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Citation: Koviˇc, K.; Ojsteršek, R.;
Palˇciˇc, I. Simultaneous Use of Digital
Technologies and Industrial Robots in
Manufacturing Firms. Appl. Sci. 2023,
13, 5890. https://doi.org/10.3390/
app13105890
Academic Editor: Jose Machado
Received: 7 April 2023
Revised: 28 April 2023
Accepted: 6 May 2023
Published: 10 May 2023
Copyright: © 2023 by the authors.
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4.0/).
applied
sciences
Article
Simultaneous Use of Digital Technologies and Industrial
Robots in Manufacturing Firms
Klemen Koviˇc, Robert Ojsteršek * and Iztok Palˇciˇc
Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia; klemen.kovic@um.si (K.K.);
iztok.palcic@um.si (I.P.)
*Correspondence: robert.ojstersek@um.si; Tel.: +386-2-2207514
Abstract:
This paper presents the use of digital technologies and industrial robots in manufacturing
firms. More importantly, we look at the relationship between the use of digital technologies and
industrial robots within the Industry 4.0 concept. We also use a specific Industry 4.0 Readiness index
to assess manufacturing firms’ Industry 4.0 readiness level and analyze the relationship between
the achieved readiness level and the use of industrial robots. The research is based on data from
118 manufacturing firms from a European Manufacturing Survey. Based on statistical analysis, we
present the results that show a significant correlation between the use of specific digital technologies
and two types of industrial robots. Our study also points out that manufacturing firms with a higher
Industry 4.0 readiness level tend to use industrial robots more frequently.
Keywords:
digital technology; industrial robot; industry 4.0; manufacturing firm; European
manufacturing survey; readiness index
1. Introduction
Commonly the general understanding of the Industry 4.0 concept is fully automated
physical systems, but it should also be considered as automated and smart decision systems,
not only automated physical systems [
1
]. Industry 4.0 is marked by highly developed
automation and digitization processes and by the use of electronics and digital technologies
in manufacturing [
2
]. Industry 4.0 can be defined briefly as the digitalization of manu-
facturing. Digitalization refers to “the manifold sociotechnical phenomena and processes
of adopting and using (digital) technologies in broader individual, organizational, and
societal contexts [
3
]. The adoption of digital technologies influences almost all areas of
modern firms (firms that have adopted or are actively trying to adopt the latest technologies,
practices or business models), including manufacturing/production processes [4].
In manufacturing firms, we must also consider, besides digital technologies, other ad-
vanced manufacturing technologies, such as industrial robots. The use of industrial robots
cannot be detached from the use of at least basic digital technologies. Kaivo-Oja et al. [
5
]
argue that robotics, digitalization and ICT technologies present three critical technology
roadmaps in the near future that are intertwined heavily. Oztemel and Gursev [
6
] per-
formed a wide systematic literature review of Industry 4.0 and related technologies, where
they addressed the use of selected digital technologies and different types of (intelligent)
robots. In their literature review, they point out some research where the authors argue
that fostering robotics can support digital transformation. They present several areas
and applications where robots and digital technologies meet. Some studies examine the
introduction of specific digital technologies in connection with robots that focus mostly on
people’s attitudes towards such new technologies [
7
,
8
]. Nevertheless, we found no studies
that examine the correlation between the use of typical Industry 4.0 digital technologies
and different types of industrial robots.
According to Schumacher et al. [9], firms have serious problems grasping the overall
idea of Industry 4.0. One of the problems is that they experience problems in determining
Appl. Sci. 2023,13, 5890. https://doi.org/10.3390/app13105890 https://www.mdpi.com/journal/applsci
Appl. Sci. 2023,13, 5890 2 of 17
their state of development concerning the Industry 4.0 vision and therefore fail to identify
concrete fields of action. To overcome growing uncertainty and dissatisfaction in manu-
facturing firms regarding the idea of Industry 4.0, new methods and tools are needed to
provide guidance and support to align business strategies and operations [
9
]. Therefore, in
the last few years, different readiness models have been developed [
10
–
13
]. These models
differ in the areas they include and the measurement approaches. It is a common approach
that these models classify manufacturing firms based on the achieved readiness level. Some
models focus predominantly just on the use of Industry 4.0 enabling technologies [
14
,
15
]
as the sum of digital technologies and advanced manufacturing technologies, including
industrial robots. All these models neglect the inter-relationship between specific types of
technology, especially based on the Industry 4.0 readiness level of a manufacturing firm.
With these issues in mind, our research presents the diffusion of seven selected digital
technologies that present vital elements of the Industry 4.0 concept and the use of five types
of industrial robots. Firstly, the relationship between digital technologies and the use of
industrial robots is analyzed in general. Besides analyzing the general diffusion of digital
technologies, this paper also presents a possible Industry 4.0 readiness index and assesses
the Industry 4.0 readiness of Slovenian manufacturing firms. Additionally, the study aims
to examine the relationship between manufacturing firm Industry 4.0 readiness level in
terms of digital technologies’ use and the use of specific industrial robots.
Since the research is focused mainly on the identification of Industry 4.0 enabling
technologies, there is a considerable research gap in studying the relationships between
enabling technologies and advanced manufacturing technologies. Our main interest was
studying the relationships between enabling technologies and the use of industrial robots
in manufacturing firms. The cited literature shows that there is insufficient knowledge
about the link between digital technologies and industrial robots. The main aim of our
study is to provide an initial understanding of the simultaneous use of specific digital
technologies and different types of industrial robots. Therefore, we propose the following
research questions:
•
RQ 1: Is the use of industrial robots independent of digital technologies, or is there
a correlation?
•
RQ 2: Which of the proposed digital technologies is more associated with the use of
Industrial robots?
•
RQ 3: Are Industry 4.0 readiness levels and the use of industrial robots independent
of each other, or is there a correlation?
The remainder of this paper is organized as follows: Section 2gives an overview of
the relevant literature on industrial robots and their connection to digital technologies.
Additionally, we describe existing Industry 4.0 readiness models. In Section 3, we describe
our data and research methodology and present the Industry 4.0 readiness index used
for the purpose of our research. Section 4presents and discusses our results in terms of
an overview of the frequency of digital technologies and industrial robots’ use in man-
ufacturing firms, as well as the relationship between both types of technology. Finally,
conclusions, limitations, directions for future research and managerial implications are
given in Section 5.
