Identificação dos fatores críticos para a aplicação de técnicas multivariadas em projetos Seis Sigma : estudo de casos

• Ano de defesa: 2012
• Autor(a) principal: Soriano, Fabiano Rodrigues
• Orientador(a): Oprime, Pedro Carlos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia de Produção - PPGEP
• País: BR
• Palavras-chave em Português: Gestão da qualidade; Análise multivariada; Fatores críticos de sucesso; Melhoria contínua; Seis sigma
• Palavras-chave em Inglês: Multivariate data analysis (MDAs); Critical factors; Continuous improvement (CI); Six sigma project
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: https://repositorio.ufscar.br/handle/ufscar/3689

The organizations face problematic situations that involve the analysis of a significant number of correlated variables to reduce the variability of production processes. There are many approaches, methods and techniques to support problem solving, however has achieved distinction in literature and adopted by Six Sigma companies, one of whose goals is to identify and eliminate the causes of variation in product and process through the use of techniques statistics. The Multivariate Data Analysis (MDAs) belongs to a set of techniques that examine both the relationship between several variables, have not been included in intensive training programs in Six Sigma. This research is qualitative and descriptive, whose purpose is to confirm the dimensions criticism from Firka (2011) and Montgomery (2010) agree that the use of statistical methods to problems of manufacturing in the context of Six Sigma programs, through the study of multiple cases. These research findings confirm the association between the use of technical barriers in the MDAs to factors of management and sociology, such as the lack of management support, focus on short-term results, the methodological factors (selection and validation of variables and results) and the statistical assumptions (multivariate normality, multicollinearity and homoscedasticity).