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FUTURE STUDIES RESEARCH JOURNAL ISSN 2175 -5825 SÃO PAUL O, V.1 2, N.1, P. 87 – 111, JAN. / APR. 2020
FUTU RE STU DIE S RE SE ARC H JO URN AL - FIA BUSI NES S SCHO OL
Scientifc Editor: Renata Giovinazzo Spers
Evaluation: Double Blind Review, pelo SE ER / OJ S
Review: Preliminary
Doi:https://doi.org/10.24023/FutureJournal/2175-5825/2020.v12i1.473
uman Factor in Smart Industry: A Literature Review
1
Vander Luiz da Silva
2
João Luiz Kovaleski
3
Regina Negri Pagani
4
Alana Corsi
5
Myller Augusto Santos Gomes
Abstract
Purpose of the study: The objective of this study is to identify the benefits and
challenges of smart industry concept to the human factor, based on the concept of
Industry 4.0.
Methodology/approach: A systematic literature review was elaborated, based on
structured protocols for the selection of a bibliographic portfolio of articles. A bibliometric
analysis of the data and content analysis was performed.
Originality/relevance: The article discusses human work, focusing on theoretical and
practical contributions of international literature. The focus scenario is smart industry, a
concept in constant improvement, which currently has acquired influences from Industry
4.0.
Main results: The discussions lead us to ponder on human factor in smart industries in
the categories physical and mental health at work, human performance and professional
career in general. The conclusions points to the need to ensure adequate working
conditions in cognitive and psychic aspects, among others.
Theoretical and methodological contributions: We present major literature articles,
smart industry definitions, main technologies, and grouping benefits and challenges to
the human factor.
Keywords: Industry 4.0. Human factor. Work. Management.
How to cite the article:
Silva, V., Kovaleski, J., Pagani, R., Corsi, A., & Gomes, M. (2020). Human factor in smart industry: a
literature review. Future Studies Research Journal: Trends and Strategies, 12(1), 87-111.
doi:https://doi.org/10.24023/FutureJournal/2175-5825/2020.v12i1.473
1 Federal University of Technology of Paraná - UTFPR, Ponta Grossa, (Brazil). E-mail:
luizvnder@gmail.com ORCID 0000-0001-9307-7127
2 Federal University of Technology of Paraná - UTFPR, Ponta Grossa, (Brazil). E-mail:
kovaleski@utfpr.edu.br ORCID 0000-0003-4232-8883
3
Federal University of Technology of Paraná - UTFPR, Ponta Grossa, (Brazil). E-mail:
reginapagani@utfpr.edu.br ORCID 0000-0002-2655-6424
4
Federal University of Technology of Paraná - UTFPR, Ponta Grossa, (Brazil). E-mail:
aaacorsi@gmail.com ORCID 0000-0002-6319-8603
5
Federal University of Technology of Paraná - UTFPR, Ponta Grossa, (Brazil). E-mail:
myller_3@hotmail.com ORCID 0000-0003-2325-6132
Received: 17/09/2019
Approved: 22/12/2019
H
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FUTURE STUDIES RESEARCH JOURNAL ISSN 2175 -5825 SÃO PAULO, V.12, N .1, P.87–111, JAN. / APR. 2020
ator Humano em Indústria Inteligente: Uma Revisão de
Literatura
Resumo
Objetivo do estudo: O objetivo deste estudo é identificar os benefícios e desafios do
conceito de indústria inteligente para o fator humano, com base no conceito da Indústria
4.0.
Metodologia/abordagem: Foi elaborada uma revisão sistemática da literatura,
baseando-se em protocolos estruturados para a seleção de um portfólio bibliográfico de
artigos. Foi realizada uma análise bibliométrica dos dados e análise de conteúdo.
Originalidade/relevância: O artigo aborda sobre o trabalho humano, concentrando-se
em contribuições teóricas e práticas da literatura internacional. O cenário foco é indústria
inteligente, um conceito em constante aperfeiçoamento, que atualmente tem adquirido
influências da Indústria 4.0.
Principais resultados: As discussões geram reflexões sobre o fator humano nas
indústrias inteligentes nas categorias saúde física e mental no trabalho, desempenho
humano e carreira profissional em geral. As conclusões apontam para a necessidade de
garantir condições adequadas de trabalho em aspectos cognitivos e psíquicos, entre
outros.
Contribuições teóricas e metodológicas: Nós apresentamos principais artigos da
literatura, definições de indústria inteligente, principais tecnologias e agrupamento de
benefícios e de desafios ao fator humano.
