Purpose: Design an industrial production model with a focus on industry 4.0 (Big Data) and decision-making analysis for small and medium-sized enterprises (SMEs) in the clothing sector that allows improving procedures, jobs and related costs within the study organization Develop a sustainable manufacturing proposal for the industrial textile sector with a focus on Big data (entry, transformation, data loading and analysis) in organizational decision making, in search of time and cost optimization and environmental impact mitigation related.Design/methodology/approach: The present research, of an applied nature, raises a value proposition focused on the planning, design and structuring of an industrial model focused on Big Data, specifically in the apparel manufacturing sector for decision-making in a structured and automated way with the methodological approach to follow: 1) Approach of production strategies oriented in Big Data for the textile sector; 2) Definition of the production model and configuration of the operational system; 3) Data science and industrial analysis, 4) Production model approach (Power BI) and 5) model validation. Methodological design of the investigation. 1) Presentation of the case study, where the current situational analysis of the company is carried out, formulation of the problem and proposal of solution for the set of data analyzed; 2) Presentation of a solution proposal focused on Big Data, on the identification of the industrial ecosystem and integration with the company's information systems, as well as the solution approach in the study and science of data in real time; 3) Presentation of the Model proposal for SQL structured databases in the loading, transformation and loading of important information for this study; 4) Information processing, in the edition of data in the M language of Power BI software, construction and elaboration of the model; 5) Presentation of the related databases, in the integration with the foreign key of the Master table and the transactional Tables; 6) Data analysis and presentation of the Dashboard, in the design, construction and analysis of the related study variables, as well as the approach of solution scenarios in the correct organizational decision makingFindings: The results obtained show an improvement in operational efficiency from the value-added proposal. Research limitations/implications: Currently, the number of studies applying Big Data technology for organizations in the textile and manufacturing sector in organizational decision making are limited. If analyzed from the local scene, there are few cases of Big Data implementation in the textile sector, as a consequence of the lack of projects and financing of value propositions. Another limiting factor in this research is the absence of digital information of high relevance for study and analysis, which leads to longer times in data entry and placement in information systems in real time. Finally, there is no data organizational culture, where there are processes and/or procedures for data registration and its transformation into clean data.Originality/value: This research integrates, as well as the correct organizational decision making For the verification of originality, the project search and systematic review of literature in the main online search engines are carried out for this research; In addition, the percentages of coincidence with online reviewers such as turnitin and plag.es are reviewed in the transparency of this study project.

La cuarta revolución industrial se presenta, en la actualidad, como un proceso de transformación y evolución productiva. El comportamiento globalizado de los mercados, las nuevas tendencias tecnológicas y el auge de innovadoras metodologías, han transformado la industria en un espacio de interacción interdisciplinar para la toma de decisiones organizacionales. Uno de estos espacios es la generación, obtención y análisis de los datos obtenidos de acuerdo con la relación hombre-máquina, también conocido en la actualidad como el Big Data. El presente artículo de investigación descriptiva tiene por objetivo exponer escenarios de aplicación, control y análisis de técnicas, tecnologías y metodologías asociados al Big Data en tres sectores principales: salud, financiero y transporte y logística. Los resultados obtenidos permiten evidenciar el impacto y relevancia con relación al mundo Big Data y sus aplicaciones en los sectores industriales de estudio como eje de reflexión y repercusiones profesionales. Se concluye que la integración de hardware y software en el campo del Big Data se hace indispensable en pro de la mejora de la calidad de servicio de teleasistencia en los pacientes, así como en los servicios logísticos y financieros

ResumoO crescimento mundial da população e o processo de urbanização tem feito aumentar a produção nas grandes indústrias e o consumo de bens no mercado, aumentando a geração da quantidade de lixo das cidades nos últimos anos. Devido ao fenômeno anterior, o número de pontos para a coleta dos Resíduos Sólidos Domiciliares aumenta, gerando custos e distâncias maiores, impactando no transito urbano e diminuindo a produtividade e qualidade dos sistemas logísticos e de transporte para as empresas de serviço de limpeza. Como solução e apoio nos processos de roteirização para a coleta dos rejeitos, utilizou-se o SIG-T software TransCAD versão 5.0. com a aplicação da rotina do Problema de Roteirização de Veículos com Janela de Tempo (PRVJT) para o planejamento, execução e análise dos roteiros dos Caminhões no Programa de Remoção Gratuita da Companhia de limpeza Urbana -COMLURB na zona sul da cidade do Rio de Janeiro. Palavras-Chaves:Resíduos Sólidos Domiciliares; Programa de Remoção Gratuita; Sistemas de Informações Geográficas para Transporte; Problema de Roteirização dos Veículos com Janela de Tempo. AbstractThe global population growth and the urbanization process has made increasing production in major industries and consumer goods in the market, increasing the generation amount of garbage from cities in recent years. Because of the previous phenomenon, the number of points for the collection of Household Solid Waste increases, generating costs and longer distances, impacting the urban traffic and decreasing the productivity and quality of logistics and transport systems for cleaning service companies. As a solution and support in routing processes for the collection of waste, we used the GIS-T TransCAD software version 5.0. with the application of Vehicle Routing Problem with Time Window (VRPTW) routine for the planning, execution and analysis of routes of trucks in Cleaning Company's Free Removal Program Urban -COMLURB in the south of Rio de Janeiro city.

La competitivad industrial y evolución operacional ha generado la necesidad de desarrollo de nuevos e innovadores métodos de sistematización industrial. Como resultado de lo anterior, la presente investigación de carácter mixto propone el desarrollo de un modelo de producción industrial enfocado en la industria 4.0 (Big data) para pymes de confección, para la mejora de procedimientos, puestos de trabajo y costos, a través de las siguientes fases: 1) Recopilación de las informaciones de las bases de datos; 2) Limpieza de bases de datos y correcta edición de informaciones ” 3) Modelado de datos e interrelación de las variables en las bases de datos; 4) Visualización gráfica de datos (Dashboard), apoyado en software Power BI, en la visualización y análisis de los datos; 5) análisis y toma de decisiones. Los resultados obtenidos permitieron generar una mejora de 20% en la identificación de fallas operacionales y no operacionales del proceso industrial interno.