Available via license: CC BY 4.0
Content may be subject to copyright.
Materials and Society: transitions in society, materials and energy
edited by Jean-Pierre Birat, Gaël Fick, Mauro Chiappini, Dominique Millet, Thècle Alix
REGULAR ARTICLE
Current and future aspects of the digital transformation in the
European Steel Industry
Teresa Annunziata Branca
1
, Barbara Fornai
1
, Valentina Colla
1,*
, Maria Maddalena Murri
2
, Eliana Streppa
2
,
and Antonius Johannes Schröder
3
1
Scuola Superiore Sant’Anna, TeCIP Institute, ICT-COISP Center, Pisa, Italy
2
RINA CONSULTING –Centro Sviluppo Materiali S.p.A. (CSM), Castel Romano (Roma), Italy
3
Technische Universität Dortmund, Dortmund, Germany
Received: 4 October 2020 / Accepted: 8 March 2021
Abstract. The technological transformation in the European steel industry is driven by digitalization, which
has the potential to strongly contribute to improving production efficiency and sustainability. The present paper
describes part of the work developed in the early stage of the project entitled “Blueprint ‘New Skills Agenda
Steel’: Industry-driven sustainable European Steel Skills Agenda and Strategy (ESSA)”, which is funded by the
Erasmus Plus Programme of the European Union. The project aims at achieving an industry driven, sustainable
and coordinated blueprint for addressing the economic, digital and technological developments, as well as
increasing energy efficiency and environmental demands through continuously update of qualification,
knowledge and skill profiles of the workforce. On the one hand, main aspects of the current state of the
technological transformation in the steel sector are described through the analysis of the main recent innovation
projects and developments. On the other hand, survey results from a dedicated questionnaire addressed to the
European steel companies are analyzed, providing an overview on the (planned) technological transformation
affecting the steel sector. The existing levels of plant automation and the possible adoption of the new paradigm
of Industry 4.0 are discussed, by also considering the possible impact on the workforce. Main results are that the
steel industry foresees an implementation of almost all Industry 4.0 technologies not only for competitive but
also environmental improvement. Because this is foreseen in an incremental way upskilling of the existing
workforce is a precondition, not only because of recruitment difficulties on the employment market but also
because the existing qualification and experience of the workplace is necessary to unfold the full potential of
digital and green transformation.
1 Introduction
The current industrial transformation results from several
industrial revolutions over the last two hundred years.
From the late 1800s and early 1900s Industry 1.0, based on
the mechanization of work, led to the electrification of work
of Industry 2.0. After this period, in the early 1960s, the
automation of work characterized Industry 3.0. More
recently, in the first decades of the new millennium the
digitalization of information, supporting the intelligent
automation and the ever-increasing computational resour-
ces, characterized Industry 4.0. The term Industry 4.0,
originated in Germany [1,2], indicates the fourth techno-
logical revolution and refers to the implementation of
advanced automation solutions in production technologies,
supported by a variety of digital technologies. Such concept
includes machine-to-machine communication, Artificial
Intelligence (AI) [3] and Industrial Internet of Things
(IIoT) [4] for improving self-monitoring, diagnostic,
forecasting and self-optimization [5] capabilities of auto-
mation systems [6]. Industry 4.0 was considered a new
industrial stage, where different emerging technologies are
converging to provide digital solutions, leading to the
smart factory concept [7]. Concerning the steel sector, the
1st industrial revolution was characterized by the
introduction of coal-powered steel engines. It represents
the first fundamental shift in the optimisation of industrial
processes, leading to productivity increase and to first
interactions between workers and machine tools. The 2nd
Industrial Revolution was characterized by the invention
of the production area, the improvement of transportation
technologies and the electrification of industrial processes.
These aspects led to produce cheaper steel through the
Bessemer process and to introduce the open-hearth
furnace. The 3rd Industrial Revolution was based on the
*e-mail: colla@sssup.it
Matériaux & Techniques 108, 508 (2020)
©SCF, 2021
https://doi.org/10.1051/mattech/2021010
Matériaux
&
Techniques
Available online at:
www.mattech-journal.org
This is an Open Accessarticle distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
introduction of a progressive automation of manufactur-
ing, reducing manual work and increasing the industrial
production. In addition, the development of work in
assembly lines, focusing on optimisation and removal of
inefficiencies, and based on the integration of computers to
control the whole production process, represented a key
aspect [8]. Finally, in Industry 4.0 breakthrough innova-
tions have been integrated in process automation, includ-
ing Cyber Physical Systems (CPS), Big Data analytics and
Decentralised Control Systems. In particular, CPS are
composed of physical subsystems equipped with computing
and networking resources/facilities to monitor and control
physical processes, where physical processes affect compu-
tations and vice-versa [9]. In addition, big data analytics
refer to processing of large, heterogeneous and unstruc-
tured process and product data for identifying quality
problems and reducing the product failures [10,11]. Finally,
self-organizing production is related to the decentral
solutions based on a new combination of resources,
equipment, and personnel, with a close mutual interaction
and supported by distributed computational resources,
leading to the real-time control of production networks [12].
