<?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>0121-5051</journal-id>
<journal-title><![CDATA[Innovar]]></journal-title>
<abbrev-journal-title><![CDATA[Innovar]]></abbrev-journal-title>
<issn>0121-5051</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Ciencias Económicas. Universidad Nacional de Colombia.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-50512022000300083</article-id>
<article-id pub-id-type="doi">10.15446/innovar.v32n85.100979</article-id>
<title-group>
<article-title xml:lang="pt"><![CDATA[Métodos de previsão de demanda: uma revisão da literatura]]></article-title>
<article-title xml:lang="en"><![CDATA[DEMAND FORECASTING METHODS: A LITERATURE REVIEW]]></article-title>
<article-title xml:lang="es"><![CDATA[MÉTODOS DE PREDICCIÓN DE DEMANDA: UNA REVISIÓN DE LA LITERATURA]]></article-title>
<article-title xml:lang="fr"><![CDATA[MÉTHODES DE PRÉVISION DE LA DEMANDE : UNE REVUE DE LA LITTÉRATURE]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ackermann]]></surname>
<given-names><![CDATA[Andres E. F.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sellitto]]></surname>
<given-names><![CDATA[Miguel A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidade do Vale do Rio dos Sinos  ]]></institution>
<addr-line><![CDATA[São Leopoldo ]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidade do Vale do Rio dos Sinos  ]]></institution>
<addr-line><![CDATA[São Leopoldo ]]></addr-line>
<country>Brazil</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2022</year>
</pub-date>
<volume>32</volume>
<numero>85</numero>
<fpage>83</fpage>
<lpage>99</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-50512022000300083&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-50512022000300083&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-50512022000300083&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="pt"><p><![CDATA[RESUMO: A previsão de demanda é uma metodologia da administração de empresas para estimar um valor futuro de uma grandeza de interesse. Realizar previsões de demanda significa reconhecer padrões de comportamento em séries históricas e predizer o comportamento futuro ou, ainda, identificar fatores causais que afetam o comportamento e extrapolá-lo. Este artigo tem por objetivo realizar uma revisão da literatura dos métodos de previsão de demanda com o propósito de reunir os métodos e modelos disponíveis acerca dos conceitos utilizados atualmente na administração de empresas relacionados ao consumo e produção de produtos e serviços. A metodologia utilizada é a revisão da literatura com abordagem qualitativa, com o propósito de dar uma visão descritiva geral dos métodos dominantes utilizados em previsão de demanda. Foi realizado o mapeamento da literatura para identificar o estado da ciência por meio da produção científica disponível nos bancos de dados Scopus e Google Scholar. Os métodos qualitativos e os causais estão mais bem associados a previsões de médio e longo prazos. A análise de séries temporais bem como os métodos dos diversos tipos de médias e de suavização exponencial são indicados como os mais adequados para previsões de curto prazo. Um recurso utilizado em diversas realidades é a construção de um modelo próprio de previsão de demanda, o qual utilize técnicas, aspectos, conceitos e características de diferentes métodos e modelos. É fundamental monitorar o modelo adotado, manter os dados de campo e de previsão sob controle e, se houver desvios, corrigir o modelo.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT: Demand forecasting is a business management methodology for estimating the future value of customer demand. Making this type of forecast means recognizing behavior patterns in historical series and predicting future behaviors, or even identifying and extrapolating causal factors that affect market behavior. Hence, this article aims to review the literature on demand forecasting methods in order to gather the methods and models currently used in business administration related to the consumption and production of goods and services. A qualitative literature review was implemented with the purpose of providing a general descriptive view of the dominant methods used in demand forecasting. A mapping of the available literature was conducted to build the state of the art on the topic through the scientific production included in Scopus and Google Scholar databases. Results show that qualitative and causal methods are better associated with medium and long-term forecasts. In addition, the analysis of time series and the methods of the diverse types of averages and exponential smoothing are mentioned as the most suitable for short-term forecasts. A resource that is commonly deployed is the construction of specific demand forecast models, using techniques, aspects, concepts and characteristics from different methods and models. However, it is important to monitor the adopted model, keep field and forecast data under control, and, in case of deviations, correct the model.