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With the emergence of new technologies, oriented to Cyber Physical Systems (CPSs) and Internet of Things (IoT) mainly, a next revolution is projected. The concept of Industry 4.0 has presented reference to the smart factory and Fourth Industrial Revolution. The implementation of this concept in companies encompasses a number of requirements and changes to be developed gradually, including the human factor management. 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. 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. The discussions lead us to ponder on human factor in smart industries in the categories physical and mental health at work, and 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.
FUTURE STUDIES RESEARCH JOURNAL ISSN 2175 -5825 SÃO PAUL O, V.1 2, N.1, P. 87 111, JAN. / APR. 2020
Scientifc Editor: Renata Giovinazzo Spers
Evaluation: Double Blind Review, pelo SE ER / OJ S
Review: Preliminary
uman Factor in Smart Industry: A Literature Review
Vander Luiz da Silva
João Luiz Kovaleski
Regina Negri Pagani
Alana Corsi
Myller Augusto Santos Gomes
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
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.
1 Federal University of Technology of Paraná - UTFPR, Ponta Grossa, (Brazil). E-mail: ORCID 0000-0001-9307-7127
2 Federal University of Technology of Paraná - UTFPR, Ponta Grossa, (Brazil). E-mail: ORCID 0000-0003-4232-8883
Federal University of Technology of Paraná - UTFPR, Ponta Grossa, (Brazil). E-mail: ORCID 0000-0002-2655-6424
Federal University of Technology of Paraná - UTFPR, Ponta Grossa, (Brazil). E-mail: ORCID 0000-0002-6319-8603
Federal University of Technology of Paraná - UTFPR, Ponta Grossa, (Brazil). E-mail: ORCID 0000-0003-2325-6132
Received: 17/09/2019
Approved: 22/12/2019
Vander Luiz da Silva, João Luiz Kovaleski, Regina Negri Pagani, Alana Corsi & Myller Augusto
Santos Gomes
ator Humano em Indústria Inteligente: Uma Revisão de
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
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
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:
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
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.
Vander Luiz da Silva, João Luiz Kovaleski, Regina Negri Pagani, Alana Corsi & Myller Augusto
Santos Gomes
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.
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.
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
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
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
Vander Luiz da Silva, João Luiz Kovaleski, Regina Negri Pagani, Alana Corsi & Myller Augusto
Santos Gomes
The software Vosviewer® was used for the bibliometric mapping. The software
allows creating a network with keywords and authors, to show tendencies and
Result and discussion
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.
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
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.
Frequency of articles
Articles on Scopus database
Articles on Web of Science database
Gross portfolio
Total articles after filtering
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.
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
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
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
Table 3. Main scientific journals.
Number of articles
Procedia Manufacturing
ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb
IEEE Access
International Journal of Advanced Manufacturing Technology
International Journal of Production Research
Sensors (Basel, Switzerland)
Computers and Industrial Engineering
International Journal of Computer Integrated Manufacturing
International Journal of Recent Technology and Engineering
International Journal of Innovative Technology and Exploring Engineering
Fme Transactions
AI and Society
Elektrotechnik und Informationstechnik
IEEE Transactions on Industrial Informatics
Sustainability (Switzerland)
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.
Vander Luiz da Silva, João Luiz Kovaleski, Regina Negri Pagani, Alana Corsi & Myller Augusto
Santos Gomes
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,
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
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
Frequency of articles
Total distinct articles
Total articles after preliminary readings of titles
Total articles after preliminary readings of abstracts
Selected articles for content analysis (articles with higher InOrdinatio
values of the Methodi Ordinatio)
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.
Study focus
and Tupa
Requirements for
Education and
Qualification of
People in Industry
Identify the competencies of
IT professionals in Industry
Case studies in
Badri et al.
Occupational health
and safety in the
industry 4.0 era: A
cause for major
Discuss the effects of
Industry 4.0 on occupational
health and safety of workers
Literature review
Sackey and
Curriculum In
Industry 4.0 In A
South African
Analyze the likely impacts of
the concept of Industry 4.0
on academic curricula
Literature review
and case study in
institutions in
Ruppert et
al. (2018)
technologies for
operator 4.0: A
Analysis of the relationship
between technologies of
Industry 4.0 (facilitators of
human labor) and worker
Literature review
Owtram, and
The new talent
challenges of
Industry 4.0
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
Broberg, and
Current research
and future
perspectives on
human factors and
ergonomics in
Industry 4.0
Identify human resources
approaches in the scientific
studies of Industry 4.0
Literature review,
relating excerpts
addressed in
articles with
contributions to
workers in
physical, cognitive
and organizational
Vander Luiz da Silva, João Luiz Kovaleski, Regina Negri Pagani, Alana Corsi & Myller Augusto
Santos Gomes
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.
Source / author
The smart factory is a manufacturing solution that provides flexible and
adaptable production processes for solving complex problems.
Radziwon et al.
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.
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.
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
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.
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.
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.
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
Chen et al. (2017)
Autonomous control and sustainable manufacturing.
Radziwon et al. (2014)
Vander Luiz da Silva, João Luiz Kovaleski, Regina Negri Pagani, Alana Corsi & Myller Augusto
Santos Gomes
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).