However, it has to be stated, the industrial develop-
ment phases from Industry 1.0 to 3.0 could not be seen as a
linear and positive development process. According to
Schumpeter’s Creative Destruction [13] the industrial
revolutions are compared by societal injuries (job losses,
emissions, deaths of workers, etc.). If this will be the same
for the 4th industrial is not quite consensual: While some
scholars expect disruptive changes [14], others foresee
mainly incremental impact via innovative technologies
updating and supporting of existing procedures (esp.
because of the already existing high automation of
production processes) and new jobs appearing [13,15].
Concerning the workforce implications, equivalent scenar-
ios are under discussion: from substitution of work via
robots and related massive job losses over the polarization,
to higher and lower qualifications and tasks (by decreasing
the middle operative level), as well as new cooperation
between different levels and working areas, and crowd
working on virtual or digital connected platforms [16,17].
In the transformation of manufacturing industry digitali-
zation is considered as a core technology. It is expected to
impact and transform industry, mainly through a substan-
tial improvement of the entire value chain. Nevertheless,
its effective implementation in the business activities
continues to be slow [18]. In the incoming years, the most
important drivers that professionals and decision makers
need to take into account are sustainability, digital
transformation, innovation and entrepreneurship [19].
In order to measure the achieved level of digitalization
in a country, the European Commission developed a
composite measure (the Digital Economy and Society
Index (DESI),) summarizing indicators related to the
digital performance and digital competitiveness of the
European Union (EU) member states [20]. According
to [21], the way to implement the Industry 4.0 paradigms
consists in integrating technologies, enabling ecosystems of
intelligent, autonomous and decentralised industries and
integrated product-services. The expected results concern
the reduction of the complexity of industry operations, as
well as the increase of their efficiency and the reduction of
costs. In this scenario, for instance, a new cross-functional
business unit (DIGI&MET) was developed in order to
implement new plant design concepts, based on digital
innovation, and also new business models [22].
In the incoming years, the adoption of the new digital
technologies, particularly based on advanced automation
and AI, is expected to produce significant transformation in
industrial activities, although this transformation is
already underway. Promotion of economic growth and
industrial competitiveness is expected. In addition, the
increase of digitalization in the industrial environment will
lead to recruitment of younger workforce, which is
generally more familiar with digital tools. However, some
negative aspects for economy and labor market can affect
companies, such as skill shortages and mismatches,
increased labor costs, production losses due to unfilled
vacancies, and higher social costs due to higher unemploy-
ment rates [23]. In this context, the European steel industry
is driving innovation in digital and green technologies, and
plays a key role in progress, economic growth, and job
opportunities [24]. This sector is highly specialised in
production and implementation of cutting-edge technolo-
gies and high value added products and solutions [25]
through the investments in Research & Development &
Innovation (R&D&I) [26]. In addition, sustainability and
CO
2
emissions reduction represent innovative challenges
and opportunities for companies to maintain their
competitiveness. On this subject, the EU should support
the steel sector in the elaboration of an industrial strategy
based on innovation, trade, sustainability and skills [27,28].
The concepts of digitization and sustainability are
becoming increasingly important and, therefore, they may
represent drivers for the evolution of the EU steel industry.
In spite of the large research activity on individual concepts
like digitalization, digital transformation, Industry 4.0
applications, etc.,the interdependency between Industry 4.0
and sustainability was shown. In particular, sustainability
is one of the main benefits of Industry 4.0, together with
productivity optimization or automated knowledge. In
addition, in manufacturing processes, the concepts of
Industry 4.0 can enhance operations to improve the
environmental sustainability of the production processes
[29,30]. The aspects related to Circular Economy have a
key position in EU policy, as the use of resources and
products in a more efficient way can contribute to reduce
industrial emissions and to improve EU industrial growth.
A recent report [31], including different sectors, such as plastic,
cement, aluminum and steel, showed an ambitious circular-
economy scenario by 2050. In addition, digitalization can help
to derive, handle, analyse and understand data and complex
information flows [32]. Furthermore, it can both catalyse
sustainable societies and circular economies, resulting in new
industries, new jobs, innovations and opportunities. The
circular transformation can provide also innovation and
prosperity to labour market and foster economic growth,
resulting in more affordable transition costs.
2 T.A. Branca et al.: Matériaux & Techniques 108, 508 (2020)
Concerning future perspectives, research programs and
activities play a crucial role for implementing Industry 4.0
concepts in steel industry. For instance, 156 projects,
funded by the Research Fund for Coal and Steel (RFCS),
involving digitalization and Industry 4.0-related technolo-
gies, were recently identified [33]. An investigation about
the current research initiatives implementing Industry 4.0
principles and technologies in the EU steel sector was based
on an approach including systematic review of funded
projects, patents analysis, expert interviews and a
qualitative survey of academics and practitioners working
in the steel sector [34]. Results showed that Industry 4.0 is
perceived as important for the steel sector, and the
expected results mainly concern process efficiency and
possibility to develop new business models. It is also seen as
an opportunity to develop intelligent support systems for
the workforce. In addition, the lack of qualified personnel is
perceived as one of the main issues. On the other hand,
short payback requirements can represent an issue for
implementing Industry 4.0. In addition, internal manage-
ment is considered a driving force for implementing
Industry 4.0 projects, due to its cross-cutting nature. Steel
manufacturers tend to rely on external expertise and to
cooperate with external partners to implement Industry 4.0
concepts and related tools and technologies.
