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Análisis multivariante de costos = y su incidencia con las ventas y producción de pymes de pulverización de áridos = de la ciudad de Guayaquil

Multivariate analysis of costs and their incidence with the sales and production of aggregate pulverization SMEs in the city of Guayaquil

= ¹	Jimmy Hurtado Pasp= uel	https://orcid.org/0000-0001-9795-2339
	Maestría en Administración de Empresas, Universidad de Guayaquil, Guayaquil, Ecuador.= jimmy.hurtadop@ug.edu.ec
= ²	Samuel Pazmińo Linares	https://orcid.org/0000-0003-3702-6653
	Maestría en Administración de Empresas, Universidad de Guayaquil, Guayaquil, Ecuador.= samuel.pazminol@ug.edu.ec
= ³	Marco Ramos Cadena	https://orcid.org/0000-0002-0826-4006
	Facultad de Ingeni= ería Industrial, Universidad de Guayaquil, Guayaquil, Ecuador. marco.ramo= sc@ug.edu.ec
= ⁴	Luis Pilacuan Bonete	https://orcid.org/0000-0002-6625-0905
	Maestría en Administración de Empresas, Universidad de Guayaquil, Guayaquil, Ecuador.= luis.pilac= uanb@ug.edu.ec Universidad de Salamanca, Salamanca, Espańa. luis.pilac= uanb@usal.es <= /span>

=	Artículo de Investigación Científica y Tecnológica Enviado: 10/12/2022 Revisado: 10/01/2023 Aceptado: 06/02/2023 Publicado:06/03/2023 DOI:&n= bsp;https://doi.org/10.33262/= ap.v5i1.1.332
<= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",= serif; mso-fareast-font-family:Calibri;color:blue'>
Cítese:		Hurtado Paspuel, J., Pazmińo Linares, S., Ramos Cadena, M., & Pilacuan Bonete= , L. (2023). Análisis multivariante de costos y su incidencia con las ventas y producción de pymes de pulverización de áridos de la ciudad de Guayaquil. AlfaPublicaciones, 5(1.1), 176–189. https://doi.org/10.33262/= ap.v5i1.1.332
		ALFA PUBLICACIONES, es una revista multidisciplinar, trimestral, que se public= ará en soporte electrónico tiene como misión contribuir a la &nbs= p; formación de profesionales competentes con visión humanística y crítica q= ue sean capaces de exponer sus resultados investigativos y científicos en la misma medida que se promueva mediante su intervención cambios positivos e= n la sociedad. https://alfapublicaciones.= com <= span style=3D'font-size:8.0pt;line-height:115%;font-family:"Times New Roman",s= erif; mso-fareast-font-family:Calibri'>La revista es editada por la Editorial Ciencia Digital (Editorial de prestigio registrada en la Cámara Ecuatoria= na de Libro con No de Afiliación 663) www.celibro.org.ec<= /a>
		Esta revista está protegida bajo una lic= encia Creative Commons Attribution Non Commercial No Derivatives 4.0 Internatio= nal. Copia de la licencia: http://creativecommons.org= /licenses/by-nc-nd/4.0/
Pala= bras claves:= Pymes, statis,= statico, áridos		Resumen Introducción: El presente estudio examina la relación y variación anual de seis empresas consideradas como pequeńas y medianas dedicadas a la pulverización de pie= dra para la fabricación de áridos de la ciudad de Guayaquil, analizando varia= bles de costos, producción y ventas en un periodo de cinco ańos antes de la pandemia. Objetivo: Analizar de una manera multivariante los costos de seis empresas pymes del sector de áridos en Ecuador, para conocer el impacto de la producción, ventas y costos y cono= cer la relación de estas en seis ańos de estudio. Metodología: Se= uso el método multivariante STATICO, para analizar la relación y el efecto de= las variables en las empresas en los diferentes periodos. Resultados: Los resultados revelaron que cinco de las seis empresas tienen un comportamie= nto similar en la estructura del análisis, donde Covitan presento un comportamiento diferente. Así mismos se observó que las ventas presentan = una alta relación con los costos Variables en las Pymes. Conclusiones: Se demostró que el análisis STATICO mejora el conocimiento de la dinámica de= los conjuntos de empresa-variables. Área de estudio general: Ingen= iería, industria y construcción. Área de estudio específica: Producción minería e industrias ex= tractivas.
Keywords: SMEs, Statis, Statics, Aggregate		Abstract: Introduction: The present study examines the relationship and annual variation of= six companies considered as small and medium dedicated to the pulverization of stone for the manufacture of aggregates in the city of Guayaquil, analyzi= ng variables of costs, production, and sales in a period of five years before the pandemic. Objective: Analyze in a multivari= ate way the costs of six SMEs in the aggregates sector in Ecuador, to know the impact of production, sales, and costs and to know the relationship of th= ese in six years of study. Methodology: The STATIC multivariate method was used to analyze the relationship and the effect of the variabl= es in the companies in the different periods. Results: The results revealed t= hat five of the six companies have similar behavior in the analysis structure, where Covitan presented a different behavior. Likewise, it was observed t= hat sales have a high relationship with variable costs in SMEs. Conclusions: It was shown that the STATIC analysis improves the knowledge of the dynamics of the company-variable sets. General area of study: Engineering, Industry, and construction. Specific area of study: Mining production and extractive industries.