2. Literature Review
Manufacturing has a profound impact on economic and social progress. The Industry
4.0 initiative is a widely accepted term for research institutions and has attracted a great
deal of attention from the business and research communities [
6
]. With the process of
digitalization, many are wondering how the introduction of new technologies will affect
manufacturing processes, job creation and job destruction. The most significant impacts are
caused almost exclusively by firms using machine-based digital technologies, such as robots,
additive manufacturing, cyber-physical systems (CPS) or the Internet of Things (IoT) [
16
].
The IoT maximizes the information and intelligence of large-scale producers and improves
the monitoring quality and efficiency of the manufacturing lines greatly [
17
]. Increasing
Appl. Sci. 2023,13, 5890 3 of 17
productivity while maintaining the quality of manufactured products is crucial in today’s
industrial context. In this sense, the use of robots in manufacturing plants is increasing due
to the advantages in terms of flexibility, repeatability, and low cost compared to workers
and unautomated machines [
18
]. When introducing robots into existing manufacturing
processes, firms need to analyze the key economic, social, and environmental challenges
of digital transformation [
5
]. In developed countries, smart factories can use industrial
equipment that communicates with users and other machines, automated processes, and
machines to facilitate real-time communication between the factory and the market, support
dynamic adjustments, and maximize manufacturing system efficiency [
19
]. Therefore, one
of the main struggles of firms trying to adopt robots is the identification of the key enabling
technologies which are related to the increased use of industrial robots.
2.1. Digital Technologies and Industrial Robots
The existing literature has focused mainly on robotization at the industrial level, re-
search focused on the enterprise level, and complementarity analysis is limited to evaluating
firms’ overall digitalization levels [
20
]. How digital technologies relate to robots is un-
known [
21
]. The integration of robots and IoT leads to the concept of the Internet of Robotic
Things, in which the innovation of digital systems opens new opportunities in both industry
and research, especially from the manufacturing perspective [
22
]. As an emerging paradigm
for digital services, a smart product service system leverages intelligent networked products
and the services they generate to act as a bundle of solutions to improve individual user
satisfaction and strengthen the global competitiveness of the firms [
23
]. The manufacturing
system based on the digital twin is a typical representative of intelligent manufacturing and
has several advantages related to cost, time and firms’ flexibility justification [
24
]. The digital
twin technology contributes effectively to the improvement of the quality and efficiency of
robotic operations, especially in time-consuming tasks [
25
]. Nikolakis et al. proposed the
implementation of the digital twin approach as part of a broader cyber-physical system to
enable the optimization of human-based production process planning and commissioning
through simulation-based approaches [
26
]. Digital twins are used to extract information
from the knowledge base to support decision-making and control of the management during
all manufacturing system operating modes [
27
]. The trends of implementing human-robot
collaboration (cobots) could represent new technological advantages in correlation with
the rapid movements and massive forces generated by industrial robots [
28
]. Although the
introduction of collaborative workplaces can be cost-effective, there is still a great deal of
uncertainty about how such workplaces affect the cost, time, and social and environmental
justification of the entire manufacturing system [
29
]. In particular, the authors emphasize
that the findings on the impact of the cobot’s auditory and visual effects on the worker when
the average operational time is reduced while increasing the cobot’s capabilities need to be
further explored. A cyber-physical production system (CPPS), which offers the advantages
of autonomy, self-organization, and interoperability, can be used to increase the flexibility
of manufacturing systems [
30
,
31
]. Longer-term discussions of technological individuality
are considered, along with sociotechnical and economic constraints on the application of
robotics and artificial intelligence (AI) in highly digitalized manufacturing systems [
32
].
The question remains to what extent the extreme automation will be accelerated by the
Internet of Things (IoT) and what impact AI and Industry 4.0 will have on the manufacturing
efficiency and overall global competitiveness of the firms [33].
2.2. Industry 4.0 Readiness Models
As manufacturing firms are quite often integrated into the global economy and global
supply chains, they face increasing competitive pressures [
34
]. Due to the complexity
of new technologies and rapid market changes, it is becoming increasingly difficult for
firms to find an answer to the question of «how to stay competitive?». Rapid technological
advances have pushed governments to create strategies and initiatives to improve the
entire manufacturing sector in their respective countries. One such example is the German
Appl. Sci. 2023,13, 5890 4 of 17
strategic initiative called “Industrie 4.0,” which was created to increase the competitiveness
of the German manufacturing sector through digitization and interconnection [
35
] and
is now synonymous with the fourth industrial revolution, or Industry 4.0. Because the
concept of Industry 4.0 is so broad and it encompasses a variety of terminologies and
techniques, it is possible that not all technologies may be equally relevant to all firms [
34
].
To address these issues, researchers have developed Industry 4.0 readiness and maturity
models. As Schumacher has already stated, there is a difference between readiness and
maturity assessment. Readiness assessment takes place before the maturity evaluation and
should not be treated as a synonym [
9
]. Some authors define the readiness model as “the
degree to which organizations are able to take advantage of Industry 4.0 technologies [
36
],
while others define it as “an instrument to conceptualize and measure the starting point
and allow for initializing the development process [
9
]. With these definitions in mind, it
can be established that an Industry 4.0 readiness model tries to represent how ready an
enterprise is to implement advanced technologies and concepts following the Industry
4.0 paradigm. As Pacchini et al. observed, the difference between the degree of matu-
rity and the degree of readiness remains undefined [
14
]. Since the majority of existing
readiness models follow very similar steps (literature review, questionnaire formulation,
data gathering, interviews/workshops with experts and model confirmation with a case
study), this paper only focuses on their attributes or dimensions. Considered by many
as the first available readiness model, the IMPULS Industry 4.0 Readiness measurement
model uses the following six key dimensions as the foundation: strategy and organization,
smart factory, smart operations, smart products, data-driven services, and employees [
11
].
This is available as an online self-check tool for Businesses and measures Industry 4.0
readiness on six levels (from level 0 to level 5) [
11
]. It was commissioned by the IMPULS
Foundation of the German Engineering Federation (VDMA) and was not published as an
article. Botha has proposed a so-called Future readiness index which would help in defining
future strategic interventions [
37
]. It is based on the future thinking space, which includes
technology, behavior, events, and the capability to do future thinking [
37
]. Castelo-Branco
and others have assessed Industry 4.0 of the European Union based on Factor analysis
of ICT usage and digitization of the corporate sector [
38
]. They have concluded that a
country’s readiness may be characterized by (ICT) infrastructure and big data maturity.