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
Table 9. Key dimensions in the adoption and development of smart industries.
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)
- 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.
Source / author
Human performance
Facilitate communication between workers
Simões et al.
Assist in human empowerment
Improved production efficiency
Creating interactive environments between humans
and robots
Vysocky and Novak
Decision making processes
Aromaa et al.
Human health at work
Reduced physical workload (weight, speed and other
Adam, Aringer-
Walch, and Bengler
(2018), Simões et
al. (2019)
Reduced dangerous and repetitive human tasks
Robla-Gómez et al.
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.
Vander Luiz da Silva, João Luiz Kovaleski, Regina Negri Pagani, Alana Corsi & Myller Augusto
Santos Gomes
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,
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.,
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
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
physical and mental health aspects at work. This includes both the worker and the
Table 11. Categories of human challenges in the context of smart industry
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
- 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
Vander Luiz da Silva, João Luiz Kovaleski, Regina Negri Pagani, Alana Corsi & Myller Augusto
Santos Gomes
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
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
be fully reviewed and any loss of quality and safety to workers, who may arise, should be
carefully managed (Stadnicka; Litwin; Antonelli, 2019).
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
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
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Appendix 1
Table 12. Main information and data of articles analyzed.
Source / Author
Article title
Benešová, A. and
Tupa, J.
Requirements for Education
and Qualification of People
in Industry 4.0
Longo, F., Nicoletti,
L. and Padovano,
Smart operators in industry
4.0: A human-centered
approach to enhance
operators’ capabilities and
competencies within the
new smart factory context
Gorecky, D.,
Khamis, M. and
Mura, K.
Introduction and
establishment of virtual
training in the factory of the
Badri, A.,
B. and Souissi,
Occupational health and
safety in the industry 4.0
era: A cause for major
Peruzzini, M. and
Pellicciari, M.
A framework to design a
human-centred adaptive
manufacturing system for
aging workers
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)
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
Kaasinen, E. et al.
Empowering and engaging
industrial workers with
Operator 4.0 solutions
Ruppert, T., Jaskó,
S., Holczinger, T.
and Abonyi, J.
Enabling technologies for
operator 4.0: A survey
Sackey, S.M. and
Bester, A.
Industrial Engineering
Curriculum In Industry 4.0
Vander Luiz da Silva, João Luiz Kovaleski, Regina Negri Pagani, Alana Corsi & Myller Augusto
Santos Gomes
In A South African Context
Ansari, F.,
Khobreh, M.,
Seidenberg, U. and
Sihn, W.
A problem-solving ontology
for human-centered cyber
physical production systems
Longo, F., Nicoletti,
L. and Padovano,
Ubiquitous knowledge
empowers the Smart
Factory: The impacts of a
Service-oriented Digital Twin
on enterprises' performance
Whysall, Z.,
Owtram, M. and
Brittain, S.
The new talent management
challenges of Industry 4.0
Faccio, M., Ferrari,
E., Gamberi, M.
and Pilati, F.
Human Factor Analyser for
work measurement of
manual manufacturing and
assembly processes
Du, G., Chen, M.,
Liu, C., Zhang, B.
and Zhang, P.
Online robot teaching with
natural human-robot
Ghani, E. and
Muhammad, K.
Industry 4.0: Employers’
expectations of accounting
graduates and its
implications on teaching and
learning practices
Pérez, L., Diez, E.,
Usamentiaga, R.
and García, D.
Industrial robot control and
operator training using
virtual reality interfaces
Mohelska, H. and
Sokolova, M.
Management approaches for
industry 4.0 - The
organizational culture
García de Soto, B.,
Agustí-Juan, I.,
Joss, S. and
Hunhevicz, J.
Implications of Construction
4.0 to the workforce and
organizational structures
Kadir, B., Broberg,
O. and Conceição,
Current research and future
perspectives on human
factors and ergonomics in
Industry 4.0
Ramingwong, S.,
Manopiniwes, W.
and Jangkrajarng,
Human Factors of Thailand
Toward Industry 4.0
Vysocky, A. and
Novak, P.
Human - Robot collaboration
in industry
Umachandran, K.
et al.
Designing learning-skills
towards industry 4.0
Longo, F., Nicoletti,
L. and Padovano,
Modeling workers’ behavior:
A human factors taxonomy
and a fuzzy analysis in the
case of industrial accidents
Ahmad, A.,
Segaran, Soon, N.,
Sapry, H. and
Omar, S.
Factors influence the
students readiness on
industrial revolution 4.0
Azahari, M., Ismail,
A. and Susanto, S.
The significance of
photographic education in
the contemporary creative
industry 4.0
Azmi, A., Kamin,
Y., Md Nasir, A.
and Noordin, M.
The engineering
undergraduates industrial
training programme in
Malaysia: Issues and
Badaruddin, Noni,
N. and Jabu, B.
The potential of ICT in
blended learning model
toward education 4.0 need
analysis-based learning
design for ELT
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
Gerasimova, E.,
Kurashova, A.,
Tipalina, M.,
Bulatenko, M. and
Tarasova, N.
New state standards of
higher education for training
of digital personnel in the
conditions of Industry 4.0
Jancikova, K. and
Milichovsky, F.
