New Professional Competencies and Skills Leaning towards
Industry 4.0
Roque Antônio Moura
1,2 a
, Marco Rogério Silva Richetto
1b
, Daniela Ercole Dale Luche
1c
,
Luiz Antonio Tozi
2
and Messias Borges Silva
1d
1
State University of São Paulo Júlio de Mesquita Filho (UNESP), Guaratinguetá, São Paulo, Brazil
2
Technology College of São José dos Campos Prof. Jessen Vidal - FATEC SJC, São Paulo, Brazil
luiz.tozi@fatec.sp.gov.br, messias.silva@unesp.br
Keywords: Employability, Human Resources, Industry 4.0, New Competencies, New Skills.
Abstract: Industry 4.0, the so-called fourth industrial revolution, has been popularized by a German government project
to promote digitization and automation, and has become a global strategy disseminated throughout several
countries. It is widely used by researchers and mentors in different contexts and studies on opportunities, risks
and challenges concerning employability. Therefore, this study aims to address the challenges of recruiters in
the face of trends in new skills, profiles, and professional competencies for safeguarding jobs. Its method
involves a systematic literature review using the keywords “Human and Machine Learning” and “Industry
4.0 and Education”. Bibliometrics was performed on works published on the theme during the latest decade.
Results demonstrate the importance of updating Human Resources and Educational policies within the
development of new skills and competencies to meet the new professional profile requirements and ensure
human employability.
1 INTRODUCTION
Mastery of technologies to produce goods and
services is the backbone of a nation's economy
(Sivathanu and Pillai, 2018). All industrial
revolutions influenced methods of production, the
labor market and the educational system, due to
changes in the manner of producing (Longo, Nicoletti
and Padovano, 2017).
In each of the four industrial revolutions, there
have been resulting in the extinction of some
professions and the generation of others, therefore it
is necessary to update the way of teaching because of
new technologies requires more qualified employees
(Crawley et al., 2014; (Benešová and Tupa, 2017).
The fourth industrial revolution conceptually
referred to as intelligent industry or simply Industry
4.0 (I4.0), marks the effect of digitization on the
production system and integrates physical, digital and
biological spheres through the Internet of Things
a
https://orcid.org/0000-0002-3036-7116
b
https://orcid.org/0000-0003-3755-4341
c
https://orcid.org/0000-0002-3949-5886
d
https://orcid.org/0000-0002-8656-0791
(IoT), Cyber-Physical Systems (CPS), Artificial
Intelligence (AI), robotics and biotechnology
(Shamim et al., 2016).
I4.0 has modified production processes and
effected a change in the necessary skills and
competencies of human workforce in their jobs
(Karre et al., 2017). Such disruptive modifications in
the manufacturing system will allow the use of
intelligent technologies, with processes monitored in
real time and products and services developed in
shorter and efficiently customized periods (Liboni et
al., 2019).
Workers will no longer be mere operators who
solve process deviation or failure problems and
perform non-routine tasks dealing with a large
amount of data (Karre et al., 2017) and combining
technical and transversal skills to interact with
modern, complex interfaces and take suitable
decisions (Kazançoglu and Özkan Özen, 2018).
622
Moura, R., Richetto, M., Luche, D., Tozi, L. and Silva, M.
New Professional Competencies and Skills Leaning towards Industry 4.0.
DOI: 10.5220/0011047300003182
In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 2, pages 622-630
ISBN: 978-989-758-562-3; ISSN: 2184-5026
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
Therefore, it is important to establish new criteria
to recruit and evaluate the human workforce
(Kazançoglu and Özkan Özen, 2018). I4.0 is not only
about intelligent algorithms, artificial intelligence,
machine learning and autonomous systems (Stachová
et al., 2019), but also workers with autonomy and
Education, as technology can provide support, but it
still does not replace human skills (Zhao et al., 2019.
