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Industry 4.0 adoption. An overview of European Union Enterprises

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Abstract

Technology and Digital transformations have been significantly disrupting and affecting business models, manufacturing processes, and corporate governance in the past decade. The constant improvements in information and communication technologies (ICT) infrastructure, and in analytical capabilities have fuelled a stream of innovation and changes at all levels of business models and value chains and the ability of companies to adopt and master them has become an element of competitive advantage in almost all economic sectors. The present study focuses on providing an overview and discussing the current status and the implications and challenges for Industry 4.0 adoption among European manufacturing SMEs.
Industry 4.0 adoption. An overview of European Union Enterprises
Lidia ALEXA
”Gheorghe Asachi” Technical University, Iasi, Romania
lidia.alexa@tuiasi.ro
Mirza Mohammad Didarul ALAM
School of Business & Economics, United International University, Bangladesh
mirza@bus.uiu.ac.bd
Abstract
Technology and Digital transformations have been significantly disrupting and affecting business models,
manufacturing processes, and corporate governance in the past decade. The constant improvements in
information and communication technologies (ICT) infrastructure, and in analytical capabilities have
fuelled a stream of innovation and changes at all levels of business models and value chains and the
ability of companies to adopt and master them has become an element of competitive advantage in almost
all economic sectors. The present study focuses on providing an overview and discussing the current
status and the implications and challenges for Industry 4.0 adoption among European manufacturing
SMEs.
Keywords
:
Industry 4.0, digitization, ICT, business models, SMEs.
Introduction
Industry 4.0 refers to recent technological advances where the internet and supporting technologies that
serve as a backbone to integrate physical objects, human actors, intelligent machines, production lines
and processes across organizational boundaries to form a new kind of intelligent, networked and agile
value chain (Schumacher et. al, 2016). The concept of Industry 4.0 has gained great importance in recent
years in all sectors of the economy, hence the vast amount of academic research and discussions.
The introduction of the term came from the German Federal Government which presents Industry 4.0 as
an emerging structure in which manufacturing and logistics systems in the form of Cyber Physical
Production System (CPPS) intensively use the globally available information and communications
network for an extensively automated exchange of information and in which production and business
processes are matched (Bahrin et. al, 2016).
Industry 4.0 brings disruptive changes to supply chains, business models, and business
processes (Schmidt et. al, 2015). The possibilities offered by the growing use of digitization in the
corporate world are changing companies’ competitive positions, how they interact with their employees
and customers (Dery et. al, 2017) and how they position themselves in the market. Some companies are
offering products and services that are apparently outside their original business model, but that are
possible because of the way that model has been adapting itself to digitization (Dongback, 2017).
So in this particular context, it is beginning to be clear that nowadays it is unnecessary to primarily focus
on awareness-raising with respect to – 1) what industry 4.0 trends are, and 2) what they can bring. These
trends are, at least in certain respects, already advancing quickly within many enterprises, and
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furthermore, their dynamics correspond to the high pace of change overall today. Thus, at the recent time,
the burning question is not “whether,” but “when” i.e., how fast 4.0 trends will penetrate into the day-to-
day lives of enterprises—and beyond them into society overall (Basl & Doucek, 2019).
Industry 4.0 key Components
The extant literature provides a wide range of definitions and key components for Industry 4.0, which
shows the growing interest and impact of the subject but can also raise confusion and adjacent challenges
in terms of understanding and adopting it in the business and manufacturing process.
Hermann et al (2016) defines Industry 4.0 as a collective term to refer it as technologies of value chain
organizations, the components of which are categorized as Internet of the Things, Cyber Physical
Systems, Internet of Services and Smart Factory.
Rüßmann et al (2015) shapes the vision of Industry 4.0 by defining nine aspects related to the concept,
which are: Big data and Analytics, Autonomous robots, Simulation, System Integration (Horizontal and
Vertical) , Internet of Things, Cyber security and Cyber Physical Systems (CPS), the Cloud, Additive
Manufacturing, and Augmented Reality.
Big data and Analytics refers to the collection and comprehensive evaluation of data from many
different sources such as production equipment and systems as well as enterprise and customer-
management systems and it will become standard to support real-time decision making (Rüßmann et al,
2015). According to Forrester’s definition, Big Data consists of four dimensions: volume of data, variety
of data, velocity of generation of new data and analysis, value of data (Witkowski, 2017). One of the
main challenges with respect to big data refers to the fact that manufacturing companies need viable and
up-to-date solutions that collect, process, and produce valuable and usable data from many diverse
sources and merge it to provide real-time perspective analytics for 24/7 automated rules and adaptive
machine learning.
