Six Sigma and Big Data in the industry 4.0 context: systematic literature review and survey on brazilian manufacturing companies
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFSCAR |
Texto Completo: | https://repositorio.ufscar.br/handle/ufscar/15402 |
Resumo: | The development of interconnected, digitized, autonomous and integrated processes in different parts of production systems has been supported by technological advances in Industry 4.0. Industry 4.0 encompasses a wide range of technologies, among which are technologies that support the generation and analysis of large volumes of data in real time, supported by technologies such as Big Data, Big Data Analytics (BDA) and Internet of Things (IoT), which support the search for operational improvements such as optimized flows and real-time anomaly identification. Similar goals are shared by operational improvement methodologies such as Six Sigma (SS) and Lean Six Sigma (LSS), which over the past 3 decades play an important role in process control and improvement following the DMAIC structured method and tools and techniques for data analysis. Technological advances from Industry 4.0 technologies can support and expand the resources of the SS methodology, making it possible to reach other levels of operational performance. To identify the main technologies of Industry 4.0 that can be integrated with the SS methodology, the main relationships and benefits and the future in this field of study, a Systematic Literature Review was carried out considering the Web of Science and Scopus databases. As a result, it was identified that the technologies that most support SS are Big Data, BDA and IoT and that the relationships presented that these technologies positively support data analysis and better decision-making in improvement projects. Considering the evidence of the relationship between the Six Sigma methodology and the BDA, the proposition of hypotheses and a theoretical model were developed with the aim of investigating through a survey of relationships between the practices of BDA, SS and quality and business performance. A survey was carried out with SS specialists from several Brazilian manufacturing companies, in a total of 171 founders. The proposed model and hypotheses were confirmed using the PLS SEM technique, showing that the BDA positively impacts SS practices and when integrated, it has a greater impact on improving quality and business performance. |
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Maia, Daniele dos Reis PereiraLizarelli, Fabiane Letíciahttp://lattes.cnpq.br/4525431419326559http://lattes.cnpq.br/693820574181137330a3ee89-c7d1-4f0e-94f1-6eec0035f5b62021-12-23T10:26:25Z2021-12-23T10:26:25Z2021-10-25MAIA, Daniele dos Reis Pereira. Six Sigma and Big Data in the industry 4.0 context: systematic literature review and survey on brazilian manufacturing companies. 2021. Dissertação (Mestrado em Engenharia de Produção) – Universidade Federal de São Carlos, São Carlos, 2021. Disponível em: https://repositorio.ufscar.br/handle/ufscar/15402.https://repositorio.ufscar.br/handle/ufscar/15402The development of interconnected, digitized, autonomous and integrated processes in different parts of production systems has been supported by technological advances in Industry 4.0. Industry 4.0 encompasses a wide range of technologies, among which are technologies that support the generation and analysis of large volumes of data in real time, supported by technologies such as Big Data, Big Data Analytics (BDA) and Internet of Things (IoT), which support the search for operational improvements such as optimized flows and real-time anomaly identification. Similar goals are shared by operational improvement methodologies such as Six Sigma (SS) and Lean Six Sigma (LSS), which over the past 3 decades play an important role in process control and improvement following the DMAIC structured method and tools and techniques for data analysis. Technological advances from Industry 4.0 technologies can support and expand the resources of the SS methodology, making it possible to reach other levels of operational performance. To identify the main technologies of Industry 4.0 that can be integrated with the SS methodology, the main relationships and benefits and the future in this field of study, a Systematic Literature Review was carried out considering the Web of Science and Scopus databases. As a result, it was identified that the technologies that most support SS are Big Data, BDA and IoT and that the relationships presented that these technologies positively support data analysis and better decision-making in improvement projects. Considering the evidence of the relationship between the Six Sigma methodology and the BDA, the proposition of hypotheses and a theoretical model were developed with the aim of investigating through a survey of relationships between the practices of BDA, SS and quality and business performance. A survey was carried out with SS specialists from several Brazilian manufacturing companies, in a total of 171 founders. The proposed model and