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Esta obra está bajo una licencia
internacional
Creative Commons Atribución-NoComercial 4.0. =
Enviado: 2024/07/12 Aceptado: 2024=
/10/02
Publicado: 2024/10/15
Resumen En el comercio internacional, la elección de un
proveedor de productos ideal puede ser un desafío. Bajo este antecedente, resulta obliga=
torio identificar cuáles =
proveedores y sus países en la importac=
ión de cierto bien resultan más adecuados. Por=
lo tanto, en este trabajo
se propone un modelo que permita realizar
análisis y visualizaciones de datos de costos de las importaciones al Ecuador dentro del
periodo 2008-2018. Para lograrlo, se analizó la información
existente sobre las importaciones a través<=
span
style=3D'letter-spacing:-.5pt'> de una revisión de la literatura relacionada. Lue=
go, se aplicó el algoritmo K-=
means
para agrupar los países por cada partida arancelaria, tomando en cuenta el valor FOB y los costos de importación. Finalmente, se diseña=
una interfaz de visualización para facilitar la toma de decisiones con base en la información obtenida. En consecuencia, el modelo prop=
uesto es útil para la toma de decisiones en las importaciones, debido a que permite realizar análisis de los dat=
os de
todos los países en conjunto con los bienes importados, demostrando =
ser
aplicable a una amplia gama de empresas.
&nbs=
p;
Palabras clave:
Clusterizaci&oac=
ute;n , Comercio Internacional, Inteligencia d=
e Negocios, Minería de
Datos, Visualización de Datos.
&nb=
sp;
Abstract In international trade ,<=
span
style=3D'letter-spacing:-.15pt'> choosing the ideal supplier =
can be a challenge. Against <=
span
style=3D'letter-spacing:-.1pt'> this background ,
<=
/o:p>
&nb=
sp;
it =
is mandatory to identify
the countries from which the
import of a certain good is most appropriate . Therefore , this paper proposes a model that allows f=
or the
analysis and visualization of cost data=
for imports <=
span
style=3D'letter-spacing:-.6pt'> into Ecuador
from 2008-2018. To achieve <=
span
style=3D'letter-spacing:-.6pt'> this , the existing =
information on imports was
analyzed by reviewing the related literature .
Then , the K-means algorithm was applied =
to group countries by tariff heading , =
considering the FOB<=
span
style=3D'letter-spacing:-.05pt'> value and import costs. Finally , a visualization <=
/span>interface facilitates decision-makin=
g based on the obtained information . Consequently , the proposed model is useful for decision-making
in imports because =
it allows data analysis from all
countries in conjunction with imported goods ,
proving to be applicable to a wide range of
companies .
&nb=
sp;
Keywords: Business Intelligenc=
e, Clustering, Data Mining , D=
ata Visualization<=
/span>, International <=
span
style=3D'letter-spacing:-.1pt'>Trade .
&nb=
sp;
Introducción El comercio internacional se posic=
iona
como uno de los pilares fundamentales para el desarrollo económico de los países. Al facilitar el intercambio de bienes, servicios y tecnolog&iac=
ute;a
entre naciones, se dinamiza la economía y se contribuye
significativamente al crecimiento del
Producto Interno Bruto (PIB) (Rahim et al., 202=
3).
Esta actividad clave genera diversos beneficios como i) aumento
del empleo: Los países exportadores experimentan un<=
span
style=3D'letter-spacing:-.4pt'> crecimiento en las oportunidades
laborales, impulsando el bienestar social; ii ) =
mayor
variedad para los consumidores: =
Los países importadores se ven beneficiados por una amplia gama de productos y
servicios a su disposición, lo cual satisface mejor sus necesidades y preferencias; iii ) información
para la toma de decisiones: El comercio internacional permite a los
consumidores acceder a informaci&oacut=
e;n sobre diferentes opciones, facilitando la elección de los bienes que mejor se ajustan
a sus necesidades y<=
span
style=3D'letter-spacing:-.75pt'> presupuesto (Tâu & Sharfeldin , 2021). Por lo que, el comercio
internacional se erige como un motor fundamental para el progreso
económico y social de las naciones, impulsando el crecimiento, la
generación de empleo y el bienestar de los ciudadanos.
&nb=
sp;
Si<=
/span> bien el comercio internacional =
es un motor <=
span
lang=3DES style=3D'letter-spacing:-.2pt'>fundamental para el crecimiento económico,
la toma=
de decisiones en este ámbito se ve afectada
por altos niveles
de incertidumbre (Ponlaem et al., 2021). Este factor eleva el riesgo asociado a las importaciones y exportaciones<=
span
style=3D'letter-spacing:-.5pt'> (Bonaime =
et al., 2018), dificultando el intercambio=
fluido
de bienes. En este contexto, el anál=
isis de datos
de
importaciones se convierte en una herramienta crucial para reducir la incertidumbre y facilitar
el comercio internacional. Las cifras del Banco Central del Ecuador
(BCE) lo confirman: En 2018, las importaciones del país alcanzaron l=
os
22,385 millones de dólares, representando el 20.81% del PIB, y compuestas principalmente por materias primas
y bienes de capital (Banco Central del Ecuador, 2023).<=
/span>
&nb=
sp;
El análisis y la
comprensión de los costos de importación de las diferentes
subpartidas es fundamental para reducir el riesgo en las decisiones de importaci&oac=
ute;n y exportación, optimizar los procesos
logísticos y de cadena de suministro, identificar nuevas oportunidades comerciales,
negociar mejores precios con proveedores internacionales y tomar
decisiones estratégicas informadas para la gestión del comerc=
io
exterior.
