Estudo de alocação de programas de melhoria em um ambiente flow shop para redução do lead time

• Ano de defesa: 2012
• Autor(a) principal: Utiyama, Marcel Heimar Ribeiro
• Orientador(a): Godinho Filho, Moacir
• 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; Planejamento e controle da produção; Teoria das restrições (Administração); Manufatura enxuta; Dinâmica de sistemas; Alocação de programas de melhoria; Factory Physics
• Palavras-chave em Inglês: Improvement allocation programs; Lead time; Theory of constraints; Lean manufacturing; System dynamics; Factory physics
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/3690

ABSTRACT Lead time reduction results in significant gains. This reduction is achieved through improvements programs in six different shop floor parameters. Two approaches, namely, Lean Manufacturing and Theory of Constraints have different tools and techniques with different improvement allocations in a production line. The tools and techniques of the first suggest improvements spread along the line. The second one advocates that the focus of improvements should be just on the constraint resource. In this context, this study investigates what is the best allocation of improvement programs regarding lead time reduction, a large improvement on the constraint resource, or, alternatively, small improvements on all workstations. In the present study, a quantitative model that relates System Dynamics (Forrester, 1961) and Factory Physics (Hopp; Spearman, 2001) approaches, created by Godinho Filho and Uzsoy (2008a; 2008b; 2009) is used. The results indicate that when constraint resource utilization is high and the difference in the utilization between constrained resource and non-constrained resource is significant (over 5%), a large investment constraint resource option is best. As the utilization of the constrained resource and the difference between the utilization of constrained and non-constrained resources decreases, the improvement program at various points of the line begins to behave better. This decrease in the difference between the utilization of constrained and non-constrained resource can be understood as a trend toward a balanced line. Lastly, if in practice, there is a balanced line and the constrained resource prevails over the non-constrained resource, the best option is a great concentrated investment at the constrained resource. However, might be situations where a great investment at the constrained resource is impracticable and the results of this work suggest that an hybrid approach is interesting when the constrained resource have higher utilizations (99,8% and 99,5%), already, when the utilization of the constrained resource is 90,5% a good option is the allocation of the improvement programs at various points of the line.