Pacchini et al. have proposed a model that can be used as a tool for the identification of
enabling technologies that need to be improved to achieve a higher degree of readiness
for the Implementation of Industry 4.0 [
14
]. This model focuses only on the technological
aspect of Industry 4.0 and does not consider other concepts. The authors believe that IoT,
big data, cloud computing, cyber-physical systems, autonomous robots, additive manu-
facturing, augmented reality, and artificial intelligence form foundations which enable
adequate Industry 4.0 implementation. Based on an exploratory sequential mixed method
design, Antony et al. identified 10 dimensions of Industry 4.0 readiness [
39
]. The identified
dimensions are technology readiness, employee adaptability with Industry 4.0, smart prod-
ucts and services, digitalization of supply chains, the extent of the digital transformation
of the organization, the readiness of the Industry 4.0 organization strategy, an innovative
Industry 4.0 business model, leadership and top management support for Industry 4.0,
organizational culture, and employee reward and recognition systems [39].
As expected, technology is a key component of many readiness models and should be
studied carefully. All the analyzed Industry 4.0 readiness models neglect the relationship
between Industry 4.0 enabling technologies and the level of achieved Industry 4.0 readiness
of the manufacturing firm. We argue that Industry 4.0 readiness is dependent, among
other things, on the number of digital technologies used in the manufacturing firm. At the
same time, we believe that the use of industrial robots also correlates with the Industry 4.0
readiness level of manufacturing firms.
Appl. Sci. 2023,13, 5890 5 of 17
3. Research Methodology
This section describes the data and research methodology used for our research. The
research is based on the Slovenian part of the largest European survey of manufacturing
activities, called the European Manufacturing Survey (EMS). The second part of this section
describes the Industry 4.0 Readiness index that was also developed within the EMS research
project as a possible tool for determining the basic Industry 4.0 readiness of manufacturing
firms. The third subsection presents the measures and statistical methods used to present
and analyze our data.
3.1. EMS
EMS is coordinated by the Fraunhofer Institute for Systems and Innovation Research—ISI.
The main objectives of the EMS project are to study the use of advanced manufacturing and
digital technologies, organizational concepts in manufacturing, as well as production and
product characteristics. The survey’s questions additionally concern manufacturing strategies,
cooperation issues, production off-shoring, back-shoring, servitization, and questions of
personnel deployment and qualification. In addition, data are collected on performance
indicators, such as productivity, flexibility, quality, and returns. The responding firms present
a cross-section of the main manufacturing industries. Included are producers of rubber and
plastics, metal works, mechanical engineering, electrical engineering, textile, and others. The
manufacturing firms in our research fall into the following NACE classification divisions:
22: Manufacture of rubber and plastic products;
23: Manufacture of other non-metallic mineral products;
24: Manufacture of basic metals;
25: Manufacture of fabricated metal products, except machinery and equipment;
26: Manufacture of computer, electronic and optical products;
27: Manufacture of electrical equipment;
28: Manufacture of machinery and equipment n.e.c.;
29: Manufacture of motor vehicles, trailers, and semi-trailers;
30: Manufacture of other transport equipment;
32: Other manufacturing.
The survey in Slovenia was performed in all of the manufacturing firms within these
divisions with at least 20 employees. Our research is based on the EMS data from a Slovenian
subsample from the year 2018/19. Around 800 questionnaires were sent, and 118 responses
were received, with a 15% response rate. In our 2018/19 subsample, manufacturing firms
from NACE divisions 22, 25, and 28 are represented most widely, with around 25% of firms
from NACE 25, around 20% from NACE 28, and around 16% from the NACE 22 division.
The structure of manufacturing firms based on their size is based on the number of employ-
ees. The largest share of respondents was from medium-sized firms (around 42%), and the
share of large firms (26%) was quite similar to the small firms’ share (32%).
Figure 1presents an example of a structural part of a question from EMS 2018 that
deals with the diffusion of technologies and represents a core question for all our analysis.
Appl. Sci. 2023, 13, x FOR PEER REVIEW 6 of 17
Figure 1. Question on the use of technologies in EMS 2018.
For each technology, we have asked for the following information:
• Use of technology (yes/no);
• Use planned in the upcoming period of 3 years (yes/no);
• Year in which this technology was used for the first time in your factory (year);
• The extent of actual utilization compared to the most reasonable potential utilization
in the factory: Extent of utilized potential, “low” for an initial attempt to utilize, “me-
dium” for partly utilized, and “high” for extensive utilization;
• Upgrade of the already implemented technology (technologies) in the last 3 years—
Follow-on investment since 2015 (yes/no);
In EMS 2018, we divided 16 technologies used in manufacturing firms into 4 groups:
• Production control: digital factory (9 technologies);
• Automation and robotics (2 technologies);
• Additive Manufacturing Technologies (2 technologies);
• Energy efficiency technologies (3 technologies).
For the purpose of our research, 7 technologies from the “production control: digital
factory” group were analyzed, and both types of industrial robots from the “automation
and robotics” group. In the separate technology questions, the use of additional industrial
robot types was investigated, such as mobile industrial robots, collaborating robots and
autonomous industrial robots.
3.2. Industry 4.0 Readiness Index
The proposed Industry 4.0 readiness index in our research was developed by Fraun-
hofer ISI [40]. The logic of the Fraunhofer Industry 4.0 readiness index is presented in
Figure 2, and it is based on the selected Industry 4.0 enabling technologies. For this index,
we are using digital (enabled) technologies that are highly process and operation-depend-
ent and come from different technology fields. Therefore, it is not sufficient simply to
count the number of technologies used. Based on the technology focus, they are divided
into 3 technology groups:
• Digital management systems: this group consists of software systems for production
planning and scheduling (also known as Enterprise Resource Planning systems; ERP)
and product lifecycle management systems (PLM);
• Wireless human-machine communication: the second group consists of digital visu-
alization technologies and mobile devices;
• Cyber-physical system (CPS)-related processes: the CPS group consists of near-real-
time production control systems, technologies for automation and management of
internal logistics, and technologies for the digital exchange of data.
