**A Maturity Level-Based Assessment Tool to Enhance the Implementation of Industry 4.0 in Small and Medium-Sized Enterprises**

#### **Erwin Rauch 1, Marco Unterhofer 1, Rafael A. Rojas 1,\*, Luca Gualtieri 1, Manuel Woschank <sup>2</sup> and Dominik T. Matt 1,3**


Received: 30 March 2020; Accepted: 22 April 2020; Published: 27 April 2020

**Abstract:** Industry 4.0 has attracted the attention of manufacturing companies over the past ten years. Despite efforts in research and knowledge transfer from research to practice, the introduction of Industry 4.0 concepts and technologies is still a major challenge for many companies, especially small and medium-sized enterprises (SMEs). Many of these SMEs have no overview of existing Industry 4.0 concepts and technologies, how they are implemented in their own companies, and which concepts and technologies should primarily be focused on future Industry 4.0 implementation measures. The aim of this research was to develop an assessment model for SMEs that is easy to apply, provides a clear overview of existing Industry 4.0 concepts, and supports SMEs in defining their individual strategy to introduce Industry 4.0 in their firm. The maturity level-based assessment tool presented in this work includes a catalog of 42 Industry 4.0 concepts and a norm strategy based on the results of the assessment to support SMEs in introducing the most promising concepts. For testing and validation purposes, the assessment model has been applied in a field study with 17 industrial companies.

**Keywords:** industry 4.0; small and medium-sized enterprises; SME; assessment model; sustainability

#### **1. Introduction**

The proclamation of the fourth industrial revolution with the aim of a digital transformation of companies changed the industrial world. While in the past a focus was given to the introduction of lean production [1,2], almost every modern enterprise aspired in recent years to become a proper smart factory according to the 'Industry 4.0' principles. In the industrial environment, a curious feeling grew. At first glance, Industry 4.0 was not really perceived as an opportunity, but more as a challenge. Since the first use of the term Industry 4.0 in 2011 [3], many studies have been conducted to investigate the impact of Industry 4.0, its significance for the sustainable competitiveness of companies, the related technologies, as well as methods and strategies for the introduction and implementation of Industry 4.0 in industrial enterprises.

Industry 4.0 is considered to be another word for the fourth industrial revolution [3]. After mechanization, electrification, and computerization, the fourth stage of industrialization aims to introduce concepts like cyber-physical systems (CPS), internet of things (IoT), automation, human–machine interaction (HMI), as well as advanced manufacturing technologies in an intelligent and digitalized factory environment [4]. A distinctive feature of the fourth industrial revolution is

the ability to combine the digital and the physical world, affecting a broad spectrum of industrial disciplines [5]. The term was introduced in 2011 by a German group of scientists during the Hannover Fair event, which symbolized the beginning of this fourth industrial revolution [6]. Since then, the term Industry 4.0, or also its synonym smart manufacturing [7], has been one of the most popular manufacturing topics among industry and academia in the world [3,8,9].

Considering recent developments in terms of the industrial progression to smart factories and the fourth industrial revolution, companies are overwhelmed and seem to be incapable of developing appropriate implementation strategies [10]. Each company has to analyze its situation and its individual needs and then choose those Industry 4.0 concepts that promise the best prospects for achieving the goals set [11]. There is a need to develop suitable models and instruments to assess the current status or maturity of applied technologies in industrial companies and to implement Industry 4.0 concepts based on the appropriateness for the individual company.

One of the remarkable works, titled 'Guideline Industry 4.0—Guiding principles for the implementation of Industry 4.0 in small and medium-sized businesses', presented by the VDMA (German Engineering Federation), proposes an 'Industry 4.0-toolbox' that should provide first guidance in the challenging change process [12]. The supplied toolbox was perceived as an acceptable starting base to assess one's own company regarding the implementation of Industry 4.0 technologies and concepts, but is limited to the evaluation of a few concepts for product design and manufacturing.

The need for instruments for the assessment of one's own company regarding the level of Industry 4.0 implementation motivated researchers to develop further models. Schumacher et al. [13] developed an assessment model for the determination of the readiness for Industry 4.0-related measures. Compared with former works [14], the degree of completeness of the model is enhanced through the inclusion and combination of technological and organizational aspects, which are categorized into nine dimensions and single specific assessment items.