Palavras-chave: Indústria 4.0. Fator humano. Trabalho. Gestão.
Como Citar:
Silva, V., Kovaleski, J., Pagani, R., Corsi, A., & Gomes, M. (2020). Fator Humano em Indústria
Inteligente: Uma Revisão de Literatura. Future Studies Research Journal: Trends and Strategies
[FSRJ], 12(1), 87-111. doi:https://doi.org/10.24023/FutureJournal/2175-5825/2020.v12i1.473
F
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Human Factor in Smart Industry: A Literature Review
FUTURE STUDIES RESEARCH JOURNAL ISSN 2175 -5825 SÃO PAULO, V.12, N .1, P. 87 – 111, JAN. / APR. 2020
Introduction
Industrial revolutions were marked by scientific / technological developments,
notably the discovery of the steam machine in the industry, mass production, electricity
use and industrial automation. With the emergence of new technologies, such as Cyber
Physical Systems and approaches like Internet of Things, a next revolution is projected
(Kazancoglu; Ozkan-Ozen, 2018).
The concept of Industry 4.0 has presented reference to the Fourth Industrial
Revolution (Park; Lee, 2017) and is configured as a smart factory. It is evidenced by
several research and strategic initiatives, proposed mainly in developed countries, which
seek to develop smarter and more sustainable industrial systems for the production of
goods and services (Dragicevic et al., 2019). This concept affects not only the production
systems, but the ways in which people organize themselves and act at work (Benešová;
Tupa, 2017, Kazancoglu; Ozkan-Ozen, 2018).
In the Industry 4.0 Scenario, the human factor needs to be studied, once the
participation of people in the work will still be necessary, and there is no perspective of
total replacement of them by artificial intelligence (Spath et al., 2013). At work, tacit
knowledge about an individual’s experience over the years cannot be transferred to
robots and computers (Postelnicu; Calea, 2019). Faced with tacit knowledge, learning
has been the main vehicle for technology transfer and knowledge through generations
(Gorman, 2002). Therefore, the human factor is needed in the smart industry and is a
great source of competitive advantages, as people can rely on their natural senses to
create solutions (Simões et al., 2019).
The adoption of the smart industry concept, including operating procedures,
technologies and systems, relies on the human factor. Therefore, it is necessary to
investigate the implications of such important factor in terms of benefits, challenges and
negative consequences for people and also for the human resources managers.
Training, professional career, well-being, human performance and physical and
mental health are elements influenced by decisions and actions of one or more
productive organizations. Therefore, multidisciplinary areas are useful to assist the
worker in achieving success in the labor market and industry, such as human resources
management, psychology, ergonomics, anthropotechnology (effects of technology on
people) and education. Each of them can provide support to the worker, observed as a
protagonist in a scenario in constant scientific and technological evolution. Both the
worker and company representatives are interested in pertinent questions about the
development of activities and work tasks. Human workers want to enter and remain in
the market, building their career and obtaining equitative wages, stability, intellectual
growth, learning and/or professional achievement. Companies, on the other hand,
generally seek the best possible human performance to increase productivity.
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In smart industries human tasks will be redirected, or reconceived (Singh;
Sellappan; Kumaradhas, 2013), and also the creation of new jobs (Ghani; Muhammad,
2019), allowing people to perform routine functions along with new technologies, mainly
in a more dynamic routine.
In this context, the objective of this study is to identify the main benefits and
challenges of smart industry concept to the human factor, based on the concept of
Industry 4.0. The benefits infer in the better performance of human work, well-being and
health, and the challenges govern elements of insertion and permanence of people within
industries (physical and mental health management and professional career, in general).
In the extant literature, much is discussed about the human factor in companies -
the focus is not on people, but on the development of skills and activities in favor of
organizational competitiveness. No modification of industrial manufacturing system
should be contemplated without discussing at length the potential effects on worker
health and safety (Badri et al., 2018) and career management.
Methodology
A systematic literature review was done, following protocols structured by Pagani,
Kovaleski, and Resende (2015; 2018), which consists of nine steps. Differently from all
the other existent in the literature, this methodology allows the researcher to ponder on
the scientific relevance of a paper using three variables: impact factor, number of
citations, and year of publication. The pondering on these variables generates an index,
the InOrdinatio, which indicates the scientific relevance of the paper. From this index, it
is possible to rank the papers individually.
The developing of the review followed these steps:
Step 1, 2 and 3: Establishing the intention of research, definition of keywords,
bibliographic databases, and making the final search. Three similar terms were defined
(Smart factory, Industry 4.0 and Fourth Industrial Revolution) and individually combined
with the keyword Human. The selected databases were Scopus and Web of Science, as
they are two important scientific bases at international level. They have two metrics to
measure the relevance of journals, CiteScore and Impact factor, respectively.