HR Marketing as a
Supporting Tool of New
Managerial Staff in Industry
M., Olid, C. and
Crespo, J.
Evolution of HR
competences in
organizations immersed in
the fourth industrial
Mokhtar, M. and
Noordin, N.
An exploratory study of
industry 4.0 in Malaysia: A
case study of higher
education institution in
Mpofu, R. and
Nicolaides, A.
Frankenstein and the Fourth
Industrial Revolution (4IR):
Ethics and human rights
Ovinova, L. and
Shraiber, E.
Pedagogical model to train
specialists for Industry 4.0
at University
Popkova, E. and
Zmiyak, K.
Priorities of training of
digital personnel for industry
4.0: social competencies vs
technical competencies
Postelnicu, C. and
Câlea, S.
The fourth industrial
revolution. Global risks, local
challenges for employment
Riveros Valdes,
B.A. and Bustos
Baez, S.S.
Training programs linked to
technological management
in Argentina, Colombia,
Mexico and Chile:
Challenges for the academy
and its relationship with the
socio productive sector
Tan, S. and Rajah,
Evoking Work Motivation in
Industry 4.0
Tinz, J., Tinz, P.
and Zander, S.
Knowledge management
models for smart
manufacturing - A
comparison of current
Ullah, A.
Fundamental issues of
concept mapping relevant to
discipline-based education:
A perspective of
manufacturing engineering
Antonelli, D. and
Bruno, G.
Dynamic distribution of
assembly tasks in a
collaborative workcell of
humans and robots
Dannapfel, M.,
Wissing, T.,
Förstmann, R. and
Burggräf, P.
Human machine cooperation
in smart production:
Evaluation of the
organizational readiness
Havard, V.,
Jeanne, B.,
Lacomblez, M. and
Baudry, D.
Digital twin and virtual
reality: a co-simulation
environment for design and
assessment of industrial
Kolbeinsson, A.,
Lagerstedt, E. and
Lindblom, J.
Foundation for a
classification of collaboration
levels for human-robot
cooperation in
Vander Luiz da Silva, João Luiz Kovaleski, Regina Negri Pagani, Alana Corsi & Myller Augusto
Santos Gomes
Nicoletti, L. and
Padovano, A.
Human factors in
occupational health and
safety 4.0: A cross-sectional
correlation study of
workload, stress and
outcomes of an industrial
emergency response
Pardi, T.
Fourth industrial revolution
concepts in the automotive
sector: performativity, work
and employment
Kinzel, H.
Industry 4.0 - Where Does
This Leave the Human
Posada, J., Zorrilla,
M., Dominguez, A.,
Simoes, B., Eisert,
P., Stricker, D.,
Rambach, J.,
Dollner, J. and
Guevara, M.
Graphics and Media
Technologies for Operators
in Industry 4.0
Richert, A., Müller,
S., Schröder, S.
and Jeschke, S.
Anthropomorphism in social
robotics: empirical results
on humanrobot interaction
in hybrid production
Laudante, E.
Industry 4.0, Innovation and
Design. A new approach for
ergonomic analysis in
manufacturing system
Johansson, J.,
Abrahamsson, L.,
Kåreborn, B.,
Fältholm, Y.,
Grane, C. and
Wykowska, A.
Work and organization in a
digital industrial context
Birtel, M., Mohr, F.,
Hermann, J.,
Bertram, P. and
Ruskowski, M.
Requirements for a Human-
Centered Condition
Monitoring in Modular
Production Environments
Jerman, A., Bach,
M. and Bertoncelj,
A bibliometric and topic
analysis on future
competences at smart
Arena, D.,
Tsolakis, A., Zikos,
S., Krinidis, S.,
Ziogou, C.,
Ioannidis, D.,
Voutetakis, S.,
Tzovaras, D. and
Kiritsis, D.
Human resource
optimisation through
semantically enriched data
Müller, S.,
Shehadeh, M.,
Schröder, S.,
Richert, A. and
Jeschke, S.
An overview of work
analysis instruments for
hybrid production
Rasca, L.
Employee experience An
answer to the deficit of
talents, in the fourth
industrial revolution
Koch, P., van
Amstel, M.,
Dębska, P.,
Thormann, M.,
Tetzlaff, A., Bøgh,
S. and
Chrysostomou, D.
A Skill-based Robot Co-
worker for Industrial
Maintenance Tasks
Richert, A.,
Shehadeh, M.,
Digital Transformation of
Engineering Education
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
Willicks, F. and
Jeschke, S.
Empirical Insights from
Virtual Worlds and Human-
Binner, H.
Industry 4.0: defining the
working world of the future
... In recent years, European manufacturing companies have constantly faced a multitude of complex challenges which are mainly triggered by exogenous influencing factors, like enhanced global competition, a tremendous change in customer behavior, the volatility and vulnerability of global supply networks, a continuous demographic change, a shifting attitude regarding the role of labor in the entire society, and an ongoing need for professionalized employability processes [1][2][3][4][5][6][7][8][9]. In this context, "Industry 4.0" research focuses on the usage of modern technologies which should ultimately enable manufacturing companies to develop new and better products, continuously improve their internal and external processes. ...