hypotheses were confirmed using the PLS SEM technique, showing that the BDA positively impacts SS practices and when integrated, it has a greater impact on improving quality and business performance.O desenvolvimento de processos interconectados, digitalizados, autônomos e integrados em diferentes partes dos sistemas de produção tem se apoiado nos avanços das tecnológicos da Indústria 4.0. A Indústria 4.0 engloba um amplo conjunto de tecnologias, dentre elas, estão as tecnologias que suportam a geração e análise de grandes volumes de dados em tempo real, apoiados por tecnologias como Big Data, Big Data Analytics (BDA) e Internet das coisas (IoT), que dão suporte à busca por melhorias operacionais como fluxos otimizados e identificação de anomalias em tempo real. Objetivos semelhantes são compartilhadas por metodologias de melhoria operacional, como Seis Sigma (SS) e Lean Seis Sigma (LSS), que durante as últimas 3 décadas desempenham um papel importante no controle e melhoria da dos processos seguindo o método estruturado DMAIC e ferramentas e técnicas para análise de dados. Os avanços tecnológicos provenientes das tecnologias da Indústria 4.0 podem apoiar e ampliar os recursos da metodologia SS, possibilitando atingir outros patamares de desempenho operacional. Para identificar as principais tecnologias da Indústria 4.0 que podem ser integradas com a metodologia SS, as principais relações e benefícios e as direções futuras neste campo de estudo, foi realizada uma Revisão Sistemática da Literatura considerando as bases de dados Web of Science e Scopus. Como resultado foram identificadas que as tecnologias que mais apoiam o SS são Big Data, BDA e IoT e que as relações mostram que estas tecnologias suportam positivamente a análise de dados e a melhor tomada de decisões nos projetos de melhoria. Consideradas as evidências da relação da metodologia Seis Sigma com o BDA, foi desenvolvida a proposição de hipóteses e de um modelo teórico com o objetivo de investigar por meio de uma Survey as relações entre as práticas de BDA, SS e desempenho da qualidade e do negócio. A pesquisa foi realizada com especialistas SS de diversas empresas de manufatura brasileiras, em um total de 171 respondentes. O modelo proposto e as hipóteses foram confirmadas por meio da técnica PLS-SEM, mostrando que o BDA impacta positivamente as práticas SS e quando integradas, tem maior impacto na melhoria do desempenho da qualidade e do negócio.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)CAPES: Código de Financiamento 001engUniversidade Federal de São CarlosCâmpus São CarlosPrograma de Pós-Graduação em Engenharia de Produção - PPGEPUFSCarAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessSeis SigmaLean Seis SigmaDesempenhoSix SigmaLean Six SigmaBig DataPerformanceDMAICENGENHARIAS::ENGENHARIA DE PRODUCAO::GERENCIA DE PRODUCAOSix Sigma and Big Data in the industry 4.0 context: systematic literature review and survey on brazilian manufacturing companiesSix Sigma e Big Data no contexto da indústria 4.0: revisão sistemática da literatura e survey sobre empresas brasileiras de manufaturainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis600600df609b60-ec93-4d6a-bf8f-caa49ff7a0f0reponame:Repositório Institucional da UFSCARinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCARORIGINALDissertação - Daniele dos Reis Pereira Maia.pdfDissertação - Daniele dos Reis Pereira Maia.pdfapplication/pdf1354054https://repositorio.ufscar.br/bitstream/ufscar/15402/1/Disserta%c3%a7%c3%a3o%20-%20Daniele%20dos%20Reis%20Pereira%20Maia.pdf10de62f33952afb4c085c18a8682322aMD512. 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dc.title.eng.fl_str_mv |
Six Sigma and Big Data in the industry 4.0 context: systematic literature review and survey on brazilian manufacturing companies |
dc.title.alternative.por.fl_str_mv |
Six Sigma e Big Data no contexto da indústria 4.0: revisão sistemática da literatura e survey sobre empresas brasileiras de manufatura |
title |
Six Sigma and Big Data in the industry 4.0 context: systematic literature review and survey on brazilian manufacturing companies |
spellingShingle |
Six Sigma and Big Data in the industry 4.0 context: systematic literature review and survey on brazilian manufacturing companies Maia, Daniele dos Reis Pereira Seis Sigma Lean Seis Sigma Desempenho Six Sigma Lean Six Sigma Big Data Performance DMAIC ENGENHARIAS::ENGENHARIA DE PRODUCAO::GERENCIA DE PRODUCAO |
title_short |
Six Sigma and Big Data in the industry 4.0 context: systematic literature review and survey on brazilian manufacturing companies |
title_full |
Six Sigma and Big Data in the industry 4.0 context: systematic literature review and survey on brazilian manufacturing companies |
title_fullStr |
Six Sigma and Big Data in the industry 4.0 context: systematic literature review and survey on brazilian manufacturing companies |
title_full_unstemmed |
Six Sigma and Big Data in the industry 4.0 context: systematic literature review and survey on brazilian manufacturing companies |
title_sort |