Autonomous robots play an important role in modern manufacturing industry. The number of
multipurpose industrial robots developed by players in the Industry 4.0 in Europe alone has almost
doubled since 2004 (Roland Berger, 2014). Robots are also becoming more autonomous, flexible, and
cooperative day by day and at certain they will interact with one another and work safely side by side
with humans and learn from them (Rüßmann et al, 2015). An autonomous robot is used to perform
autonomous production method more precisely and also work in the places where human workers are
restricted to work. Autonomous robots can complete given task precisely and intelligently within the
given time limit and also focus on safety, flexibility, versatility and collaboratively (Bahrin et. al, 2016).
The most important impact of autonomous robots is on job market structure, as many jobs become
obsolete and companies need workers with a different set of competences and skills.
Simulations will leverage real-time data to mirror the physical world in a virtual model, which can
include machines, products and humans (Rüßmann et al, 2015). This element allows operators to test and
optimize the machine settings for the next products in line with the virtual world before the physical
changeover, thereby driving down machine setup times and increasing the end product quality.
System Integration: Horizontal and Vertical System Integration. Integration and self-optimization are
the two major mechanisms used in industrial organization (Schuh et. al, 2014). The paradigm of Industry
4.0 is essentially outlined by three dimensions of integration: (a) horizontal integration across the entire
value creation network, (b) vertical integration and networked manufacturing systems, and (c) end-to-end
engineering across the entire product life cycle (Stock & Seliger, 2016).
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Internet of Things refers to a worldwide network of interconnected and uniform addressed objects that
communicate via standard protocols (Hozdić, 2015). There is also another approach which talks about the
Internet of Everything (IoE) which consists of Internet of Service (IoS), Internet of Manufacturing
Services (IoMs), Internet of People (IoP), an embedded system and Integration of Information and
Communication technology (IICT) (Neugebauer et. al, 2016). The factories of the future are all about
intelligent planning, agility and networking with all elements of the process interconnected and in
constant real-time communication.
Cyber security and Cyber Physical Systems (CPS). Cyber security has become paramount with the
increased connectivity and use of standard communications protocols that come with Industry 4.0 and so,
the need to protect critical industrial systems and manufacturing lines from cyber security threats
increases dramatically (Vaidyaa et al, 2018). The term CPS has been defined as the systems in which
natural and human made systems (physical space) are tightly integrated with computation,
communication and control systems (cyber space) (Bagheri et. al, 2015) and is characterized by the
decentralization and autonomy of the production process.
The Cloud. Within Industry 4.0, fast and reliable data sharing is and so cloud-based IT-platforms serve
as a technical backbone for the connection and communication of manifold elements of the Application
Centre Industry 4.0 (Landherr et al, 2016).
Additive Manufacturing methods are also widely used in Industry 4.0 to produce small batches of
customized products, reducing time to market, and increasing customer satisfaction by proving products
and services that better respond to the consumers’ needs and expectations. At the same time, high-
performance, decentralized additive manufacturing systems will reduce transport distances and stock on
hand, diminishing costs.
Augmented Reality systems support a variety of services meant to both increase productivity and
decrease errors, such as providing workers with real-time information to perform procedures and make
decisions, sending repair instructions on how to replace a particular part as they are looking at the actual
system needing repair (Rüßmann et al, 2015)
Opportunities and challenges of Industry 4.0 adoption
In this thriving context, manufacturing enterprises are currently facing substantial challenges with regard
to the disruptive concept of Industry 4.0. It is obvious that such a far-reaching vision will lead to an
increased complexity of manufacturing processes on the micro and macro level (Schuh et. al, 2014) and
especially small and medium sized manufacturing companies are uncertain about the financial effort
required for the acquisition of such new technology and the overall impact on their business model.
Subsequently, increasing complexity on all firm levels creates uncertainty about respective organizational
and technological capabilities and adequate strategies to develop them (Schumacher et. al, 2016).
In order to face the new challenges, companies will need virtual and physical structures that allow for
close cooperation and rapid adaption along the whole lifecycle from innovation to production and
distribution (Gligor, D.M., Holcomb, 2012) and so industrial companies are digitizing essential functions
within their internal operations processes, as well as with their partners along the value chain (De Carolis
et. al, 2017). In addition, they are enhancing their product portfolio with digital functionalities and
introducing innovative, data-based services (GMIS, 2016). This is a journey they are taking towards a
complete value chain transformation and at the end, successful companies will become true digital
enterprises, with physical products at the core, augmented by digital interfaces and innovative services
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(Lee et. al, 2016; Porter et. al, 2014). In a futuristic and disruptive vision, these digital enterprises will
work together with customers and suppliers in industrial digital ecosystems (GMIS, 2016).
Despite the clear advantages and opportunities provided, extensive research on company’s perspective
over Industry 4.0 (Erol et al, 2016, Schumacher et. al 2016) have shown that companies have serious
problems to grasp the overall idea of Industry 4.0 and particular concepts hereof:
Companies perceive the concepts of Industry 4.0 as highly complex with no strategic guidance
offered
Companies are not able to relate it to their specific domain and their particular business strategy.