After defining the combinations of keywords and aligning them with the research
proposal, definitive searches were carried out, adopting three basic criteria, applied in
the platforms of each database: i) Keywords inserted in abstract-title-keywords; ii)
Journal articles, and; iii) All year’s period ≤ November, 2019. A total of six searches were
executed, three combinations in each database.
In step 4, a gross portfolio of articles was found and stored in bibtex file format:
Collecting the papers, using the reference manager Mendeley. The results are in Table 1,
Section of Results.
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Step 5: Filtering out the papers: gross portfolio was subjected to filtering
procedures: i) eliminating articles that were in duplicate, using the JabRef® software,
and; ii) articles not related to the topic under study (exclusion of articles by means
preliminary readings of titles and abstracts).
In addition to the scope of the research, another selection criterion was applied,
ordering based on the relevance of scientific indicators, inserted in steps 6 and 7 below.
Step 6: Finding the impact factor (metrics of the journal) and the number of
citations: the data collected by the reference manager were then exported to an
electronic spreadsheet. In the spreadsheet, the year of publication, the number of
citations (obtained from Google Scholar) and the metrics of the paper (impact factor) are
added. The metrics of the papers (impact factor) was also manually obtained from the
Clarivate Analytics list from 2018, in Web of Science database. This information along
with the year of publication is necessary to calculate the InOrdinatio (1) index (Pagani,
Kovaleski, and Resende, 2015; 2018).
Step 7: Establishing the rank for the papers: this step is designed to rank the
papers according to its scientific relevance, determined by the pondering of the most
important elements in a paper: year of publication (once new researches means new
contributions and advancements of science); the metrics, which show the significance of
a scientific journal; and, the number of citations, which proves the recognition of the
work in the scientific community (Pagani; Kovaleski; Resende, 2015; 2018).
InOrdinatio = (IF / 1000) + (α* (10 - (ResearchYear – PublishYear))) + (Ci) (1)
The IF is the impact factor, α (alfa value) is a weighting factor ranging from 1 to 10
to be attributed by the researcher; Research Year is the year which the bibliographic
research was developed; Publish Year is the year which the selected paper was
published; and, Ci is the number of times the paper has been cited on Google Scholar
(Pagani; Kovaleski; Resende, 2015; 2018).
Therefore, from the application of the filtering procedures, for selecting the final
portfolio of articles, step 7 of the Methodi Ordinatio, called InOrdinatio, was applied. This
phase allows qualifying and sorting the articles according to scientific relevance, equating
the impact factor, year of publication and number of citations for each article. Thus, it is
possible to obtain relevant studies regarding the scientific factors mentioned. It should be
emphasized that together with the method application (articles with best InOrdinatio
results of Methodi Ordinatio), it was used the relevance criterion of the themes
addressed.
Step 8 and 9: finding full papers and systematic reading: after finding the full
versions of the papers, the final reading, and the bibliometric and content analysis were
performed.
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The software Vosviewer® was used for the bibliometric mapping. The software
allows creating a network with keywords and authors, to show tendencies and
connections.
Result and discussion
Bibliometrics
In the two international databases, using three keywords combinations, a gross
portfolio of 754 articles was obtained (Table 1).
Table 1. Gross total of articles for literature review.
Keywords
Database
Scopus
Web of Science
"Smart factory" and "Human"
69 articles
22 articles
"Industry 4.0" and "Human"
301 articles
161 articles
"Fourth Industrial Revolution and "Human"
117 articles
84 articles
Total
487 articles
267 articles
We opted for a keyword that best expressed the research proposal, "Human". This
includes "human factor", "human resource", "human and machine cooperation", among
others. The combination of these with the term Industry 4.0 was responsible for the
higher gross return of articles, compared to the other terms used, in both databases.
The Scopus database presented a gross total of 487 articles of journals, followed by
the Web of Science, with 267 articles. Only articles published in journals were
considered. It was necessary to eliminate duplicate articles and, therefore, a portfolio of
482 was obtained (Table 2).
Table 2. Article filtering procedure.
Procedure
Frequency of articles
Articles on Scopus database
(+)
487
Articles on Web of Science database
(+)
267
Gross portfolio
(=)
754
Duplicates
(–)
272
Total articles after filtering
(=)
482
In the same database, duplicates of articles were found, due to three combinations
of keywords being used. In addition, duplicates are also generated because the same
article is indexed in more than one database.