2 LITERATURE REVIEW
2.1 Industry 4.0 (I4.0)
Industry 4.0 (I4.0) is a term that was coined in
Germany during the Hannover fair and has been
influencing factories in different branches and
segments worldwide (Gorecky et al., 2014; Gabor,
Szabó and Ahmed, 2017; Fernández-Caramés and
Fraga-Lamas, 2018).
It starts a one-way process to automate repetitive
and monotonous activities performed by humans,
while at the same time requiring human decisions in
solving more complex problems (Bauer, Schlund and
Vocke, 2017). Among the fundamental concepts of
I4.0, it is interconnection to physical and virtual
assets by sensors and actuators driven by
microcomputers linked to autonomous machines
(Spöttl, 2017).
Human participation in the development of I4.0
towards digitalization through studies that address
different objectives indicate a lack of qualified
workers concerning needed skills (Shamim et al.,
2016). In addition to technological changes in
Education and work organization, I4.0 brings changes
regarding job profiles and competencies (Dumont,
Rayp and Willemé, 2012), which leads to job losses
according to the level of qualification (Daling et al.,
2018) and how the individual achieves success in
specific tasks (Holm, 2018).
2.2 Cyber Physical Systems (CPS)
This CPS is the foundation of I4.0 by connecting all
physical devices to the IoT and incorporating the
functions of computing, communications, control and
coordination of a virtual environment integrated with
the physical world (Zhou, Liu and Zhou, 2015).
CPS requires workers with technical skills due to
its high degree of automation. A connection
established between the real world and the cyber
environment through sensors and actuators allows
acquiring actual data to be fed to the cyber elements
that in turn provide feedback for professionals
(Centea, Singh and Elbestawi, 2018).
2.3 Internet of Things (IoT)
The IoT is understood as a cloud of data and
information which is similar to that of CPS (Gehrke
et al., 2015). It is the point at which CPS interacts with
the connection of elements (Fernández-Caramés and
Fraga-Lamas, 2018) that are not only machines, but
all devices and humans that are able to detect, identify
and communicate via the internet (Mueller, Jaeger
and Hanewinkel, 2019).
IoT plays an important role in I4.0, since devices
and sensors spread throughout facilities allow
interoperability through a multilingual and
multiprotocol (Centea, Singh and Elbestawi, 2018).
Managing the IoT in industrial environments implies
a massive implantation of sensors, actuators and
machines with remote detection and actuation
capabilities (Fernández-Caramés and Fraga-Lamas,
2018).
2.4 Artificial Intelligence (AI)
AI is a field of computer science in which machines
perform tasks that the human mind usually performs,
such as learning and reasoning by combining
software, logic and computing measures that do not
depend on human decisions, thus becoming
autonomous agents and affecting logistics and
manufacturing processes due to providing greater
safety, speed, precision and productivity (Kaasinen et
al., 2019).
With the adoption of AI, there is an opportunity to
discuss new demands for qualification and Education
by focusing on the labor market (Venkatraman,
Souza-Daw and Kaspi, 2018).
2.5 Machine Learning (ML)
AI encompasses ML that acquires knowledge and
data patterns (Ciolacu et al., 2017) by focusing on
autonomous knowledge acquisition which has been
increasingly found in industrial environments
(Pozdneev et al., 2019) due to the ease of algorithmic
application in innovations (Longo, Nicoletti and
Padovano, 2017).
ML plays a key role in Education and
personalized training (Daling et al., 2018) by
answering routine questions (Bauer, Schlund and
Vocke, 2017) through computer technology and
mathematical models without distorting folk
knowledge (Li, Fast-Berglund and Paulin, 2019),
New Professional Competencies and Skills Leaning towards Industry 4.0
623
such as problem recognition (Kinkel, Schemmann
and Lichtner, 2017) and recommendations for
possible preemptive solutions, thus allowing workers
to develop their skills virtually (Antkowiak et al.,
2017).