&nbs=
p;
El análisis de datos ha
demostrado ser una herramienta invaluable para optimizar la gestión de cadenas de suministro y aumentar la ventaja competitiva en el comercio internacional.
Al reducir la incertidumbre en factores como la demanda y las
condiciones del mercado, es posible tomar decisiones más informadas y
estratégicas (Dubey et al., 2021). Las
técnicas de minería de datos son capaces
de realizar análisis robustos del comportamiento comercial de los
&nb=
sp;
países, <=
span
lang=3DES style=3D'letter-spacing:-.2pt'> facilitando la toma de decisiones acertadas en materia de importaciones =
y exportaciones=
. Sin embargo, la complejidad de la interpretación y
comunicación de los resultados obtenidos mediante estas técni=
cas
puede dificultar su comprensión a los usuarios (Chambers, 2018).
&nbs=
p;
Así pues, la visualización de datos es importante, ya que a través de resúmenes visuales y paneles de control
o d=
ashboards permite
comunicar informaci&oacu=
te;n de manera clara, concisa
y comprensible (J. Kim et al., 2016). Su aplicación en el ámb=
ito
del comercio internacional ha
demostrado ser particularmente útil para visualizar el comportamiento
del comercio entre diferentes regiones, considerando variables como el volu=
men
de compraventa, el costo y su evolución
a lo largo del tiempo (S. Kim et al., 2020). La principal ventaja
de la visualización de
datos radica en su capacidad para ofrecer al usuario una comprensión intuitiva de la información
presentada. En el caso de Ecuador, la aplicación de estas
técnicas a gran escala permitiría representar gráficamente los costos de importación,
facilitando su análisis y la toma de decisiones estraté=
;gicas
en materia de comercio exterior.
&nbs=
p;
Conforme con los antecedentes expuestos, en<=
span
style=3D'letter-spacing:-.25pt'> esta investigación se propone un modelo
de análisis y visualización de datos de importaciones del Ecu=
ador
en el período 2008-2018 en base a técnicas de minerí=
;a de datos. Con la finalidad de reducir la incertidumbre en la toma de
decisiones comerciales internacionales al conocer cuáles son los
países con los que se puede lograr una mejor relación comerci=
al
en base a los costos de importación.
&nbs=
p;
De este modo, este artículo=
se
estructura de la siguiente manera: en la Sección 2 se presentan
los trabajos relacionados; la Sección 3 expone la metodología empleada; en la Sección
4 se detallan los resultados obtenidos; la Sección 5 manifies=
ta
las discusiones y finalmente en la última Sección se exhiben =
las
conclusiones y los trabajos futuros.
&nb=
sp;
Trabajos Relacionados=
En el ámbito del comercio
internacional, el análisis de datherramienta fundamental para comprender las dinámicas complejas que lo caracterizan. Diversos
estudios han demostrado varias técnicas para identificar
patrones, clasificar países y tomar decisiones estratégicas. =
Siendo así, en un estudio
realizado por Simić et al. (2016),
examinaron las importac=
iones de telas de algodón a los Estados
Unidos durante el período 1990-2010. Para ello, prop=
usieron un enfoque original
que clasificaba a los 200 países proveedores en distintos gru=
pos
según su desempeño económico.
&nbs=
p;
Los investigadores obtuvieron dato=
s de
valor monetario y peso de las importaciones del repositorio de=
comercio
exterior de Estados
Unidos. Posteriormente,=
aplicaron el algoritmo K-=
means , una técnica de agrupamiento para identi=
ficar
grupos de países con características similares. Los resultados
revelaron una correlación significativa entre el crecimiento económico
de un país y su volumen de exportaciones de algodón a =
los
Estados Unidos. Los países con =
economías
de rápido crecimiento tienden a ser los principales proveedores de
algodón al mercado estad=
ounidense.
&nbs=
p;
Un<=
/span> estudio adicional analiza las importaciones de arroz =
a Indonesia desde =
diversos países
durante el período 2000-2015, inc=
luyendo Vietnam,
Tailandia, China, India,
Pakistán, Estado=
s Unidos,
Taiwán, Singapur y Myanmar. Con el objetivo de identificar los principales proveedores
de arroz, se agruparon los países de importación en tr=
es
clústeres: alto nivel de importación, introducción med=
ia y
bajo nivel de importación. Para ello, emplearon el algoritmo K-means y el software RapidMiner,
considerando las variables de peso de la mercadería en toneladas, su=
&nb=
sp;
valor de costo, seguro y flete o Cost ,
Insurance , Freight =
(CIF).