Six Sigma and Big Data in the industry 4.0 context: systematic literature review and survey on brazilian manufacturing companies |
author |
Maia, Daniele dos Reis Pereira |
author_facet |
Maia, Daniele dos Reis Pereira |
author_role |
author |
dc.contributor.authorlattes.por.fl_str_mv |
http://lattes.cnpq.br/6938205741811373 |
dc.contributor.author.fl_str_mv |
Maia, Daniele dos Reis Pereira |
dc.contributor.advisor1.fl_str_mv |
Lizarelli, Fabiane Letícia |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/4525431419326559 |
dc.contributor.authorID.fl_str_mv |
30a3ee89-c7d1-4f0e-94f1-6eec0035f5b6 |
contributor_str_mv |
Lizarelli, Fabiane Letícia |
dc.subject.por.fl_str_mv |
Seis Sigma Lean Seis Sigma Desempenho |
topic |
Seis Sigma Lean Seis Sigma Desempenho Six Sigma Lean Six Sigma Big Data Performance DMAIC ENGENHARIAS::ENGENHARIA DE PRODUCAO::GERENCIA DE PRODUCAO |
dc.subject.eng.fl_str_mv |
Six Sigma Lean Six Sigma Big Data Performance DMAIC |
dc.subject.cnpq.fl_str_mv |
ENGENHARIAS::ENGENHARIA DE PRODUCAO::GERENCIA DE PRODUCAO |
description |
The development of interconnected, digitized, autonomous and integrated processes in different parts of production systems has been supported by technological advances in Industry 4.0. Industry 4.0 encompasses a wide range of technologies, among which are technologies that support the generation and analysis of large volumes of data in real time, supported by technologies such as Big Data, Big Data Analytics (BDA) and Internet of Things (IoT), which support the search for operational improvements such as optimized flows and real-time anomaly identification. Similar goals are shared by operational improvement methodologies such as Six Sigma (SS) and Lean Six Sigma (LSS), which over the past 3 decades play an important role in process control and improvement following the DMAIC structured method and tools and techniques for data analysis. Technological advances from Industry 4.0 technologies can support and expand the resources of the SS methodology, making it possible to reach other levels of operational performance. To identify the main technologies of Industry 4.0 that can be integrated with the SS methodology, the main relationships and benefits and the future in this field of study, a Systematic Literature Review was carried out considering the Web of Science and Scopus databases. As a result, it was identified that the technologies that most support SS are Big Data, BDA and IoT and that the relationships presented that these technologies positively support data analysis and better decision-making in improvement projects. Considering the evidence of the relationship between the Six Sigma methodology and the BDA, the proposition of hypotheses and a theoretical model were developed with the aim of investigating through a survey of relationships between the practices of BDA, SS and quality and business performance. A survey was carried out with SS specialists from several Brazilian manufacturing companies, in a total of 171 founders. The proposed model and hypotheses were confirmed using the PLS SEM technique, showing that the BDA positively impacts SS practices and when integrated, it has a greater impact on improving quality and business performance. |
publishDate |
2021 |
dc.date.accessioned.fl_str_mv |
2021-12-23T10:26:25Z |
dc.date.available.fl_str_mv |
2021-12-23T10:26:25Z |
dc.date.issued.fl_str_mv |
2021-10-25 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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masterThesis |
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publishedVersion |
dc.identifier.citation.fl_str_mv |
MAIA, Daniele dos Reis Pereira. Six Sigma and Big Data in the industry 4.0 context: systematic literature review and survey on brazilian manufacturing companies. 2021. Dissertação (Mestrado em Engenharia de Produção) – Universidade Federal de São Carlos, São Carlos, 2021. Disponível em: https://repositorio.ufscar.br/handle/ufscar/15402. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufscar.br/handle/ufscar/15402 |
identifier_str_mv |
MAIA, Daniele dos Reis Pereira. Six Sigma and Big Data in the industry 4.0 context: systematic literature review and survey on brazilian manufacturing companies. 2021. Dissertação (Mestrado em Engenharia de Produção) – Universidade Federal de São Carlos, São Carlos, 2021. Disponível em: https://repositorio.ufscar.br/handle/ufscar/15402. |
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https://repositorio.ufscar.br/handle/ufscar/15402 |
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eng |
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eng |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
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openAccess |
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Universidade Federal de São Carlos Câmpus São Carlos |
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Programa de Pós-Graduação em Engenharia de Produção - PPGEP |
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Universidade Federal de São Carlos Câmpus São Carlos |
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