... The project is focusing on the realignment and, therefore, the professionalization of engineering education in the respective focus areas of operations management in European manufacturing companies forced by the two predominant and policy-driven drivers "Industry 4.0" (smart operations management) and "Sustainability" (sustainable operations management). This transformation is further challenged by continuous demographic change, a shifting attitude regarding the role of labor in the entire society, and an ongoing need for professionalized employability and structured lifelong learning approaches [1,4,5,7,9]. The project will establish a dialogue with public and private stakeholders to (1) elaborate the needs and expectations of the industry, (2) establish a constant knowledge exchange within the knowledge triangle to foster skills ecosystems, and (3) and ensure the co-creation of educational materials, tools, and concepts. ...
Conference Paper
Full-text available
Based on the project "Engineering Excellence for the Mobility Value Chain" (EE4M), this paper addresses the increasing need for training, re-, and upskilling of engineers in manufacturing enterprises in the mobility value chain. Recently, the European mobility value chain is influenced by a multitude of hy-per-dynamic factors, like changing consumer behavior, disruptive technologies, etc., which leads to the fact that a continuous realignment of engineering education is indispensable. The focus is placed on operations management (OM) which is changing due to the two policy-driven and predominant drivers "Industry 4.0" and "Sustainability". The implementation of smart and sustainable concepts in OM in the mobility value chain entails both a transformation of production processes and a change in the working and learning processes of the employees. Companies are increasingly required to design, manage, and integrate learning processes and learning environments to provide a lifelong learning ecosystem and to prepare employees for changes in work and tasks. Moreover, educational institutions are challenged to successfully address those demands. Therefore, this paper introduces the European research project EE4M which focuses on the professional development of smart and sustainability competences of engineers in the mobility value chain through innovative educational modules supported by a transnational platform between the main drivers of the European mobility value chain. The innovation of the project can be explained by the fact that OM serves as the basis for empirically based realignment of engineering education to create requirement-orientated competence profiles.
... E koncepció bevezetése a vállalati életbe nem kis kihívásokat hordoz magában. A vállalatoknak számos követelménynek kell megfelelniük, valamint reagálniuk kell a folyamatos változásokra, az emberi tényezők hatékony menedzselése mellett (Belli et al., 2019, Silva et al., 2019. ...
... Ennek értelmében az emberi tényezők alkalmazása és menedzselése kiemelten fontos az okos gyárak esetében is, és képes lehet kompetitív előnyt nyújtani a vállalat számára (Simões et al., 2019, da Silva, et al., 2019. ...
Full-text available
A magyar demográfiai folyamatok hatásai az elmúlt évtizedekben komoly kihívást jelentettek a hazai vidéki területek fenntarthatóságában. Ebből a szempontból az egyik leginkább érintett terület Dél-Zala (Murafölde) volt, amelynek társadalma a versenyképesség szempontjából jelentős kihívásokkal küzd. Az esettanulmányunkban azt vizsgáljuk meg, hogy mely tényezők vezettek Dél-Zala demográfiai helyzetének fenntarthatósági problémáihoz, illetve a tapasztalt kihívásokra a Mura-menti régió – vagyis a Mura folyó által érintett stájer, muravidéki és muraközi területek – azonosított jó gyakorlatai közül mely szakpolitikai eszközök alkalmazása számít referencia értékűnek, így lehetőség nyílik a számunkra releváns külföldi tapasztalatok becsatornázására is. Az esettanulmányunk meghatározza az urbanizációt támogató tényezőket, mint a munka- és termékpiacot, a fejlett közvetítő szolgáltatásokat, az oktatási externáliákat, a tovagyűrűző agglomerációt, a közszolgáltatásokat és az önérdek erősebb kifejeződését. Ilyen módon azonosíthatjuk azokat a stratégiai tényezőket, amelyeket a vidékpolitikának kezelnie kell a hazai migráció negatív hatásainak csökkentése érdekében. Eredményeink alapján arra a következtetésre juthatunk, hogy a hármas és négyes hélix modelleken alapuló helyi gazdaságfejlesztési hálózatok és klaszterek támogatása, a helyi földtulajdonosok piacszerzésének erősítése, az innovatív üzleti megoldások és jobb szolgáltatások támogatása és az „okos” technológiák használata segíthet a vidéki térségeknek – így Muraföldének is – a demográfiai visszaesés megállításában.
... An analysis of the professional activities of workers involved in the offshore development and operation of oil and gas fields, through the prism of the impact of working conditions, everyday life and external factors on their health, shows that offshore development and operation of oil and gas fields take place in difficult and often extreme working and living conditions [15][16][17]. An analysis of the causes of accidents shows that many of them are associated with an unforeseen health deterioration of workers [21,24]. ...
... Acquiring and evaluating real time information on the health status of each employee and making automatic decisions according to the critical situation and providing prompt feedback will allow for more effective management of each employee's health, as well as the prevention of accidents due to the human factor, and these are currently possible with the application of digital technologies, especially IoT technologies. However, it should be noted that the development and application of IoT solutions to eliminate possible representation of the human factor and to support the health and safety of workers in oil and gas industry and, particularly, the offshore industry has been poorly studied yet [15,16], although in a number of increased risk facilities, such studies are already being carried out. Thus, [17] highlights the possibilities of modern network platforms and applications for solving healthcare problems based on IoT. ...
