<?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>2007-7467</journal-id>
<journal-title><![CDATA[RIDE. Revista Iberoamericana para la Investigación y el Desarrollo Educativo]]></journal-title>
<abbrev-journal-title><![CDATA[RIDE. Rev. Iberoam. Investig. Desarro. Educ]]></abbrev-journal-title>
<issn>2007-7467</issn>
<publisher>
<publisher-name><![CDATA[Centro de Estudios e Investigaciones para el Desarrollo Docente A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-74672025000200971</article-id>
<article-id pub-id-type="doi">10.23913/ride.v16i31.2631</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Uso de Herramientas Avanzadas de Análisis de Datos para automatizar DMAIC: Revisión Sistemática y Esquema de Simulación en RStudio]]></article-title>
<article-title xml:lang="en"><![CDATA[Using Advanced Data Analysis Tools to Automate DMAIC: Systematic Review and Simulation Framework in RStudio]]></article-title>
<article-title xml:lang="pt"><![CDATA[Usando ferramentas avançadas de análise de dados para automatizar DMAIC: revisão sistemática e esquema de simulação no RStudio]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ortega Mendoza]]></surname>
<given-names><![CDATA[Michell Ricardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rodríguez-Picón]]></surname>
<given-names><![CDATA[Luis Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández Gómez]]></surname>
<given-names><![CDATA[Jesús Andrés]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Romero López]]></surname>
<given-names><![CDATA[Roberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Ciudad Juárez  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de Ciudad Juárez  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Autónoma de Ciudad Juárez  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad Autónoma de Ciudad Juárez  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<volume>16</volume>
<numero>31</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-74672025000200971&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-74672025000200971&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-74672025000200971&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  La literatura es extensa en cuanto a la integración de herramientas avanzadas de análisis de datos (HAAD) -como análisis de big data (ABD), minería de datos (MD), minería de procesos (MP) y aprendizaje automático (AA) - con la metodología de mejora continua &#8220;Definir, Medir, Analizar, Mejorar, Controlar&#8221; (DMAIC, por sus siglas en inglés). Este estudio buscó identificar patrones de vinculación entre HAAD y DMAIC para el desarrollo de un sistema de mejora continua autogestionados (SMCA). Se realizó una revisión sistemática de literatura (RSL) aplicando criterios de selección y análisis de estudios estandarizados en cinco bases de datos principales (IEEE Xplore, ScienceDirect, Springer, Taylor &amp; Francis y Wiley), analizando 1,189 estudios para seleccionar 18 documentos clave.  Los resultados revelaron que técnicas de AA - como red neuronal artificial (RNA) y red neuronal profunda (RNP) ambas con 30% de vinculación - predominaron en la fase de Analizar, siendo RNP utilizada además en la fase de Mejorar en un 66%, no obstante, con vacíos críticos en Definir y Medir. Por otro lado, en el área de MD se identificó un vacío de implementación en la fase de Controlar. Se desarrolló un esquema ilustrativo de simulación (EIS), que utiliza vinculaciones secuenciales en DMAIC: (1) Definir: descubrimiento de proceso, SIPOC (por sus siglas en inglés: Suppliers, Inputs, Process, Outputs, Customers); (2) Medir: MD, capacidad de proceso (Cp, Cpk); (3) Analizar: análisis predictivo, análisis de regresión; (4) Mejorar: RNP, metodología de superficie de respuesta (MSR); (5) Controlar: monitoreo en tiempo real, gráficos de control. RStudio fue plataforma central y librerías clave como &#8220;bupaR&#8221; (Minería de Procesos) y &#8220;caret&#8221; (Modelos Predictivos) fueron consideradas para el desarrollo metodológico del EIS. Este marco muestra la viabilidad teórica de sistemas SMCA que puede ser usado en futuras investigaciones para validar su funcionalidad en diferentes entornos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The literature extensively covers the integration of advanced data analysis tools (ADAT) - including big data analytics (BDA), data mining (DM), process mining, and machine learning (ML) - with the &#8220;Define, Measure, Analyze, Improve, Control&#8221; (DMAIC) continuous improvement methodology. This study identified linkage patterns between ADAT and DMAIC for developing self-managed continuous improvement systems (SMCIS). A systematic literature review was conducted using standardized selection and analysis criteria across five major databases (IEEE Xplore, ScienceDirect, Springer, Taylor &amp; Francis y Wiley), analyzing 1,189 studies to select 18 key documents. Results revealed that machine learning techniques - specifically artificial neural networks (ANN) and deep neural networks (DNN), each showing 30% linkage - predominated in the Analyze phase, while DNN showed 66% usage in the Improve phase. Critical gaps were identified in the Define and Measure phases. In data mining, a gap was found in the Control phase. An illustrative simulation framework (ISF) was developed using sequential DMAIC linkages: (1) Define: discovery, SIPOC (Suppliers, Inputs, Process, Outputs, Customers); (2) Measure: DM, process capability (Cp, Cpk); (3) Analyze: predictive analysis, regression analysis; (4) Improve: DNN, response surface methodology; (5) Control: real-time monitoring, control charts. RStudio served as the central platform, with key libraries such as &#8220;bupaR&#8221; (Process Mining) and &#8220;caret&#8221; (predictive models) employed for the ISF development. This framework demonstrates theoretical viability for SMCA systems, though future research should validate functionality in different environments.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo A literatura é extensa sobre a integração de ferramentas de análise avançada de dados (ADA) - como big data analytics (BDA), mineração de dados (DM), mineração de processos (PM) e aprendizado de máquina (ML) - com a metodologia de melhoria contínua Definir, Medir, Analisar, Melhorar, Controlar (DMAIC). Este estudo buscou identificar padrões de ligação entre ADA e DMAIC para o desenvolvimento de um sistema de melhoria contínua autogerenciado (SMCI). Uma revisão sistemática da literatura (SLR) foi conduzida aplicando critérios padronizados de seleção e análise de estudos em cinco grandes bases de dados (IEEE Xplore, ScienceDirect, Springer, Taylor &amp; Francis e Wiley), analisando 1.189 estudos para selecionar 18 documentos-chave. Os resultados revelaram que técnicas de ML, como rede neural artificial (ANN) e rede neural profunda (DNN), ambas com 30% de ligação, predominaram na fase de Análise, com DNN também sendo usada na fase de Melhoria com 66%, porém, com lacunas críticas em Definir e Medir. Por outro lado, na área de MD, uma lacuna de implementação foi identificada na fase de Controle. Foi desenvolvido um framework de simulação ilustrativa (EIS), que utiliza ligações sequenciais em DMAIC: (1) Definir: descoberta de processos, SIPOC (Fornecedores, Entradas, Processo, Saídas, Clientes); (2) Medir: MD, capacidade do processo (Cp, Cpk); (3) Analisar: análise preditiva, análise de regressão; (4) Melhorar: DNN, metodologia de superfície de resposta (RSM); (5) Controlar: monitoramento em tempo real, gráficos de controle. O RStudio serviu como plataforma central, e bibliotecas-chave como "bupaR" (Mineração de Processos) e "caret" (Modelos Preditivos) foram consideradas para o desenvolvimento metodológico do EIS. Este framework demonstra a viabilidade teórica de sistemas SMCA que podem ser utilizados em pesquisas futuras para validar sua funcionalidade em diferentes ambientes.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[análisis de big data]]></kwd>
<kwd lng="es"><![CDATA[DMAIC]]></kwd>
<kwd lng="es"><![CDATA[minería de datos]]></kwd>
<kwd lng="es"><![CDATA[minería de procesos]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[big data analytics]]></kwd>
<kwd lng="en"><![CDATA[DMAIC]]></kwd>
<kwd lng="en"><![CDATA[data mining]]></kwd>
<kwd lng="en"><![CDATA[process mining]]></kwd>
<kwd lng="pt"><![CDATA[aprendizado de máquina]]></kwd>
<kwd lng="pt"><![CDATA[análise de big data]]></kwd>
<kwd lng="pt"><![CDATA[DMAIC]]></kwd>
<kwd lng="pt"><![CDATA[mineração de dados]]></kwd>
<kwd lng="pt"><![CDATA[mineração de processos]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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<article-title xml:lang=""><![CDATA[Six-Sigma Quality Management of Additive Manufacturing]]></article-title>
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<numero>4</numero>
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