2.6 Deep Learning (DL)
DL is a branch of machine learning based on a set of
algorithms that mathematically model abstractions
using deep learning with multiple processing layers
and linear and nonlinear transformations from an
artificial neural network (Whysall, Owtram and
Brittain, 2019) that recognizes visual objects, natural
language processing and logical reasoning (Zhao et
al., 2019).
2.7 Technical and Non-Technical Skills
Skill and competency are interrelated. Competency
consists in a combination and coordination of
knowledge, attitudes, skills and ethics (Enke et al.,
2018) and it comprises a set of technical and non-
technical skills that individuals must develop (Gabor,
Szabó and Ahmed, 2017) so as to perform
coordinated, strategic and creative activities (Longo,
Nicoletti and Padovano, 2017) while the concept of
skill is to apply acquired theories and concepts into
practice (Hecklau et al., 2016).
Educational institutions must gather information
on competencies required in the future (Crawley et
al., 2014) for imparting knowledge, developing skills
and assigning responsibility to students (Enke et al.,
2018), therefore this development process requires a
set of skills necessary for an education process
considered qualification (Benešová and Tupa, 2017).
For example, guaranteeing the collective competence
of working as a team to achieve a higher level of
synergy (Holm, 2018).
Some authors classify competencies into four
main categories (Benešová and Tupa, 2017). The first
one refers to the technical competencies that
encompass knowledge and mastery, the second one
comprises the methodological competencies that
include problem solving and decision making
(Cvetic, Vasiljevic and Danilovic, 2017), the third
one is the social skills that encompasses attitude and
communication and the fourth one is associated with
personal competencies, which include the concept of
values and motivation (Hecklau et al., 2016). Non-
technical competencies are also known as transversal
or behavioral skills (Longo, Nicoletti, and Padovano,
2017).
Skill is the quality of someone who is skilled at
performing activities using dexterity, mastery or
aptitude (Hecklau et al., 2016), which can be
classified as technical and non-technical skills
(Kazançoglu and Özkan Özen, 2018). Non-technical
skills (soft skills) refer to personal and social skills
such as knowing how to lead, express oneself, listen
and be empathetic (Hecklau et al., 2016).
Unlike technical skills (hard skills), non-technical
skills (soft skills) are recognized as transversal skills
(Cotet, Balgiu and Zaleschi, 2017) found between
technical and behavioral skills (Gehrke et al., 2015).
Relatively, technical skills assist the candidate in
succeeding in an interview, but without transversal
skills, they do not remain employed and do not reach
professional success (Shamim et al., 2016), therefore
it is recommended that qualification and technical
skills should also be developed towards personal
Education, such as teamwork (Crawley et al., 2014).
Technical skills (hard skills) are enhanced
through professional courses and technical learning,
and their development is predicated on the time spent
and dedication. Technical skills are those possessed
by specialists and technicians who are able to handle
events and technological demands that affect
production systems, in addition to controlling and
monitoring technological developments (Bruno and
Antonelli, 2018), identifying boundaries,
understanding roles and systemic relationships, thus
promoting wholistic functioning (Holm, 2018) by
considering its complexity and interconnectivity
(Benešová and Tupa, 2017) and carrying out
maintenance and repairs to solve technological
problems (Karre et al., 2017).
Hecklau et al. (2016) classified of technical skills
which can be learned and assimilated through training
and in school institutions (Crawley et al., 2014), and
non-technical or transversal skills that must be
developed through relationship (Cotet, Balgiu and
Zaleschi, 2017).
2.8 Education and the Teaching of New
Competencies and Skills
There is a lack of qualified workers to meet the I4.0
requirements, which makes it a critical factor in the
process of adjusting companies to a I4.0 environment,
thus compelling executives and recruiters to seek
innovative behavior, critical thinking, knowledge and
adaptation to technology during the recruitment and
screening of applicants during selection processes
(Hecklau et al., 2016).