Although some first attempts to apply AI to process
modelling and optimization were carried out in the 90-ies,
over the last decade the significant evolution and increased
availability of computational resources opened far greater
opportunities for AI in steelworks. Therefore, nowadays in
the steel sector significant technological applications are
ongoing. For instance, applications of Big Data and
Integrated Intelligent Manufacturing (I2M), as well as
applications of AI to process modelling and optimisation
are currently being explored to increase energy efficiency
[35,36] and reduce CO
2
emissions [37,38]. In addition, to
reduce energy consumption and environmental impact,
new methodological approaches can lead to better
management of resources, such as waste materials reduc-
tion, recycling improvement, and reduced natural resour-
ces exploitation [39–41]. Furthermore, Digital Twins can
be used for planning and control plant activities. If
products and plants are digitally connected, intelligent
automation of processing steps can be achieved, including
real-time traceability of objects and their states [42–44].
In a recent review paper, current technological trans-
formations and main developments funded by EU Research
Programs were described and analyzed along with impact of
digitalization on workforce and economic developments in
the steel sector [45]. As highlighted in this work, the future of
digitalization in the steel sector will be based on integration,
for instance, of Information Technology (IT), automation,
optimization technologies, adaptive online control, through-
process optimization, through-process synchronization of
data, zero-defect manufacturing, traceability and intelligent
and integrated manufacturing. In addition, the challenge of
digitalization is based on vertical, horizontal and transversal
integration of systems and production units as well as along
the whole lifecycle of the plant production. However,
automation of processes already involved the steel produc-
tion chain, leading to reduction of manual labour.
The recruitment shortage in the steel sector is mainly
due to different factors (e.g. lack of adequate profiles,
difficult integration of new technologies, in particular
among older workers and employees, age gap between
current and potential workforce, low investments in
training and education, lack of strategies on long-term
competences). Even in the transition from Industry 3.0 to
Industry 4.0, the main fear was represented by workers’
replacement by machinery. In particular, automation and
robotic generate new job profiles, but can also produce job
losses. For this reason, creation of new jobs and workers’
retraining represent significant future challenges [46]. On
this subject, potentials and opportunities of human-robot
interaction [47] were implemented in a steelmaking process,
by also considering a social innovation paradigm [48]. In
particular, robot can contribute to improve workers’health
and safety conditions by carrying out the most cumber-
some activities, while workers supervise the operations [49].
For all these reasons, and due also to skills gaps,
mismatches and shortages, the steel industry is committed
to face these issues through new strategies and initiatives.
They are based on up-skilling and re-skilling programmes
to improve interdisciplinary skills and preserve competi-
tiveness. In the context of Industry 4.0, the workforce will
be required to interact with digital devices and trust in new
technologies, moving away from monotonous and repeti-
tive works. This will lead to improve skills, such as
teamwork, problem-solving and decision-making, as well as
to a continuous learning process. The implementation of
smart and digital components in the steel sector could
attract more highly educated candidates. Furthermore, the
process to low-carbon competitive steel industry will
produce an increasing demand for research engineers and
technicians holding competences on material recycling and
resource optimization. Strategies for attracting and
retaining qualified people, such as educational programs
implemented in collaboration with universities and
Vocational Education and Training (VET) institutions,
can help to enroll more qualified workforce. A recent study
was focused on gathering perceptions on current and future
steelmaking workforce in the European steel sector from
key stakeholders, students, graduates and jobseekers with
a STEM (Science, Technology, Engineering, and Mathe-
matics) background [50]. In addition, the main drivers and
barriers affecting steelmaking as a possible career choice
were identified, with a specific focus on skills supply and
demand, aiming at providing a clear understanding of the
mismatch between demand and supply to a long-term skill
strategy. The work presented in this paper is part of the
Erasmus+ Blueprint project entitled “New Skills Agenda
Steel: Industry-driven sustainable European Steel Skills
Agenda and Strategy”(ESSA), started in January 2019.
The ESSA project focuses on the development of a
Blueprint to achieve sustainability in the steel industry,
according to the fast increase of influence and requirements
of Industry 4.0. The project aims at improving the
competitiveness of the European steel sector via proactive
skills adjustment, also including energy efficiency and
environmental aspects. Based on continuous training
activities, workforce’s skill improvements can be achieved,
according to the sector demands. In particular, some
T.A. Branca et al.: Matériaux & Techniques 108, 508 (2020) 3
significant challenges, crucial for Industry 4.0, can be faced,
such as re-skilling of current employees and increased
attraction and recruitment of talents. [51,52].
This paper presents a (still limited) first spotlight on
the foreseen implementation of Industry 4.0 technologies in
the steel sector and its impact on the workforce by the
results of a survey carried out to overview the current state
of digitalization in European Steel Industry. The existing
level of plant automation and the adoption of the new
paradigm of Industry 4.0, including the resulting impact on
the workforce, were assessed. To this aim, a questionnaire
was developed and launched during the early stages of the
project, which was addressed to European steel companies.
The paper is organized as follows: Section 2 presents the
structure of the questionnaire; then, the survey results are
discussed in the Section 3, by considering the planned
Strategy, Technical Aspects and Human Resources (HR)
impact; finally, in the Section 4 some concluding remarks as
well as some notes on the future work are presented.