However, there is still room for further improvements in the development of models for the evaluation of companies introducing Industry 4.0. Therefore this work aimed to enrich the evolution of Industry 4.0 assessment models giving a specific focus to small and medium-sized enterprises. Further, it opens doors to new and enriched evaluation possibilities that are no longer addressing a problem of readiness of enterprises, but one affiliated to the maturity level associated to the Industry 4.0 implementation. Small and medium-sized enterprises (SMEs) are usually less informed about Industry 4.0 concepts. Therefore, an assessment model must clearly describe the concepts and also show the companies which different possibilities of concepts and technologies Industry 4.0 has to offer. Subsequently, such a model should support SMEs simply and pragmatically to show their status of implementation and the potential for future Industry 4.0 projects. Thus, the model's objective was (i) to inform SMEs about the existing Industry 4.0 concepts, (ii) to assess the current progress in the implementation and application of these concepts, and (iii) to signalize to SMEs which of the Industry 4.0 concepts are the most important ones for the individual company.

The paper is structured as follows: after a first introduction to the topic in Section 1, the authors provide in Section 2 the research methodology for this work. Section 3 follows a literature review of existing assessment and maturity models, as well as for the identification of Industry 4.0 concepts and technologies. Section 4 provides a detailed insight into the structure and development of the proposed maturity level-based assessment tool. Section 5 describes the evaluation and testing of the developed model in several industrial companies. Section 6 provides a discussion about the proposed model, the implications for practitioners, as well as its limitations. Finally, the paper ends with a summary and a look at further research needed in the future.

#### **2. Research Methodology**

In this work, we applied a research approach in three phases (see also Figure 1): (i) in the first phase a literature review was conducted in 2018 and Industry 4.0 concepts and technologies were identified; (ii) in the second phase the assessment tool for SMEs was developed in 2018–2019; (iii) in the third phase the developed assessment model was tested and validated by using a field study with industrial companies in 2019.

In the first phase (see also Section 3), we conducted a literature review to analyze already existing assessment models and their suitability for SMEs. In addition, we looked into the scientific literature for Industry 4.0 concepts and technologies to assess their implementation in SMEs in the assessment model.

In the second phase (see also Section 4), we developed the maturity level-based assessment tool for Industry 4.0 to be used in SMEs. In the first step, we defined the maturity levels for each of the Industry 4.0 concepts and technologies. The assessment model foresees the evaluation of the current implementation of Industry 4.0 in SMEs as well as the future target implementation. The difference between current and target state defines the gap that needs to be overcome by SMEs. Further, the assessment model should include also the assessment of the potential of each concept/technology for the single SME. Based on the gap and the potential of Industry 4.0 concepts/technologies, a norm strategy matrix indicates to SMEs a meaningful prioritization of final implementation measures.

In the third phase (see also Section 5), we conducted a field study with 17 industrial companies from Italy, Austria, Slovakia, and the United States to test and validate the developed assessment model. First, we will describe the structure of the field study describing the dimensions of the analyzed companies, their origin, as well as the approach of the field study. Then we present the results of the field study for the application of the assessment model and discuss the lessons learned to focus on feedback from SMEs.


**Figure 1.** Research methodology used in this work.

#### **3. Literature Review and Identification of Industry 4.0 Concepts and Technologies**

#### *3.1. Overview of Existing Assessment and Maturity Models for SMEs*

The analysis of existing assessment and/or maturity models for Industry 4.0 was based on a literature search in SCOPUS database, as this is one of the leading and most complete scientific databases for industrial and manufacturing engineering. The research team used for the literature analysis the following research query: (TITLE-ABS-KEY ("Industry 4.0") AND TITLE-ABS-KEY (assessment OR maturity) AND TITLE-ABS-KEY (sme OR "small and medium sized")) AND (LIMIT TO (LANGUAGE,"English")), with an output of 32 search hits. In the search, only works in the English language were considered, excluding 1 work in German. The remaining 31 works were published mainly as conference papers (22 works) and conference reviews (2 works), while 7 works were articles/reviews in journals and 1 had been published as a book chapter. This shows that the development of assessment models for Industry 4.0 in SMEs is a quite new topic and is already mainly discussed in scientific conferences and not yet so much in peer-reviewed journals. Also, the year of publication confirmed this as 2 works were published in 2016, 14 works in 2018 and 16 works in 2019. In the first screening of title and abstract, a total of 19 works were encoded as firmly pertinent. In a

second screening reading all remaining works, a total of 13 research papers were considered for a further content analysis.