After eliminating duplicities, a portfolio of 482 distinct articles was submitted to
bibliometric analysis. The bibliometric data of articles are oriented to human work and
equivalent approaches in smart industries, presented below, years of publications, major
journals, and authors with higher numbers of publications, frequent terms and articles
most cited in other studies.
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Figure 1. Years of publication of articles on human labor in smart industries.
Source: Data from Scopus and Web of Science (2019).
The years with a higher number of publications were 2019 (208 articles), 2018
(152 articles) and 2017 (74 articles). In the period surveyed, 2019 remained limited until
November.
The main journals of publications of the articles are presented in Table 3. The
frequency of articles per Journal is low due to the high number of available scientific
sources.
Table 3. Main scientific journals.
Journal
Number of articles
IFAC-PapersOnLine
19
Procedia Manufacturing
15
ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb
14
IEEE Access
11
International Journal of Advanced Manufacturing Technology
10
International Journal of Production Research
8
Sensors (Basel, Switzerland)
8
Computers and Industrial Engineering
7
International Journal of Computer Integrated Manufacturing
7
International Journal of Recent Technology and Engineering
7
International Journal of Innovative Technology and Exploring Engineering
7
Fme Transactions
6
AI and Society
6
Elektrotechnik und Informationstechnik
5
IEEE Transactions on Industrial Informatics
5
Sustainability (Switzerland)
5
Source: Data from Scopus and Web of Science (2019).
The main authors, according to the involvement in articles, and the most frequent
terms included in the articles, are presented in Figures 2 and 3, respectively.
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Figure 2. Main authors on human labor in smart industries.
Source: Data from Scopus and Web of Science (2019).
Figure 3. Frequent terms included in selected articles.
Source: Data from Scopus and Web of Science (2019).
Prominent words associated with “human” were Worker and Operator, working in
smart manufacturing, smart factory and industries in general.
For content analysis, it was necessary to limit the number of articles, prioritizing
studies with greater proximity to the research focus and scientific relevance. Therefore,
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out of 482 distinct articles, 59 were selected for full readings, through the procedures of
Table 4.
Table 4. Total articles selected for content analysis
Procedure
Frequency of articles
Total distinct articles
482
Total articles after preliminary readings of titles
177
Total articles after preliminary readings of abstracts
94
Selected articles for content analysis (articles with higher InOrdinatio
values of the Methodi Ordinatio)
59
After filtering articles - based on two criteria (researched scope and relevance of
themes), articles with higher values of InOrdinatio were selected. In this context, another
criterion was decisive, the scientific relevance. Main information and data from these
articles are described in Appendix 1.
The articles with greater similarities to our proposed research objective are briefly
addressed in Table 5.
Table 5. Main studies on human factor in the Smart Industry scenario.
Author
Title
InOrdinatio
value
Study focus
Methodological
procedure
Benešová
and Tupa
(2017)
Requirements for
Education and
Qualification of
People in Industry
4.0
193
Identify the competencies of
IT professionals in Industry
4.0
Case studies in
companies
Badri et al.
(2018)
Occupational health
and safety in the
industry 4.0 era: A
cause for major
concern?
129
Discuss the effects of
Industry 4.0 on occupational
health and safety of workers
Literature review
Sackey and
Bester
(2016)
Industrial
Engineering
Curriculum In
Industry 4.0 In A
South African
Context
107
Analyze the likely impacts of
the concept of Industry 4.0
on academic curricula
Literature review
and case study in
educational
institutions in
industrial
engineering
Ruppert et
al. (2018)
Enabling
technologies for
operator 4.0: A
survey
107
Analysis of the relationship
between technologies of
Industry 4.0 (facilitators of
human labor) and worker
Literature review
Whysall,
Owtram, and
Brittain
(2019)
The new talent
management
challenges of
Industry 4.0
103
Discuss the impact of
Industry 4.0 on
contemporary human
resource management
practice, focusing on
recruitment, training and
career development
Interviews with
human resources
directors at UK
companies
Kadir,
Broberg, and
Conceição
(2019)
Current research
and future
perspectives on
human factors and
ergonomics in
Industry 4.0
102
Identify human resources
approaches in the scientific
studies of Industry 4.0
Literature review,
relating excerpts
addressed in
articles with
contributions to
workers in
ergonomic
physical, cognitive
and organizational
categories
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Discussion
The Industry 4.0, a concept of smart industry, had its discussions begun in 2011 at
the Hannover technology fair in Germany (Kazancoglu; Ozkan-Ozen 2018). It was
proposed by a group of business representatives, the government and academia to
increase competitiveness of German companies (Jeganathan et al., 2018).