Full-text available
This paper proposes a methodological approach for the decision synthesis in a geographically distributed intelligent health management system for oil workers working in offshore industry. The decision-making methodology is based on the concept of a person-centered approach to managing the health and safety of personnel, which implies the inclusion of employees as the main component in the control loop. This paper develops a functional model of the health management system for workers employed on offshore oil platforms and implements it through three phased operations that is monitoring and assessing the health indicators and environmental parameters of each employee, and making decisions. These interacting operations combine the levels of a distributed intelligent health management system. The paper offers the general principles of functioning of a distributed intelligent system for managing the health of workers in the context of structural components and computing platforms. It presents appropriate approaches to the implementation of decision support processes and describes one of the possible methods for evaluating the generated data and making decisions using fuzzy pattern recognition. The models of a fuzzy ideal image and fuzzy real images of the health status of an employee are developed and an algorithm is described for assessing the deviation of generated medical parameters from the norm. The paper also compiles the rules to form the knowledge bases of a distributed intelligent system for remote continuous monitoring. It is assumed that embedding this base into the intelligent system architecture will objectively assess the trends in the health status of workers and make informed decisions to eliminate certain problems
... Human workers want to enter the market, stay in the market, building their careers, earned fair wages, stability, intellectual growth, learning, and / or professional achievement. On the other hand, companies generally want the best possible human performance to improve productivity [6]. The limitations and requirements placed on the devices and systems used depend heavily on the application chosen and the layers considered [7]. ...
... Carry out research and development efforts [4] Collaborate with organizations that specialize in agriculture and / or meteorology [5] Implementing an integrated SI, especially in the production, membership (HR), finance and reporting business processes [6] Improvement of education methods and capital systems [7] ...
Full-text available
The Indonesian coffee industry has become a trend that has a strategic role and potential for the livelihoods of the business people in it, as well as Indonesia's economic growth. One of the trends that stole attention is the concept of smart industry, the concept of a digital-based industry that is highly relevant to technological developments in this era. When companies want to implement a smart industry, companies need a strategy to implement IT (Information Technology) so that the investment spent is right to build the company's targets. This study aims to design a systematic IS/IT strategy to realize the concept of smart industries that are effective. The analysis and design method used is the Ward & Peppard framework which consists of two phases, namely the input and output phases. The input phase consists of internal business analysis, external business, IT internal and external. The output stage includes the design of IT management strategies, business information systems and IT strategies. The results of this study are in the form of a portfolio of IT designs at the Margamulya Coffee Producers Cooperative consisting of business strategy designs and IT management.
... Theoretically, the value of the product is determined by the function of the product and the cost of the product. In the information society, the information itself has value, so if the information can be used well, the cost of the product can be reduced, and the value of the information will be realized through the transfer [16]. The four key technologies of the Internet of Things are shown in Figure 1: ...
Full-text available
With the emergence of the IoT era, wireless sensor networks will be more and more widely used. In addition to collecting, transmitting and processing simple data such as humidity, temperature and density of the dome, they can also provide multimedia information services such as video and images. It enables more comprehensive and accurate environmental monitoring. Therefore, MSDs have a huge demand in military, daily, forestry, biomedicine and other fields. The intensive city model has obvious advantages in meeting people's diverse needs and comfortable life. Most obviously, it speeds up the rhythm of life for residents, thereby increasing efficiency and saving time. Starting from this aspect, this paper conducts a research on the evaluation index system of public built on the following areas of open space IoT and mental health. In this paper, the GRNN neural network model is constructed, the mean condition is calculated, the density function can be estimated, the network output, and the schematic diagram of the generalized regression neural network is improved. Using the established system, the index in 2018 is selected as the base year, and after transformation, the standardized values of the past years are formed, which are substituted into the cells to form different matrices. The value of each cell is counted to obtain the subsystem coordination degree, and the global coordination degree is obtained through calculation. The evaluation results of ecological civilization construction and development in 2018 and 2019, 2020 and 2021 were compared. The experimental data shows that compared with 2018, economic development will change from 1 to 2.000, social harmony will change from 1 to 2.480, ecological health will decrease to 0.850, environmental friendliness will decrease to 0.750, and comprehensive evaluation will decrease to 0.513. This shows that while the economy is developing this year, the construction of ecological civilization has been gradually carried out, and good results have been achieved. This reflects the effectiveness of the system. The subject of the evaluation index system of green public open space based on the Internet of Things and mental health has been well completed.
... As the result of the study of the professional activities of workers engaged in offshore development and operation of oil and gas fields through the prism of the impact of working conditions, daily life and external factors on their health, the following specific features are revealed [7,8]: ...