2 Structure of the questionnaire
The survey aimed at collecting information directly from
the various company’s representatives, concerning the
current state of the digitalization in the European Steel
Industry. The on-line questionnaire was partly structured
and partly unstructured. Structured questions provided a
predefined set of responses, while unstructured questions
(i.e. questions with open answer) provided deeper
information on the respondents’opinion, to explore and
collect new ideas. The questionnaire was organized into
different sections, as follows: General information, Strate-
gy, Technical Aspects and HR. The first section mainly
includes country, company size (e.g. Small and Medium-
sized Enterprise-SME, Medium Enterprise, Large Enter-
prise), type of product, production route and output. The
sample considered in the analysis mainly includes large
companies (see Fig. 1) located in several European
countries, as depicted in Figure 2.
The second section includes questions related to the
implemented strategy, such as traditional solutions applied
before Industry 4.0, state of digitalization, priorities on
digital technologies and which of them will be adopted, how
they will affect workforce, company’s involvement in
research projects on digitalization (past, current and
future/planned ones). The third section proposes questions
related to technical aspects, such as awareness of
opportunities and threats from additive manufacturing,
areas of applications of digital technologies, expected
benefits and major barriers. The fourth section, concerning
HRs, includes questions about gender balance, age profile,
percentage of each category of employers with higher
education, foreseen evolution of workforce size in the
incoming years, awareness of each staff category about
digital competences demands, and training programs on
Industry 4.0 topics.
The representativeness of the survey results in terms of
general information, such as origin country of respondents,
company size, production route and product types, was
taken into account. In particular, answers primarily
involved several professional profiles, e.g. board director,
plant managers, Information Communication Technology
(ICT) managers, HR managers. In addition, both produc-
tion routes are represented (i.e. blast furnace route, based
on iron ore, and electric route, based on scrap), with a
majority of respondents coming from the integrated route.
Finally, as far as product types are concerned, they were
mainly represented by flat products.
3 Results and discussion
3.1 Strategy
In the first part of the questionnaire the state of
digitalization and plant automation in the steel industry
before Industry 4.0 was evaluated. In addition, the level of
knowledge and interest on Industry 4.0 enabling technolo-
gies were assessed. Results provided significant level
(77,78%) of automation, while lower levels of Basic
Automation (BA) and full Process Integration (PI) were
detected. Furthermore, standard solutions, such as
Computer Aided Design (CAD), Product Data Manage-
ment (PDM), production control system, are currently
adopted. As shown in Figure 3, results confirm the
commitment of European steel companies to technological
improvements as a way to increase competitiveness.
The priority and importance for more or less all
companies involved in the survey were mainly focused on
Internet of Things (IoT), Analytics, Cyber Security and PI,
both horizontal and vertical integrations. In particular, PI,
mainly based on IoT, allows communication among
different units, but also leads to security issues. In
particular, IoT concerns a fully connected world, in which
various objects networked and connected by collecting and
exchanging data and information [53]. An online monitor-
ing system based on IoT architecture is typically composed
of four layers: sensing, network, service resource and
applications. For instance, a similar system was imple-
mented in a real continuous steel casting production
line [54]. Therefore, the listed priorities reflect future trends
about interconnectivity and automation.
The survey results showed a general trend about the
high awareness of new technologies, although they have not
been applied yet. In particular, currently the most
investigated and applied technological fields in the steel
Fig. 1. % of the enterprises involved in the survey.
4 T.A. Branca et al.: Matériaux & Techniques 108, 508 (2020)
sector are IoT, Cloud Computing, Analytics, Cyber
Security and Product/Process Virtual Simulation. Con-
cerning investment plans, results highlighted the strategic
relevance of the selected technologies in the short term. In
particular, as shown in Figure 4, in the next few years, the
investments on Industry 4.0 technologies will be particu-
larly focused on Cyber Security, Analytics and IoT
applications (highlighted in orange in Fig. 4). Additive
Manufacturing (AM) is a specific 3D printing process,
which in industry makes it possible to build lighter,
stronger components and systems, by depositing material
according to digital 3D design data. In the steel sector this
technology opens up the possibility to develop innovative
alloys. The answers of a specific question on AM confirmed
some awareness about the opportunities and threats,
although AM is not among the main planned investments.
Fig. 2. The European countries involved in the survey.
Fig. 3. Traditional solutions currently applied in the European
steel companies.
Fig. 4. The planned investments within 3 years.
T.A. Branca et al.: Matériaux & Techniques 108, 508 (2020) 5
These results confirm steel industry commitment towards
Industry 4.0 through planned investments, to increase its
competitiveness.
Concerning the impact of digital technologies on
workforce, increasing training, requirement of new skills
and upgrading of existing ones were considered as the main
impacts, while workforce deskilling was indicated as less
affected. On the other hand, impacts on employees were
identified, in particular: improvement of work conditions
(e.g. workplace), health and safety, increased working time
and work-life balance.
Finally, another aspect, which was taken into account
in the survey, concerned the participation in the European
joint research projects on digitalisation. Results showed the
current involvement of some companies, while other ones
planned to participate. Nevertheless, significant percen-
tages of the interviewed companies are not involved. In
particular, about half is currently involved in few projects
(<5) and the remaining is equally divided in up to 10 or
more. In addition, increasing interest in joint research
projects was detected.
3.2 Technical aspects
Concerning the investigated technical aspects, by consid-
ering the areas where digital technologies are applied,
expected benefits and major barriers to be overcome during
their application were considered.