Introducción<= /b>

Las pequeńas y medianas empresas (Pymes) son contribuyentes importantes para las actividades financieras de muchos países en desarrollo (Liu et al., 2022), estas a menudo presentan muchas dificultades, entre la más severa tenemos el acceso a la liquidez (Emrouznejad & Amin, 2009), buscando diferentes fuentes de financiamiento entre las que se encuentran las sociedades de garantías (Gan & Xia, 2022), por lo tanto el riesgo de quiebra de algunas de estas empresas es a= lto, siendo un causal el flujo de efectivo de estas.

Uno de los factores que obstaculizan el crecimiento de la productividad de las Pymes es la crisis financieras de los países emergentes en comparación con países de Europa, denominada brecha de productividad de las Pymes (Chen & Lee, 2023), la cual con los cambios paradigmáticos en la globalización, guerras= y conflictos internacionales afectan al desempeńo de estas, varios estudios se han realizado para el análisis de las Pymes en diferentes entornos globales, tales como, la agilidad estratégica en mercados emergentes de estas = (Adomako et al., 2022), la versatilidad para afrontar la pandemia del covid-19 en América Latina (Benítez et al., 2020), la transformación digital a la que se enfrentan en la sociedad digi= tal (Skare et al., 2023), entorno de la economía circular (Soni et al., 2023), el manejo enfocado a los objetivos de desarrollo sostenible = (Smith et al., 2022), siendo importante estudiar los costos asociados a los que se enfren= tan las pymes.

El sector de pulverizaci= ón y producción de áridos a nivel mundial está por encima del rango de más 100 millones de toneladas de procesamiento por ańos, el 90% de esta actividad se concentra en diez países, como, China, Italia, India, Irán, Espańa, Turquía, Brasil, Egipto, Grecia y Portugal con un estimado de más de 2 millones de toneladas por ańo (Napoli, 2010), la mayoría de las actividades de extracción y procesamiento de pied= ra en las canteras de áridos la realizan las pequeńas y medianas empresas (EUROSTAT, 2008)<= !--[if supportFields]>, ubicándose estas pymes en las cercanías a zonas montańosas. Los principales problemas de este sector es la tecnificación de los procesos, l= as enormes cantidades de desechos durante la producción, así como los altos co= stos y la falta de gestión de una manera eficiente y sostenible (Founti & Giannopoul= os, 2010), se han aplicado estudi= os para el manejo eficiente de la energía y la evaluación ambiental en este ti= po de empresas (Gazi et al., 2012), para mitigar los impactos negativos de esta industria, lo cual representa el aumento de costos aplicados en el sector. <= /p>
Las pymes deben evaluar = sus productos y servicios para poder manejar el impacto a la que se enfrentan frente a los objetivos estratégicos de las mismas (Bagur-Femenias et al., = 2013; Silva et al., 2019), por lo cual las mismas deben mantener un orden en sus sistema finan= ciero y contable, las pymes representan el 88,4% de las empresas Latinoamérica, u= na revisión de las pymes en América latina encontró que estas deben mantener un control en varias áreas, siendo la más importante la financiera, siendo el recurso más importante las ventas y los costos (Padilla-Angulo et al., = 2023).

El presente estudio anal= iza de una manera multivariante los costos de seis empresas pymes del sector de áridos en Ecuador, para conocer el impacto de la producción, ventas y costo= s y conocer la relación de estas en seis ańos de estudio.