Full-text available
Oil and gas companies have an urgent need for technologies that provide complete and reliable information about the actual state of health and safety of personnel. To solve this problem, the article proposes a concept solution for the development of a system monitoring of the psychophysiological health of workers employed on offshore oil platforms. The concept is based on a person-centered approach and allows monitoring of health of employees simultaneously linking them to the context of the environment. The urgency of the problem is confirmed by statistical data, according to which workers in the oil and gas industry are 8 times more likely to get injured. The article analyzes the specific features of the professional activity of the workers employed on offshore oil platforms and shows that the deterioration of their health and psychological condition due to the long-term “sea environment” is unavoidable. It offers to develop an intelligent system for monitoring the psychophysiological condition of workers employed on offshore oil platforms and to assess its suitability for their position with the reference to the Cattell test and fuzzy patterns recognition. The development and systematic operation of such a system may timely detect undesirable consequences for the health status of workers employed on offshore oil platforms and prevent wrong decisions due to the “human factor”
... Also, the globalisation of markets demanded a new set of aptitudes for accountants to perform effectively across a wide range of work environments, countries and cultures (Winterton & Turner, 2019). Recent industry research suggests that while shifts in work and skill demands arising from the 4IR may be little different to those experienced in earlier periods of rapid technological change the accounting profession is expected to be significantly impacted (Rumbens et al., 2019;Silva et al., 2020). ...
Full-text available
The fourth industrial revolution (4IR) presents many opportunities and challenges in a digitised world of work. This paper draws on a systematic literature review of recent research published by accounting professional bodies outlining the impact of digital technologies on the accounting profession. By taking advantage of this work this study critically assesses the types of skills and personal qualities that graduates as future accountants will need and explores the implications for accounting education and university curricula. The analysis reveals that necessary skills for future accountants may be summarised into four categories: (a) Ethical skills; (b) Digital skills; (c) Business skills; and (d) Soft skills. The analysis reveals ‘adaptability’ and ‘lifelong approach to CPD’ as the two essential personal qualities for future accountants. The practical implications for university accounting education are summarised in a proposed conceptual framework. The proposed conceptual framework: (1) acts as a roadmap for universities to align their accounting curricula with the developments in professional body syllabi; (2) helps university accounting education teachers to update, enrich, and refocus their teaching and learning approach to the requirements of the 4IR; and (3) promotes the coordination and rationalisation of the skills and personal qualities currently pursued by employability agendas at university, course, and module levels.
The business environment has seen rapid changes due to the Fourth Industrial Revolution (4IR) and the influence of exponential technological advancements. The enhancement of technology-based work environments has changed the skill set needed by graduates entering the workforce. In this regard, higher education institutions have shown signs of struggling to adapt curricula to prepare graduates with the skills needed by the fast-changing workforce environment. The South African Institute of Chartered Accountants (SAICA) developed a framework with competencies aimed to address the needed graduate skills, and for institutions to use as guiding documents in the amendment of their curricula. In this regard, the purpose of this study was to evaluate the top universities in South Africa’s accountancy module documents to determine whether the curricula are addressing the 4IR workforce needs. The SAICA-proposed competency framework (2025CF) was used as a conceptual framework for the evaluation of the university’s curricula documents, and findings indicate that universities still need to make substantial changes in order to include newly-added pervasive skills as prescribed by 2025CF. Findings from the systematic literature review indicate that universities adequately address business acumen skills; however, the categories of digital-, relational and decision-making acumen were insufficiently evident within the current curricula of the top five South African universities. The apparent lack of specific skills development for graduates needs urgent attention considering the technological changes brought on by the 4IR within the accountancy domain.
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Objective: This work brings light to the concept of Smart Sustainable Cities, creating a correlation between the terms Smart Cities and Sustainable Development, and maps out the technologies and projects implemented in this model of urban agglomeration. Methodology: A content analysis was carried out using the Methodi Ordinatio, a systematic literature review multicriteria methodology. Relevance: Smart Sustainable Cities arise with the aim of promoting technological development, but also facing the problems generated by cities. However, the concepts and structures of these cities are not clear, as well as which technologies are implemented and their impacts on cities. Results: Smart Sustainable Cities generate benefits for the three sustainable axes, in greater proportion for the Social axis, followed by the Environmental axis, and with less impact for the Economic axis. Contributions: This paper contributes to the academy by increasing the theoretical material, and to decision-makers, by highlighting the structures that make up the Smart Sustainable Cities.
Industries are revolutionizing everyday as we step into industrial 4.0 with enhanced and smart technologies ruling every micro hub. The Information Technology industry is running with thousands of employees at once needs more power management system. The proposed system identifies the entry of any person at the entrance. The identified person’s cabin or workspace power is turned on with simpler but efficient way of communication as LoRa module is used. The person is face recognized with the assistance of cameras at the entrance and their ID is transmitted to the Distribution box controller where the Air conditioner and other local supplies are automated to be turned on meanwhile. An employee’s ID and his position in the industry are matched in the industry with distribution box control which is reprogrammable. This LoRa WAN network is used effectively with gateways to transport less data to longer range as the system deals with large network. The end node of this network is fitted with energy meter and localized controller to calculate the power consumed as well control the appliances, which also gives a leverage of using implementing this at home.
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A fim de tornar a abordagem de Indústria 4.0 uma realidade, diversos requisitos precisam ser atendidos, um deles, a necessidade de qualificar pessoas para o trabalho nas indústrias. Este estudo teve por objetivo identificar as competências fundamentais para o trabalho na Indústria 4.0. Foi elaborada uma revisão sistemática de literatura, selecionando artigos relevantes por meio de critérios metodológicos. A pesquisa é de natureza descritiva e exploratória. Na Indústria 4.0 foi constatada uma série de competências básicas para o trabalho humano, como criatividade, capacidade de inovação tecnológica, conhecimento de tecnologias digitais e TI (Tecnologia da Informação), entre outras.