The first 2 technology groups cover typical ICT-related processes (ERP and PLM sys-
tems, technologies for digital visualization and mobile devices) that, by themselves, can-
not form the Industry 4.0 concept. On the other hand, CPS-related technologies already
had production elements in cyber-physical systems and are therefore considered to be
among the advanced I4.0 technologies [40].
Figure 1. Question on the use of technologies in EMS 2018.
For each technology, we have asked for the following information:
•Use of technology (yes/no);
•Use planned in the upcoming period of 3 years (yes/no);
Appl. Sci. 2023,13, 5890 6 of 17
•Year in which this technology was used for the first time in your factory (year);
•The extent of actual utilization compared to the most reasonable potential utilization
in the factory: Extent of utilized potential, “low” for an initial attempt to utilize,
“medium” for partly utilized, and “high” for extensive utilization;
•
Upgrade of the already implemented technology (technologies) in the last 3 years—
Follow-on investment since 2015 (yes/no);
•In EMS 2018, we divided 16 technologies used in manufacturing firms into 4 groups:
•Production control: digital factory (9 technologies);
•Automation and robotics (2 technologies);
•Additive Manufacturing Technologies (2 technologies);
•Energy efficiency technologies (3 technologies).
For the purpose of our research, 7 technologies from the “production control: digital
factory” group were analyzed, and both types of industrial robots from the “automation
and robotics” group. In the separate technology questions, the use of additional industrial
robot types was investigated, such as mobile industrial robots, collaborating robots and
autonomous industrial robots.
3.2. Industry 4.0 Readiness Index
The proposed Industry 4.0 readiness index in our research was developed by Fraun-
hofer ISI [
40
]. The logic of the Fraunhofer Industry 4.0 readiness index is presented in
Figure 2, and it is based on the selected Industry 4.0 enabling technologies. For this index,
we are using digital (enabled) technologies that are highly process and operation-dependent
and come from different technology fields. Therefore, it is not sufficient simply to count
the number of technologies used. Based on the technology focus, they are divided into
3 technology groups:
•
Digital management systems: this group consists of software systems for production
planning and scheduling (also known as Enterprise Resource Planning systems; ERP)
and product lifecycle management systems (PLM);
•
Wireless human-machine communication: the second group consists of digital visual-
ization technologies and mobile devices;
•
Cyber-physical system (CPS)-related processes: the CPS group consists of near-real-
time production control systems, technologies for automation and management of
internal logistics, and technologies for the digital exchange of data.
Appl. Sci. 2023, 13, x FOR PEER REVIEW 7 of 17
Figure 2. Industry 4.0 readiness index.
Based on the presented grouping, manufacturing firms can be classified into different
levels of readiness for the Industry 4.0 concept. Some firms still do not use any Industry
4.0 enabling technologies (non-users), with no signs of digital production. Included in the
Basic readiness group are manufacturing firms that use mostly digital technologies from
Digital management systems and Wireless human-machine communication fields. The
number of technologies can vary; therefore, there are 3 basic levels. In the 3
rd
level, it is
assumed that firms also use 1 CPS-related process. The High readiness group consists of
firms that use and combine several technology fields in production and, at the same time,
use several of the CPS-related processes in their production. Accordingly, the Industry 4.0
readiness index results in the following main groups and levels:
Non-users who are not (yet) ready for Industry 4.0:
• Level 0: Firms that do not use any of the Industry 4.0 enabling technologies and tend
still to rely on traditional production processes;
• Basic levels, as the basis on the way to Industry 4.0, with little readiness;
• Level 1 (beginners): Firms that use IT-related processes in 1 of the 3 technology fields;
• Level 2 (advanced beginners): Firms that use IT-related processes in 2 of the 3 tech-
nology fields;
• Level 3 (advanced users): Firms that are active in all 3 technology fields and use both
IT-related processes and 1 technology in the CPS-related group;
• Top group, firms on the way to Industry 4.0, with a slightly higher readiness:
• Level 4: Firms that are active in all technology fields and use at least 2 technologies
of CPS-related processes;
• Level 5: Firms that are active in all technology fields and use at least 3 technologies
of CPS-related processes;
With each level, the Industry 4.0 readiness status increases, or the distance to digital
production decreases. However, even at levels 4 and 5, it cannot be assumed that these
are firms that embrace the Industry 4.0 concept fully. Nevertheless, the presented Industry
4.0 readiness index maps the change from traditional production to production close to
Industry 4.0 [40].
3.3. Measures and Statistical Methods
The following statistical methods were used to test the relationship between indus-
trial robot types and the selected digital technologies and the use of industrial robots
based on specific firm characteristics: Chi-square test of independence, Phi coefficient, and
logistic regression [41].
Figure 2. Industry 4.0 readiness index.
Appl. Sci. 2023,13, 5890 7 of 17
The first 2 technology groups cover typical ICT-related processes (ERP and PLM
systems, technologies for digital visualization and mobile devices) that, by themselves,
cannot form the Industry 4.0 concept. On the other hand, CPS-related technologies already
had production elements in cyber-physical systems and are therefore considered to be
among the advanced I4.0 technologies [40].
Based on the presented grouping, manufacturing firms can be classified into different
levels of readiness for the Industry 4.0 concept. Some firms still do not use any Industry
4.0 enabling technologies (non-users), with no signs of digital production. Included in the
Basic readiness group are manufacturing firms that use mostly digital technologies from
Digital management systems and Wireless human-machine communication fields. The
number of technologies can vary; therefore, there are 3 basic levels. In the 3rd level, it is
assumed that firms also use 1 CPS-related process. The High readiness group consists of
firms that use and combine several technology fields in production and, at the same time,
use several of the CPS-related processes in their production. Accordingly, the Industry 4.0
readiness index results in the following main groups and levels:
Non-users who are not (yet) ready for Industry 4.0:
•
Level 0: Firms that do not use any of the Industry 4.0 enabling technologies and tend
still to rely on traditional production processes;
•Basic levels, as the basis on the way to Industry 4.0, with little readiness;
•
Level 1 (beginners): Firms that use IT-related processes in 1 of the 3 technology fields;
•
Level 2 (advanced beginners): Firms that use IT-related processes in 2 of the 3 technol-
ogy fields;
•
Level 3 (advanced users): Firms that are active in all 3 technology fields and use both
IT-related processes and 1 technology in the CPS-related group;
•Top group, firms on the way to Industry 4.0, with a slightly higher readiness:
•
Level 4: Firms that are active in all technology fields and use at least 2 technologies of
CPS-related processes;
•
Level 5: Firms that are active in all technology fields and use at least 3 technologies of
CPS-related processes;
With each level, the Industry 4.0 readiness status increases, or the distance to digital
production decreases. However, even at levels 4 and 5, it cannot be assumed that these are
firms that embrace the Industry 4.0 concept fully. Nevertheless, the presented Industry
4.0 readiness index maps the change from traditional production to production close to
Industry 4.0 [40].