Los resultados revelaron que Vietnam =
y Tailandia, pertenecientes =
al clúster de alto nivel
de importación,<=
span
style=3D'letter-spacing:-.6pt'> eran los principales proveedores de arroz pa=
ra
Indonesia, seguidos por China, India y Pakistán en el clúster=
de
introducción media. Los países restantes
se ubicaron en el clúster de bajo nivel de importación (Windarto ,
2017).
&nb=
sp;
Dankevych et al. (2018) identificaron socios potencial=
es para el comercio
internacional de productos agrícolas a largo plazo con Ucrani=
a.
Se analizaron datos del período 2014-2016 utilizando el algoritmo K-=
means para agrupar países en tres categor&iacu=
te;as:
"Orientados a la exportación", "Orientados a la importación" y "Socios potenciales". Austria, Bulga=
ria, Croacia, Chipre, República C=
heca,
Dinamarca, Estonia, Finlandia, Grecia, Irlanda, Letonia, Lituania, Luxembur=
go y
España fueron identificados como socios potenciales basados en su
potencial comercial y perspectivas a largo plazo.
=
;
Batarseh et al. (2019) exploraron la viabilidad de utilizar técnic=
as de
minería de datos para a=
grupar países según s=
u nivel
de comercio de productos agrícolas. Se analizaron<=
span
style=3D'letter-spacing:-.35pt'> datos
del sistema de agricultura de Estados Unidos entre 1989 y 2018. El
algoritmo K-means aplicado con Python agrupa los
países en tres clústeres, principales importadores y exportad=
ores
entre Estados Unidos y China, países con comercio significativo entre
Japón, Alemania, Canadá, Reino Unido, India y Francia, y el r=
esto
de los países. Este tipo de análisis puede ser útil pa=
ra
informar la formulación de políticas comerciales.
&nbs=
p;
En otro estudio, se evalúa =
la
eficacia del algoritmo K-means para clasificar
países desarrollados y<=
span
style=3D'letter-spacing:-.75pt'> en vías de desarrollo. Se utilizaron datos
de 167 países =
obtenidos del repositorio
Kaggle , sin especificar el períod=
o de tiempo analizado. Los investigador=
es analizaron factores relacionados con las importaciones, exportaciones y
otras variables. El algoritmo K-means logr&oacu=
te;
clasificar correctamente 32 países como desarrollados y 135 como en
vías de desarrollo (Wulandari & Yogantara , 2022).
&nbs=
p;
Los datos del comercio internacion=
al
ofrecen información valiosa para comprender las dinámicas económicas globales, identificar
oportunidades comerciales y tomar decisiones estratégicas. La visualización de datos juega un papel crucial en la transformación de estos datos complejos
en información accesible y útil para diversos actores, como
gobiernos, empresas y organismos internacionales.
&nbs=
p;
En el estudio de SIKOS T & Meirmanova (2020), examinaron las interconexiones en =
el
comercio mundial de trigo utilizando representaciones geográficas y
grafos. Se identifica una red de escala libre, donde algunos países
tienen un papel central en el comercio, mientras que otros particip=
an de manera marginal. Esta informaci=
ón puede
ser útil para comprender
la vulnerabilidad del mercado ante eventos disruptivos y para diseñar
políticas comerciales efectivas=
.
&nbs=
p;
Otro
estudio evalúa el comercio internacional de Corea del Sur durante
el período 2007- 2017, creando visualizaciones=
que
muestran las importaciones y exportaciones por país y tipo de bien (=
Dar
et al., 2020). Se identificó a China, Estados Unidos, Vietnam, Hong =
Kong
y Japón como los principales socios comerciales de Corea del Sur. Es=
ta
información puede ser utilizada=
por empresas y autoridades gubernamentales para identificar oportunidades de mercado
y optimizar las estrategias comerciales.
&nb=
sp;
Pacini
et al. (2021) investiga=
ron sobre el comercio
internacional de residuos
de plástico en 20=
18
utilizando visualizaciones en forma de grafos. En este estudio, el pa&iacut=
e;s
de Alemania fue determinado como el mayor comerciante, seguido de Estados
Unidos, Bélgica, Italia y otros países. Esta informació=
;n
puede ser útil para comprender los flujos de residuos y para impleme=
ntar
políticas de gestión ambiental efectivas.
&nbs=
p;
Una investigación explora l=
as
exportaciones de frutas de Brasil entre 1986 y 2017, utilizando visualizaci=
ones
en forma de grafos para mostrar los destinos y el nivel de ingresos de los
países importadores, Bornal et al. (2021=
). En
este estudio se señala a Europa como el principal mercado, destaca=
ndo la importancia de la visualización de datos para comprender las tendencias del comercio internac=
ional
de productos agrícolas.
&nbs=
p;
En esta investigación se pr=
opone
una interfaz interactiva para visualizar los precios de franco a bordo o Free on Board (FOB)
y costos de importación del Ecuador. La interfaz incluye gráficos de barras, grafos y filtros para facilitar la toma de decisiones comerciales internacionales.
