<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2306-9155</journal-id>
<journal-title><![CDATA[Retos de la Dirección]]></journal-title>
<abbrev-journal-title><![CDATA[Rev retos]]></abbrev-journal-title>
<issn>2306-9155</issn>
<publisher>
<publisher-name><![CDATA[Centro de Estudios de Dirección Empresarial Territorial de la Universidad de Camagüey Ignacio Agramonte Loynaz]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2306-91552020000200354</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Metodología para pronosticar demanda y clasificar inventarios en empresas comercializadoras de productos mayoristas]]></article-title>
<article-title xml:lang="en"><![CDATA[A Methodology to Forecast the Demand and Classify Inventories in Wholesale Supplier Companies]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Madariaga Fernández]]></surname>
<given-names><![CDATA[Carlos Jesús]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lao León]]></surname>
<given-names><![CDATA[Yosvani Orlando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Curra Sosa]]></surname>
<given-names><![CDATA[Dagnier Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lorenzo Martín]]></surname>
<given-names><![CDATA[Rafael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Holguín Departamento de Desarrollo de Sistemas ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Holguín Facultad de Ciencias Empresariales y Administración ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad de Holguín Facultad de Ingeniería ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad de Holguín Dirección de Ciencia Tecnología e Innovación ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<volume>14</volume>
<numero>2</numero>
<fpage>354</fpage>
<lpage>373</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2306-91552020000200354&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2306-91552020000200354&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2306-91552020000200354&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Objetivo:  Proponer una metodología que permita la clasificación de inventarios y el pronóstico de la demanda, en empresas comercializadoras de productos mayoristas, los cuales son factores claves para optimizar su desempeño.  Métodos y técnicas:  La metodología se sustenta en el uso de una red neuronal artificial tipo perceptrón multicapa creada con el software Weka; con el agregado de resolver problemas de clasificación de ítems del inventario, basados en ABC y el proceso de análisis jerárquico AHP. La metodología constó de tres fases, la primera encargada de la clasificación de los inventarios, la segunda del pronóstico, y la tercera del análisis integrado de los resultados.  Principales resultados:  Se propuso una escala jerárquica de variables para la clasificación de ítems del inventario, así como de los pesos de los criterios y subcriterios que la conforman, y su rango de selección. Se mostró una manera efectiva para pronosticar la demanda de forma individualizada para cada ítem del inventario. Conclusiones: La aplicación de la herramienta metodológica en la empresa ACINOX UEB Holguín comercializadora, de la provincia Holguín, Cuba, validó su efectividad para resolver problemas de clasificación de inventarios y pronóstico de demanda. Como derivado de su aplicación, se proporcionó a sus directivos, un instrumento que permite la toma de decisiones en aras de favorecer aquellos ítems mejor clasificados y sus pronósticos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Objective:  To recommend a methodology that allows for inventory classification and demand forecast, by wholesale supplier companies, as critical factors to implement performance optimization.  Methods and techniques:  The methodology relies on the use of a multilayer artificial neural network developed with Weka software, which adds the solution of inventory item classification problems, based on ABC and Analysis of hierarchical processes (AHP). The methodology was developed in three phases, the first one was in charge of inventory classification, the second was engaged in forecasting, and the third, in integrated result analysis.  Main results:  A hierarchical scale of variables was suggested for inventory item classification, as well as weighing opinions and sub-opinions in it, and its selection scope. An effective way of forecasting individual demands was presented for every inventory item.  Conclusions:  The application of this methodological tool by ACINOX sales company in Holguin province corroborated its effectiveness to solve inventory classification problems and demand forecasting. Deriving from the application, all the executives have access to a tool that contributes to decision-making, in order to favor better classified items and their forecasts.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[pronóstico de la demanda]]></kwd>
<kwd lng="es"><![CDATA[planeación agregada]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales artificiales]]></kwd>
<kwd lng="es"><![CDATA[clasificación de inventarios]]></kwd>
<kwd lng="es"><![CDATA[clasificación ABC]]></kwd>
<kwd lng="en"><![CDATA[demand forecasting]]></kwd>
<kwd lng="en"><![CDATA[aggregate planning]]></kwd>
<kwd lng="en"><![CDATA[artificial neural networks]]></kwd>
<kwd lng="en"><![CDATA[inventory classification]]></kwd>
<kwd lng="en"><![CDATA[ABC classification]]></kwd>
</kwd-group>
</article-meta>
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