Adding technical job skills to transversal skills
(Shamim et al., 2016) improves creativity and leads
CSEDU 2022 - 14th International Conference on Computer Supported Education
624
to innovative problem solving. The process of
developing and assembling a mini baja vehicle for
academic-professional purposes by gathering a
multifunctional and multidisciplinary work team
involving individuals of both genders that together
will create, develop, implement and operationalize a
project for assembling a vehicle represents a clear
example of this. In this sense, ML (Ciolacu et al.,
2017), Virtual Reality (Pozdneev et al., 2019) and
exoskeletons play a vital role to develop technical
skills and competencies (Cotet, Balgiu and Zaleschi,
2017).
3 MATERIALS AND METHODS
Bibliographic research was adopted with detailed
scientific reports and reviews in order to better
analyze the past and thus prepare for the future
(Kinkel, Schemmann and Lichtner, 2017), as in
Education, in the development of skills so as to
safeguard employability and the use of technologies.
Specific and multidisciplinary themes were
searched on the databases of Scientific Electronic
Library Online (SciELO), Web of Science (WoS),
Science Direct (SD) and Scopus (Jerman, Bach and
Bertoncelj, 2018).
SciELO (http://www.scielo.org/php/ index.php)
allows access to over 544 journals at WoS
(https://www.webofknowledge.com), which is
organized and run by Reuters, and provides access to
a file with over 925 journals and the SD
(https://www.sciencedirect.com) makes 2558
journals available. Scopus (https://www.scopus.com)
has a multidisciplinary content with a range of over
4300 documents in the areas of life sciences and more
than 6800 in the area of health sciences that have been
filtered by categories (Li, Fast-Berglund and Paulin,
2019).
On the Scopus, WoS, SciELO and SD databases,
it was performed a bibliometric analysis by
combining several keywords and areas of I4.0, which
support the types of competencies and the manner to
acquire them as shown in Table 1.
It can be observed that technology exerts a
worldwide impact on the production system due to
predicting the participation of humans and machines
in shared environments (Prifti et al., 2017). While
combining the keywords “Human and Machine” on
the Scopus, WoS, SCIELO and SD databases, over
20,000 publications were found between the years
2010 and 2020, as shown in Fig. 1.
Table 1: Bibliometric analysis combining keywords.
Source: Scopus, WoS, SciELO e SD (2021).
Source: Authors (2021).
Figure 1: Publications with: “Human” and “Machine”.
The countries that published the largest number of
studies using the keywords “Human and Machine”
between 2010 and 2021 are shown in Fig. 2.
Source: Authors (2021).
Figure 2: Countries that published using the keywords
“Human and Machine”.
As follows, a bibliometric analysis was also
carried out by searching the terms "Industry 4.0 and
New Professional Competencies and Skills Leaning towards Industry 4.0
625
Education". Over 350 publications were found
between the years 2010 - 2020, according to Fig. 3.
Source: Authors (2021).
Figure 3: Publications using the keywords: “Industry 4.0
and Education”.
In Fig. 4, the countries that published the most by
using the keywords “Industry 4.0 and Education”
between 2010 and 2020 were researched. It should be
noted that SciELO database generated no significant
results, since their articles are mostly published in
South America, Portugal and Spain (Jerman, Bach
and Bertoncelj, 2018).
Source: Authors (2021).
Figure 4: Countries that most published the keywords
“Industry 4.0 and Education”.
It is observed that technology has had a worldwide
impact on Education, as well as on human resources,
replacing human participation to make decisions and
resolve conflicts in I4.0 environments (Prifti et al.,
2017), and literature data demonstrates such a
scenario in related studies that these have been an
increasingly interesting subject in recent years and a
trend towards future research involving terms
associated with Education, Machines and Humans in
I4.0, e.g. the skills and competencies required for the
human workforce in an I4.0 environment.