&n=
bsp;
Materi=
ales y Métodos Para la elaboración del mod=
elo se
contemplan las siguientes fases: i) limpieza y construcción de los
datos, ii ) aplicación de técnicas=
de
minería, iii ) diseño de interfaz =
de
visualización y iv ) evaluación de
resultados. La Figura 1 presenta las fases de la metodología utiliza=
ndo el Meta-Modelo 2.0 de Ingeniería de Procesos de Software y Sistemas (SPEM
2.0).
&nbs=
p;
Figura 1 Fases de elaboración del modelo propuesto
Limpieza y construcción de
datos Para esta fase se utilizó c=
omo
entrada la base de datos de importaciones del Ecuador obtenida del sitio web
del Banco Central del Ecuador (Banco Central del Ecuador, 2023). El
análisis fue ejecutado para el periodo 2008-2018. Este conjunto de d=
atos
fue seleccionado debido a la disponibilidad de una base de datos consolidad=
os
de todos los países de origen y partidas arancelarias i=
mportadas entre
esos años (Medina
López et al., 2023). En esta instancia, el objetivo fue transfor=
mar la
base disponible en un conjunto de datos adecuado y aplicar las técni=
cas
de minería de datos necesarias. Para ello, se evaluó la neces=
idad
y utilidad de cada variable y se utilizó el lenguaje de
programación Python.
=
;
El<=
/span> conjunto de datos <=
span
lang=3DES style=3D'letter-spacing:-.4pt'> consta =
de 1,471,125 registros, una cantidad grande =
para ser manejada en un solo archivo. Por este motivo,=
fue
dividida en 2 hojas del documento, las mismas que fueron cargadas al procesador de forma individual y luego fueron
unificadas. Luego de la carga, se evidenció que la base de datos constaba
de 42 variables Categ&=
oacute;ricas, Nominales, Numéricas, Categóricas Ordinales y
Dicotómicas.
&nb=
sp;
El análisis fue ejecutado e=
n base
a los datos de los precios del franco a bordo o Free O=
n
Boad (FOB) y costos de importación. Para=
esto,
fueron consideradas las variables “Valor FOB de la importación” y “Costo de importación”. Sin embargo, los valores de estas variables se presentaban en base al=
total
de importaciones anuales de cada partida arancelaria, por lo que estaban en
función de la cantidad de toneladas importadas. A fin de evitar sesg=
os
en la interpretación, se analizaron los valores por tonelada. Para esto, se generaron las variables “FOB
por tonelada” y “Costo de importación por tonelada”. Esto se logró dividiendo res=
pectivamente
los valores de las variables “Valor FOB de la Importación̶=
1; y
“Costo de importación” entre los datos de la variable “Toneladas importadas” =
para
cada año, país de importación y partida arancelaria.
=
;
Por otra parte, para el proceso de=
clusterización , es preferible trabajar con aqu=
ellas
variables que brinden información útil durante el
análisis. Por este motivo, una metodología conocida para
prescindir de variables que no aportan valor es eliminar aquellas que brind=
an la misma información o tienen=
un
alto nivel de correlación (Hsu et al., 2=
010).
Así, no todas las variab=
les
disponibles fueron necesarias. El objetivo de este estudio es conocer los
países importadores m&a=
acute;s adecuados
para cada partida,
por lo que se seleccionaron las variables como el
nombre del país de importaci&o=
acute;n, el año de la importación, código de la partida
“NANDINA” actual y el costo de importación por
tonelada y FOB por tonelada. Los criterios fueron seleccionados en base a
cuáles variables pueden brindar información sobre si es
conveniente o no importar un bi=
en
seleccionado de un país específico desde el punto de vista
económico.
=
;
Luego,
se evidenció la existencia de valores en blanco en ciertas variables. Estos valores =
pueden
repercutir en complicaciones al aplicar los algoritmos. Sin embargo, dichos
valores pueden ser reemplazados o eliminados (Sehgal =
&
Bhargava , 2018). En los análisis comerciales, la ausencia de registros implica que no
existió intercambio alguno, por lo tanto, a los datos en blanco se l=
os
reemplazó con ceros.
&nbs=
p;
Para ejecutar el análisis,
primero se obtuvieron los valores promedio de las variables “FOB por tonelada” y “Costo de importación por tonelada” en función de las variables “Año de
la importación”,<=
span
style=3D'letter-spacing:-.35pt'> “Nombre del paí=
s de importación en inglés” y “Código de partida NANDINA actual”. De esta manera, se consiguió un total de 124,150 registros correspondientes a 224 países
y 7,131 partidas arancelarias.
&nbs=
p;
Aplicac=
ión de Técnicas de Minería Una=
vez culminado el preprocesamiento del conjunto de datos, fue aplicada la técni=
ca de clusterización=
. Como se mencionó anteriormente, el algoritmo K-means ha sido ampliamente utilizado para clusterización en varios estudios
relacionados a agrupación de países para análisis
comerciales (Dankevych et al., 2018; Simić et al., 2016; Windart=
o ,
2017). Sin embargo, este método requiere que previamente se defina el
número de clústeres para trabajar.
