total de unidades inspeccionadas

Se procede luego, a calcular los límites de control, mínimo, máximo y promedio= , a través de las siguientes ecuaciones:

·      =    Límite Inferior de Control =3D LIC

·      =    LIC =3D p – 3 s

·      =    Límite Superior de Control =3D LSC

·      =    LSC =3D p + 3 s

·      =    Línea Central =3D p<= /p>

Donde:

·      =    Fracción defectuosa =3D p

·      =    Varianza =3D s

s =3D

p (1 – p)

Unidades por cada muestra tomada

Habiendo obtenido los límites superior e inferior de control, se construye el gráfic= o de control correspondiente, que representa la carta de control p. La fracción defectuosa p, constituye el porcentaje de defectos de todas las muestras (lotes) y este se debe comparar con los parámetros establecidos en la norma= de calidad correspondiente, para determinar si cumple o no cumple los parámetr= os de calidad y si se requiere la acción correctiva o preventiva.

Resultados

Siguiendo el procedimiento para el cálculo de la fracción defectuosa, se ha procedido= a realizar el registro de los lotes, indicando la cantidad de productos defectuosos por cada lote:

·      =    Número de muestras =3D 8 (1 muestra po= r cada hora laborable)

·      =    Tamaño de la muestra =3D 50 unidades (= tabla No. 2: registro No. 2)

Tabla 1

Cálculo de la fracción defectuosa p<= o:p>

Tiempo

 

Unidades con defectuosidad

Unidades revisadas en la inspec= ción

p

9h00-10h00

2

50

0,04

10h00-11h00

3

50

0,06

11h00-12h00

2

50

0,04

12h00-13h00

3

50

0,06

13h00-14h00

3

50

0,06

14h00-15h00

2

50

0,04

15h00-16h00

2

50

0,04

16h00-17h00

4

50

0,08

Total=

21

400

0,0525

Nota: Tomado de la observación directa realizada mediante la revisión de productos defectuosos por lotes, en la empresa manufacturera de artículos plásticos.<= o:p>

Es necesario calcular la varianza de las muestras tomadas, considerando la siguiente ecuación:

 


s =3D

p (1 – p)

Unidades por cada muestra tomad= a

En la siguiente tabla se realizan los cálculos correspondiente= s a la varianza de la muestra considerada:

Tabla 2

Cálculo de la fracción defectuosa p<= o:p>

Tiempo

 

Fracción Defectos

p

Unidades inspeccionadas

p * (1-p)

 

(p * (1-p)) / (50)

 

s

 

9h00-10h00

0,04

50

0,0384

0,000768

0,02771281

10h00-11h00

0,06

50

0,0564

0,001128

0,03358571

11h00-12h00

0,04

50

0,0384

0,000768

0,02771281

12h00-13h00

0,06

50

0,0564

0,001128

0,03358571

13h00-14h00

0,06

50

0,0564

0,001128

0,03358571

14h00-15h00

0,04

50

0,0384

0,000768

0,02771281

15h00-16h00

0,04

50

0,0384

0,000768

0,02771281

Tabla 2

Cálculo de la fracción defectuosa p (continuación)

Tiempo

 

Fracción Defectos

p

Unidades inspeccionadas

p * (1-p)

 

(p * (1-p)) / (50)

 

s

 

16h00-17h00

0,08

50

0,0736

0,001472

0,03836665

Total

0,0525

400

0,04974375

0,00099488

0,03154164

Nota: Tomado de la observación directa realizada mediante la revisión de productos defectuosos por lotes, en la empresa manufacturera de artículos plásticos.<= o:p>

De esta manera, se obtuvo que la varianza s fue igua= l a 0,03154164 (3,15%). Con estos datos, se calcularon los límites superior e inferior de control:

·      =    LIC =3D p – 3 s

·      =    LSC =3D p + 3 s

·      =    LC =3D p

Donde:

·&nb= sp;        LC =3D 0,0525 (5,25%) =

Entonces:

·      =    LSC =3D 0,0525 + (0,03154164 X 3)=

·&nb= sp;        LSC =3D 0,1471 (14,71%)

Luego:

·      =    LIC =3D 0,0525 - (0,03154164 X 3)=

·&nb= sp;        LIC =3D 0

Basado en estos cálculos, se obtuvo la siguiente tabla de lími= tes de control con la carta de control p.

Tabla 3

Cálculo de la fracción defectuosa p<= o:p>

Tiempo

 

Fracción defectuosa

P

LIC

 

LSC

 

9h00-10h00

0,04

0,0525

0

0,1471

10h00-11h00

0,06

0,0525

0

0,1471

11h00-12h00

0,04

0,0525

0

0,1471

12h00-13h00

0,06

0,0525

0

0,1471

13h00-14h00

0,06

0,0525

0

0,1471

14h00-15h00

0,04

0,0525

0

0,1471

15h00-16h00

0,04

0,0525

0

0,1471

16h00-17h00

0,08

0,0525

0

0,1471

Nota: Tomado de la observación directa realizada mediante la revisión de productos defectuosos por lotes, en la empresa manufacturera de artículos plásticos.