Survey results (see Fig. 5)showedthatdigital
technologies are applied in the process chain control,
and where management of large amounts of data is
required (i.e. production, business, etc.). In addition,
maintenance, administration, quality control and HR
management are mainly considered in the application of
digitalisation.
Concerning expected benefits from adoption of enabling
technologies, results showed a homogeneous distribution of
listed benefits (see Fig. 6), with higher results mainly
focused on production in terms of cost reduction and
quality improvement, as well as on increased workplace
safety. In addition, environmental benefits, such as
reductions of emissions, wastes and resources consumption,
are ranked high in the results.
Fig. 5. Digitalization in the areas of companies involved in the survey.
Fig. 6. The expected benefits from adopting enabling technologies.
6 T.A. Branca et al.: Matériaux & Techniques 108, 508 (2020)
Concerning the main barriers affecting adoption and
application of new technologies, results were mainly
focused on the central categories, based on their impor-
tance (Less important, Moderately important, Very
important), as shown in Figure 7.
In particular, the proposed barriers were enough
uniformly distributed as Moderately important. Further-
more, cost of investment is the most relevant barrier among
Important categories. On the other hand, as shown in
Figure 7, know-how protection was considered as the least
important barrier.
As also shown in Figure 7, the barriers affecting aspects
related to workforce, such as lack of highly skilled
workforce, skill gap and acceptance of new technologies
by the workforce, were highlighted as Moderately impor-
tant, but they were also pointed out as significant among
the important barriers. In addition, obsolescence of plant/
infrastructures and equipment and compatibility with
existing technologies and process were highlighted as the
most Important barriers.
3.3 Human resources
Regarding workforce profiles, focused on HRs and current
and future workforce over the last 5 years, age and
education were taken into account. Although lack of data
and data inhomogeneity were detected, the following
aspects were highlighted:
–An imbalance between male and female percentages,
with higher percentage of males in all the three
considered areas, i.e. operations, administration and
services, with the maximum imbalance in the operation
area.
–Concerning age profile, the values are reported in
absolute value. According to the available valid data,
results showed a slight workforce age redistribution
during the period from 2015 to 2019. In particular, a
certain stability in each presented age class (lower than
25 and between 25 and 34 years old) can be estimated,
with a substantial rising in the last two years for
personnel from 35–44 years old. On the other hand, the
45–54 age class showed an opposite trend, i.e. a decrease
from 2015–2016 to 2019. The last groups (between 55 and
64 years old) showed a stabilization in the overall
considered period, while the over 64 age group presented
a moderate increase.
–Higher education represents a requisite for most
production managers and engineers compared to tech-
nicians, operators and apprentices/trainees.
–Concerning workforce development in the next 3–5 years,
the sample was subdivided between the increase and the
decrease of the trend (50% yes, 50% no). Nevertheless,
the common trend focuses on the increase of women
employment, compared to the past, as well as on
recruitment of people with higher qualification, to
incorporate deeper knowledge and stronger skills for
exploiting new technologies.
–A further noticeable aspect is represented by the
increased employee awareness related to the need for
digital competences. Among the different profiles,
production managers and engineers (compared to
technicians, operators and apprentices/trainees) were
the most aware of the need for digital competences.
Finally, although staff training programs can be crucial
to meet Industry 4.0 challenges, results showed that few
training programmes are currently scheduled on com-
pany’s digital products and services, communication,
technology and innovation.
4 Conclusions
Although this article is a small and initial attempt to
analyse the digital and green transition of the steel
industry, the results of the conducted survey analysis
support a deeper understanding of the companies’trend
related to implementation of digital technologies in their
production processes and, consequently, about the evolu-
tion of workforce skills. In particular, the survey results
showed significant levels of automation in the involved
steel companies. This represents a starting point for
improving technological aspects. Concerning Industry 4.0
technologies, they resulted to be generally widely known.
In addition, companies showed interest in IoT, Analytics,
Cyber Security and PI (both horizontal and vertical). Some
Fig. 7. The distribution of the main digitalization barriers.
T.A. Branca et al.: Matériaux & Techniques 108, 508 (2020) 7
of these technologies have already been integrated in the
production processes, and are taken into account in
planned future investments within short time (i.e. 3 years).
Concerning the impact of the digital technologies on
workforce, results highlight the need of improving work
conditions, such as workplace environment and health and
safety aspects, and increased working time and the work-
life balance. In addition, a growing interest in European
research projects is highlighted, although no funding
programmes are mentioned, and some companies have
not been involved in such projects yet.
Currently, digital technologies are applied in all areas of
the companies, in particular in process chain control, and in
areas requiring management of large volumes of data, e.g.
production and business. The expected benefits are mainly
dealing with production, such as cost reduction and quality
improvement, working conditions in terms of safer and
healthier workplaces, and improvement of environmental
impacts, such as reduction of wastes, emissions and
resources consumptions. On the other hand, the highlight-
ed barriers for adoption and application of enabling
technologies concern investment costs. These costs should
be assessed by taking into account obsolescence of plant/
infrastructures and equipment. In addition, the compati-
bility with existing technologies should be considered.
Furthermore, the barriers affecting the workforce aspects
consist in lack of highly skilled workforce, skills gap and
lack of confidence of some workers (especially older ones) in
new technologies.