3.3. Measures and Statistical Methods
The following statistical methods were used to test the relationship between industrial
robot types and the selected digital technologies and the use of industrial robots based on
specific firm characteristics: Chi-square test of independence, Phi coefficient, and logistic
regression [41].
Pearson’s chi-square test of independence is a method for evaluating if 2 variables
are independent of each other. It is based on the assumption that the data are categorical
or nominal (mutually exclusive categories such as yes or no), the analyzed data represent
a random sample, and that the expected frequency of each cell is at least 5 or greater.
Fischer’s exact test should be used if the frequency is less than 5 [41].
The Phi coefficient is a special case of the Pearson product-moment correlation coeffi-
cient, which is employed when we wish to analyze the relationship between the levels of
2 dichotomous variables (in our case, there are many yes or no variables that are examples
of dichotomous variables). If the values of the phi correlation coefficient are in the range of
0.1 and 0.3, then it is considered that there exists a small effect size. If they are in the range
of 0.3 and 0.5, it is considered a medium effect size, and if the values are greater than 0.5,
then we can say that the effect size is large [41].
Lastly, the logistic regression will provide us with the likelihood that an observed
type of robot will fall into 1 of 2 categories (Using or Not using) [
41
]. In our case, we will
Appl. Sci. 2023,13, 5890 8 of 17
determine the likelihood that the firm is using a specific type of robot based on 7 digital
technologies and the readiness index level.
Several variables from the EMS questionnaire were used for the purpose of our
research. The following questions were used as dependent variables:
1.
Type of industrial robot (industrial robot for manufacturing processes, industrial
robots for handling processes, mobile industrial robots, collaborating robots and
autonomous industrial robots);
The following questions were used as independent variables:
2. Digital technologies (7 selected technologies);
3. Firm size (number of employees);
4. Readiness Index levels (level 0 to level 5).
Simple descriptive statistics were used for the presentation of the general use of
selected digital technologies and industrial robots. Descriptive statistics were also used to
present the distribution of firms within all 6 Industry 4.0 Readiness Index levels.
4. Results and Discussion
This section first presents the results of the overall use of selected digital technologies
in Slovenian manufacturing firms. Later, digital technologies, according to the previously
described Industry 4.0 readiness index, were combined and presented the share of manu-
facturing firms in each readiness level. The distribution is also presented of all five types of
industrial robots.
In the second part of the Results section, several statistical tests were performed to
address our research questions on the relationship between industrial robots and digital
technologies.
4.1. Descriptive Statistics
Table 1presents the diffusion of the seven selected digital technologies included in our
research. Analysis shows that software for production planning and scheduling (e.g., the
ERP system) is the most frequently used technology, but other “Digital factory” technologies
are catching up. This is especially evident for digital technologies to provide drawings,
work schedules or work instructions directly on the shop floor for product and process data
exchange with suppliers and customers (Electronic Data Interchange; EDI). There is also a
rise in the implementation of real-time production control systems and the introduction of
mobile/wireless devices for programming and controlling facilities and machinery.
Table 1.
Descriptive data from analysis of the adoption of digital technologies in Slovenian manufac-
turing firms.
Digital Technology Share [%]
Mobile/wireless devices for programming and controlling facilities and
machinery (e.g., tablets) 32.2%
Digital solutions to provide drawings, work schedules or work instructions
directly on the shop floor 54.2%
Software for production planning and scheduling (e.g., the ERP system) 62.7%
Digital exchange of product/process data with suppliers/customers (Electronic
Data Interchange; EDI) 51.7%
Near-real-time production control system (e.g., systems of centralized operating
and machine data acquisition, Manufacturing Execution System MES) 39.8%
Systems for automation and management of internal logistics (e.g., Warehouse
management systems, Radio Frequency Identification—RFID) 20.3%
Product-Lifecycle-Management-Systems (PLM) or Product/Process Data
Management (PDM) 19.5%
Appl. Sci. 2023,13, 5890 9 of 17
Table 2presents the use of five industrial robot types. As expected, the use of “tradi-
tional” industrial robots for manufacturing and handling processes is much higher than for
other types of robots that are gaining importance. When looking at the use of industrial
robots for manufacturing and handling processes together, it was found that at least one
type of robot is present in 64% of firms.
Table 2. Industrial robot adoption in Slovenian manufacturing firms.
Industrial Robot Type Share [%]
Industrial robots for manufacturing processes 50.0%
Industrial robots for handling processes 35.6%
Mobile industrial robots 4.2%
Collaborating robots 15.3%
Autonomous industrial robots 19.5%
Table 3depicts the share of manufacturing firms in each of the previously described
Industry 4.0 readiness levels. Almost 17% of firms have so far not implemented any digital
technologies in production. Around 57% of all firms already have IT-related processes
in their production and form the basic levels. This basic user group includes groups of
beginners who only use technologies from one or two IT-related areas (around 43% of
firms). The basic level group also included advanced firms that are combining technologies
from all three technology fields (almost 14%; level 3). In the two highest levels, 4 and 5, this
high-users group consisted of 26.3% of all firms. About every fourth firm is, consequently,
active in all three technology fields and uses not only IT-related processes but also several
CPS-related processes simultaneously.
Table 3. Share of manufacturing firms in the Industry 4.0 Readiness Index levels.
