Desenvolvimento de indicadores da manufatura enxuta utilizando ferramentas de business intelligence : uma aplicação na manufatura de calçados

• Ano de defesa: 2009
• Autor(a) principal: Escodeiro, José Roberto
• Orientador(a): Pereira, Neocles Alves
• 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: Engenharia de produção; Produção enxuta; Data warehouse; Indústria do calçado; Manufatura Enxuta (ME); Business Intelligence (BI)
• Palavras-chave em Inglês: Lean Manufacture (LM); Shoe industry
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: https://repositorio.ufscar.br/handle/ufscar/3604

In the past decades companies around the world have implemented their concepts of Lean Manufacture (LM) with their main focus on business. Together with LM s implementation comes the need of continuous improvement as several sources and historical data volume originate from its processing. Whenever those historical data are, for any reason, discarded without generating indicators the oportunity of transforming data into strategic information is missed. Such situation brings about the need of an adequate performance measurement system of LM. Having this constant monitoring need of LM s performance in mind as a strategic mean for a company to achieve competitivity in the market, this paper aims to develop through Information Technology (IT) and the tools of Business Intelligence (BI) a proposal of managing performance indicators of LM present in shoe production, as an alternative to improve decision making. In order to develop this research, an overview of the literature of LM, BI, Information Systems (IS), performance indicators and shoe manufacturing from the basic concept of LM is offered that, on the other hand, has to do with cost reduction and total cut of waste. This study leads to the seven waste groups of LM, which, together with other published works, can guide and focus so as to reach indicators. By setting indicators and pointing strategy, and also collecting data, the next step is modelling and developing data load in dimentional format prepared to use BI s tools as On-line Analytical Processing (OLAP). Finally, the application is tested in a shoe industry with the load of waste indicator for over production. The final result of the research is a series of analyses of waste information for over production as well as the contribution of the proposed method in terms of easiness, flexibility and practical availability in companies.