In Table 6 some of the definitions of smart factory/industry as well as of a more
specific concept (Industry 4.0) are presented.
Table 6. Smart industry definitions.
Definition
Source / author
The smart factory is a manufacturing solution that provides flexible and
adaptable production processes for solving complex problems.
Radziwon et al.
(2014)
The smart factory is a production solution in a flexible and efficient way to
meet the needs of today's market. It links the physical components of the
production system and the digital, abstract, and virtual components.
Hozdić (2015)
Smart Factory is the integration of all recent technological advances in
computer networks and physical processes.
Lee (2015)
The smart factory is a virtual, planned and real scenario model for design,
planning, and management of operations.
Shariatzadeh et al.
(2016)
Smart Factory is a set of intelligent production systems that integrates
communication, computing and control processes to meet industrial demands.
Chen et al. (2017)
Industry 4.0 is a broad concept covering a variety of systems, technologies,
principles and procedures designed to make production processes more
autonomous, dynamic, flexible and precise.
Tortorella and
Fettermann (2017)
“Industry 4.0 describes the transition from centralized production towards one
that is very flexible and self-controlled. Within this production the products and
all affected systems, as well as all process steps of the engineering, are
digitized and interconnected to share and pass information and to distribute
this along the vertical and the horizontal value chains, and even beyond that in
extensive value networks.”
Leyh et al. (2017)
Industry 4.0 is defined as an intelligent network of machines and industrial
processes, which is formed with the aid of information and communication
technologies for physical and digital connectivity’s of resources.
BMWi (2018)
The concept of Industry 4.0 encompasses a new generation of technologies,
existing or under tests, through technological advances in Information and
Communication Technologies, Robotics and other areas (Robla-Gómez et al., 2017).
Silva et al. (2019) addressed empirical evidence in Industry 4.0, highlighting the
technologies of this concept, as described in Table 7. These technologies have also been
found in articles on human labor in smart industries.
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Table 7. Key technological requirements for Industry 4.0.
Tecnological requirement
- Robots
- RFID technologies
- Advanced traceability systems
- Generation of wireless sensors and actuators
- Mobile Technologies
- Complex network protocol, IPv6.
- Cyber-Physical System (CPS)
- Internet of Things (IoT) e Internet of Services (IoS).
- Artificial intelligence (software), Big Data and IoT are core
technologies for automating operational activities.
- Cloud platform and cloud services
- Cyber security systems
- Augmented reality systems
- Virtual reality systems
- Simulation of technologies and resources
- Digital Twins technologies
- Real Time Location System (RTLS)
Source: Silva et al. (2019).
A marked change in the industrial sector corresponds to an advanced process of
automation and digitization, combining physical and virtual resources and internet (Belli et al.,
2019). Smart factory defines a new approach to manufacturing by introducing recent
industrial Internet technologies, intelligent sensors, cloud computing, predictive analytics of
data and scenarios by Big Data, simulations, and resilient control technologies (Lee, 2015).
In particular, technologies and core approaches of Industry 4.0 are Physical Cyber
Systems (CPSs), Internet of Things (IoT), artificial intelligence and software (Jeganathan et
al., 2018; Pejic-Bach et al., 2019), virtual reality, augmented reality, simulation (Aromaa et
al., 2018), Big Data (Belli et al., 2019; Pejic-Bach et al., 2019), cloud computing (Pejic-Bach
et al., 2019) and Cyber security (Carías et al., 2019).
Therefore, the adoption of the concept of smart industry, based on the concept of
Industry 4.0, presents advantages to companies (Table 8).
Table 8. Advantages of smart industry concept to companies.
Advantage
Source / author
Greater efficiency in resources processing
Albers et al. (2016), Gökalp, Şener, and
Eren (2017), Jasiulewicz-Kaczmarek,
Saniuk, and Nowicki (2017), Belli et al.
(2019)
Flexibility in production
Radziwon et al. (2014), Gökalp, Şener,
and Eren (2017)
Production cost reduction
Belli et al. (2019)
Better product quality and control
Albers et al. (2016), Belli et al. (2019)
Real-time fault detection
Jasiulewicz-Kaczmarek, Saniuk, and
Nowicki (2017), Belli et al. (2019)
Production optimization
Chen et al. (2017), Rajnai and Kocsis
(2018), Bányai et al. (2019), Belli et al.