Companies experience problems in determining their state-of-development with regard to the
Industry 4.0 vision and therefore fail to identify concrete fields of action, programs and projects.
In this specific context, one of the main challenges industrial companies are now facing is to
define their Industry 4.0 transformation roadmap. In order to support companies to start defining
their digitalization transformation roadmap, a model to analyse their status quo is needed.
To overcome growing uncertainty and dissatisfaction in manufacturing companies regarding the idea of
Industry 4.0, new methods and tools are needed to provide guidance and support to align business
strategies and operations.
Various readiness indexes and maturity models can help companies to make easier, and also faster,
decisions concerning the question of in which areas they should build up Industry 4.0, and at what tempo.
Both of these, meanwhile, indicate not only a company’s own position, but also the positions of its
competition. At present, attention is shifting towards tasks connected with the execution of needed
changes and towards specifying the expectations other than merely profit that are connected with their
deployment (Basl & Doucek, 2019).
Studies on Industry 4.0 Adoption
Academic investigation addressing Industry 4.0 extensively focuses on large enterprises (Arnold et al.,
2016; Radziwon et al., 2014) and only marginally on SMEs (small and medium-sized enterprises)
(Schmidt et al., 2015). Yet many large companies act as suppliers to SMEs and have SMEs as suppliers.
Their actions affect the actions of their smaller supply chain partners and their requirements influence the
positioning of SMEs towards the technological developments derived from Industry 4.0. Therefore, it is
important to consider how SMEs implement Industry 4.0 and consequently, how it impacts industrial
value creation in SMEs (Muller et al., 2018). SMEs also provide a fruitful research sample, as those
represent over 99% of the companies located in the EU and hire between 50% and 70% of the full time
equivalent of persons employed.
Industry 4.0 adoption in the European Union
The European Union understands the potential of advanced technologies to transform EU industries and
so there are several policies at European level that have been further translated to national level meant to
encourage and support adoption of digital technologies.
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In May 2015, the European Commission adopted a Digital Single Market Strategy as one of its top 10
political priorities for 2015-2019. The Digital Single Market Strategy had 16 initiatives that covered three
broad pillars: promoting better online access to goods and services across Europe, designing an optimal
environment for digital networks and services to develop and ensuring that the European economy and
industry takes full advantage of the digital economy as a potential driver for growth.
In the same spirit, most of the EU governments have made Industry 4.0 a priority adopting large-scale
Industry 4.0 policies to increase productivity and competitiveness and improve the high-tech skills of
their workforce.
Within the Strategy for the Single Digital Market, the European Commission (2015) has been working on
the identification and gathering of data that allows for the measurement and characterization of the digital
society, including data related with the digitization of production processes.
Data Covering Industry 4.0 Adoption In EU
Most of the data used throughout this study is based on the The Digital Economy and Society Index
(DESI) (2019), which is a composite index that summarizes relevant indicators on Europe’s digital
performance and tracks the progress of EU Member States in digital competitiveness and Eurostat
database which is the outcome of the answers to the questionnaire ICT usage and e-commerce in
enterprises (Eurostat, 2018) and refers solely to enterprises from the manufacturing sector, according to
the statistical classification of economic activities in the European Community. This survey is conducted
periodically and enquires the habits, practices, constraints, expectations, and intentions related to ICT
usage with the intention of gathering information that allows the characterization of the degree of
adoption across time and countries in the EU.
According to DESI (2019) Finland, Sweden, the Netherlands and Denmark, have the most advanced
digital economies in the EU followed by the UK, Luxembourg, Ireland and Estonia. Bulgaria, Romania,
Greece and Poland have the lowest scores on the index.
In terms of Internet access, according to Eurostat data, in 2018, the vast majority (97 %) of EU
enterprises with at least 10 persons employed used a fixed broadband connection to access the internet,
going up 3% from 2011, suggesting that at EU level the uptake of this technology has reached saturation.
9 countries have declared that 100% of their companies have internet access, while at the other end of the
spectrum are Romania and Greece with 86% of companies with internet access. With almost all
enterprises connected to the internet via broadband, the attention of policymakers has more recently
switched to the speed of fixed broadband connections.
Internet connection speed. In Industry 4.0 context, speed is equally important to access, and so, the
share of enterprises using the fastest internet connections tripled between 2011 and 2018. In 2018, one
fifth (20 %) of enterprises in the EU-28 had an internet connection speed that was within the range of 2
Mb/s but < 10 Mb/s, with a slightly higher share (24 %) having a connection that was in the range of 10
Mb/s but < 30 Mb/s. One quarter (25 %) had a connection in the range of 30 Mb/s but < 100 Mb/s,
while the fastest internet connections (at least 100 Mb/s) were enjoyed by more than one sixth (18 %) of
enterprises in the EU-28.