4 DISCUSSIONS
A bibliometric analysis allowed observing the
evolution of scientific literature along the years
(Jerman, Bach and Bertoncelj, 2018) by covering
several areas of knowledge in publications to identify
opportunities and gaps, once literature on I4.0, as well
as its required competencies and skills, are still in the
process of transition. However, bibliometric results
indicate that most works are conceptual and
technological, and there is still a lack of studies on the
themes of educational changes, employment and
infrastructure to develop new competencies and skills
for digital age (Li, Fast-Berglund and Paulin, 2019).
The researched documents contextualize the
development of I4.0 by highlighting how technology
and knowledge (Liboni et al., 2019) can be applied in
organizations (Ras et al., 2017) and presenting risks,
opportunities and challenges (Shamim et al., 2016).
Regarding risks (Liboni et al., 2019), possible job
losses (Grenciková and Vojtovic, 2017) and distance
between the economies of emerging and developed
countries are related, which suggests an update of
HRM (Schroeder et al., 2017), as digital
transformation demands changes in the way of
working and qualification (Bauer, Schlund and
Vocke, 2017), such as teamwork and decision-
making to resolve conflicts (Ciolacu et al., 2017).
As for technological developments, the
implementation of new forms of working that involve
an interaction between humans and machines
(Shamim et al., 2016; Daling et al., 2018), the
dissemination of information will assist human
beings cognitively (Li, Fast-Berglund and Paulin,
2019), e. g. self-configured layouts specifically aimed
to activities controlled by sensors, actuators and CPS
(Fernández-Caramés and Fraga-Lamas, 2018) such as
the use of scanners to measure human well-being
(Pozdneev et al., 2019) and check their position in a
shared cellular layout (Bruno and Antonelli, 2018).
Currently, management is beginning to realize
that it needs to be transformed and recruit through
group dynamics based on a model that combines
technical skills with non-technical skills (Ras et al.,
2017), thus suggesting that specialists and technicians
should also develop a transversal competence profile
that requires multifunctionality (Gehrke et al., 2015)
with technological support so as to creatively solve
problems in real time (Gorecky et al., 2014).
Oriented and didactic group dynamics will
explore non-technical competencies, such as ethical
creativity (Shamim et al., 2016), willingness to learn
(Gabor, Szabó and Ahmed, 2017) and responsible
data sharing (Hecklau et al., 2016; Cotet, Balgiu and
Zaleschi, 2017) in which heterogeneous and
multidisciplinary teams will enhance their creativity
and productivity (Bauer, Schlund and Vocke, 2017).
Thus, a new educational system by combining the real
and virtual world will improve skills (Benešová and
CSEDU 2022 - 14th International Conference on Computer Supported Education
626
Tupa, 2017), as workers are going to have a decisive
role in connection and control (Hirsch-Kreinsen,
2016), as maestras (Bauer, Schlund and Vocke, 2017)
in the era of digitalization and technological
transformation (Störmer et al., 2014).
Individuals possessing highly technical expertise
are fit for the competitive culture of an organization,
however, they are often not hired on account of not
knowing how to demonstrate non-technical skills,
such as adjusting to a teamwork environment and
flexible production (Crawley et al., 2014) and the
update or creation of curricula and disciplines
(Benešová and Tupa, 2017), and rethinking how to
teach (Antkowiak et al., 2017) through technology or
learning factories to develop technical and transversal
skills during the design and operationalization of
projects (Enke et al., 2018). The concept of educating
and teaching, mainly aimed at new generations
(Crawley et al., 2014), should be reviewed and
updated by creating experiences that promote the
learning of technical fundamentals (hard skills) and a
behavioral manner to develop transversal skills (soft
skills) through a modern pedagogical approach
(Stachová et al., 2019).
A good example is shown in literature regarding
the CEO of Apple Inc. According to many scholars,
the brand success was achieved due to transversal
leadership skills and how Steve Jobs extracted the
best results and ideas from his employees, instead of
harnessing their technical skills (Shamim et al.,
2016).