In the following, a summary of the content analysis of the remaining works (see Table 1) will be given to explain the current status of assessment and maturity models with a special focus on SMEs.

The first group of works focused on maturity-based assessment models determining the maturity/implementation of concepts and technologies from Industry 4.0. The authors in [15] were focused more on the maturity levels of the general implementation of an Industry 4.0 strategy but did not go into detail regarding single Industry 4.0 concepts. The model in [16] was based on five maturity levels (novice, beginner, learner, intermediate, expert) as well as five dimensions (finance, people, strategy, process, product). Also in this model, the assessment provided only a rough overview of seven categories of Industry 4.0 so-called toolboxes (manufacturing/fabrication, design and simulation, robotics and automation, sensors and connectivity, cloud/storage, data analytics, and business management), but not of the single Industry 4.0 concepts and technologies. The authors in [17] presented a study of the maturity of Industry 4.0 in German companies, focusing on six dimensions (product development process, steering, and control, manufacturing and operation, smart services, process organization, big data). The authors in [18] presented a maturity level assessment with five fields of action and 29 subordinated action elements. The action elements showed only areas of implementation (e.g., production logistics or communication), thus the results gave only an overview of a general assumption of the implementation of Industry 4.0 in each action element, but not based on single Industry 4.0 concepts and technologies. In [19] the authors proposed a maturity model based on five dimensions (strategy, technology, production, products, people) and a Likert scale from 1 to 5. The study used a Likert scale from 0 to 6. In [20] the authors used five dimensions (strategy, people, processes, technology integration) with a Likert scale from 1 to 5. Also in the latter cases, the maturity assessment did not go in deep on the concept and technology level, but instead remained on a very superficial level of exploration.

The second group of works concentrated on the assessment model for evaluating the level of readiness of Industry 4.0. [21] determined the readiness for Industry 4.0 based on six dimensions (1—strategy and organization; 2—smart factory; 3—smart products; 4—data-driven services; 5—smart operations; 6—employees) based on a Likert scale from 0 to 5. The works did not provide any further insight into the readiness regarding detailed Industry 4.0 concepts. The work described in [22] presented a readiness self-assessment for craftsmanship companies with the three dimensions production/operations, digitalization, and ecosystem. In these three dimensions, a total of 23 items (e.g., data security, perception of digitalization, quality of internet connection) were assessed with a Likert scale from 1 to 5. Also, in [23] the model provided only a rough overview of two dimensions (smart factory, strategy, and culture). The authors in [24] provided a readiness assessment based on five dimensions (manufacturing and operations, people capability, technology-driven process, digital support, and business and organization strategy) and 43 subdimensions with a Likert scale from 0 to 4. Summarizing, most of the models discussed did not go very in depth and followed a different scope to the one in this work as they assessed the readiness for Industry 4.0 and not the maturity of applied Industry 4.0 concepts and technologies.

The third group of works dealt with a specific assessment model for Industry 4.0 in SMEs. In [25] the following process modules of intralogistics were assessed: incoming goods, internal transport, storage, order picking, packaging, and outgoing goods. In [26], four dimensions (data, communication, processes, and intellectual capital) were assessed based on a Likert scale from 1 to 5. A characteristic of this work was that each maturity level was described in detail with examples, which makes it easier for SMEs to validate the right maturity level. The work in [27] referred to nine characteristics of SMEs (e.g., ability to produce customized products) and assessed the maturity of lean and Industry 4.0 components). In summary, this group of assessment models dealt only with specific areas of an SME, like logistics, or with additional concepts from lean manufacturing and is therefore not suitable for the scope of the work proposed in this article.



Most of the authors used a Likert scale from 1 to 5 for measuring the maturity of Industry 4.0, which was applied also in our work. All works did not go into detail about the single Industry 4.0 concepts and technologies and evaluated only dimensions or fields of application. As one of the most difficult tasks for SMEs is to understand what kind of concepts belong to Industry 4.0, we maintained it was important to develop a much more detailed assessment model. The evaluation might be difficult for SMEs if only a Likert scale with a vague description is shown (e.g., from beginner to expert). We used here the procedure as shown in [26], where each maturity level is well described with examples to simplify the use of the assessment model for SMEs. Further, in all the shown approaches, an evaluation to assess also a target maturity level for SMEs was missing. Not all SMEs need the maximum level of Industry 4.0 for their purposes, thus the maximum maturity level should not be seen as something that must be attained at all costs. In addition, the analyzed assessment models do not show any procedure to rank the potential implementation steps or technologies for a single company. All these points should be included in the new proposed assessment model.

