Behavioral training of engineering professionals and students for Industry 4.0

Fernandes, Maria Ângela de S.; Rodrigues, Ricardo C.; Antunes, Adelaide Maria S.

doi:10.1590/1678-6971/eRAMR230084.en

Maria Ângela de S. Fernandes

National Institute of Industrial Property, Rio de Janeiro, RJ, Brazil

Authors’ notes Maria Ângela de S. Fernandes is now an innovation maturity assessment at Foundation for Supporting the Development of Science and Technology (Fundação de Apoio ao Desenvolvimento da Ciência e Tecnologia – Facto); Ricardo C. Rodrigues is now an innovation and development coordinator at the Academy of Intellectual Property, Innovation, and Development of National Institute of Industrial Property (Instituto Nacional da Propriedade Industrial – Inpi); Adelaide Maria S. Antunes is now a senior specialist at the Academy of Intellectual Property, Innovation, and Development of Inpi.

https://orcid.org/0000-0001-6374-6051

Ricardo C. Rodrigues Correspondence concerning this article should be addressed to Ricardo C. Rodrigues, Rua Mayrink Veiga, 9, Centro, Rio de Janeiro, Rio de Janeiro, Brazil, ZIP code 20090-910. Email: ricardo.rodrigues@inpi.gov.br

National Institute of Industrial Property, Rio de Janeiro, RJ, Brazil

https://orcid.org/0000-0002-2614-4323

Adelaide Maria S. Antunes

National Institute of Industrial Property, Rio de Janeiro, RJ, Brazil

https://orcid.org/0000-0002-2245-7517

Correspondence concerning this article should be addressed to Ricardo C. Rodrigues, Rua Mayrink Veiga, 9, Centro, Rio de Janeiro, Rio de Janeiro, Brazil, ZIP code 20090-910. Email: ricardo.rodrigues@inpi.gov.br

Authors’ notes

Maria Ângela de S. Fernandes is now an innovation maturity assessment at Foundation for Supporting the Development of Science and Technology (Fundação de Apoio ao Desenvolvimento da Ciência e Tecnologia – Facto); Ricardo C. Rodrigues is now an innovation and development coordinator at the Academy of Intellectual Property, Innovation, and Development of National Institute of Industrial Property (Instituto Nacional da Propriedade Industrial – Inpi); Adelaide Maria S. Antunes is now a senior specialist at the Academy of Intellectual Property, Innovation, and Development of Inpi.

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Gilberto Perez

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Gabriel Henrique Carille

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Big eight behavioral competencies	Description
1. Supporting and cooperating	Supports and demonstrates respect in teamwork.
2. Interacting and presenting	Communicates and influences others with confidence.
3. Analyzing and interpreting	Demonstrates analytical thinking for complex problem solving and is quick to assimilate new technologies.
4. Creating and conceptualizing	Actively seeks new learning opportunities and employs broad, strategic thinking.
5. Organizing and executing	Provides services or products that meet the agreed quality standards.
6. Adapting and coping	Adapts and responds well to change and pressure.
7. Enterprising and performing	Shows understanding of business and finance and seeks out opportunities for their own career development.
8. Leading and deciding	Takes control and exercises leadership, initiates action, gives direction, and takes responsibility.