Further discussions in the ESSA project underlines that
the impact on the workforce mainly concerns the
requirement of T-shaped skills with a focus on horizontal,
transversal soft-skills (digital, green, social, methodologi-
cal, individual and personal skills) as well as the need of
continuous learning in an interdisciplinary perspective
(and environment). However, the concrete impact of
digitalization on the low skilled workers and the effect
on employment as well as recruitment difficulties for high-
skilled workers remain open to further discussion.
As the steel industry is evolving towards industry 4.0
starting mainly from a high level of automation it is more
an evolution or incremental adjustment instead of an
industrial revolution. The survey and discussions in ESSA
stress that the challenge of digitalization concerns the
integration of all systems (sensors, automation, and IT
systems) and all production units in different dimensions
(horizontal, vertical and transversal). In this context, the
steel industry’s expectations from digital and green
transformation concentrate on quality, flexibility and
productivity through the optimization and new interac-
tions of the individual production units. Economic aspects
include not only the reduction operational costs (i.e.
energy/row materials consumption reduction), but also the
introduction of new business models and organizational
structures.
However, it can already be emphasised that people (or
the human factor) are still needed with their experience and
skills to implement and run the new systems. Therefore, an
integration of technological innovation development
within a social innovation process (with co-creation and
teamwork) is supporting an effective technological innova-
tion as well as an upskilling of the workforce is needed to
unfold the potential of these innovations at the workplace.
As discussed with the stakeholders and companies of
the steel industry, even in Industry 4.0 social and
organisational factors are still the driver of productivity
and innovation. To say it with Klaus Schwab [55], Founder
and Executive Chairman of the World Economic Forum:
Leaders and citizens “together shape a future that works for
all by putting people first, empowering them and
constantly reminding ourselves that all of these new
technologies are first and foremost tools made by people for
people”[48].
The ESSA project, which delivered the described
results, is still in ongoing. The questionnaire will remain
online to involve additional companies and, consequently,
to enlarge the analyzed sample. This will provide
increasingly representative data on the situation of the
European steel sector about the implementation of the new
concepts of industry 4.0.
Acknowledgments. The research described in the present paper
was developed within the project entitled “Blueprint ‘New Skills
Agenda Steel’: Industry-driven sustainable European Steel Skills
Agenda and Strategy (ESSA)”and is based on a preliminary
deliverable of this project. The ESSA project is funded by
Erasmus Plus Programme of the European Union, Grant
Agreement No 2018-3019/001-001, Project No. 600886-1-2018-
1-DE-EPPKA2-SSA-B. The sole responsibility of the issues
treated in the present paper lies with the authors; the Commission
is not responsible for any use that may be made of the information
contained therein. The authors wish to acknowledge with thanks
the European Union for the opportunity granted that has made
possible the development of the present work. The authors also
wish to thank all partners of the project for their support and the
fruitful discussion that led to successful completion of the present
work.
References
1. H. Kagermann, W. Wahlster, J. Held, Bericht der promo-
torengruppe kommunikation. Im fokus: Das zukunftsprojekt
industrie 4.0, Handlungsempfehlungen zur umsetzung, For-
schungsunion, 2012
2. R. Anderl, Industrie 4.0-Technological approaches, use cases,
and implementation, At-Automatisierungstechnik 63(10),
753–765 (2015)
3. G. Duft, P. Durana, Artificial intelligence-based decision-
making algorithms, automated production systems, and big
data-driven innovation in sustainable industry 4.0, Econ.
Manag. Finan. Mark. 15(4),9–18 (2020)
4. I. Bisio, C. Garibotto, A. Grattarola, F. Lavagetto, A.
Sciarrone, Exploiting context-aware capabilities over the
internet of things for industry 4.0 applications, IEEE Netw.
32(3), 108–114 (2018)
5. H.-C. Möhring, P. Wiederkehr, K. Erkorkmaz, Y. Kakinuma,
Self-optimizing machining systems, CIRP Ann. 69(2), 740–
763 (2020)
8 T.A. Branca et al.: Matériaux & Techniques 108, 508 (2020)
6. S.I. Tay, T.C. Lee, N.Z.A. Hamid, A.N.A. Ahmad, An
overview of industry 4.0: Definition, components, and
government initiatives, J. Adv. Res. Dynam. Control Syst.
10(14), 1379–1387 (2018)
7. P. Osterrieder, L. Budde, T. Friedli, The smart factory as a
key construct of industry 4.0: A systematic literature review,
Int. J. Prod. Econ. 221, 107476 (2020)
8. L. Gehrke, A.T. Kühn, D. Rule, et al., A Discussion of
Qualifications and Skills in the Factory of the Future: A
German and American Perspective, VDI The Association of
German Engineers, Düsseldorf, Germany, 2015
9. V. Iannino, V. Colla, J. Denker, M. Göttsche, A CPS-based
simulation platform for long production factories, Metals
9(10), 1025 (2019)
10. J. Brandenburger, V. Colla, G. Nastasi, F. Ferro, C. Schirm,
J. Melcher, Big Data Solution for Quality Monitoring and
Improvement on Flat Steel Production, IFAC-PapersOnLine
49(20),55–60 (2016)
11. M. Vannocci, A. Ritacco, A. Castellano, et al., Flatness De-
fect Detection and Classification in Hot Rolled Steel Strips
Using Convolutional NeuralNetworks, in: Rojas I, Joya G,
Catala A, eds., Advances in Computational Intelligence,
IWANN 2019, Lecture Notes in Computer Science, Springer,
Cham, 2019, 11507 p.