In terms of Cloud computing, the Eurostat data showed that more than 1 in 4 (26 %) EU enterprises
reported that they used cloud computing services. Compared to 2014, this represents an increase of 7%.
However, there are significant differences across countries. In Finland, Sweden, Denmark, the
Netherlands, Ireland, the United Kingdom and Belgium at least 40 % of enterprises used cloud computing
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in 2018. On the other hand, in Romania and Bulgaria only 10 % or fewer enterprises did so, confirming
the results of the DESI conclusions. Another relevant element refers to the significant differences
between large enterprises and small ones when cloud computing use is discussed: the first ones use
cloud computing much more (56% of enterprises employing 250 persons or more) than small ones (23%
of enterprises employing 10 to 49 persons). And over the last four years (between 2014 and 2018), the
highest increase in cloud computing usage was also registered for large enterprises ( 21% increase),
compared with 12% in medium sized enterprises and 6% in small enterprises.
The use of 3D printing, also known as 'additive layer manufacturing', refers to the use of special printers
either by the enterprise itself or the use of 3D printing services provided by other enterprises
(outsourcing) for the creation of three-dimensional physical objects using digital technology.
In 2018, 4 % of EU enterprises with at least 10 persons employed used 3D printing, either their own 3D
printers or printing services provided by other enterprises. The largest shares of enterprises using 3D
printing were observed in Finland (7 %), Belgium, Denmark, Malta and the United Kingdom (all 6 %).
The smallest shares were reported by enterprises in Cyprus and Latvia (both 1 %), followed by Bulgaria,
Estonia, Greece, Hungary, Poland and Romania (all 2 %).
In large enterprises, the share of 3D printing stood at 13 % compared with 4 % in SMEs. This technology
was most used in the manufacturing sector (9 %), followed by enterprises in professional, scientific and
technical activities (6 %) and in information and communication (5 %).
Big data usage. In recent years, the quantity of digital data created, stored and processed in the world has
grown exponentially. Each activity conducted online or by using information and communication
technologies generates series of digital imprints which, given their volume, variety and velocity, are
referred to as big data.
According to the data gathered from companies in the EU, in 2018, 12 % of enterprises with at least 10
persons employed reported analysing big data. The big data analysis is predominantly done by large
enterprises (33%) and medium sized enterprises (19%). Among EU Member States, the largest shares of
enterprises analysing big data were observed in Malta (24 %), the Netherlands (22 %), Belgium and
Ireland (both 20 %). The smallest shares were noted in Cyprus (5 %), Hungary and Austria (both 6 %),
Bulgaria and Italy (both 7 %).
Enterprises that analysed big data used a variety of data sources, the most popular ones used being
geolocation and social media data. Almost half of all enterprises analysed geolocation data from the use
of portable devices e.g. portable devices using mobile telephone networks, wireless connections or GPS
(49 %), followed by data generated from social media e.g. social networks (45 %). Less than one third of
enterprises analysed own big data from smart devices or sensors (29 %) or data from other sources
(26 %).
As shown in Figure 1, in SMEs, the use of geolocation data from portable devices and data from social
media was prevailing whereas large enterprises mostly used data from the enterprise's own smart devices
or sensors and other sources.
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Figure 1: Structure of the data sources used and size class of EU enterprises
In large enterprises, big data analysis was performed mainly by own employees (90 % of enterprises
analysing big data) rather than by an external service provider (75 %). Among SMEs, slightly more
enterprises relied on external service providers (42 %) to analyse big data than on own employees (40 %).
Conclusions
The high interest and impact of Industry 4.0 technology and applications is highly visible both in terms of
literature written and research conducted and in the actual evolution statically proven. What is still under
discussion is the fact that there are still significant discrepancies between different countries in the EU
and also between large corporations and SMEs, showing that internet access alone is not enough anymore
to ensure access and competitiveness.
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The Industrial Internet of Things (IIoT) poses large impacts on business models (BM) of established manufacturing companies within several industries. Thus, this paper aims at analyzing the influence of the IIoT on these BMs with particular respect to differences and similarities dependent on varying industry sectors. For this purpose, we employ an exploratory multiple case study approach based on semi-structured expert interviews in 69 manufacturing companies from the five most important German industries. Owing the lack of previous research, our study contributes to the current state of management literature by revealing the following valuable insights with regard to industry-specific BM changes: The machine and plant engineering companies are mainly facing changing workforce qualifications, the electrical engineering and information and communication technology companies are particularly concerned with the importance of novel key partner networks, and automotive suppliers predominantly exploit IIoT-inherent benefits in terms of an increasing cost efficiency.