Metodología

Las empresas analizadas se encuentran ubicadas en la ciudad de Guayaquil, mucha= s de estas ubicadas en el sector de vía a la costa, para nuestro análisis se estudiara el comportamiento de CODEMET, Calizas y Minerales (CAL y MIN), COVITAN, HUAYCO, EXPLOSA, MISANSA, donde se analizan los datos obtenidos de= los estados financieros de la Superintendencia de Compańías, Valores y Seguros = (2022), para el anál= isis se tomaron datos antes de la pandemia, considerando un periodo del 2015 al 2019.

En total, se recolectaron datos de seis variables, tales como: Costos Fijos (C. FIJOS), Producción en toneladas por ańo (PRODUC), Costos en Maquinarias (MA= Q), Costos Variables (C. VARIAB), Costos de Mantenimiento (C. MANT) y Nivel de Ventas (VENTAS), todos estos datos se cuantificaron anualmente durante el periodo de estudio, como se muestra en la tabla1.

Tabla 1

Cuantificación de las variables de las Pymes duran= te el periodo de estudio

EMPRESA

YEAR

VENTAS

EQ Y MAQ

C.MANT

C.FIJOS

C.VARIAB

PRODUC

CODEMET

2019

2883717

2578729

38955

325579

891805

52904

CODEMET

2018

3550971

2543697

556429

98469

1771533

52212

CODEMET

2017

2253822

1845473

354962

48041

1138103

51035

CODEMET

2016

2253170

453302

166666

623906

800646

56326

CODEMET

2015

1782170

352992

118117

170026

613631

63003

MISANSA

2019

1059662

2027843

31132

100751

446476

50222

MISANSA

2018

2085879

1517763

148626

12678

808418

42243

MISANSA

2017

1826999

1349863

179742

14480

1149701

42876

MISANSA

2016

237750

74090

69206

116935

554406

39741

MISANSA

2015

313059

75025

76987

120125

488.245

41904

CAL Y MIN

2019

1394022

1559432

114290

125411

1035529

38962

CAL Y MIN

2018

2877027

1341190

126113

48574

1277176

46203

Tabla 1

Cuantificación de las variables de las Pymes duran= te el periodo de estudio (continuación)

EMPRESA

YEAR

VENTAS

EQ Y MAQ

C.MANT

C.FIJOS

C.VARIAB

PRODUC

CAL Y MIN

2017

2120025

1251479

61499

251233

1320913

46932

CAL Y MIN

2016

1058213

895412

85232

163626

780885

47549

CAL Y MIN

2015

1110208

1007704

59779

44772

778766

44146

HUAYCO

2019

5701317

4197333

322666

1338960

3288135

215636

HUAYCO

2018

6057017

5340616

465484

575355

4701805

213365

HUAYCO

2017

5242674

4338545

276677

529222

4161940

211226

HUAYCO

2016

4821859

1916262

244556

488141

3817822

208340

HUAYCO

2015

3919884

760362

553985

369692

1325639

218367

COVITAN

2019

311269

308750

111166

21667

102412

2818

COVITAN

2018

828176

307252

80178

71806

164161

5763

COVITAN

2017

883794

306099

105701

73540

428466

5783

COVITAN

2016

882979

298815

39258

161934

310579

6071

COVITAN

2015

836686

247181

42910

31251

282153

6109

EXPLOSA

2019

1172852

56075

71122

142203

466354

8659

EXPLOSA

2018

930466

50346

86957

101141

519415

8067

EXPLOSA

2017

858391

13798

21849

56221

410729

7023

EXPLOSA

2016

743261

14320

56743

62353

289975

5992

EXPLOSA

2015

501949

13258

50271

42679

235519

3769

Fuente: Superintendencia de Compańías, Valores y Seguros (2022)

Para el análisis, los datos se organizaron en seis tablas, cada una representa u= na empresa, cada tabla está compuesta en las columnas por las variables de est= udio y en las filas los periodos a ser analizados, los datos de las tablas se analizaron mediante el método STATICO (Thioulouse et al., 2004), el cual nos permite analizar los datos en tres etapas: la primera etapa con= siste en la aplicación de un análisis de componentes principales normalizado de l= as variables de estudio y un análisis de componentes principales centrado de l= os datos de los individuos analizados (análisis de la inter estructura), la segunda etapa es el análisis de la coinercia (Dolédec & Chessel, 1994), es decir la variabilidad conjunta entre los conj= unto de variables, (análisis de la matriz compromiso) para poder obtener la coestructura de la combinación de empresas y variables, y la tercera parte consiste en la aplic= ación de un análisis triádico parcial (Thioulouse & Chessel, 1987), mediante el cual se analizan las K-Tablas obtenidas (análisis de la infraestructura).