Industry 4.0 Readiness Index Level Share [%]
Level 0 16.9%
Level 1 19.5%
Level 2 23.7%
Level 3 13.6%
Level 4 12.7%
Level 5 13.6%
4.2. Statistical Tests
The association and correlation between selected digital technologies, readiness levels
and types of industrial robots were analyzed with the use of inferential statistics. The
technologies were coded as T1: PLM systems, T2: ERP Systems, T3: digital exchange of
data, T4: automation and management of internal logistics, T5: near-real-time production
control systems, T6: mobile devices for programming and/or operating systems and/or
machines, and T7: digital visualization (solutions) on the shop floor. Industrial robots
were coded as R1: robots for manufacturing processes, R2: robots for handling processes,
R3: mobile robots, R4: collaborative robots, and R5: autonomous robots. For analysis, IBM
SPSS Statistics was used, and each technology and readiness level was tested with different
types of industrial robots. Pearson’s chi-square and its corresponding significance value
were used when testing for the association. Values of Pearsons’ chi-square significance
below 0.05 indicate a significant association or relationship between the two variables,
which implies that the observed enabling technology (or readiness level) affects the specific
type of robots. If the value is above 0.05, then there is no significant relationship, and
the enabling technology has no effect on the use of industrial robots, and the values can
Appl. Sci. 2023,13, 5890 10 of 17
be ignored. Additionally, the Phi coefficient was used to gain a clearer understanding of
the strength and direction of the (correlation) effect. As previously mentioned, the Phi
correlation coefficient values between 0.1 and 0.3 were considered to represent a small
effect size, whereas values ranging from 0.3 to 0.5 were considered to indicate a medium
effect size. If the values exceed 0.5, it can be concluded that the effect size is large.
Table 4summarizes the results of the association and correlation analysis. As shown in
the Table, industrial robots for handling processes have a positive association and correla-
tion with each of the selected technologies. PLM systems, ERP systems, the digital exchange
of data, and near-real-time production control systems have a medium correlation effect
size, while the rest of the technologies have only a small effect size. Industrial robots for
manufacturing processes are correlated with five out of the seven technologies. Although
all five correlations are weak, the correlation between near-real-time production control
systems and robots for manufacturing processes and mobile devices for programming
and robots for manufacturing processes are very close to having a medium-sized effect.
Collaborative robots are correlated weakly with the use of ERP systems, near-real-time
production control systems and digital visualization (solutions) on the shop floor. No
association or correlation was found between the selected digital technologies and mobile
robots and autonomous robots.
Table 4. Association and correlation results for technologies and types of robots.
Technology Statistic R1 R2 R3 R4 R5
T1 Pearson χ20.008 <0.001 0.414 0.151 0.357
Phi 0.245 0.317 0.075 0.132 0.085
T2 Pearson χ20.011 <0.001 0.977 0.004 0.170
Phi 0.235 0.304 0.003 0.267 0.123
T3 Pearson χ20.017 <0.001 0.196 0.167 0.235
Phi 0.220 0.364 0.119 0.127 0.109
T4 Pearson χ20.188 0.004 0.355 0.338 0.501
Phi 0.121 0.263 −0.085 0.088 0.062
T5 Pearson χ20.001 <0.001 0.264 0.034 0.229
Phi 0.295 0.327 0.103 0.196 0.111
T6 Pearson χ20.002 0.008 0.174 0.079 0.894
Phi 0.290 0.245 0.125 0.162 0.012
T7 Pearson χ20.065 0.005 0.237 0.007 0.067
Phi 0.170 0.257 0.109 0.248 0.168
After conducting the initial association and correlation analysis, logistic regression was
carried out to determine the likelihood of using a certain type of industrial robot based on
the specific digital technology. When interpreting the logistic regression results, the main
focus was on the significance of the predictor variable and the likelihood of using a certain
type of robot when a certain enabling technology is utilized. The lower bound (LB) and
upper bound (UB) were also reported at the 95% confidence interval. Variable significance
(Sig.) indicates that a variable has a significant effect on prediction, while Exp(B) provides
an odds ratio or likelihood of using a certain type of industrial robot if a particular digital
technology is utilized. Table 5summarizes the results for logistic regressions for each
selected technology and the five types of industrial robots. As expected, technologies that
have a significant effect on predicting the use of a certain type of industrial robots are the
same as in the association and correlation analysis. All of the selected technologies have
a significant effect on predicting the use of industrial robots for handling processes, and
only the technologies T1–T3 and T5–T6 have a significant effect on the prediction of the
Appl. Sci. 2023,13, 5890 11 of 17
industrial robots for manufacturing process use. In the case of collaborative robots, the
use of this type of robot is predicted mainly by the use of ERP systems, near-real-time
production control systems and digital visualization (solutions) on the shop floor. Again,
no significant effect was found for mobile robots and autonomous robots.
Table 5. Logistic regression results for technologies and types of robots.
Technology Statistic R1 R2 R3 R4 R5
T1
Sig. 0.009 0.001 0.428 0.160 0.359
Exp(B) 2.836 4.744 2.457 2.333 1.576
95% CI LB 1.304 1.876 0.266 0.716 0.596
UB 6.168 11.997 22.714 7.601 4.169
T2
Sig. 0.014 0.002 0.977 0.006 0.186
Exp(B) 3.575 4.722 1.034 4.533 2.007
95% CI LB 1.296 1.796 0.110 1.540 0.716
UB 9.865 12.419 9.716 13.344 5.631
T3
Sig. 0.018 0.001 0.227 0.173 0.238
Exp(B) 2.453 5.186 3.930 2.082 1.739
95% CI LB 1.170 2.222 0.426 0.724 0.693
UB 5.143 12.105 36.260 5.981 4.364
T4
Sig. 0.189 0.005 0.373 0.341 0.502
Exp(B) 1.645 3.072 0.364 1.632 1.363
95% CI LB 0.782 1.404 0.039 0.595 0.552
UB 3.461 6.722 3.363 4.472 3.367
T5
Sig. 0.003 <0.001 0.282 0.04 0.233
Exp(B) 5.130 6.703 2.758 3.107 1.865
95% CI LB 1.766 2.486 0.434 1.053 0.669
UB 14.905 18.075 17.517 9.165 5.198
T6
Sig. 0.002 0.009 0.197 0.085 0.894
Exp(B) 3.682 2.929 3.343 2.448 1.067
95% CI LB 1.603 1.311 0.535 0.883 0.411
UB 8.457 6.545 20.903 6.789 2.767
T7
Sig. 0.066 0.006 0.266 0.013 0.073
Exp(B) 1.993 3.088 3.533 5.204 2.429
95% CI LB 0.955 1.377 0.383 1.418 0.922
UB 4.158 6.927 32.605 19.098 6.398
To interpret our results, an example of one of the included digital technologies is given:
near-real-time production control systems. If a firm is using near-real-time production
control systems, then it is 6.7 times more likely to use industrial robots for handling
processes, 5.1 times more likely to use industrial robots for manufacturing processes, and
3.1 times more likely to use collaborative robots.
