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Field surveys are frequently used to monitor quality levels in the automotive industry. They generate models that reveal the factors which drive customer satisfaction and purchase intent. In this way, companies can determine the areas which require improvement actions or planning for future products that are better suited for customer expectations. Unlike standard regression models, the binary logistic regression is appropriate for non-continuous binary responses. It matches customer satisfaction metrics, which can be evaluated as either “satisfied” or “not satisfied”. This paper presents the binary logistic regression as an alternative to construct customer satisfaction models. A case study of the analysis of a vehicle in the Brazilian market is used to illustrate its application.

This paper describes the steps utilized in the development of the Flex Fuel program by the Ford South America Product Development team to implement a reliability plan. A reliability plan, understood as series of tools to avoid failure mode occurrence, is particularly important when introducing a new technology. Robustness, as the ability of a system to perform its intended function in the presence of variable operational conditions, is contained in the reliability concept and is a key aspect of this plan. Several factors that could affect the vehicle performance were listed, classified and prioritized in order to establish a preventive action plan. The tools were used first at the vehicle level, and then cascaded down to subsystem and component level. Also, with the results of this analysis, design verification methods were enhanced to capture real world usage conditions.