Simultaneous Use of Digital Technologies and Industrial Robots in Manufacturing Firms

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.

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 manufacturing. 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 advanced 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] performed 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 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 manufacturing 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 [10,11,12,13]. 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 2 gives 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 4 presents and discusses our results in terms of an overview of the frequency of digital technologies and industrial robots’ use in manufacturing 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 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, research focused on the enterprise level, and complementarity analysis is limited to evaluating firms’ overall digitalization levels [20]. How digital technologies relate to robots is unknown [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 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 maturity 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 manufacturing, 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 products 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.

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 employees. 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 1 presents 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.

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, “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 Fraunhofer 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 visualization 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 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 technology 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 coefficient, 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 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:

• 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 manufacturing 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 1. Descriptive data from analysis of the adoption of digital technologies in Slovenian manufacturing 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%

presents 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 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%

presents the use of five industrial robot types. As expected, the use of “traditional” 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 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%

depicts 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.

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 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 4. Association and correlation results for technologies and types of robots.

Technology	Statistic	R1	R2	R3	R4	R5
T1	Pearson χ2	0.008	<0.001	0.414	0.151	0.357
	Phi	0.245	0.317	0.075	0.132	0.085
T2	Pearson χ2	0.011	<0.001	0.977	0.004	0.170
	Phi	0.235	0.304	0.003	0.267	0.123
T3	Pearson χ2	0.017	<0.001	0.196	0.167	0.235
	Phi	0.220	0.364	0.119	0.127	0.109
T4	Pearson χ2	0.188	0.004	0.355	0.338	0.501
	Phi	0.121	0.263	−0.085	0.088	0.062
T5	Pearson χ2	0.001	<0.001	0.264	0.034	0.229
	Phi	0.295	0.327	0.103	0.196	0.111
T6	Pearson χ2	0.002	0.008	0.174	0.079	0.894
	Phi	0.290	0.245	0.125	0.162	0.012
T7	Pearson χ2	0.065	0.005	0.237	0.007	0.067
	Phi	0.170	0.257	0.109	0.248	0.168

summarizes the results of the association and correlation analysis. As shown in the Table, industrial robots for handling processes have a positive association and correlation 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.

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 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

summarizes 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 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.

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. Association and correlation results for readiness levels and types of robots.

Technology	Statistic	R1	R2	R3	R4	R5
Readiness level	Pearson χ2	0.021	<0.001	0.920	0.400	0.450
	Spearman correlation	0.283	0.437	0.096	0.198	0.141

). The highest correlation coefficient value was found for readiness levels and industrial robots for handling processes. Readiness levels are also correlated with the use of industrial robots for manufacturing processes but not with the other types of robots.

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. 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

). 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. 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

). 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 processes. 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 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.

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.

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 “traditional” 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 specific digital technologies and specific robots but to scratch the surface of these relationships. 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.