(2019)
Industrial process transparency
Kambatla et al. (2014), Avventuroso,
Silvestri, and Pedrazzoli (2017)
More consistent and real-time decisions
Chen et al. (2017), Kiel et al. (2017),
Ardito et al. (2018)
Self-organization and self-adaptability in data and product
processing
Chen et al. (2017)
Autonomous control and sustainable manufacturing.
Radziwon et al. (2014)
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Industry 4.0 combines intelligent sensors, artificial intelligence and data analysis to
optimize real time manufacturing (Xu; Xu, and Li, 2018). Information systems and
technologies such as the Internet of Things are used to improve resource management
(Chen et al., 2017) and reduce unnecessary work and waste (Radziwon et al., 2014).
High variability and reduced product life cycles require agile and flexible production
structures that can be quickly reconfigured in companies (Gorecky; Khamis, and Mura,
2017). Smart factory modeling can provide the capacity for self-organizing, self-learning
and self-adapting production processes (Chen et al., 2017), allowing flexibility in
development, diagnosis and maintenance, operationalization and control of systems. Also
in terms of flexibility, products can be tailored to the specific and individual needs of
customers (Jazdi, 2014).
Industry 4.0 requires effective integration between equipment, people, processes
and products (Gebhardt; Grimm; Neugebauer, 2015; Haddara and Elraga 2015),
providing competitive advantages such as cost efficiency and time in production and
improved product quality (Albers et al., 2016). The smart factory integrates technologies
to improve the performance, quality and transparency of manufacturing processes
because its systems assist people and machines in the execution and control of their
tasks based on data and information from the physical and virtual scenarios (Mabkhot et
al., 2018).
In addition, another advantage is that Big Data analytics can provide support for
decision making (Chen et al., 2017).
Manufacturing systems are now able to monitor physical processes, and make
smart decisions through real-time communication and cooperation (Zhong et al. 2017).
Intelligent processes provide rapid responses to changes in production and failures along
the industrial production chain (Jasiulewicz-Kaczmarek; Saniuk; Nowicki, 2017; Haddara;
Elraga, 2015).
As explained by researchers and experts, the concept of Industry 4.0 has
advantages, but in addition to the technology dimension, other dimensions must be
considered in smart industries (Table 9).
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Table 9. Key dimensions in the adoption and development of smart industries.
Dimension
Source / author
- Technology,
- Organizational management
De Carolis et al. (2017) and
Rajnai and Kocsis (2018)
- Strategy,
- Client,
- Product,
- Leadership,
- Employees,
- Organizational culture,
- Governance,
- Technology
Schumacher, Erol, and Sihn (2016)
People,
- Strategies,
- Processes,
- Technologies
- Products
Canetta, Barni, and Montini (2018)
- Technology,
- Organizational management,
- People
Kravčík, Ullrich, and Igel (2017)
- Finance,
- Employees,
- Strategy,
- Process,
- Products
Mittal, Romero, and Wuest (2018)
- Strategy,
- Technology,
- Organizational management
Biegler et al. (2018)
Focusing on Human dimension, it is necessary that companies and institutions
ponder on developing the human resources sincetheir participation in the work will still
be necessary and source of competitive advantages; moreover, there is not a perpective
for total replacement of human labor by artificial intelligence (Spath et al. 2013). In face
of this situation, this important resource must be managed adequately.
In smart industries, in addition to competitive advantages generated for companies,
people will also provide other benefits in terms of human performance, well-being and
health at work (Table 10).
Table 10. Benefits of smart industry concept to the human factor.
Category
Benefit
Source / author
Human performance
Facilitate communication between workers
Simões et al.
(2019)
Assist in human empowerment
Improved production efficiency
Creating interactive environments between humans
and robots
Vysocky and Novak
(2016)
Decision making processes
Aromaa et al.
(2018)
Human health at work
Reduced physical workload (weight, speed and other
aspects)
Adam, Aringer-
Walch, and Bengler
(2018), Simões et
al. (2019)
Reduced dangerous and repetitive human tasks
Robla-Gómez et al.
(2017)
Data processing and information with analytical
complexities, facilitated.
Robla-Gómez et al.
(2017), Simões et
al. (2019)
Monitoring of physical activities and health conditions
of workers in real time
Ruppert et al.
(2018)
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Technologies such as robots and intelligent systems provide changes in work. One
of them is the redirection of human activities and the creation of new jobs. It is expected
an intensification of activities with intellectual nature (Ghislieri; Molino; Cortese, 2018).
Changes in technology and work configurations present the main purpose of improving
production efficiency (Simões et al., 2019).