#### *3.2. Identification of Industry 4.0 Concepts and Technologies*

To design an assessment model for maturity in the application of Industry 4.0 technologies and concepts, the assessment units must first be identified and defined. As mentioned before in Section 3.1, the aim is to provide a comprehensive method catalog with Industry 4.0 concepts and technologies to support SMEs in the selection of measures to be implemented. Therefore, the research team carried out a systematic literature analysis with the keyword "Industry 4.0" in the database SCOPUS in the first year of the research project. From initially 733 works a large number of publications were excluded based on exclusion criteria like type of paper (only journal articles, reviews, and articles in press were considered), the cover period (from 2011 to 2017), the language (only works in English), the subject area (only papers belonging to engineering, computer science, business, materials science, social sciences, decision sciences, management and accounting, energy, econometrics and finance, multidisciplinary, economics and psychology were considered in the search) were analyzed. The remaining 102 works were screened by three independent researchers to guarantee objectivity in the analysis. The screening process resulted in a total of 27 publications that were defined as firmly pertinent for further analysis, which means that they provided clear information about Industry 4.0 technologies and concepts.

These identified works formed the basis for a subsequent content analysis, from which Industry 4.0 concepts and technologies could be extracted. The content analysis resulted in a first step in 75 Industry 4.0 topics that were condensed by the research team into 42 meaningful Industry 4.0 concepts and technologies used as a basis for the proposed assessment model. The reason for the adopted consolidation lay in the fact that many of the identified Industry 4.0 topics were characterized by a subject or industry-specific nature with the need for generalization.

The broad spectrum of the identified Industry 4.0 concepts and technologies required structuring into several levels of Industry 4.0 dimensions (see also Table 2). In the first dimension level the concepts were classified as follows:


In the second dimension level, a total of 21 subdimensions were identified. Table 2 summarizes the identified Industry 4.0 concepts and technologies, including dimension level I and II in alphabetical order.



#### **4. Maturity Level-Based Assessment Tool of Industry 4.0 for SMEs**

#### *4.1. Maturity Levels Used in the Assessment Model*

First of all, SMEs have to insert in the assessment model their general data, like operating sector, number of employees, annual revenue, and balance sheet total. These data are used in a later stage to compare their own results with the average of other SMEs of the same size and the same sector. In the next step, SMEs have to define the maturity level of the identified Industry 4.0 concepts and technologies (see Section 3.2) in their own company. As already seen in Section 3.1, many assessment models use such maturity levels to express the progress of implementation of Industry 4.0 in their own company. We wanted to apply the same approach also in our assessment model for Industry 4.0 in SMEs. Each level should specify the peculiarities and the progress of the evaluated enterprises or desires to achieve to become a smart enterprise.

As already mentioned in Section 3.1, we wanted to adopt also a Likert scale with five maturity levels. According to the results of the review of maturity level-based assessment models, it can be confirmed that five stages are most common and suitable to map the implementation or maturity of Industry 4.0 concepts. To facilitate the application of the assessment model in SMEs, the maturity levels are expressed and described not only by numbers from 1 to 5 but with a combination of a single term and a brief statement/example. First, tests with an SME have shown that persons asked to do the assessment based only on a Likert scale from 1 to 5 (therefore described only by numbers) have difficulties deciding which is the right value for the stage of implementation in their company as for many Industry 4.0 concepts they have no experience with what could be the lowest stage or the highest stage of implementation. Next, tests that added a term to the numbers facilitated the assessment, but still caused uncertainty (and therefore a lower quality of the assessment data) due to many new and innovative Industry 4.0 concepts where people needed some more explanation and, if possible, also an example. The explained maturity levels are illustrated in Figure 2 as follows.


**Figure 2.** Exemplary representation of maturity levels of Industry 4.0 concepts.

#### *4.2. Analysis of the Current Status of Implementation and the Target Status*

The company completes the assessment of the current status regarding the implementation of Industry 4.0 concepts by selecting one of the maturity levels shown above. In our assessment model, this maturity level is called the 'firm's I4.0 score'. In addition, the company also indicates a degree of

maturity that it would like to achieve in the medium term, and where the firm also realistically sees the feasibility of implementation. This objective of a future maturity level is called the 'target level'.