Level 1	Level 2	Level 3
Big eight behavioral competencies	Competency dimensions	Competencies
Big eight behavioral competencies	Competency dimensions	Information Systems	Computer Science	Engineering
Supporting and cooperating	Adhering to principles and values	Ethics, environmental and ergonomic awareness
Supporting and cooperating	Working well with people	Teamwork, collaboration, communication
Interacting and presenting	Networking	Compromising, networking, customer relations
	Influencing	Negotiation, emotional intelligence
	Presenting information	Presenting and communicating
Analyzing and interpreting	Writing and reporting	Technical communication, speaking/writing skills
	Applying expertise and technology	Information and technology; economics extracts business value from media
		Service orientation/product-service offerings	Network security
		Business process management	Information technology (IT) architecture
		Business change management	Machine learning
		Coordination of workflows
Analyzing and interpreting			System development; technology integration
			Mobile technologies, sensors/embedded systems
			Network technology, machine-to-machine (M2M) communication
			Robotics and artificial intelligence
			Predictive maintenance
		Modeling and programming, big data analysis
		Cloud computing/architectures, databases
		Statistics/data security
	Analyzing	Problem solving, optimization, analytical skills, cognition
Creating and conceptualizing	Learning and researching	Lifelong learning, knowledge management
	Creating and innovating	Innovating, creativity, critical thinking, change management
	Formulating strategies and concepts	Business strategy, abstraction, managing complexity
Organizing and executing	Planning and organizing	Project management, planning and organizing work, management
	Delivering results and meeting customer expectations	Customer orientation, customer relationship management
	Following instructions and procedures	Legislation, security, individual responsibility
Adapting and coping	Adapting to change	Work in an interdisciplinary environment, interculturality, flexibility, open-mindedness
Adapting and coping	Persuading and influencing	Work-life balance
Enterprising and performing	Achieving goals and objectives	Self-management
Enterprising and performing	Entrepreneurial thinking	Understanding of business model, entrepreneurship
Leading and deciding	Deciding and initiating action	Decision-making, taking on responsibility
Leading and deciding	Leading and supervising	Leadership

Employing competency-based training	Engineering education should be based on competencies involving the diversity of people’s expectations and behaviors. Techniques should be used to transform observation into an economically viable problem formulation and problem-solving, with the application of technologies to meet user and market demands.
Adopting innovative methodologies	This implies adopting teaching methodologies that better meet the needs of the global reality, combined with developing behavioral competencies and motivating students to seek out different sources of input. The idea is to make the engineering learning process more dynamic and autonomous, engaging students in solving concrete problems that require interdisciplinary knowledge in a bid to raise the quality of teaching and reduce drop-out rates.
Encouraging innovative institutional policies	The idea is to promote a greater diversity of engineers to meet the different needs of society, such as housing, security, education, and health. The new guidelines are more flexible so that each higher education institution can structure its courses as it wishes to foster the competencies needed for the graduates of each program.
Managing the learning process	The new curriculum guidelines are designed to encourage the development of a culture within higher education institutions that fosters the management of competency development. This means developing academic and professional profiles that are aligned with international benchmarks and training students to work effectively in the area of engineering anywhere in the world.
Strengthening ties with different organizations	The programs should promote interactions with organizations for the development of projects of mutual interest while also encouraging the students’ final-year projects to address concrete problems faced by companies.
Valuing the qualification of professors	Professors should be encouraged to train in new teaching/learning methods and strategies, develop their pedagogic and academic management skills, strike a balance between functional incentives, academic incentives, research funding, outreach, and teaching activities and involve business professionals in academic activities.

Level of quality of evidence	Definition
High	High level of confidence in the quality of the evidence.
Moderate	Moderate level of confidence in the quality of the evidence: estimate is very similar to reality but could diverge.
Low	Low level of confidence in the quality of the evidence: estimate may diverge from reality.
Very low	Very low level of confidence in the quality of the evidence: estimate may diverge substantially from reality.

Engineer	Machine (AI application)
Defines problem and expresses objective: makes a decision regarding the development of new products.	Checks for permission and infers the engineer’s intention and knowledge.
Expresses questions: what types of material are most suitable for the top and bottom coatings of the product?	Receives and processes information – presents a drawing and list of materials.
Provides information: conductivity/elasticity parameters of the material for the upper and lower coating of the product cylinder.	Processes information and offers alternatives for decision-making. Presents options of materials, suggesting which is best. Presents a video with a performance test, similar product(s), and suppliers and awaits approval from the engineer before taking further action.
Perceives the machine’s behavior, perceives the information presented and monitors and requests information: VR simulation/3D prototyping of product to gain insights on its handling and performance; quality simulation of the complete product using AR to check for potential gaps.	Does a simulation, generates a 3D prototype of the product and advises the engineer as to the correct way to handle the product.
Checks inferences: production cost.	Presents a holographic projection of the production area, monitors movements of other production workers and requests collaboration.
Makes a decision.