12. V. Iannino, M. Vannocci, M. Vannucci, V. Colla, M. Neuer, A
multi-agent approach for the self-optimization of steel
production, Int. J. Simul.: Syst. Sci. Technol. 19(5), 20.1–
20.7 (2018)
13. J.A. Schumpeter, Kapitalismus, Sozialismus und Demokra-
tie. 7, Auflage. Tübingen., 1993
14. C.B. Frey, M.A. Osborne, The future of employment: How
susceptible are jobs to computerisation?, Technol. Forecast.
Soc. Change 114, 254–280 (2017)
15. R.D. Atkinson, J.J. Wu, False alarmism: Technological
disruption and the US labor market, Inform. Technol. Innov.
Found. ITIF, 1850–2015 (2017)
16. J. Abel, H. Hirsch-Kreinsen, T. Wienzek, Akzeptanz von
Industrie 4.0, Abschlussbericht zu einer explorativen empiri-
schen Studie über die deutsche Industrie. München: acatech,
2019
17. Acatech-Deutsche Akademie der Technikwissenschaften,
“Kompetenzentwicklungsstudie Industrie 4.0 –Erste Ergeb-
nisse und Schlussfolgerungen”, 2016
18. P. Kilimis, W. Zou, M. Lehmann, U. Berger, A Survey on
Digitalization for SMEs in Brandenburg, Germany, IFAC-
PapersOnLine 52(13), 2140–2145 (2019)
19. J. Ordieres-Meré, T. Prieto Remón, J. Rubio, Digitalization:
An Opportunity for Contributing to Sustainability From
Knowledge Creation, Sustainability 12, 1460 (2020)
20. DESI 2018, Digital Economy and Society Index Methodo-
logical Note, EU Comm., Bruxelles, 2018
21. C. Santos, A. Mehrsai, A.C. Barros, M. Ara
ujo, E. Ares,
Towards Industry 4.0: an overview of European strategic
roadmaps, Proc. Manuf. 13, 972–979 (2017)
22. A. Merluzzi, G. Brunetti, Metals industry: Road to
digitalization, in: Proc. of 40th International Con-vention
on Information and Communication Technology, Electron.
Microelectron. (MI-PRO) 2017, 2017, pp. 967–973
23. J. Bughin, E. Hazan, S. Lund, P. Dahlström, A. Wiesinger, A.
Subramaniam, Skill Shift: Auto-mation and the Future of
Workforce, McKinsey Global Institute, 2018
24. European Commission, The future of European steel.
Innovation and sustainability in a competitive world and
EU circular economy, Publications Office of the European
Union, Luxembourg, 2017
25. European Commission, Steel: Preserving sustainable jobs
and growth in Europe, COM (2016) 155 final, European
Commission, Brussels, 2016
26. European Commission Directorate-General for Enterprises
and Industry, Study on the Competitiveness of the European
Steel Sector Final report, Ecorys SCS Group, Rotterdam,
2008
27. EUROFER, European steel –A manifesto: 2019–2024, 2019,
Available from http://www.eurofer.org/News%26Events/
News/MANIFESTO%20European%20Steel%202019.fhtml
28. K. Peters, E. Malfa, V. Colla, The European steel
technology platform’s strategic research agenda: A further
step for the steel as backbone of EU resource and energy
intense industry sustainability, Metall. Ital. 111(5),5–17
(2019)
29. A.B.L. De Sousa Jabbour, C.J.C. Jabbour, C. Foropon, M.G.
Filho, When titans meet: Can in-dustry 4.0 revolutionise the
environmentally-sustainable manufacturing wave? The role
of critical success factors, Technol. Forecast. Soc. Chang.
132,18–25 (2018)
30. V. Colla, I. Matino, F. Cirilli, et al., Improving ener-gy and
resource efficiency of electric steelmaking through simulation
tools and process data analyses, Matériaux & Techniques
104(6-7), 602 (2016)
31. Material Economics, The Circular Economy A Powerful
Force For Climate Mitigation, 2018, Available from https://
materialeconomics.com/publications/the-circulareconomy
32. A. Larsson, L. Lindfred, Digitalization, circular economy and
the future of labor: How circular economy and digital
transformation can affect labor, in: The Digital Transforma-
tion of Labor: Automation, The Gig Economy and Welfare,
1st ed., Routledge, 2020, 16 p., DOI: 10.4324/
9780429317866-16
33. M. Arens, Policy support for and R&D activities on digitising
the European steel industry, Resour. Conserv. Recycl. 143,
244–250 (2019)
34. C. Neef, S. Hirzel, M. Arens, Industry 4.0 in the European
Iron and Steel Industry: Towards an Overview of Imple-
mentations and Perspectives, Fraunhofer Institute for
Systems and Innovation Research ISI, Karlsruhe, Germany,
2018
35. F. Marchiori, M. Benini, S. Cateni, et al., Agent-based
approach for energy demand-side management, Stahl und
Eisen 138,25–29 (2018)
36. S. Dettori, I. Matino, V. Colla, V. Weber, S. Salame, Neural
Network-based modeling methodol-ogies for energy transfor-
mation equipment in integrated steelworks processes, Energy
Proc. 158, 4061–4066 (2019)
37. G.F. Porzio, B. Fornai, A. Amato, et al., Reducing the energy
consumption and CO
2
emissions of energy intensive indus-
tries through decision support systems –An example of
application to the steel industry, Appl. Energy 112, 818–833
(2013)
38. A. Maddaloni, G.F. Porzio, G. Nastasi, V. Colla, T.A.
Branca, Multi-objective optimization ap-plied to retrofit
analysis: A case study for the iron and steel industry, Appl.