These two values are the basis for the calculation of the 'I4.0 gap' that describes the difference between target level and the firm's I4.0 score. This gap should act as a measure of the difficulty for the SME to achieve the future target level of Industry 4.0 for each single Industry 4.0 concept.

Figure 3 shows a screenshot of the MS Excel-based maturity level-based assessment tool with the fields to be filled in by the SME to determine the firm's I4.0 score and the target level. For the development of a prototype of the assessment model, MS Excel was chosen as it is widely spread in SMEs and easy to use also for nonexpert users of MS Excel. In the final stage of the project a web-based self-assessment model is developed to maximize dissemination.

**Figure 3.** Fields to be filled in for firm's I4.0 score, target level, and the importance of each Industry 4.0 concept.

Figure 4 shows an example of the visualization of the firm's I4.0 score and the target level automatically generated by the MS Excel prototype of the assessment model. The representation in the radar chart is intended to simplify the evaluation of the assessment for SMEs, as the gap between today and tomorrow is directly visible.

The radar charts illustrate the firm's Industry 4.0 score as well as the firm's target score for each Industry 4.0 concept categorized in an operational I4.0 level, an organizational I4.0 level, a social–cultural I4.0 level, as well as a technological I4.0 level (further subdivided into data-driven and process-driven technological level).

In the visual representation of the results, the technology-related Industry 4.0 concepts are subdivided into a group of data-driven technologies and process-driven technologies (see Table 3).


**Table 3.** Data-driven and process-driven technologies.

**Figure 4.** Exemplary visualization of the gap between firm's I4.0 score and the target level.

#### *4.3. Identification and Ranking of Industry 4.0 Concepts and Technologies with the Highest Potential in Implementation*

In the next step, the company is asked to provide an assessment of the potential ('importance') of the respective Industry 4.0 concept (see also Figure 3). Also, this evaluation is based on a 5-step Likert scale and is meaningful according to the authors, because not every Industry 4.0 concept might be important for the individual company. While for example, the traceability of products is essential and of the highest importance for certain companies and industries (e.g., automotive sector, food sector), this concept will play a much less important role in many other SMEs.

In this respect, the estimation of the importance is used to express the potential of each Industry 4.0 concept for the individual SME. To prioritize the Industry 4.0 concepts and thus to define the basis for implementation measures, the assessment tool automatically generates a ranking based on the identified potential (see Figure 5).

**Figure 5.** Exemplary visualization of the potential for each Industry 4.0 concept.

#### *4.4. Norm Strategy Matrix for Defining Implementation Measures*

Not all of the 42 Industry 4.0 concepts listed will be of high potential and not all of them require the same amount of effort and time for implementation. Therefore it makes sense, especially for SMEs with financial and personnel limits, to strive for a gradual implementation of the Industry 4.0 concepts. Both criteria, namely the gap and the identified potential, are therefore combined in a norm strategy matrix as shown in Figure 6, to facilitate the selection and scheduling of implementation measures for Industry 4.0 in terms of time (short-term vs. medium-term implementable I4.0 concepts). Figure 6 shows the matrix schematically with the following standardization strategies depending on the respective quadrant in the diagram:


**Figure 6.** Norm strategy matrix to facilitate the selection and definition of Industry 4.0 measures/projects.

Depending on the position of the Industry 4.0 concepts, the SMEs can then define whether, and to what extent and in what time frame, implementation measures have to be defined.

#### **5. Validation of the Industry 4.0 Assessment in a Field Study with SMEs**

#### *5.1. Structure and Procedure of the Field Study*

A number of 17 SME companies operating in different industrial sectors from Italy (10), Austria (3), Slovakia (1), and the USA (3) participated in the field study. The participating companies in the field study were mainly operating in industrial goods manufacturing (6), industrial supplies and materials (4), construction (3), food (2), and other industries (2). From these companies, seven were small enterprises with up to 49 employees and 10 were medium-sized enterprises with 50–249 employees. The companies had been collaborating with the research team in the H2020 research project "SME 4.0—Industry 4.0 for SMEs". In the case study companies, the assessment was completed by the company owner/manager or the head of production. In most of the cases, they called for specific concepts experts from product development, IT department, or other operational departments. To identify difficulties in completing the assessment, a representative of the research team was always present. This was done to monitor the time spent on completing the assessment, to record difficulties in completing the assessment, and then to ask the companies for their feedback on the user-friendliness of the assessment. At no time did the researcher present intervene in the evaluation or influence the participant in the assessment.