&nbs=
p;
Aplicación =
de clusterización Aunque
la aplicación del algoritmo K-means =
es el método más uti=
lizado para clusterizar (Sinaga &=
Yang,
2020), la cantidad de clústeres (k) en los que se desea agrupar los
datos es requerida como entrada; lo cual puede resultar complejo de determi=
nar.
Por otra parte, dicho número <=
/span>puede ser predefinido en base a las necesidades del negocio (Kodinariya et al., 2013). Así, trabajos previ=
os que han agrupado
países en base a variables económicas han dividido
los datos en 3 niveles –alto, medio y bajo– (Dankevych et al., 2018).
&nb=
sp;
En esta investigación se pe=
rsigue
un objetivo análogo y se ha considerado definir tres grupos de países por cada partida arancelaria (k =3D 3). Por otra parte, existen partidas arancelarias
que fueron importadas desde una cantidad de países menor o ig=
ual
a la cantidad de grupos predefinida. En estos casos, se utilizó un
número de clústeres igual a la cantidad de naciones desde las=
que
se importó el bien, resultando en un país dentro de cada
clúster.
&nbs=
p;
Una vez definido
el número de clústeres, se utilizó el algoritmo K-means <=
span
style=3D'letter-spacing:-.2pt'> con los datos de
importaciones utilizand=
o la librería scikit-learn de Python. El script desarrollado realiza una
iteración por cada partida arancelaria. En cada repetición se
agrupa a los países desde donde se importó la partida en base=
a
las variables “FOB por tonelada” y “Costo de
importación por tonelada” de acuerdo
con el número =
de clústeres definido para<=
span
style=3D'letter-spacing:-.05pt'> dicha
clasificación ar=
ancelaria.
En esta instancia, los clústeres obtenidos fueron representados por =
los
números 0, 1 y 2 y no siempre están conformados por los mismos
grupos de países. Luego, se añadió un campo al dataset=
o
conjunto de datos para especificar el número de clústeres
obtenidos del proceso, de modo que pueda ser incluido en la fase de
visualización.
&nb=
sp;
Dise&nt=
ilde;o
de Interfaz de Visualización Para facilitar la interpretaci&oac=
ute;n
de los resultados obtenidos y la toma de decisiones, se diseñó=
; un
entorno para la visualización de los grupos obtenidos y su
relación con los datos iniciales mediante interacción del usuario final.
Este proceso fue realizado mediante
el software Microsoft <=
span
class=3DSpellE>Power BI, debido a que permite representar los datos =
de
forma efectiva y el tipo de visualización depende de factores
como los tipos
de datos, las preguntas que se busca
responder y al público que va dirigido
el informe. Lo que se busca con la visualización de los datos
es la simplicidad, clar=
idad,
coherencia, interactividad, y accesibilidad de los datos.
&nbs=
p;
Interfaz e<=
span
style=3D'letter-spacing:-.2pt'> Interacción del usuario Primero, se vinculó el
código de la partida de la base de datos cluste=
rizados
con la información de las partidas arancelarias de la base inicial, =
de
modo que en la interfaz fueron colocadas las variables
“Nivel 1 de la Partida”, “Nivel 2 de la Partida”, “Nivel 3 de la Partida” y “Nivel 4 de la Partida” para facilitar la búsqueda de información con respecto a cada tipo de
bien importado. Por otra parte, fue incluida la variable
“Clúster” para identificar a qué grupo pertenece =
cada
país desde la interfaz.
=
;
Como siguiente paso, en la interfa=
z se
colocaron los valores promedio de las variables “Free On Board (FOB)” y “Costo=
de importación” de cada grupo
de países clusterizados<=
/span> por su
importancia en el análisis de importaciones. Además, fue incluido un elemento de identificación
de las unidades en las que se mide cada partida arancelaria. Finalme=
nte,
se incluyeron dos representaciones de las variables “Free On Board (FOB)” y
“Costo de importación” mediante grafos con el país
Ecuador en el nodo central. En esta instancia, los tamaños de los no=
dos
de los grafos fueron parametrizados de tal manera que resulten proporcional=
es a
los valores que toman las variables, mientras que su color fue utilizado para indicar el clúster al que pertenecía el país
representado.
&nb=
sp;
El objetivo del trabajo es facilitar la toma de decisiones para el usuario
final a través de representaciones visuales de los datos y que permita comprender de forma sencilla las complejas
relaciones de los datos. Para ello, se utilizó la visualización de los datos como una herramienta
que permita identificar a los países con los que sería m&aacu=
te;s
conveniente ejecutar procesos de importación en base al costo de
importación y valor FOB. Así,
para facilitar la interacción del usuario, se colocaron opciones de
filtrado a las variables “Nivel 1 de la Partida”, “Nivel =
2 de
la Partida”, “Nivel 3 de la Partida”, “Nivel 4 de la
Partida” de modo que se pueda acceder de
&nb=
sp;
manera s=
encilla e intuitiva a la información requerida. Ade=
más, se incluyó el campo “Clúster”, que
también puede ser filtrado, para poder identificar los países
pertenecientes a cada clúster de las partidas.