When testing the results of association and correlation analysis for readiness levels
and types of industrial robots, the Phi coefficient was substituted with the Spearman rank
coefficient since readiness level is an ordinal variable (Table 6). The highest correlation
coefficient value was found for readiness levels and industrial robots for handling processes.
Appl. Sci. 2023,13, 5890 12 of 17
Readiness levels are also correlated with the use of industrial robots for manufacturing
processes but not with the other types of robots.
Table 6. Association and correlation results for readiness levels and types of robots.
Technology Statistic R1 R2 R3 R4 R5
Readiness level Pearson χ20.021 <0.001 0.920 0.400 0.450
Spearman correlation
0.283 0.437 0.096 0.198 0.141
After modeling relationships with the help of logistic regression between individual
digital technologies and types of industrial robots, the same procedure was applied to
analyze Industry 4.0 readiness levels and industrial robots. Since readiness level is a
categorical variable, it had to be specified as such in SPSS, and a reference category had
to be chosen. For each combination of readiness levels and a type of industrial robot, the
logistic regression was conducted twice, once with Level 0 as the reference category and
once with Level 5 as the reference category. The results of the logistic regression were
compared to the reference category and had to be interpreted in the same way.
In the first iteration of predicting different types of robots with readiness levels, it
was found that Level 5 was the most significant level for predicting the use of industrial
robots compared to firms in Level 0 (Table 7). In comparison with the reference category,
the firms that are considered to be in the top level of readiness (Level 5) are 16.3 times more
likely to use industrial robots for manufacturing processes and 82.3 times more likely to use
industrial robots for handling processes than firms that are in Level 0. For industrial robots
for manufacturing processes, no other level of readiness was significant in predicting the
use of such robots. However, in the case of industrial robots for handling processes, Level 2
was also significant in predicting the usage of this type of robot. Firms that are in Level 2 are
13.1 times more likely to use industrial robots for handling processes The logistic regression
results for mobile robots (R3) were inconclusive and could not be included in the Table
due to their limited usage in firms. For collaborative robots and autonomous robots, none
of the readiness levels showed a statistically significant prediction for the usage of these
two types of robots.
If the reference category is switched to Level 5, then the odds of using industrial
robots can be obtained for firms in Levels 0–4 compared to firms in Level 5 (Table 8). As
expected, the results of logistic regression confirm that firms in lower readiness levels
have lower odds (or chances) of using industrial robots for manufacturing processes and
industrial robots for handling processes. For the other three types of robots, the results were
statistically insignificant. The results for firms in Level 0 indicate that the odds of them using
industrial robots for manufacturing processes are only 0.002, or 0.2%, in comparison with
firms in Level 5. This can also be interpreted as having 99.8% lower odds of using industrial
robots for manufacturing processes. Interestingly, the firms in Level 3 have no statistically
significant odds of using industrial robots for manufacturing processes than firms in Level 5.
In the case of industrial robots for handling processes, the odds of using this type of robot
are lower across all levels. For mobile robots, collaborative robots and autonomous robots,
no conclusions could be made about the significantly lower odds of using these robots.
Our first and second research questions dealt with the inquiry about the correlation and
its strength between digital technologies and industrial robots. As results have shown, both
types of “traditional” industrial robots—for manufacturing and handling processes—are
correlated to the majority of selected digital technologies. The only exception is digital
visualization (solutions) on the shop floor for the industrial robots for manufacturing pro-
cesses. In general, these correlations are weak and, on some occasions, medium. Digital
technology near-real-time production control systems have the strongest correlation with
both “traditional” industrial robot types. Our findings indicate that digital technologies and
advanced manufacturing technologies, such as industrial robots, are not isolated in their use,
regardless of the type of digital technology. Our finding is supported further by our results
Appl. Sci. 2023,13, 5890 13 of 17
when including Industry 4.0 readiness levels. In our readiness model, these levels depended
on the number and specific combination of selected digital technologies. It was shown that
the use of “traditional” industrial robots is correlated with the number of implemented
digital technologies in a manufacturing firm. For industrial robots for handling processes,
this correlation is almost strong. This is also the answer to our third research question.
Table 7. Logistic regression results for readiness levels and types of robots (ref.: Level 0).
Readiness
Level Statistic R1 R2 R4 R5
Level 1
Sig. 0.425 0.159 0.665 0.502
Exp(B) 1.667 5.000 1.727 0.571
95% CI LB 0.475 0.533 0.145 0.112
UB 5.842 46.93 20.578 2.923
Level 2
Sig. 0.214 0.019 0.303 0.641
Exp(B) 2.167 13.062 3.304 0.696
95% CI LB 0.647 1.518 0.340 0.151
UB 7.327 112.413 32.112 3.199
Level 3
Sig. 0.117 0.117 0.117 0.925
Exp(B) 3.000 6.333 6.333 0.923
95% CI LB 0.759 0.630 0.630 0.174
UB 11.864 63.639 63.639 4.885
Level 4
Sig. 0.316 0.007 0.199 0.643
Exp(B) 2.042 21.714 4.750 1.455
95% CI LB 0.506 2.284 0.441 0.298
UB 8.231 206,482 51.106 7.092
Level 5
Sig. 0.002 0.000 0.117 0.250
Exp(B) 16.333 82.333 6.333 2.400
95% CI LB 2.8 7.692 0.630 0.540
UB 95.3 881.258 63.639 10.67
The situation with the other three types of robots is a bit different. Although some
weak correlation was found between collaborative robots and three digital technologies, in
general, these types of robots showed no correlation with the selected digital technologies.
There are at least two reasons for this finding. One might be the small number of these
robots in our research sample as a result of the total number of included manufacturing
firms. At the same time, it is important to note that these types of robots are much younger
in terms of first installations in manufacturing firms compared to the two “traditional”
industrial robot types. Collaborative robots, mobile robots, and autonomous robots are
slowly gaining importance, and a higher diffusion share can be expected in the near future.
Appl. Sci. 2023,13, 5890 14 of 17
Table 8. Logistic regression results for readiness levels and types of robots (ref.: Level 5).