From the year 2000, three generations started
being present in the labor market, the so-called baby
boomer generations, X and Y (Comazzetto et al.,
2016), with their competencies and divergent skills in
relation to technology and work organization
(Grenciková and Vojtovic, 2017). As a consequence,
conflicts and questions about the relationship
between generations will arise, especially regarding
Y (Comazzetto et al., 2016) on account of features,
such as an ambitious personality for professional
growth (Grenciková and Vojtovic, 2017), being
easily frustrated and not staying long in their jobs,
which leads to a turnover that affects intellectual
capital and financial aspects of an organization
(Comazzetto et al., 2016), in addition to loss of
knowledge (Shamim et al., 2016). In this context,
from the advent of generation Y (Comazzetto et al.,
2016), personal or non-technical competencies have
led employers to start seeking workers who have
transversal skills as illustrated in Fig. 5.
Source: Authors (2021).
Figure 5: Relationship between technical and non-technical
skills.
For several decades, practice and science prevailed
with a strong emphasis on technical foundations
(Crawley et al., 2014). In the 90s and following years,
representatives of industries started expressing
concern about technical competencies by articulating
the need for a broader view that also emphasized
(Antkowiak et al., 2017) transversal skills and
competencies, recognizing the need for change in
knowledge (Cotet, Balgiu and Zaleschi, 2017),
training and attitudes towards reaching a balance
(Pfeiffer, 2016; Müller and Hopf, 2017) between
teaching personal, interpersonal and technical skills
(Crawley et al., 2014).
As a reaction to the high levels of job turnover,
employers and recruiters revised selection criteria and
their group dynamics (Shamim et al., 2016) to hire
applicants with transversal skills and competencies
aimed to avoid replacement costs or conflicts between
workers (Sivathanu and Pillai, 2018).
5 CONCLUSIONS
Since the very first studies by German academics, I4.0
is undergoing a rapid transition process that has been
exerting impacts through opportunities to modernize
the means of production, employment profile and
education, and new technological infrastructure
resources have been proposed which demonstrates
the need to develop new skills and competencies.
The I4.0 trends reflect the non-technical skills
(soft skills) required to maintain good interactions
and relationships between workers which are difficult
to be taught, but with the primary aim of
complementing the technical skills (hard skills) that
can be acquired through vocational education or
training.
In this context, technical competencies and skills
can be taught and trained with the aid of technology
and machines (ML, DL and RV). Non-technical or
transversal skills can be better developed and
New Professional Competencies and Skills Leaning towards Industry 4.0
627
improved with the assistance of recruiters and
behavioral activities in state-of-the-art learning
factories.
Thus, in order to help individuals to remain
employed, especially the new generations, there is a
need for a review of the content of the curriculum of
educational institutions when sending out their
undergraduates to the labor market in order to allow
a more modern and attractive education, rethinking
Education in the sense of conceiving, designing,
implementing and operationalizing processes, and
combining technical skills with non-technical or
transversal skills.
Therefore, it is important to establish new criteria
for recruiting and assessing the human workforce,
although there are still few studies about it in
literature, since I4.0 is not only about intelligent
algorithms, artificial intelligence and autonomous
systems, but also regarding workers having autonomy
and qualification, as technology does not replace
human skills yet, such as emotion and creative
problem solving. Future work, should research and
investigate how new skills and abilities, can be
developed on the job training in the context of human
behavior in a real-time transfer process of learning,
reducing the time interval and increasing safety in
Manufacturing 4.0.
ACKNOWLEDGEMENTS
The authors are grateful for the reviewer's comments,
which allow the authors to improve the article. The
authors also acknowledge the financial support of the
Brazilian research promotion agencies (CNPq) and
FAPESP.
CONFLICT OF INTEREST
The authors are responsible for researching and
writing this article, and there is no conflict of interest
on the part of them.
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