&nbs=
p;
Evaluac=
ión de resultados El<=
/span> modelo fue sometido a una evaluaci&oac=
ute;n por parte de expertos en el campo =
del comercio internacional y la minería de datos, quienes
confirmaron su validez
y aplicabilidad en diversos
escenarios. Los expertos
destacaron la solidez
de la metodología, <=
/span>la precisión de los resultados y la utilidad potencial =
del
modelo para la toma de decisiones estratégicas. Por lo tanto, los
expertos evaluaron el modelo en base a la solidez
de la metodología, <=
/span>precisión de los resultados, aplicabilidad del model=
o y el
análisis para identificar los aspectos positivos y negativos del mod=
elo,
así como las áreas de mejora potenciales.
&n=
bsp;
Resultados Como parte del proceso de investigación, fue elaborada una base de datos completa
que contiene promedios de precios FOB por tonelada y costos de
importación por tonelada para cada país de origen de las importaciones realizadas en Ecuador
durante el período 2008-2018. La i=
nformación se encuentra organizada por partida arancelaria “NANDINA”, =
lo que facilita el análisis y la comparaci&oa=
cute;n
de datos entre diferentes productos. Para ilustrar la aplicación de =
la
base de datos, fue seleccionada la partida arancelaria 85171200 , que corresponde a "teléfonos móviles (celulares) y los de otras redes inalámbricas". Esta partida fue elegida por su simplicidad
y por ser un producto de uso común en la actualidad. La
descripción de la partida arancelaria con el código 85171200 es:
&nb=
sp;
• =
Sección VIII:
Máquinas, aparatos y material eléctrico; sus partes; aparatos=
de grabación o reproducción de sonido, aparatos de grabación o reproducción de imagen
y sonido en televisión, y las partes y accesorios de estos
aparatos.
• =
Capítulo 85: Máquinas, aparatos y material
eléctrico, y sus partes.
•
Partida 8517: Teléfonos,
incluidos los teléfonos móviles (celulares) y los de otras redes inalámbricas, los demás aparatos para emisión, transmisión o recepción de voz, imagen
u otros datos, incluidos los de comunicación en red con o sin cable.=
• =
Subpartida 851712: No hay descripción disponible.
• =
Ítem 85171200: Teléfonos móviles (celulares) y los de otras redes
inalámbricas.
&nbs=
p;
El análisis del conjunto de=
datos
para la partida arancelaria 8517120=
0 permitió
identificar los países de origen con los precios FOB y costos de
importación más favorables para la importación de
teléfonos móviles hacia Ecuador. Esta información puede
ser de gran utilidad para empresas importadoras, entidades gubernamentales y
otros actores interesados en e=
l comercio
internacional de este tipo de productos. En la Tabla
1 se muestra la agrupación obtenida de la pa=
rtida
arancelaria de ejemplo.
=
;
Tabla 1 Conjunto de datos resultante (Fragmento)
COSTO DE IMPORTACIÓN POR TONELADA
FOB POR TONELADA
NOMBRE =
DEL PA&Iacut=
e;S =
DE
IMPORTACIÓN
GRUPO
=
28,808.81
=
1,295,847.76
Canada
=
0
=
16,425.13
=
783,938.49
=
Colombia
=
0
COSTO DE IMPORTACIÓN POR TONELADA
FOB POR TONELADA
NOMBRE =
DEL PA&Iacut=
e;S =
DE
IMPORTACIÓN
GRUPO
=
58,716.67
=
799,096.67
Denmark
=
0
5,293.19
369,255.89
United
States of
America
2
=
6,652.95
=
532,318,35
=
Vietnam
=
2
En la Tabla 1 se observa que cada
país presente en el campo “Nombre del País de
Importación” perte=
nece a un clúster asignado en el campo “Grupo” identificado por=
los valores
0, 1 y 2 (3 clústeres) en base a las variables planteadas ini=
cialmente. Para el ejemplo
presentado, la interpretación puede ser la siguiente: <=
/p>
&nbs=
p;
• =
Clú=
ster 0: <=
span
lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1=
00%;
letter-spacing:-.65pt'> Se <=
span
lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1=
00%;
letter-spacing:-.65pt'> encuentran=
los =
paí=
ses desde los =
cuales el <=
span
lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1=
00%;
letter-spacing:-.65pt'> costo promedio de <=
span
lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1=
00%;
letter-spacing:-.65pt'> importaci&=
oacute;n
es 30,383.00 dólares por tonelada y el valor FOB
promedio de la importación es 1,078,415.29 dólares por tonela=
da.
• =
Clú=
ster 1: <=
span
lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1=
00%;
letter-spacing:-.65pt'> Se <=
span
lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1=
00%;
letter-spacing:-.65pt'> encuentran=
los =
paí=
ses desde los =
cuales el <=
span
lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1=
00%;
letter-spacing:-.65pt'> costo promedio de <=
span
lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1=
00%;
letter-spacing:-.65pt'> importaci&=
oacute;n
es 56.411,34 dólares por tonelada y el valor FOB
promedio de la importación es 6,644,241.87 dólares por tonela=
da.