Great eight competencies	Relevant information extracted from interviews with leaders from the schools of engineering of UFRJ	Level of quality of evidence
1. Supporting and cooperating	[Our] alliances have been with CREA [engineering council], the Engineers’ Club, and companies. The university’s portfolio of alliances comes from research projects and the hiring of students by partner companies, which see the university as an opportunity to publicize their selection processes and need students to fill their vacancies.	Moderate confidence in the quality of evidence, but the reality could be different.
2. Interacting and presenting	A plan to expand our interactions would be important to showcase the students’ true profile. This is being planned by the coordinators of the internships, and once our partner companies have been analyzed, it will be possible to prospect other companies.	Low confidence in the quality of evidence, the reality may be substantially different.
3. Analyzing and interpreting	As for Industry 4.0 and AI in engineering education, this means taking action to include, in the courses taught, scientific computing and tools that can introduce the students to this expertise. The actions should be executed by the faculty, provided they are properly incorporated into the course syllabuses and the descriptions of the courses that use these approaches. Many tasks that engineers perform aren’t core engineering activities, which have to do with thinking about systems, creating and proposing solutions, identifying and analyzing problems, and innovating. In other words, everything a machine can do is not what an engineer does. In their continuing education, engineers should aim to be like a conductor, conducting an orchestra of different types of expertise, and every virtuoso conductor begins being an expert in one musical instrument (degree in Engineering).	Moderate confidence in the quality of evidence, but the reality could be different.
4. Creating and conceptualizing	Engineering is, by definition, multidisciplinary. It’s been broken down by an excess of the scientific method applied to the professional area – the optimal method for research but really bad for doing a project (design). We have to get back to the method of developing solutions for problems, identifying problems, studying problems, identifying requirements, devising solutions, scaling up projects (design), studying their feasibility (now with multiple criteria, sustainable), and detailing their execution. The curriculum reform is moving in this direction.	Moderate confidence in the quality of evidence, but the reality could be different.
5. Organizing and executing	There are a lot of methodologies to bring about a reformulation of the teaching and training methods for these professionals, always maintaining the relationship between practice and theory: active methodologies, integrated projects, flipped classroom, use of blogs, robotics, Moodle, modeling, videos, forums, group problem-solving, workshops.	Low confidence in the quality of evidence, the reality may be substantially different.
6. Adapting and coping	The students who drop out of the program are the ones who are extremely intelligent, and so they stand out, even on a course that wasn’t their first choice. When they realize this, they drop out in search of their real aptitudes. I think the main way of measuring a student’s success during an Engineering program is whether they enter the market, whether it is through internships during the program or even getting a job after completing the program. Today, about 80% of our students take an internship in companies, and at the end of the program, 70% are employed, and 10% do graduate studies. It’s become standard practice to analyze the student’s average grade, which is actually valued more by the professors than by the market, which evaluates other skills.	Moderate confidence in the quality of evidence, but the reality could be different.
7. Enterprising and performing	No evidence.
8. Leading and deciding	As for encouraging students to monitor their own learning/performance throughout the program, it’s tricky. If it isn’t very clearly structured, pedagogically and psychologically speaking, it can make them frustrated and even hinder the process. I think the best thing is to oversight together with a professor because then the student can understand their real potential according to what stage of life they’re at and what their current needs are.	Low confidence in the quality of evidence, the reality may be substantially different.

[1] Correspondence concerning this article should be addressed to Ricardo C. Rodrigues, Rua Mayrink Veiga, 9, Centro, Rio de Janeiro, Rio de Janeiro, Brazil, ZIP code 20090-910. Email: ricardo.rodrigues@inpi.gov.br

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Behavioral training of engineering professionals and students for Industry 4.0

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