In large corporations, the higher degree of process digitization implies in lower
quantitative workload and better operational benefits to workers (Adam; Aringer-Walch;
and Bengler, 2018). Specific technologies, in addition to reducing the physical workload
(weight, speed and other aspects), facilitate the execution of operational activities on
human labor, communication between workers and machines, detailed analytical
analyses, among other advantages (Simões et al., 2019).
Industrial robots, for example, are widely used in companies, replacing dangerous
human and repetitive tasks or difficult processing or with greater analytical complexities
(Robla-Gómez et al., 2017). Increasingly flexible robots are desired for industrial work,
from sensors, devices, software and hardware modules and advanced interfaces
(Gonçalves et al., 2019). Human and machine collaboration emerges as an innovative
strategy to build interactive environments between humans and robots, safely (Vysocky;
Novak, 2016).
Augmented reality and virtual reality approaches have the potential to empower
workers, generating information and knowledge about processes, products,
manufacturing and assembly (Simões et al., 2019). In addition to better human
performance, they can support less stressful work by removing information that is not
currently applicable and facilitating assimilation and learning (Kadir; Broberg; Conceição,
2019).
New technology frameworks, such as Big Data and analytical tools, for instance,
generate large volumes of data for monitoring, analysis and improvement of production
processes (Faccio et al., 2019).
In practice, successive industrial revolutions decrease physical loads of human
work, but the cognitive burden is increased due to the complexity of information and
tasks (Kumar; Kumar, 2019). Intelligent technologies can help workers in cognitive
terms, one of which makes decisions by sharing information on platforms (Aromaa et al.,
2018).
Solutions through activity monitoring are also achievable, such as specific
technologies measure activity complexity, heart rate, ergonomic postures, among other
important metrics for human health management (Ruppert et al., 2018).
Despite the benefits, human resources are exposed to the challenges of insertion
and permanence in smart industries, associated with training, professional career and
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physical and mental health aspects at work. This includes both the worker and the
manager.
Table 11. Categories of human challenges in the context of smart industry
Category
Requirement
Education and formation
Promising knowledge areas and new competences development
Professional career
Positions and functions
Human motivation, satisfaction, leadership and instability
Physical and mental health
management at work
Worker’s role in the intelligent industrial environment.
Important aspects of studies are
- security of data and personal information,
- Physical security in contact with collaborative robots and other
technologies,
- Social relationship at work
- Intellectual management
- Challenges in cognitive and mental health
People can adapt to changes in companies without interrupting production, but it is
necessary to empower them in terms of intellectual, cognitive and emotional aspects
(Simões et al., 2019). It is important to create and improve knowledge about digital
technologies. A promising area for the human learning is Information Technology - IT
(Ghislieri; Molino; and Cortese, 2018). In addition to the area of Information and
Communication Technology, other promising areas for companies are the industrial
automation, robotics, nanotechnology, biotechnology, materials science and engineering
(Postelnicu; Calea, 2019).
The academy presents a vital role in meeting demands for knowledge and human
skills development in Industry 4.0 (Jeganathan et al., 2018). The complexity of the
information generated and its influence on production is a multidisciplinary challenge. In
human resources management, knowledge is needed in cognitive psychology,
ergonomics, operations management, communication technology and computer science,
industrial design, manufacturing technologies, instrumentation engineering and others
(Kumar; Kumar, 2019).
To ensure that workers perform their tasks efficiently in increasingly complex
environments, the implementation of qualification measures is necessary, at the
technological and organizational management levels (Gorecky; Khamis; Mura, 2017).
Qualification of people for work is one of the challenge requirements for industries,
educational institutions and government, which must create policies and actions directed
to work issues in smart industries (Silva; Kovaleski; Pagani, 2019a).
Hecklau et al. (2016) identified human competencies for work in the Industry 4.0,
classifying them into four categories, technical (in-depth knowledge of technologies,
understanding of the intelligent process, technical, media and system coding skills and
security understanding of Information Technology - IT), methodological competences
(creativity, business perspective, problem solving, decision making, conflict resolution
ability, research skills, and analytical skills), social skills (intellectual skills, language and
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communication skills, networking skills, commitment, ability to transfer knowledge and
leadership) and personal competencies (flexibility, motivation to learn, ability to work
under pressure and sustainable mindset).
Silva, Kovaleski and Pagani (2019b) identified basic competencies for the Industry
4.0, communication, creativity, innovation, leadership, decision making facility and
analytical skills. They are fundamental to diversity of different professions and positions,
this is, technology development, project management, management and supervision of
systems and people, among others.
Gebhardt, Grimm, and Neugebauer (2015) discuss the importance of developing
Information Technologies skills and interdisciplinary human thought ability as basic
curricular elements for people in Industry 4.0.