•
Clú=
ster 2: <=
span
lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1=
00%;
letter-spacing:-.65pt'> Se <=
span
lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1=
00%;
letter-spacing:-.65pt'> encuentran=
los =
paí=
ses desde los =
cuales el <=
span
lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1=
00%;
letter-spacing:-.65pt'> costo promedio de <=
span
lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1=
00%;
letter-spacing:-.65pt'> importaci&=
oacute;n
es 26,975.34 dólares por tonelada y el valor FOB
promedio de la importación es 361,856.33 dólares por tonelada=
.
=
;
De acuerdo con las necesidades del negocio, es posible identificar cuáles =
son los países
desde los que resulta más conveniente impo=
rtar una partida “NANDINA” específica en base a los costos y valor FOB en los que se incurre para adquirir el bien descrito
desde el Ecuador.
A su vez, estos resultados fueron representados en un informe visual=
(dashboard ), cuya interfaz se diseñó =
para facilitar el acceso a la información disponible de<=
span
style=3D'letter-spacing:-.05pt'> todas las partidas
y países con interacciones sencillas e intuit=
ivas
por parte del usuario, como se representa en la Figura 2.
&nbs=
p;
Figura 2 Interfaz de visualización de datos de costos de importaciones al
Ecuador
En el informe visual presentado, c=
ada
cuadro permite el acceso a la información que requiera el usuario de acuerdo con el tipo de bien que desea
importar. Para esto,
los filtros y los
gráficos dinámicos facilitan la comparación entre los costos de importación y FOB de cada país.
Los campos de la sección superior izquierda de la Figura 2
muestran todos los posibles filtros de=
l informe visual, desde lo más general (Nivel de Partida 1) a lo más específico (Clúster), como se
puede observar en la Figura 3.
=
;
Figura 3 Filtros utilizados del informe
Los valores de la sección media izquierda<=
span
style=3D'letter-spacing:-.5pt'> muestran
el promedio de las variables
FOB y “Costo de importación”. Se colocaron como elementos separados por su relevancia en el análisis,
como se observa en la Figura 4.
&nbs=
p;
Figura 4 Valores visualizados en el informe
y unidades de la partida
arancelaria
En la sección inferior izqui=
erda, se muestra la gráfica de barras que puede ser utilizada
para comparar la diferencia<=
span
style=3D'letter-spacing:-.75pt'> entre
los valores promedio
que tomaron las variables FOB y “Costo de importación&=
#8221;
para cada clúster, como se muestra en la Figura 5.
&nbs=
p;
Figura 5 Comparación
de valores entre los clústeres
Finalmente, en la Figura
6 se aprecia la representación de las variables FOB y “Costo de importación”
en dos visualizaciones =
de grafos para facilitar
la comparación de los valores
que adquieren en base al país de origen, así como el
clúster al que pertenece cada nación.
&nb=
sp;
Figura 6 Valores visualizados en el informe
y unidades de la partida
arancelaria
El modelo obtenido fue validado, de
manera cualitativa, por un equipo de expertos conformado por profesores de =
las
escuelas de Estudios Internacionales y Administración. Asimismo, par=
a la
validación se compararon los resultados obtenidos con la
información provenientes de otras fuentes, como las bases de datos
oficiales, estudios previos e informes de mercado. El resultado de esta fase
indica que, si bien hay aspectos en los que se podría profundizar pa=
ra
determinar cuál es el mejor socio comercial, el modelo es un buen pu=
nto
de partida para identificar cuáles son los mejores países para
importar cada partida arancelaria.
&n=
bsp;
Discusión Este trabajo revisa la literatura
existente sobre la visualización y el procesamiento de datos en estu=
dios
sobre importaciones internacionales. Si bien estudios previos han analizado
datos de importaciones de países, este trabajo se distingue por su
enfoque práctico, dirigido a fa=
cilitar la toma de decisiones por parte de empresarios que buscan identificar los países óptimos
para importar determinados bienes.
&nbs=
p;
El estudio de Simić
et al. (2016) ha analizado datos de importaciones para agrupar países
mediante la técnica de clusterización.
Sin embargo, su enfoque se limita a un solo tipo de producto, lo que restri=
nge
su aplicación a empresas de un sector específico. En contrast=
e,
este trabajo presenta un modelo
de análisis generalizable a todos los tipos de productos (Partida NANDINA) importados a Ecuador.
Esta amplitud lo convierte en una herramienta valiosa para u=
na gran variedad de empresas, ya que les permite identificar los paí=
ses óptimos para importar
cualquier tipo de bien según sus necesidades<=
span
style=3D'letter-spacing:-.6pt'> específicas. Windarto (2017), quien agrupó países importadores de un solo producto, observó una tendencia a clasificarlo=
s en 3 clústeres, enfoque
&nb=
sp;
tambi&e=
acute;n utilizado por Dankevych et al. (2018) y Batarseh =
et al. (2019). Considerando la validez
de este enfoque, se replicó para agrupar a los países proveedores de<=
span
style=3D'letter-spacing:-.05pt'> Ecuador.