Therm. Eng. 91, 638–646 (2015)
T.A. Branca et al.: Matériaux & Techniques 108, 508 (2020) 9
39. I. Matino, E. Alcamisi, V. Colla, S. Baragiola, P. Moni,
Process modelling and simulation of electric arc furnace
steelmaking to allow prognostic evaluations of process
environmental and energy impacts, Matériaux & Techniques
104, (2016)
40. I. Matino, V. Colla, F. Cirilli, et al., Environmental impact
evaluation for effective resource manage-ment in EAF
steelmaking, La Metallurgia Italiana 109,48–58 (2017)
41. I. Matino, T.A. Branca, B. Fornai, V. Colla, L. Romaniello,
Scenario analyses for by-products reuse in integrated
steelmaking plants by combining process modeling, simula-
tion, and optimi-zation techniques, Steel Res. Int. 90, (2019)
42. H. Zsifkovits, J. Kapeller, H. Reiter, C. Weichbold, M.
Woschank, Consistent Identification and Traceability of
Objects as an Enabler for Automation in the Steel Processing
Industry, in: Matt D, Modrák V, Zsifkovits H, eds.,
Industry 4.0 for SMEs, Palgrave Macmillan, Cham, 2020
43. L.-W. Kang, Y.-T. Chen, W.-C. Jhong, C.-Y. Hsu, Deep
learning-based identification of steel products, Smart
Innovation, Syst. Technol. 110, 315–323 (2019)
44. C.-Y. Hsu, L.-W. Kang, H.-Y. Lin, R.-H. Fu, C.-Y. Lin, M.-F.
Weng, D.-Y. Chen, Depth-based feature extraction-guided
automatic identification tracking of steel products for smart
manufacturing in steel 4.0, in: Proceedings of 4th IEEE
International Conference on Applied System Innovation
2018, ICASI 2018, 2018, pp. 145–146
45. T.A. Branca, B. Fornai, V. Colla, M.M. Murri, E. Streppa, A.
J. Schröder, The Challenge of Digitalization in the Steel
Sector, Metals 10, 288 (2020)
46. D. Autor, A. Salomons, Is automation labor-displacing?
Productivity growth, employment, and the labor share,
National Bureau of Economic Research, 2018
47. W. Bauer, C. Vocke, Work in the Age of Artificial
Intelligence Challenges and Potentials for the Design of
New Forms of Human-Machine Interaction, in: Kantola J,
Nazir S, eds., Advances in Human Factors, Business
Management and Leadership, AHFE 2019, Adv. Intell. Syst.
Comput., Vol. 961, Springer, Cham, 2020, DOI: 10.1007/
978-3-030-20154-8_45
48. V. Colla, A.J. Schroeder, A. Buzzelli, D. Abbà, A. Faes, L.
Romaniello, Introduction of symbiotic human-robot-cooper-
ation in the steel sector: an example of social innovation,
Matériaux & Techniques 105, 505 (2017)
49. V. Colla, R. Matino, A. Faes, L. Romaniello, A.J. Schröder,
Robot-assisted replacement of the refractory components of
the ladle sliding gate in a steel shop, in: Proceedings of the
10th European Metallurgical Conference, EMC 2019, Vol. 4,
2019, pp. 1441–1454
50. European Commission, Blueprint for Sectoral Cooperation
on Skills: Towards an EU Strategy Addressing the Skills
Needs of the Steel Sector: European Vision on Steel-Related
Skills of Today and Tomorrow Study, European Commis-
sion, Bruxelles, Belgium, 2019, Available from <https://op.
europa.eu/en/publication-detail/-/publication/ff0f8660-
ca07-11e9-992f-01aa75ed71a1>
51. T.W. Malone, The Future of Work. How the New Order of
Business Will Shape Your Organization, Your Management
Styles, and Your Life, Harvard Business School Press,
Cambridge, MA, 2004
52. L.A. Berger, D.R. Berger, The Talent Management Hand-
book: Creating Organisational Excellence By Identifying,
Developing & Promoting Your Best People, McGraw-Hill,
New York, 2004
53. F. Xia, L.T. Yang, L. Wang, A. Vinel, Internet of things, Int.
J. Commun. Syst. 25(9), 1101–1102 (2012)
54. F. Zhang, M. Liu, Z. Zhou, W. Shen, An IoT-based online
monitoring system for continuous steel casting, IEEE Intern.
Things J. 3(6), 1355–1363 (2016)
55. K. Schwab, The Fourth Industrial Revolution, Currency,
2017
Cite this article as: Teresa Annunziata Branca, Barbara Fornai, Valentina Colla, Maria Maddalena Murri, Eliana Streppa,
Antonius Johannes Schröder, Current and future aspects of the digital transformation in the European Steel Industry, Matériaux &
Techniques 108, 508 (2020)
10 T.A. Branca et al.: Matériaux & Techniques 108, 508 (2020)