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Research advances in the last decades have allowed the introduction of Internet of Things (IoT) concepts in several industrial application scenarios, leading to the so-called Industry 4.0 or Industrial IoT (IIoT). The Industry 4.0 has the ambition to revolutionize industry management and business processes, enhancing the productivity of manufacturing technologies through field data collection and analysis, thus creating real-time digital twins of industrial scenarios. Moreover, it is vital for companies to be as “smart” as possible and to adapt to the varying nature of the digital supply chains. This is possible by leveraging IoT in Industry 4.0 scenarios. In this paper, we describe the renovation process, guided by things2i s.r.l., a cross-disciplinary engineering-economic spin-off company of the University of Parma, which a real manufacturing industry is undergoing over consecutive phases spanning a few years. The first phase concerns the digitalization of the control quality process, specifically related to the company's production lines. The use of paper sheets containing different quality checks has been made smarter through the introduction of a digital, smart, and Web-based application, which is currently supporting operators and quality inspectors working on the supply chain through the use of smart devices. The second phase of the IIoT evolution—currently on-going—concerns both digitalization and optimization of the production planning activity, through an innovative Web-based planning tool. The changes introduced have led to significant advantages and improvement for the manufacturing company, in terms of: (i) impressive cost reduction; (ii) better products quality control; (iii) real-time detection and reaction to supply chain issues; (iv) significant reduction of the time spent in planning activity; and (v) resources employment optimization, thanks to the minimization of unproductive setup times on production lines. These two renovation phases represent a basis for possible future developments, such us the integration of sensor-based data on the operational status of production machines and the currently available warehouse supplies. In conclusion, the Industry 4.0-based on-going digitization process guided by things2i allows to continuously collect heterogeneous Human-to-Things (H2T) data, which can be used to optimize the partner manufacturing company as a whole entity.
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Since changes in job characteristics in areas such as Industry 4.0 are rapid, fast tool for analysis of job advertisements is needed. Current knowledge about competencies required in Industry 4.0 is scarce. The goal of this paper is to develop a profile of Industry 4.0 job advertisements, using text mining on publicly available job advertisements, which are often used as a channel for collecting relevant information about the required knowledge and skills in rapid-changing industries. We searched website, which publishes job advertisements, related to Industry 4.0, and performed text mining analysis on the data collected from those job advertisements. Analysis of the job advertisements revealed that most of them were for full time entry; associate and mid-senior level management positions and mainly came from the United States and Germany. Text mining analysis resulted in two groups of job profiles. The first group of job profiles was focused solely on the knowledge related to Industry 4.0: cyberphysical systems and the Internet of things for robotized production; and smart production design and production control. The second group of job profiles was focused on more general knowledge areas, which are adapted to Industry 4.0: supply change management, customer satisfaction, and enterprise software. Topic mining was conducted on the extracted phrases generating various multidisciplinary job profiles. Higher educational institutions, human resources professionals, as well as experts that are already employed or aspire to be employed in Industry 4.0 organizations, would benefit from the results of our analysis.
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Since the first the image or photograph was first found around 1800, has become one of the fundamental elements of communication in human daily live. Those images which come from various sources from electronic and printed media contain various types of messages. Fundamentally all these images can be clustered into two major of 'realistic' and 'interpretative' strands. It functions encompass a broad range of contemporary living, from communication, the economy, education to art and culture. It is therefore, all images even the simple one requires to be understood by the large of society. Its importance is like a verbal and written languages. In some parts of the nation especially some developing countries however, image or photograph or can be referred as photographic education has not been given a proper position or is not well flourished in their formal Education System. Arguably, the subject of image-based or photographic education is given as secondary importance in education hierarchy ranging from the lower level of primary education to the highest level of tertiary education. Its importance has not been given as important as others established subjects like mathematics, business and others. This paper therefore, aim to analyse and highlight the significance of the theory and practice of image-based subject as important as others subjects in the formal education sphere. This paper can be concluded that visual images in education lies between two continua-the theory of epistemology (knowledge) and the theory of learning. The study argues that the importance of visual images in education provides significant opportunities for the development of individuals and for social, cultural and economic development. Photographic education can provide a platform for visual intelligence competencies of visual thinkers, specialist visual practitioners and contributes to visual literacy. Finally, photographic education is recommended to become as one of the subjects to offer at various levels of lower, middle and tertiary education in the formal education system.
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In this paper, we discuss how Cross Reality (XR) and the Industrial Internet of Things (IIoT) can support assembly tasks in hybrid human-machine manufacturing lines. We describe a Cross Reality system, designed to improve efficiency and ergonomics in industrial environments that require manual assembly operations. Our objective is to reduce the high costs of authoring assembly manuals and to improve the process of skills transfer, in particular, in assembly tasks that include workers with disabilities. The automation of short-lived assembly tasks, i.e., manufacturing of limited batches of customized products, does not yield significant returns considering the automation effort necessary and the production time frame. In the design of our XR system, we discuss how aspects of content creation can be automated for short-lived tasks and how seamless interoperability between devices facilitates skills transfer in human-machine hybrid environments.