All participants assessed the firm's I4.0 score, the target level and the importance of the respective Industry 4.0 concepts for their company. As a result and for their benefit, they learned possible Industry 4.0 concepts and technologies, they got an overview of their status and the gap for each concept, and they received a visual ranking of the most potential concepts for their firm.

#### *5.2. Results of the Field Study*

In general, the assessed importance/potential of each single Industry 4.0 concept was analyzed in the form of the average value, the respective standard deviation, and the coefficient of variation as a measure of relative variability of the answers as well as the number of answers. This analysis was used by the research team to identify suitable/potential Industry 4.0 concepts from the viewpoint of SMEs. These results are summarized and presented in [28].

In this work, we were more interested in the overall results of the field study and the feedback from the participating persons using the assessment model. Therefore we analyzed the data of the field study to investigate in the first step the maturity level and target level for Industry 4.0 concepts according to the participating SMEs (see Figure 7).

The overall results showed that the current maturity level of Industry 4.0 concepts in SMEs is generally very low. Comparing the single categories and I4.0 concepts depicted in Figure 7 we can see that the 10 concepts with the highest Firms I4.0 score were (from highest to lowest value): (1) agile manufacturing, (2) cloud computing, (3) product data management (PDM) and product lifecycle management (PLM), (4) enterprise resource planning (ERP) and manufacturing execution system (MES), (5) role of the operator, (6) simulation, (7) automation in manufacturing and assembly, (8) integrated and digital real-time monitoring systems, (9) collaboration network models, and (10) self-adapting manufacturing models.

The 10 Industry 4.0 concepts with the highest target level were (from highest to lowest value): (1) integrated and digital real-time monitoring systems, (2) agile manufacturing systems, (3) ERP/MES, (4) Industry 4.0 roadmap, (5) cloud computing, (6) digital and connected workstations, (7) cultural transformation, (8) PDM and PLM, (9) big data analytics, and (10) training 4.0.

Further, it is interesting to know those Industry 4.0 concepts that showed the greatest gap between the current level and the target level (from highest to lowest value): (1) integrated and digital real-time monitoring systems, (2) Industry 4.0 roadmap, (3) Training 4.0, (4) cultural transformation, (5) digital and connected workstations, (6) ERP/MES, (7) internet of things and cyber-physical systems, (8) decision support systems, (9) predictive maintenance, and (10) big data analytics.

In the next step, we analyzed the data to determine the average potential of the Industry 4.0 concepts according to the participating SMEs in the field study (see Figure 8).

**Figure 7.** Visualization of average Firms I4.0 Score and Target level of SMEs in the field study.


**Figure 8.** Ranking of the average potential of Industry 4.0 concepts according to SMEs in the field study.

Based on the evaluations of the companies in the field study the most promising Industry 4.0 concepts are the ones illustrated in Figure 8. Interestingly, most of these concepts were also evaluated very highly when companies had to determine the current maturity level and the target level. This led to the conclusion that the most promising concepts are not completely new to them and that they are already targeting to increase their competences in these fields. The only concept that was not mentioned before in Figure 7 was cybersecurity. This led to the hypothesis that SMEs are already aware of the future importance of cybersecurity, but they do not feel they are at a good level and also fail to raise their level in this area.

In the third step of the field study, we combined the average data of the Industry 4.0 gap and the identified potential to generate the norm strategy matrix (see Figure 9). In principle, it can be seen that none of the Industry 4.0 concepts mentioned had a low potential and a large gap in implementation. This means that it makes sense to introduce all of the 42 concepts in the long term. There are a few so-called "low hanging fruits" that have a moderate potential with little effort for introducing the target level (which might be high or moderate according to the needs of SMEs). Examples of such concepts are additive manufacturing, servitization and sharing economy, digital upgrade, freemium, or artificial intelligence. It might sound surprising that a technology like artificial intelligence is clustered as a low hanging fruit technology. Here, it must be specified that SMEs in this field study defined a relatively low target level for artificial intelligence. The matrix also showed some concepts that can only be implemented with great effort, but which require a long-term "must have" strategy due to their high potential. Examples for this norm strategy are big data analytics, predictive maintenance, collaborative robotics, real-time monitoring systems, digitalized workplaces, improvement of their ERP, the introduction of MES systems, as well as a cultural change of the company towards developing an Industry 4.0 roadmap and qualifying their people with Training 4.0. The large number of Industry 4.0 concepts mentioned fall within the norm strategy "quick wins", which means that the effort for introduction is relatively moderate, but the concepts show a high potential. Examples of concepts where it makes sense to apply this strategy are agile manufacturing systems, automation, e-kanban, operator 4.0, simulation, cloud computing, smart assistance systems, as well as appropriate cybersecurity solutions for SMEs. 

