Abstract
Industry 4.0 has been discussed in the scientific community since its inception in 2011. Discussions have focused on characterising what Industry 4.0 is. This industry will increasingly require professionals to perform complex and indirect tasks, such as working together with machines in their daily work. This article aims to analyse the skills and competencies required by Industry 4.0, and to compare them with the scope of production engineering disciplines in six Brazilian universities. As a methodological option, the study is classified as exploratory, bibliographic, and qualitative. It is established that the current syllabus of production engineering courses of Brazilian universities needs to be improved, seeking alignment with the skills and competencies required by Industry 4.0. Although the Industry 4.0 theme is being discussed in the scientific field in Brazilian universities, it is not yet possible to identify any key actions taken to adjust and improve production engineering courses.
Highlights
Innovation is the engine that increases industry’s competitiveness
We found that most of the disciplines in the production engineering courses focus on production processes, while only a small number of the disciplines related to computer science and statistics, which are among the pillars of Industry 4.0, appear in production engineering courses
The curricula of the production engineering courses of some of the analysed universities underwent revision in 2017, it is not clear that this revision has taken into account the skills and competencies required by Industry 4.0
Summary
Innovation is the engine that increases industry’s competitiveness. Over the centuries, industrial revolutions have taken place, ranging from the mechanisation of artisanal manufacturing to the automation of processes. The first industrial revolution occurred in England in the late eighteenth century, and was marked by innovation and the mechanisation of manual labour, focusing on increasing production through increased efficiency, speed, and quantity. This was in marked contrast with the practice that preceded it, in which production was carried out in an artisanal way. Probability and electrical, mechanical, and statistics computer components Statistical methods and Applied engineering statistics data analysis techniques Develop innovative. Complex problems need to be solved more efficiently, for example by analysing Time series analysis increasing amounts of data Article: [25]
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