Figura 1

Carta de con= trol p

LIC =3D 0

p =3D 0,0525

LSC =3D 0,1471

Nota: Tomado de la observación directa realizada mediante la revisión de productos defectuosos= por lotes, en la empresa manufacturera de artículos plásticos

Los hallazgos obtenidos, evidenciaron que la fracción defectuo= sa p se encuentra en 5,25%, donde el límite superior se encuentra en 14,71% y lí= mite inferior es cero, significando ello, que los lotes de artículos plásticos se encuentran dentro de los parámetros de control por atributos, los cuales, además, se encuentran dentro del rango establecido en la normativa internacional MIL-STD-105D, adaptada por el Instituto Ecuatoriano de Normalización (INEN) para la aceptación o rechazo en muestreo por lotes.

Discusión

Los resultados obtenidos evidenciaron que, al aplicar la carta= de control (p), por atributos, a una muestra de 8 lotes de producción, cada uno con 50 tuberías plásticas para instalaciones eléctricas, se pudo calcular u= na fracción defectuosa (p) de 5,25%, que indicó que por cada lote hubo un prom= edio de 2,62 unidades con defectos, que representaría alrededor de 5 defectos por cada 100 unidades.

Ante esta situación, se pudo evidenciar que estos resultados g= uardan concordancia con el hallazgo obtenido por Pérez & Montoya (2017)= , quienes evidenciaron una fracción defectuosa del 6%, un poco mayor a lo registrado = en este trabajo investigativo, donde las principales causas de defectuosidad fueron los defectos del zurcido y las composturas por costura recta.

Prosiguiendo, los principales tipos de defectos encontrados en este caso fueron ralladuras en las tuberías plásticas p= ara la construcción de instalaciones eléctricas, así como los ojos de pez que s= on especies de hendiduras en este tipo de productos, también se evidenciaron manchas en estos artículos y partes quebradas.

Sobre este particular, al revisar la investigación de Vélez (2021), realizada en = una empresa siderúrgica que fabrica varillas de acero, en la ciudad de Guayaqui= l, se pudo observar que las principales causas de defectuosidad estuvieron asociadas a variaciones en los parámetros de masa, fluencia y resistencia, donde se aplicó la carta de control XR, hallazgo di= ferente a lo presentado en los resultados del presente trabajo investigativo, pero = que establece que las cartas de control pueden medir la variabilidad en los procesos productivos.

Además, es digno de destacar que al verificar en la norma internacional MIL-STD-105D, adaptada por el Instituto Ecuatoriano de Normalización (INEN) para la aceptación o rechazo en muestreo por lotes de producción, se pudo reconocer que, en efecto, en un lote de 50 unidades, solo se deben presenta= r un máximo de 3 unidades, para declarar al lote como aceptado, es decir, para garantizar la calidad del producto manufacturado.<= /p>

Al respecto, al revisar la investigación de Villegas (2021), realizada en = la empresa Plastigama de Guayaquil, con el producto tuberías de desagüe, se pu= do observar que la comparación de los resultados obtenidos con la aplicación d= e la carta de control p, con una fracción defectuosa de 2,55%, también se verifi= có cumplimiento con la norma internacional MIL-STD= -105D, adaptada por el Instituto Ecuatoriano de Normalización (INEN) para la aceptación o rechazo en muestreo por lotes de producción, hallazgo casi sim= ilar a lo calculado en los hallazgos del presente trabajo investigativo.

Conclusiones

·      =    En conclusión, la aplicaci= ón de la carta de control (p) por atributos, evidenció que en una muestra de 8 lotes de producción, cada uno con 50 tuberías plásticas para instalaciones eléctricas, la fracción defectuosa (p) fue igual a 5,25%, donde los princip= ales tipos de defectos encontrados en este caso, fueron ralladuras, ojos de pez, manchas y quebraduras en las tuberías plásticas para la construcción de instalaciones eléctricas, hallazgo que al verificarse en la norma internacional MIL-STD-105D, adaptada por = el Instituto Ecuatoriano de Normalización (INEN) para la aceptación o rechazo = en muestreo por lotes de producción, permitió declarar al lote como aceptado, porque se garantiza la calidad del producto de plástico.<= /span>

·      =    Esto demuestra la utilidad= del control estadístico de procesos, así como el empleo de la carta de control = p, para verificar la variabilidad del proceso de producción de tuberías plásti= cas, con base en material reciclado, razón por la cual, esta herramienta de gest= ión de calidad, facilita la toma de decisiones de acciones correctivas y preventivas para reducir el desperdicio, y también, cumple con el objetivo = de verificar la influencia que tiene el control estadístico de procesos en la disminución del despilfarro.