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**Figure 9.** Norm strategy matrix with average values of the Industry 4.0 gap and the potential in the field study.

The norm strategy matrix illustrated in Figure 9 supports SMEs to plan short-term, medium-term, as well as long-term-oriented measures for introducing Industry 4.0 in their company. Thus, the application of the assessment model with a subsequent definition of norm strategies is recommended.

Finally, we asked the participating SMEs about the usability of the assessment model and feedback for improvement. All participants found the assessment to be very instructive and a good exercise for thinking about the status of Industry 4.0 in their own company. Concerning the number of items to be evaluated, the participants were not all of the same opinion. Most felt it very positive that an Industry 4.0 concept catalog was provided to get a better overview of existing Industry 4.0 concepts. However, the catalog of 42 Industry 4.0 concepts requires a lot of time to fill out the assessment, which was considered quite exhausting by the participants. According to the participants, there were no major difficulties in assigning the level of maturity due to the detailed description and examples provided in the assessment model. The companies found it very useful to have an overview in the form of an individual norm strategy matrix at the end of the assessment, as this supported the definition of future Industry 4.0 measures and projects. All in all, the participants found the assessment to be effortful to conduct, but extremely useful for paving the way to implement Industry 4.0 in a systematic manner in the company.

#### **6. Discussion**

#### *6.1. Novel Aspects of the Proposed Assessment Model*

As described in the introduction and review in Section 3.1, our assessment model differs from existing models. While general assessment models [13–15] (developed for both SMEs and large companies) are often limited to a general Likert scale assessment, our model offers a significant advantage for smaller companies. The additional detailed description of each maturity level for each Industry 4.0 concept (including practical examples) makes it easier for SMEs to determine their own maturity level. This assistance is particularly important as many SMEs do not have highly qualified employees who have experience with all concepts. Another advantage over all previous models is the depth and number of Industry 4.0 concepts that can be evaluated, which means that many companies doing the assessment even start to think about certain perhaps less well-known concepts. Finally, we saw the subsequent derivation of the norm strategy matrix as a decisive difference and novelty of our model, since it is a practical aid especially for SMEs in the selection and introduction of important Industry 4.0 concepts for each individual firm.

#### *6.2. Implications for Academia and Practitioners*

For research purposes, the presented assessment model represents an extension and enrichment of already existing assessment models for Industry 4.0. On the one hand, the proposed model is designed for use in SMEs and has also been developed and tested with the involvement of SMEs. On the other hand, the implementation of Industry 4.0 in the whole company or parts of the company is not just evaluated superficially, but existing Industry 4.0 concepts are specifically evaluated on a very detailed level. On the one hand, this type of evaluation goes much deeper than all other currently existing models and at the same time offers a catalog of 42 Industry 4.0 concepts, which have been determined based on scientific contributions. For future research, the assessment model offers the possibility to examine the implementation of the Industry 4.0 concepts in detail. If the assessment is carried out in the future with a sufficient number of SMEs from different industrial sectors, these data can be used for many purposes. For example, it will be possible to determine which Industry 4.0 concepts are most important for different company sizes. In addition, this analysis can also be reduced to individual industrial sectors to understand where and why different dimensions of SMEs or different industrial sectors gave a different assessment. The same analysis can also be extended to the current and target maturity level. Finally, it is a new and original approach, as the assessment of an Industry 4.0 gap and its potential is combined in a norm strategy matrix.

For practitioners from SMEs, the assessment model is an ideal tool to evaluate the current situation of the company and to systematically plan future projects and initiatives. One of the biggest advantages for SMEs is the Industry 4.0 catalog with different concepts and technologies, which also helps small companies to get an overview of Industry 4.0 methods. The validation in practice has shown that the model can be applied in small companies, although the evaluation itself takes some time. The subsequent systematic analysis of the Industry 4.0 gaps and the potential in the norm strategy matrix especially serves SMEs as an orientation guide to better plan future initiatives and to transfer them into an implementation plan for Industry 4.0. The application of the assessment model provides companies practical recommendations for the application of selected Industry 4.0 concepts and how and with which priority they should be implemented.