&nbs=
p;
El análisis de los resultad=
os
evidenció la robustez del algoritmo K-means
para la agrupación de paí=
;ses,
corroborando su efectividad, como lo mencionan (Wuland=
ari
& Yogantara , 2022). En cuanto a la visualización, se optó por métodos tradicionales como los propuestos por SIKOS T & Meirmanova (2020) y Dar et al. (2020). Si bien estos trabajos se enfocan en variables
como el nivel de importaciones y exportaciones, el presente trabajo
mantiene el foco en el usuario <=
span
style=3D'letter-spacing:-.35pt'> final, permitiendo identificar diferencias en las variables de interés, en este caso, el FOB y
“Costo de importación”. Bornal et al. (2021) y Pacini et al. (2021), quienes
exploraron con éxito la=
representación de datos de comercio internacional mediante grafo=
s. Este estudio adoptó este enfoque.
Cada país se representa como un nodo, cuyo tamaño se relaciona directamente con el
costo total (bien e importación) y cuyo color indica el clúst=
er
al que pertenece, facilitando una identificación rápida y
sencilla.
&nb=
sp;
En consonancia con el trabajo de L=
iu et
al. (2021) sobre la utilidad de los análisis de datos de comercio pa=
ra
la gestión de rutas comerciales enfocadas en el transporte de bienes,
este estudio se presenta como un punto de partida para identificar potencia=
les
nuevas rutas comerciales o potenciar las existentes entre Ecuador y los
países con los que resulta más conveniente comerciar desde una
perspectiva económica.
&n=
bsp;
Conclusiones Ele=
gir el país adecuado para <=
span
lang=3DES style=3D'letter-spacing:-.1pt'>importar productos puede =
ser un desafío sin las herramientas de análisis correctas. Para<=
span
style=3D'letter-spacing:1.5pt'> abordar
esto, se desarrolló un modelo para analizar y visualizar
los costos de importación de bienes a Ecuador entre 2008 y 2018. Este
modelo facilita la identificació=
;n de los mejores países para importar cada producto, lo que reduce la incertidumbre
en la toma de decisiones comerciales internacionales.
&nbs=
p;
Tras revisar la literatura sobre
visualización y procesamiento de datos de comercio internacional, se encontró que el algoritmo K-means es el modelo de clusterización más utilizado
debido a su facilidad
de uso y efectividad. Además, la representación de datos en forma de grafos se ha vuelto popular
para mostrar relaciones entre entidade=
s, ya que permite
transmitir una gran cantidad de información=
a
través de recursos visuales como el color y el tamaño de los
nodos. La clusterización , utilizando el
algoritmo K-means , demostró que para enc=
ontrar
patrones de comportamiento y
información relev=
ante de una base de datos, era crucial
comprender las necesidades del usuario y seleccionar las variables
correctas antes de realizar cualquier =
procesamiento tecnológico. La creación del modelo de visualización basado en representaciones
gráficas revel&oa=
cute; una amplia gama de posibilidades para transmitir<=
span
style=3D'letter-spacing:-.2pt'> información de forma visual. Esto lleva a la conclusión que enfocarse en la funcionalidad del modelo y en la creatividad para simplificarlo sin sacrificar la
calidad de la información transmitida son aspectos esenciales en la
visualización de datos.
&nb=
sp;
La evaluación de expertos
confirmó la validez y aplicabilidad del modelo en diversos escenarios. Un ejemplo es su uso por parte de empresarios que buscan el mejor país para importar
productos específicos de manera más económica, =
lo
que se traduce en una mayor rentabilidad para sus empresas. Este
análisis también es útil para que los funcionarios
públicos formulen y establezcan=
políticas comerciales
que mejoren la competitividad de Ecuador. Además, el pers=
onal que establece rutas comercia=
les hacia
Ecuador puede utilizar
el modelo para identificar nuevas conexiones potenciales en
función de los costos de importación.
&nb=
sp;
Entre las recomendaciones para fut=
uros
trabajos está la incorporación de otras herramientas visuales
que permitan la observación de los datos
de importaciones por un periodo mayor al 2018, con la finalidad de conglomerar=
más datos y que se presente
de forma simple
y estructurada. Asimism=
o, con respecto al análisis temporal, sería fac=
tible comparar
la variación de l=
os
costos entre un período y otro.
&n=
bsp;
Reconocimientos Los autores desean agradecer al
Vicerrectorado de Investigaciones de la Universidad del Azuay por el apoyo financiero y académico, así
como a todo el personal de la escuela de Ingeniería de Ciencias de la
Computación y el Laboratorio de Investigación y Desarrollo en
Informática (LIDI).
&nb=
sp;
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<=
span
class=3DSpellE>using
k-means clustering method . =
International Journal of
Artificial Intelligence=
Research , 1(2),
26–33.
Wulandari Yogantara , B. O. (2022). Algorithm analysis of K-means and fuzzy C-means for
clustering countries based on economy
and health . Faktor Exacta,
15(2).
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