Método para identificar falhas representativas em produtos modulares através da análise de dados de aplicações em campo
Increasingly, manufacturing companies are focusing their efforts on exploring new markets. This new reality makes them strive for more efficient ways to offer their products at a lower cost and without losing their customization. As a result, the compromise between volume and customization (i.e. mas...
| Autor principal: | Cerqueira Filho, Evandro Cavalcante |
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| Formato: | Dissertação |
| Idioma: | Português |
| Publicado em: |
Universidade Tecnológica Federal do Paraná
2020
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| Assuntos: | |
| Acesso em linha: |
http://repositorio.utfpr.edu.br/jspui/handle/1/4902 |
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| Resumo: |
Increasingly, manufacturing companies are focusing their efforts on exploring new markets. This new reality makes them strive for more efficient ways to offer their products at a lower cost and without losing their customization. As a result, the compromise between volume and customization (i.e. mass customization) is necessary and to support these product platforms have become a standard practice in the industry, especially the automotive one. However, another challenge arises with the use of platforms: the lack of an efficient way to develop product platforms that will bring a high level of customer satisfaction. The present work aims to develop a method capable of assisting global project groups for identifying representative failures in modules of product platforms and to set up product variations. It is intended to solve the problem of inefficient platform configuration for different markets, taking into account the specific application characteristics of each one. The methodological procedure is based on the Design Science Research (DSR) framework, according to which the work is carried out in six steps. The demonstration and evaluation steps of the solution were performed in the context of an automotive partner industry. The results show that is possible to use the method as a way to improve product platform configuration. The main contribution comes from the fact that the method performs a data analysis based on actual usage information under different product application conditions. |
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