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Abstract(s)
A presente dissertação, realizada no âmbito do Mestrado em Engenharia Eletrotécnica e de Computadores, foi desenvolvida na empresa Simoldes Plásticos em Oliveira de Azeméis. Com um universo a rondar as 300 máquinas de injeção, o controlo de produção de todas as unidades de produção era feita através de registos manuais realizados pelos operadores em cada posto de trabalho e com o preenchimento manual de indicadores de performance e supervisão em quadros visuais. Assim sendo, no que diz respeito à análise dos dados de produção, tornava-se num processo demasiado complexo, pesado e moroso. Esta dissertação apresenta a implementação de um sistema de aquisição de dados de produção, que se comprometeu a atingir três grandes objetivos: aumentar a performance dos indicadores de desempenho, nomeadamente o OEE (Overall Equipment Effectivenes), através da resposta rápida a problemas de performance; aumentar o desempenho dos colaboradores reduzindo as tarefas administrativas sem valor acrescentado; e aumentar e digitalizar as iniciativas de melhoria contínua, promovendo a partilha de know-how; De forma a dar resposta a este desafio, foi implementada uma plataforma cloud de aquisição de dados denominada proGrow, que possibilitou a visualização de informação agregada em tempo real e a gestão de todo o universo Simoldes numa visão única. A plataforma possibilitou a geração de relatórios de produção automáticos, configurados para acompanharem todas as reuniões de avaliação de desempenho e permitiu a digitalização das iniciativas de melhoria contínua. Espera-se que os resultados obtidos no final do projeto, reflitam impactos positivos para a empresa tanto a nível qualitativo como quantitativo. Face aos três objetivos que se pretendiam atingir, relativamente ao rendimento operacional espera-se um aumento do valor do OEE entre 1% e 1,5%. No que diz respeito ao trabalho administrativo espera-se uma redução em 13 mil horas/ano e por último espera-se um aumento de cerca de 5% relativamente ao valor das poupanças associadas a iniciativas de melhoria contínua.
This dissertation carried out within the scope of master’s degree in Electrical and Computer Engineering, was developed at Simoldes Plásticos in Oliveira de Azeméis. With a universe of around 300 injection machines, the production control of all production units was carried out through manual records carried out by the operators at each workstation and with the manual filling of performance indicators and supervision on visual boards. Therefore, regarding the analysis of production data, it became a complex, cumbersome and time-consuming process. This dissertation reports the implementation of a production data acquisition system, which proposes to digitize this process and commits to achieving three main objectives: to increase the performance of performance indicators, namely the OEE, through the rapid response to problems of performance; increase employee performance as non-valued administrative tasks; and increase and digitize continuous improvement initiatives, promoting the sharing of know-how; In order to respond to this challenge, a cloud platform for data acquisition called proGrow was implemented, which enabled the visualization of aggregated information in real time and the management of the entire Simoldes universe in a single view. The platform enabled the generation of automatic production reports, configured to accompany all performance evaluation meetings and allowed the digitization of continuous improvement initiatives. It is expected that the results obtained at the end of the project, reflect positive impacts for the company both qualitatively and quantitatively. In view of the three goals that were intended to be achieved, in terms of operating income, an increase in the value of the OEE is expected between 1% and 1.5%. Regarding to the administrative work, a reduction of 13 thousand hours/year is expected and, finally, an increase of around 5% is expected in relation to the value of savings associated with continuous improvement initiatives.
This dissertation carried out within the scope of master’s degree in Electrical and Computer Engineering, was developed at Simoldes Plásticos in Oliveira de Azeméis. With a universe of around 300 injection machines, the production control of all production units was carried out through manual records carried out by the operators at each workstation and with the manual filling of performance indicators and supervision on visual boards. Therefore, regarding the analysis of production data, it became a complex, cumbersome and time-consuming process. This dissertation reports the implementation of a production data acquisition system, which proposes to digitize this process and commits to achieving three main objectives: to increase the performance of performance indicators, namely the OEE, through the rapid response to problems of performance; increase employee performance as non-valued administrative tasks; and increase and digitize continuous improvement initiatives, promoting the sharing of know-how; In order to respond to this challenge, a cloud platform for data acquisition called proGrow was implemented, which enabled the visualization of aggregated information in real time and the management of the entire Simoldes universe in a single view. The platform enabled the generation of automatic production reports, configured to accompany all performance evaluation meetings and allowed the digitization of continuous improvement initiatives. It is expected that the results obtained at the end of the project, reflect positive impacts for the company both qualitatively and quantitatively. In view of the three goals that were intended to be achieved, in terms of operating income, an increase in the value of the OEE is expected between 1% and 1.5%. Regarding to the administrative work, a reduction of 13 thousand hours/year is expected and, finally, an increase of around 5% is expected in relation to the value of savings associated with continuous improvement initiatives.
Description
Keywords
Indústria 4.0 Smart Factory Transformação Digital Processo Produção Dados Produção 4.0 Industry Digital Transformation Productive Process Production Data