 

Referencias bibliográficas

Arispe, C., Yangali, J., Guerrero, M., Lozada, O., Acuña, L., & Arellano, C. (2020). La investigación científica. Una aproximación para los estudios = de postgrado. Universidad Internacional del Ecuador. https://doi.org/ISBN: 978-9942-38-578-9

Camargo, A., Guete, E., & Roa, P. (2018). Diseño de un sistema de calidad bas= ado en cartas de control multivariadas para el monitoreo del proceso de potabilización del agua en la etapa de control de calidad de la empresa Tri= ple A S.A E.S.P. Ibarra: Universidad Técnica del Norte. https://doi.org/http://hdl.handle.net/10584/7980

Camero, J., & Huamaní, V. (2016). Criterios de aseguramiento de calidad para resultados de ensayo en la determinación de bario por espectrometría de absorción atómica en agua residual mediante cartas de control de Shewart. <= i>Tecnia, 23(1), 10. https://doi.org/http://190.119.192.195/index.php/tecnia/article/view/98/85<= o:p>

Carhuancho, I., Nolazco, F., Monteverde, L., Guerrero, M., & Casana, K. (2019). = Metodología de la investigación holística (Primera edición ed.). Guayaquil: UIDE. https://doi.org/ISBN 978-9942-36-316-9

Herrera, R., & Herrera, R. (2019). Aplicación de la carta EWMA con base en la varianza efectiva para monitorear variabilidad en procesos de control de calidad multivariada de fungicidas. I+D Revista de Investigaciones, 14(2), 15. https://doi.org/https://doi.org/10.33304/revinv.v14n2-2019003

Hurtado, C., Villada, S., & Correa, J. (2020). Carta de control bayesiana asocia= da a no conformidades en las historias clínicas. Lámpsakos, 2(24), 10. https://doi.org/https://doi.org/10.21501/21454086.3362

Neill, D., & Cortez, L. (2018). Procesos y fundamentos de la investigación científica. Editorial UTMACH: REDES 2017. https://doi.org/ISBN: 978-9942-24-093-4

Ortiz, Y., & González, I. (2018). Control estadístico de procesos en organizaciones del sector servicios. Respestas, 23(1), 10. https://doi.org/https://doi.org/10.22463/0122820X.1500

Pérez, G., & Montoya, M. (2017). Implementación de herramientas de control de calidad en MYPEs de confecciones y aplicación de mejora continua PHRA. I= ndustrial Data, 20(2), 10. https://doi.org/ISSN: 1560-9146

Rahmer, B., Solana, J., Garzón, H., & Ortiz, G. (2020). Diseño de una carta de control basada en Análisis de Componentes Principales. Un caso de estudio. = Revista de métodos cuantitativos para la economía y la empresa, 8(1), 18. https://doi.org/https://upo.es/revistas/index.php/RevMetCuant/article/view/= 3509/4616

Ramírez, E., & Cantú, M. (2017). Carta de control para procesos multinomiales: estudio de desempeño. Ingeniería, investigación y tecnología, 18(1),= 15. https://doi.org/ISSN 2594-0732

Restrepo, L. (2018). Cartas de control para optimizar el proceso de pintura de lámina= s de aluminio. Espacios, 39(22), 15. https://doi.org/http://www.revistaespacios.com/a18v39n22/18392234.html=

Robles, B. (2019). Población y muestra. Pueblo Continente, 30(1), 10. https://doi.org/http://doi.org/10.22497/PuebloCont.301.30121

Uribe, J. (2021). Fundamentos de control estadístico de procesos para gestores y administradores tecnológicos. Medellín: Fondo Editorial ITM. https://doi.org/https://doi.org/10.22430/9789585122352

Vélez, K. (2021). Control estadístico de procesos en la fabricación de varillas de acero. Matemática ESPOL FCNM Journal, 19(1), 11. https://doi.org/http://www.revistas.espol.edu.ec/index.php/matematica/artic= le/view/631/833

Villegas, J. (2021). Implementación del control estadístico de procesos de envases= y tapas metálicas para verificar el desperdicio e incrementar la eficiencia e= n el área de producción de FADESA. Guayaquil: Universidad de Guayaquil. https://doi.org/http://repositorio.ug.edu.ec/handle/redug/56685<= /span>

 

 

 

 

 

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

 


El artículo queda en propiedad d= e la revista y, por tanto, su publicación parcial y/o total en otro medio tiene = que ser autorizado por el director de la Revista Alfa Publicaciones.

 

 

 

 


=

 

 

 

 

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ISSN: 2773-7330

Vol. 4 No. 3.1  pp= . 6 – 19. agosto 2022

 

 

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                                      Científico e Industrial              Página 6 | 1

 

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