Este análisis nos permite trazar las proyecciones de los ańos de muestreo de cada tabla original en los ejes del compromiso, en términos de valores mayores de las variables y su estructura con las empresas. Siendo así posible discutir= la correlación entre las empresas y las variables. Los gráficos y cálculos par= te del estudio se realizaron utilizando el software libre Rstudio (Posit Software, 2023), en el cual se usó el paquete AD= E4 versión 1.7-22 (Dray & Dufour, 2007).

Resultados

La Inter estructura del análisis STATICO, nos muestra las seis empresas de ári= dos, donde se observa en la figura 1, la longitud de las flechas en la proyección del primer eje, reflejan el peso de las empresas de muestreo con el comprom= iso, lo que indica que todas las empresas presentan una alta influencia para el compromiso entre estas y las variables de estudio. Se puede apreciar que las empresas analizadas presentan una estructura común a excepción de la Pymes Covitan.

Figura 1

Análisis de la Interstructura

Nota<= /b>: Se observa= a la Empresa COVITAN como difiere de las demás empresas, los ejes principales de= la figura representan el 12,11% y el 2,71% de la variabilidad total respectivamente.

Fuente: Superintendencia de Compańías, Valores y Seguros (2022)

Para el análisis del mapa de factores de compromiso representan el primer eje (horizontal) dominante que explica el 96,06% de la varianza y el segundo eje (vertical) solo el 0,23% de la varianza (eigenvalues), del análisis del compromiso se observa en la Figura 2a como el ańo 2019 y 2016 muestran un gradiente con valores altos y el resto de ańos muestran valores bajos en el mapa de factores del compromiso,= en la figura 2b se muestra que la variable producción tiene una representación débil en este plano factorial, el cual se demuestra por la longitud de la flecha, así mismo se muestra como las ventas, costos de maquinarias y costos variables tiene una relación significativa de acuerdo al ángulo de los vect= ores que lo forman, mientras que los costos fijos presenta una baja correlación = con las variables de estudio en los diferentes ańos analizados.

Figura 2

Análisis del Compromiso

a

b

Nota<= /b>: Mapa de fa= ctores de compromiso del análisis STATICO. Este mapa muestra la parte estable de l= as relaciones de las empresas con los ańos de estudio. (a) análisis de los ańos proyectados en el plano factorial, (b) variables de estudio proyectadas en = los ejes factoriales para el análisis de la variabilidad explicada de las misma= s.

Fuente: Superintendencia de Compańías, Valores y Seguros (2022)
En la figura 3 se puede observar cola empres COVITAN tiene un peso menor en el análisis del compromiso, siendo el resto de las empresas analizadas las de = un mayor peso de la variabilidad explicada por las variables de estudio a trav= és de los ańos.

Figura 3

Análisis del peso en el compromiso de las empresas=

Nota<= /b>: La Empresa COVITAN difiere de las demás empresas, los pesos en el compromiso son bajos= , lo que refleja que esta empresa tiene un comportamiento menor al resto.

Fuente: Superintendencia de Compańías, Valores y Seguros (2022)
Los mapas de las trayectorias se enfocaron en el comportamiento de las variable= s en los diferentes ańos en cada una de las empresas, se puede observar en la fi= gura 4a cómo se comportan las variables en su conjunto en los diferentes ańos en= las seis empresas analizadas, por ejemplo se observa como CODEMET tiene un comportamiento diferente en su conjunto todos los ańos, y COVITAN tiene comportamientos similares todos los ańos a excepción del 2016 que muestra un comportamiento diferente con respecto a los otros ańos analizados. En la fi= gura 4b se observan las relaciones de las variables en cada una de las empresas, donde la longitud del vector representa la variabilidad explicada y el ángu= lo entre vectores la relaciones entre estas. Por ejemplo, en la empresa Explosa todas las variables analizadas presentan una alta relación entre estas, mientras que en Huayco las variables de Producción y mantenimiento tienen p= oca variabilidad explicada y su relación con las otras variables es inversa o b= aja. En CODEMET se puede observar como la producción tiene un vector en sentido contrario a las Ventas

Figura 4

Análisis de las trayectorias en la coestructura

a

b

Nota<= /b>: (a) Coestr= uctura de las empresas explicada por los ańos. (b) Coestructura de las variables p= or cada una de las empresas.