Readiness
Level Statistic R1 R2 R3 R4 R5
Level 0
Sig. 0.002 <0.001 0.998 0.117 0.250
Exp(B) 0.061 0.012 0.000 0.158 0.417
95% CI LB 0.010 0.001 0.000 0.016 0.094
UB 0.357 0.130 / 1.587 1.852
Level 1
Sig. 0.008 <0.001 0.769 0.166 0.075
Exp(B) 0.102 0.061 0.652 0.273 0.238
95% CI LB 0.019 0.012 0.038 0.043 0.049
UB 0.553 0.300 11.242 1.713 1.153
Level 2
Sig. 0.017 0.014 0.705 0.411 0.098
Exp(B) 0.133 0.159 0.577 0.522 0.290
95% CI LB 0.025 0.036 0.034 0.111 0.067
UB 0.700 0.691 9.911 2.462 1.257
Level 3
Sig. 0.062 0.003 1000 1.000 0.245
Exp(B) 0.184 0.077 1000 1.000 0.385
95% CI LB 0.031 0.014 0.057 0.202 0.077
UB 1.090 0.417 17.509 4.955 1.929
Level 4
Sig. 0.023 0.103 0.962 0.740 0.521
Exp(B) 0.125 0.264 1.071 0.750 0.606
95% CI LB 0.021 0.053 0.061 0.137 0.132
UB 0.753 1.325 18.820 4.095 2.793
5. Conclusions
The main aim of our study was to provide an initial understanding of the simultaneous
use of specific digital technologies and industrial robots. Our results show that the “tradi-
tional” types of industrial robots, such as industrial robots for manufacturing processes and
industrial robots for handling processes, do correlate with the use of practically all digital
technologies that are also addressed by us as Industry 4.0 enabling technologies. This
finding showed a clear positive relationship when analyzing these two types of industrial
robots with each selected digital technology and when the Industry 4.0 readiness concept
was introduced. Additionally, the likelihood was analyzed that a certain type of industrial
robot would be used when using a specific digital technology. Again, for “traditional”
types of industrial robots, significant relationships were found that indicate the likelihood
of using them with almost all the selected digital technologies.
As seen, the Industry 4.0 readiness model used in our research is based on the number
and combination of digital technologies used in manufacturing firms. The highest levels of
Industry 4.0 readiness correlate strongly with the use of industrial robots for manufacturing
processes and industrial robots for handling processes. Our results also indicate that the
distribution of digital technologies in Industry 4.0 readiness levels can serve as predictors
for industrial robots, such as industrial robots for manufacturing processes and industrial
robots for handling processes’ use.
The results for the other three types of industrial robots were, in general, not significant.
This can be attributed to the novelty of these types of robots since their diffusion is, in
comparison to “traditional” types of industrial robots, rather low.
According to our best knowledge, this is the first study that addresses this relationship.
It was not our attempt to analyze the deeper interconnection for the simultaneous use of
Appl. Sci. 2023,13, 5890 15 of 17
specific digital technologies and specific robots but to scratch the surface of these relation-
ships. Another novelty of our study is also the introduction of the Industry 4.0 readiness
concept and its relationship with the use of specific manufacturing technologies, including
industrial robots. These readiness models focus on different blocks that constitute the
Industry 4.0 concept but neglect empirical findings on the relationships between different
enabling technologies.
As in every research, ours also has several limitations. One of the limitations of this
research is that it does not cover the full range of digital technologies that are typical for
the Industry 4.0 concept. Given the high number of identified digital technologies in the
literature and the limited space available in the EMS questionnaire, it was not possible
to include all digital technologies. Additionally, the diffusion of some emerging digital
technologies is still rather low, making it difficult to examine the relevant relationship with
the use of industrial robots. The same applies to collaborative robots, mobile robots and
autonomous robots. Another limitation is the fact that our study was limited to just one
country. Although 10 European countries participated in the EMS 2018 survey, Slovenia
was the only country that included a sufficient number of digital technologies and industrial
robots in its national questionnaire. Finally, our research was focused on examining the
correlation between digital technologies and industrial robots and not on the causality in
their implementation.
Based on the limitation of our presented research, our future research will address
these challenges. Although the Slovenian sample is not small, further research will go
in the direction of a larger sample of more countries. The next EMS research round in
2023 will include more digital technologies in all partner countries. Digital technologies
that are gaining in their use will be included, such as artificial intelligence. The larger
sample of firms from different countries will enable more in-depth analysis based on firm
characteristics, such as size, firm status as the final producer or supplier, specific industries
and technology intensity, firms’ innovation and financial performance, and production and
product characteristics. The international research sample will also allow a multi-country
analysis. The causality factor will also be added to our analysis.
Our research findings have initial managerial implications for manufacturing firms.
The results of the adoption of digital technologies and industrial robots can serve managers
as a roadmap for future investments. Our findings indicate which digital technologies are
linked more frequently to the use of industrial robots. Although this information is not
sufficient to make a final decision on the adoption of specific technologies and robots, it
provides the current state of the simultaneous use of both types of technologies. As was
pointed out in the previous paragraph, in the future, manufacturing firms’ innovation
and financial performance results will be examined when using specific combinations of
types of digital technologies and industrial robots. The planned analysis will certainly
strengthen the perception of benefits when using the observed technologies. Additionally,
our presented Industry 4.0 readiness index provides managers with a simple tool to observe
their firm’s readiness for the Industry 4.0 concept. When combining the readiness level with
the use of industrial robots, this tool also serves as an initial indicator of digital technologies
and industrial robots’ simultaneous use frequency. In addition, we are developing a
decision support model that addresses the need for criteria for firm managers who require
assistance in making technology investment decisions.
Author Contributions:
Conceptualization, I.P.; methodology, K.K. and I.P.; validation, I.P. and R.O.;
formal analysis, K.K.; investigation, I.P.; resources, I.P.; writing—original draft preparation, I.P.;
writing—review and editing, R.O.; visualization, R.O.; supervision, I.P.; project administration, I.P.
All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the Slovenian Research Agency (Research Core Funding
No. P2-0190).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Appl. Sci. 2023,13, 5890 16 of 17
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author. The data are not publicly available due to the consortium agreement of
project partners.
Conflicts of Interest: The authors declare no conflict of interest.
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