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Purpose The transformational changes to business environments brought about by the fourth industrial revolution create a perfect storm for strategic human resource management, prompting a need to explore the implications of this context for talent management theory and practice. The paper aims to discuss these issues. Design/methodology/approach In-depth interviews were conducted with HR directors and senior leaders within engineering-led organisations to explore current challenges experienced across each stage of the talent pipeline: attraction and recruitment, training and development, career development, talent mobility and succession planning. Findings The speed of technological change brought about by Industry 4.0 had created a significant gap between current capability of employees and the rapidly evolving requirements of their roles, prompting a need to consider new and more effective approaches to talent development. Middle managers are increasingly recognised as overlooked critical talent within this context of unprecedented change, given their essential role in change management. In addition, whilst lateral hiring remains a common talent management practice, in the case of Industry 4.0 this equates to fighting a war for talent that does not exist. Practical implications This study suggests that there is a need for evolution of talent management theory and practice towards a more dynamic, systems-thinking orientation, acknowledging the interrelated nature of different talent management activities. Originality/value This paper provides an in-depth insight into the impact of the unprecedented change brought about by Industry 4.0 on contemporary talent management practice, considering how theory and practice might need to evolve to enable individuals and organisations to keep up with the rate of technological change.
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The novel generation of production facilities fostered by the fourth industrial revolution widely adopts different technologies to digitalise the manufacturing and assembly processes. In this context, work measurement techniques are one of the main candidates for the application of these new technologies because of the time, cost, and competences required to analyse manual production activities and considering the limited precision of the traditional approaches. This paper proposes a new hardware/software architecture devoted to the motion and time analysis of the activities performed by human operators within whatsoever industrial workplace. This architecture, called Human Factor Analyser (HFA), is constituted by a network of ad hoc depth cameras able to track the worker movements during the task execution without any interference with the monitored process. The data provided by these cameras are then elaborated in a post-process phase by the HFA to automatically and quantitatively measure the work content of the considered activities through an accurate motion and time analysis. The developed architecture evaluates the worker in a 3D environment considering his interaction with the industrial workplace through the definition of appropriate control volumes within the layout. To test the accuracy of HFA, an extensive experimental campaign is performed at the Bologna University Laboratory for Industrial Production adopting several realistic industrial configurations (different workplaces, operators and tasks). Finally, the HFA is applied to a real manufacturing case study of an Italian company producing refrigerator metal grates. A wide and deep analysis of the obtained key results is presented and discussed.
Technological and scientific advances set out the expanding of new opportunities for intelligent industries. The concept of Industry 4.0 is observed by expert groups as an important industrial configuration approach, however, much is still under discussion, especially in the emerging countries. There is a lack of practical studies and procedures on the implementation of this concept in companies. This paper aims to present intrinsic scientific contributions to Industry 4.0 deployment in companies, so that this concept can be set in motion. A literature review was elaborated in the Scopus, Web of Science and Science Direct databases, using structured protocols to select scientific articles. Only empirical studies were considered-empirical evidences - interview with experts on Industry 4.0 themes, case studies in companies, among others. As result, the barriers were reported, such as lack of financial resources and lack of infrastructure, the challenging impacts, such as relocation of people in the labour market, and benefits, respectively, and finally the basic technological and managerial requirements for Industry 4.0.
Nowadays, we are involved in the fourth industrial revolution, commonly referred to as “Industry 4.0,” where cyber-physical systems and intelligent automation, including robotics, are the keys. Traditionally, the use of robots has been limited by safety and, in addition, some manufacturing tasks are too complex to be fully automated. Thus, human-robot collaborative applications, where robots are not isolated, are necessary in order to increase the productivity ensuring the safety of the operators with new perception systems for the robot and new interaction interfaces for the human. Moreover, virtual reality has been extended to the industry in the last years, but most of its applications are not related to robots. In this context, this paper works on the synergies between virtual reality and robotics, presenting the use of commercial gaming technologies to create a totally immersive environment based on virtual reality. This environment includes an interface connected to the robot controller, where the necessary mathematical models have been implemented for the control of the virtual robot. The proposed system can be used for training, simulation, and what is more innovative, for robot controlling in an integrated, non-expensive and unique application. Results show that the immersive experience increments the efficiency of the training and simulation processes, offering a cost-effective solution.
Technological advancements are giving rise to the fourth industrial revolution – Industry 4.0 – characterized by the mass employment of smart objects in highly reconfigurable and thoroughly connected industrial product-service systems. The purpose of this paper is to propose a theory based knowledge dynamics model in the smart grid scenario that would provide a holistic view on the knowledge-based interactions among smart objects, humans, and other actors as an underlying mechanism of value co-creation in Industry 4.0. A multi-loop and three-layer – physical, virtual, and interface – model of knowledge dynamics is developed by building on the concept of ba – an enabling space for interactions and the emergence of knowledge. The model depicts how big data analytics are just one component in unlocking the value of big data, whereas the tacit engagement of humans-in-the-loop – their sense-making and decision making – is needed for insights to be evoked from analytics reports and customer needs to be met.