Fuente: Superintendencia de Compańías, Valores y Seguros (2022)
En general, la empresa COVITAN presenta un comportamiento diferente en cada un= o de los ańos, en relación con las otras analizadas, sin embargo, muestra dos variables como los costos de mantenimiento y ventas con un comportamiento diferente a lo largo de los periodos analizados, las flechas en su mayoría tienen vectores con una longitud alta lo que muestra una variabilidad expli= cada en los ejes en el espacio factorial alto.

Discusión

El presente estudio se ha centrado en el comportamiento de las variables analizadas en las empresas con mayor contribución a la coestructura entre las variables y los periodos de estudi= o. Se puede observar que la relación de las empresas en los periodos de estudio tiene una relación similar en el 2019, ya que se encuentra en el mapa facto= rial en el lado superior a excepción de la empresa COVITAN, mientras que las variables de estudio se observan que las ventas tienen una alta relación con los costos variables, pero comportamiento diferentes en relación a las otras variables, la dinámica del método STATICO permite evaluar el comportamiento= de las variables en diferentes periodos de seis empresas, donde se observa que= las empresas presentan un patrón homogéneo entre las medianas empresas, a diferencia de las pequeńas empresas, consideradas así por su volumen de ven= tas.

Otras técnicas multivariantes, como el análisis canónico de correspondencia (CCA) es útil para evaluar relaciones entre variables y la distribución de estas en diferentes ańos. Sin embargo,= la variabilidad puede enmascararse en los primeros ejes principales, a diferencia del STATI= CO, el cual es una variante de los métodos statis, permiten analizar la coestructura entre las variables y la distribución en los diferentes period= os, y la relación entre las empresas. Este estudio permite observar la capacidad del método para distinguir el comportamiento de las variables en los difere= ntes ańos y su efecto general sobre las empresas y como estas actúan durante un periodo, lugar o condición determinada.

Conclusiones

· Se puede ver durante el estudio como los diferentes costos se relacio= nan con la producción y ventas de seis empresas pymes del sector de áridos en Ecuador, en un periodo de seis ańos antes de la pandemia, se puede identifi= car como las ventas se relacionan con los costos variables con una similaridad entre las empresas. Los resultados presentados agregan nueva información en= el comportamiento de las empresas de pulverización de áridos con las variables analizadas, en estudios posteriores, se pueden incluir nuevas variables al estudio, podría ser interesante analizar el comportamiento de las empresas post-pandemia en el mismo sector o ampliar el estudio a nuevas empresas del sector.

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Conflicto de intereses

Los autores deben declarar si existe o no conflicto de intereses en relación co= n el artículo presentado.

=

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

El artíc= ulo queda en propiedad de la revista y, por tanto, su publicación parcial y/o t= otal en otro medio tiene que ser autorizado por el director de la Revista Alfa Publicaciones.<= /o:p>

&nb= sp;

&nb= sp;

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EMPRESA	YEAR	VENTAS	EQ Y MAQ	C.MANT	C.FIJOS	C.VARIAB	PRODUC
CODEMET	2019	2883717	2578729	38955	325579	891805	52904
CODEMET	2018	3550971	2543697	556429	98469	1771533	52212
CODEMET	2017	2253822	1845473	354962	48041	1138103	51035
CODEMET	2016	2253170	453302	166666	623906	800646	56326
CODEMET	2015	1782170	352992	118117	170026	613631	63003
MISANSA	2019	1059662	2027843	31132	100751	446476	50222
MISANSA	2018	2085879	1517763	148626	12678	808418	42243
MISANSA	2017	1826999	1349863	179742	14480	1149701	42876
MISANSA	2016	237750	74090	69206	116935	554406	39741
MISANSA	2015	313059	75025	76987	120125	488.245	41904
CAL Y MIN	2019	1394022	1559432	114290	125411	1035529	38962
CAL Y MIN	2018	2877027	1341190	126113	48574	1277176	46203