Six Sigma and Big Data in the industry 4.0 context: systematic literature review and survey on brazilian manufacturing companies

• Ano de defesa: 2021
• Autor(a) principal: Maia, Daniele dos Reis Pereira
• Orientador(a): Lizarelli, Fabiane Letícia
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia de Produção - PPGEP
• Palavras-chave em Português: Seis Sigma; Lean Seis Sigma; Desempenho
• Palavras-chave em Inglês: Six Sigma; Lean Six Sigma; Big Data; Performance; DMAIC
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA DE PRODUCAO::GERENCIA DE PRODUCAO
• Link de acesso: https://repositorio.ufscar.br/handle/ufscar/15402

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.