Análise, modelagem e simulação de um processo produtivo de massa de linguiça frescal

• Ano de defesa: 2024
• Autor(a) principal: Melo, Lorrana Magalhães
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso embargado
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Engenharia de Alimentos
• Palavras-chave em Português: ARENA; gargalos; dados; embutidos; frigorífico; software; bottlenecks; data; sausages; slaughterhouse; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO::GERENCIA DE PRODUCAO; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO::ENGENHARIA DO PRODUTO::PROCESSOS DE TRABALHO; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO::PESQUISA OPERACIONAL::PROCESSOS ESTOCASTICOS E TEORIAS DA FILAS; Alimentos - Indústria; Simulação (Computadores digitais); Alimentos - Biotecnologia; Alimentos - Controle de produção; ODS::ODS 12. Consumo e produção responsáveis - Assegurar padrões de produção e de consumo sustentáveis.
• Link de acesso: https://repositorio.ufu.br/handle/123456789/41291; http://doi.org/10.14393/ufu.di.2024.32

Computer simulation softwares are tools with the potential to directly assist a company's production line. They can be applied to reduce costs, constant improvement and maximize production. The positive point of simulation is modeling a production process while maintaining a controllable environment, testing modifications and analyzing results without changing the real structure of the process. In this work, we sought to identify, simulate and analyze the results of a production process for fresh sausage dough in a slaughterhouse in Minas Gerais. The identification of process variables took place through on-site visits and analysis of the occupation time of each piece of machinery using a stopwatch. Statistical analysis was carried out with the chi-square test and simulation of the process using the student version of the ARENA software; with the simulations, it was possible to define scenarios through the identified bottlenecks, working with a scenario with more equipment, for example. It was possible to identify that the biggest bottleneck in the plant is the equipment called mixer and that there were two scenarios that suited the manufacturing plant, the first containing two mixers and the second containing two grinders, two brine teams and two mixers. These scenarios were better suited due to the potential reduction in waiting time for queues formed at the equipment (from 30 minutes to 9 and 2 minutes, respectively) and guarantee a lighter operating time for the equipment and employees (98.2% of occupancy to 59.3%). Therefore, the use of software to simulate the process was of great value, identifying industrial bottlenecks and simulating scenarios with more aligned production.