Método de tomada de decisão AHP aplicado na gestão da manutenção industrial: estudo de caso em uma empresa de bens de consumo
The report “A situação da manutenção no Brasil” ABRAMAN (2017), reports that the electronics sector obtained 136 billion reais in net sales in 2017. Of this total, approximately 9.52 billion reais (7%) were allocated to industrial maintenance. Considering the high amount of financial resources alloc...
| Principais autores: | Oliveira, André Finco de, Bonora, Taicimara Ferreira, Kloss, Thiago de Almeida |
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| Formato: | Trabalho de Conclusão de Curso (Graduação) |
| Idioma: | Português |
| Publicado em: |
Universidade Tecnológica Federal do Paraná
2021
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| Assuntos: | |
| Acesso em linha: |
http://repositorio.utfpr.edu.br/jspui/handle/1/26587 |
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| Resumo: |
The report “A situação da manutenção no Brasil” ABRAMAN (2017), reports that the electronics sector obtained 136 billion reais in net sales in 2017. Of this total, approximately 9.52 billion reais (7%) were allocated to industrial maintenance. Considering the high amount of financial resources allocated for this purpose, maintenance has a strategic function in an industry, as it is responsible for the availability of assets and it is directly related to the productivity, reliability and results of the company. Therefore, for decision-making in the strategic planning of maintenance in an industry, to be carried out according to the established internal objectives, mathematical models of decision making become essential. In order to assist in improving the decision-making process in the maintenance management of a thermoforming machine for the thermoforming process in a consumer goods industry located in the city of Curitiba – PR, the hierarchical decision-making method AHP (Analytic Hierarchy Process) was applied. The result obtained was that predictive maintenance is the most appropriate maintenance strategy to be used on the equipment, with a relative weight of 40.45% compared to other strategies. This result is aligned with the current position of the factory in relation to the asset, and expands the possibilities of deploying new predictive maintenance techniques to monitor the machine concerned. |
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