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN: La predicción de demanda es una metodología de la administración de empresas para estimar un valor futuro de una grandeza de interés. Realizar predicción de demanda significa reconocer estándares de conducta en series históricas y predecir la conducta o, aun, identificar factores causales que afectan la conducta y extrapolarla. El artículo tiene el propósito realizar una revisión de la literatura de los métodos de predicción de demanda con el fin de reunir los métodos y modelos disponibles acerca de los conceptos utilizados actualmente en la administración de empresas relacionados al consumo y producción de productos y servicios. La metodología utilizada es la revisión de la literatura con enfoque cualitativo, con el propósito de dar una visión descriptiva general de los métodos dominantes utilizados en predicción de demanda. Se realizó el mapeo de la literatura para identificar el estado de la ciencia por medio de la producción científica disponible en los bancos de datos Scopus y Google Scholar. El análisis de series temporales, así como los métodos de los diversos tipos de medianas y suavización exponencial se indican como los más adecuados para predicciones de corto plazo. Un recurso utilizado en diversas realidades es la construcción de un modelo propio de predicción de demanda, que utilice técnicas, aspectos, conceptos y características de diferentes métodos y modelos. Es fundamental monitorear el modelo adoptado, mantener los datos de campo y predicción bajo control y, si hubo desviaciones, corregir el modelo.]]></p></abstract>
<abstract abstract-type="short" xml:lang="fr"><p><![CDATA[RÉSUMÉ : La prévision de la demande est une méthodologie de gestion d'entreprise permettant d'estimer la valeur future d'une quantité d'intérêt. La prévision de la demande consiste à reconnaître les normes de comportement dans les séries historiques et à prédire le comportement ou, même, à identifier les facteurs causaux qui affectent le comportement et à l'extrapoler. L'objectif de l'article est de passer en revue la littérature sur les méthodes de prévision de la demande afin de rassembler les méthodes et les modèles disponibles sur les concepts actuellement utilisés dans la gestion des affaires liées à la consommation et à la production de produits et de services. La méthodologie utilisée est une revue de la littérature avec une approche qualitative, dans le but de donner un aperçu descriptif des méthodes dominantes utilisées dans la prévision de la demande. On a réalisé une cartographie de la littérature pour identifier l'état de la science au moyen de la production scientifique disponible dans les bases de données Scopus et Google Scholar. L'analyse des séries temporelles, ainsi que les méthodes des différents types de médianes et du lissage exponentiel sont indiquées comme les plus appropriées pour les prédictions à court terme. Une ressource utilisée dans diverses réalités est la construction d'un modèle propre de prévision de la demande, qui utilise des techniques, des aspects, des concepts et des caractéristiques de différentes méthodes et modèles. Il est essentiel de surveiller le modèle adopté, de garder sous contrôle les données de terrain et de prévision et, en cas d'écarts, de corriger le modèle.]]></p></abstract>
<kwd-group>
<kwd lng="pt"><![CDATA[métodos de previsão de demanda]]></kwd>
<kwd lng="pt"><![CDATA[previsão]]></kwd>
<kwd lng="pt"><![CDATA[previsão de demanda]]></kwd>
<kwd lng="pt"><![CDATA[revisão]]></kwd>
<kwd lng="en"><![CDATA[Demand forecasting methods]]></kwd>
<kwd lng="en"><![CDATA[forecasting]]></kwd>
<kwd lng="en"><![CDATA[demand forecasting]]></kwd>
<kwd lng="en"><![CDATA[literature review]]></kwd>
<kwd lng="es"><![CDATA[métodos de predicción de demanda]]></kwd>
<kwd lng="es"><![CDATA[previsión]]></kwd>
<kwd lng="es"><![CDATA[revisión]]></kwd>
<kwd lng="fr"><![CDATA[méthodes de prévision de la demande]]></kwd>
<kwd lng="fr"><![CDATA[prévision]]></kwd>
<kwd lng="fr"><![CDATA[révision]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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