This study analyses the Brazilian auto-industry based on Porter's diamond model, looking for the four competitive advantage factors and how the new automotive industrial policy (INOVAR-AUTO), recently launched in 2012, impacts on the local automobile industry. Results show that the Brazilian government's goal is in line with Porter's theoretical model, but it leads us to conclude that Brazil needs to devise, design and implement a focused and integrated industrial policy of research and development (R&D) in the country. The survey, conducted with Asian new comer car and auto-part manufacturers, shows they have quickly adapted to the new policy changes of INOVAR-AUTO without consolidating a real local R&D function. The absence of such a technological specialisation policy increases the chances for Brazil to become a centre of production and commercialisation of vehicles conceived externally and produced with increasingly less local content and local design with higher prices due to less competition.

As global competition intensifies, transnational corporations are internationalising even the most knowledge-intensive corporate functions, such as research and development (R&D). Today, corporations in industries such as automobiles are establishing R&D facilities in selected developing countries. They do this to enhance their efficiency, to access expanding pools of scientists and engineers, and to meet the demands in these countries. These recent trends have important implications for the international division of labour and stress the need for coherent national policies to ensure benefits from this evolution. This study aims to identify the localisation of product development activities based on Porter's (1998) diamond model for competitive advantage of location, and Kuchiki's (2005) flowchart approach for government policies. The analysis is based on case studies of automobile assemblers that have headquarters in Triad markets and facilities in Brazil. Brazilian competitive advantages of location are investigated, and implications for host government policies proposed, from the viewpoint of leveraging location specific advantages for R&D localisation.

The Brazilian Automotive and Aeronautic Industries are both important sectors for the National Innovation System. Through this research, we aim to understand the Innovation System of both sectors in order to collect lessons learned for the Automotive Industry Innovation Policy called Inovar-Auto (Innovate-Auto), which was effective from the beginning of 2013 until the end of 2017. This study was conducted based on the quantitative analysis of sectoral investment in R&D and the number of patents as well as on the qualitative analysis of some critical projects performed by companies in both industry sectors, the Automotive and the Aeronautic. All data were collected via interviews with the agents of the sectoral innovation system. This article aims to contribute to the analysis of a sectoral innovation system based on an analytical tool to measure the maturity level on specific key variables.

To enhance the competitiveness of the Brazilian automotive industry, the government approved a new automotive policy in October 2012. It is called the Inovar-Auto program and will be implemented over the course of five years from 2013 to 2017. This program encourages automakers and their suppliers to produce more efficient, technologically advanced vehicles and auto parts by investing in R&D and engineering. The objective of this paper is to determine whether the Inovar-Auto program will effectively improve the competitiveness of the Brazilian automotive industry. In this sense, the impact of each Inovar-Auto requirement in relation to the Porter's diamond model and flowchart approach model was investigated. Although the Inovar-Auto program represents an advance over previous industrial policies, particularly in relation to aspects concerning improvement in energy efficiency, encouraging innovation, increased local content, the traceability of auto-parts and supplier development policy, the result of this research indicates that its implementation has not been stimulating a substantial evolution on the competitiveness of the Brazilian automotive industry.

Background: The growing complexity of production processes in the metal–mechanical industry demands ever more effective strategies for managing machine and equipment maintenance, as unexpected failures can incur high operational costs and compromise productivity by interrupting workflows and delaying deliveries. However, few studies have combined end-to-end data analytics and data mining methods to proactively predict and mitigate such failures. This study aims to develop and validate a comprehensive framework combining data analytics and data mining to prevent machine failures and support decision-making in a metal–mechanical manufacturing environment. Methods: First, exploratory data analytics were performed on the sensor and logistics data to identify significant relationships and trends between variables. Next, a preprocessing pipeline including data cleaning, data transformation, feature selection, and resampling was applied. Finally, a decision tree model was trained to identify conditions prone to failures, enabling not only predictions but also the explicit representation of knowledge in the form of decision rules. Results: The outstanding performance of the decision tree (82.1% accuracy and a Kappa index of 78.5%), which was modeled from preprocessed data and the insights produced by data analytics, demonstrates its ability to generate reliable rules for predicting failures to support decision-making. The implementation of the proposed framework enables the optimization of predictive maintenance strategies, effectively reducing unplanned downtimes and enhancing the reliability of production processes in the metal–mechanical industry.