For work in Industry 4.0, Kazancoglu, and Ozkan-Ozen (2018) mention human
skills, ability to act in complex situations, critical thinking, flexibility at work,
interdisciplinary learning, IT knowledge, knowledge of technologies, ability to interact
with modern interfaces and analytical skills. Ghislieri, Molino, and Cortese (2018)
highlight social human skills, flexibility, and ability to work in multifunctional teams, and
deal with complex situations, leadership, efficient communication and innovation (Silva;
Kovaleski; Pagani, 2019a).
People should gain knowledge and learn new daily tasks, as well as understand and
know how to use high-tech devices (Trstenjak; Cosic, 2017) and manage smart systems.
Educational programs will be designed to meet current demands, new content, skills and
knowledge (Azahari; Ismail; Susanto, 2019).
Promising human professions in smart industries are project management, product
development, computational systems development, engineering (mechanics, industrial,
electronics and software), industrial automation, quality management, production
management (business, data, production processes, advanced technologies and
software) and predictive maintenance (Pejic-Bach et al., 2019). Other professions are
strategic corporate manager, software developer, systems programmer, process
supervisor and manager and operator of technologies and devices (Silva; Kovaleski;
Pagani, 2019a).
On the other hand, the widespread use of different technologies for work execution
can result in negative implications such as reducing human relationships in the
environment, informal learning of interrupted people, dissatisfaction, demotivation and
stress. One of the reasons is the greater sense of control and oppression of workers by
technologies (Ghislieri; Molino; Cortese, 2018).
It should be ensured that the worker feels comfortable and safe when interacting
with digital devices, robots and systems. The psychological factor must be preserved and
managed (Robla-Gómez et al., 2017). In Industry 4.0, the organization of work should
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be fully reviewed and any loss of quality and safety to workers, who may arise, should be
carefully managed (Stadnicka; Litwin; Antonelli, 2019).
Conclusion
The smart industry is widely influenced by the concept of Industry 4.0 and other
concepts and principles such as lean production, resource efficiency such as energy,
sustainable development, smart manufacturing and advanced manufacturing, among
others. As noted in the literature, human work will be indispensable in smart industries,
both for the development of this concept as the management and operationalization of
advanced production systems, technologies and processes.
In this intelligent environment, workers will have reduced physical efforts, more
efficient internal and external communication by artificial intelligence, companies and
people, decision-making processes based on sets of criteria, tools and data other positive
implications.
It is necessary to ensure adequate conditions of human work, interventions and
actions in cognitive, emotional and psychic aspects, mainly. Changes are projected not
only to the operator, but to technicians, managers and other employees at operational,
tactical and strategic levels.
Studies with worker-centered focuses are suggested, seen from the perspective of
protagonists, regarding professional career, challenges in the labor market, performance
and health management in the context of smart industry.
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Appendix 1
Table 12. Main information and data of articles analyzed.
N.
Source / Author
Article title
Year
Citation
Impact
factor
InOrdinatio
value
1
Benešová, A. and
Tupa, J.
Requirements for Education
and Qualification of People
in Industry 4.0
2017
113
0
193
2
Longo, F., Nicoletti,
L. and Padovano,
A.
Smart operators in industry
4.0: A human-centered
approach to enhance
operators’ capabilities and
competencies within the
new smart factory context
2017
101
3,518
181
3
Gorecky, D.,
Khamis, M. and
Mura, K.
Introduction and
establishment of virtual
training in the factory of the
future
2017
59
2,09
139
4
Badri, A.,
Boudreau-Trudel,
B. and Souissi,
A.S.
Occupational health and
safety in the industry 4.0
era: A cause for major
concern?
2018
39
3,619
129
5
Peruzzini, M. and
Pellicciari, M.
A framework to design a
human-centred adaptive
manufacturing system for
aging workers
2017
46
3,772
126
6
Richter, A.,
Heinrich, P.,
Stocker, A. and
Schwabe, G.
Digital Work Design: The
Interplay of Human and
Computer in Future Work
Practices as an
Interdisciplinary (Grand)
Challenge
2018
23
3,6
113
7
Pinzone, M., Albè,
F., Orlandelli, D.,
Barletta, I., Berlin,
C., Johansson, B.
and Taisch, M.
A framework for operative
and social sustainability
functionalities in Human-
Centric Cyber-Physical
Production Systems
2019
12
3,518
112
8
Kaasinen, E. et al.
Empowering and engaging
industrial workers with
Operator 4.0 solutions
2019
10
3,518
110
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