#### *6.3. Limitations of the Proposed Assessment Model*

Despite the many advantages mentioned, the model has certain limitations. One limitation is certainly the already mentioned effort for the evaluation. The validation phase has shown that most companies spend about one to two hours on the evaluation and usually also bring in experts from different areas of the company. Therefore, the user must invest the necessary time to be able to draw the right conclusions from the results. Furthermore, it can be limited if only one person carries out the evaluation because a certain subjective opinion cannot be excluded. Therefore, it is recommended that a team completes the evaluation to improve not only the quality of the answers but also the objectivity of the result. A third limitation to be mentioned is the catalog of 42 Industry 4.0 concepts used as a basis. In the course of the four-year research project from 2017–2020, a literature analysis was conducted to identify existing Industry 4.0 concepts until 2017. A first check by the research team after the development of the model in 2018 and its validation in 2019 showed that the Industry 4.0 concepts are still up-to-date and can be used in this way. However, it cannot be excluded that a few Industry 4.0 concepts might be added in the meantime. The proposed assessment model can be applied to every kind of industry sector. In the sample of SMEs we used for validation there were many different industrial sectors and the model could be used successfully in each of the industries. Of course the proposed model provides a catalog of 42 Industry 4.0 concepts, which not all might be of interest in each sector. This can easily be handled as the companies can assess the importance for their individual company as very low.

#### **7. Conclusions**

Industry 4.0 is a chance for SMEs to achieve a new level of competitiveness. Many SMEs are already trying to implement Industry 4.0 [28], but there is still a lack of specific instruments for introducing them [29,30]. In this paper, a maturity level-based assessment model for SMEs is presented. The model is based on 42 Industry 4.0 concepts identified by literature analysis, which are rated on a Likert scale of 1 to 5. In addition to the current maturity level, a target level and the importance and potential of the Industry 4.0 concepts are evaluated by the user. The collected results show in a developed norm strategy matrix, which of the Industry 4.0 concepts should be approached immediately and which may need more time or are not immediately implemented due to a lower potential.

The proposed assessment model shows several advantages compared to already existing models. It provides a detailed overview of existing Industry 4.0 concepts and is very easy for SMEs to adopt. The evaluation of the maturity level is facilitated through a brief description of the five maturity levels for each of the 42 Industry 4.0 concepts. Further, the evaluation and combination of a target level and the potential of each of the Industry 4.0 concepts in the norm strategy matrix allow SMEs to plan and schedule the implementation of Industry 4.0 in a very systematic way.

Further research is planned in the development of a web-based version of the assessment model and its dissemination in SMEs (ongoing work). As soon as enough SMEs complete the assessment model online and allow the use and processing of the anonymized data the research team plans to use these data for further analysis and a benchmarking functionality. In its final version, the assessment model will also show SMEs their position for each Industry 4.0 concept compared to those of the average of companies of a similar size and the same industrial sector. This will allow SMEs to better identify their own competitive situation and researchers to better understand the implementation of Industry 4.0 for each company size and industrial sector as well as its development over the time.

**Author Contributions:** E.R. led the research team, developed the structure of the maturity level-based assessment model, and conducted the field study for testing and validation purposes; M.U. worked on the literature review and the development of the maturity level-based assessment model; R.A.R. and L.G. contributed to the literature review and screening process as well as in the content analysis; M.W. conducted the analysis of field study data; D.T.M. supervised the work as scientific coordinator of the research project. All authors have read and agreed to the published version of the manuscript.

**Funding:** This project has received funding from the European Union's Horizon 2020 R&I programme under the Marie Skłodowska-Curie grant agreement No 734713.

**Acknowledgments:** This work belongs to the project "SME 4.0 – Industry 4.0 for SMEs" (funded in the European Union's Horizon 2020 R&I program under the Marie Skłodowska-Curie grant agreement No 734713). The authors would like to thank all industrial companies that contributed to the field study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Industry 4.0—Awareness in South India**

#### **Leos Safar 1,\*, Jakub Sopko 1, Darya Dancakova <sup>1</sup> and Manuel Woschank <sup>2</sup>**


Received: 28 February 2020; Accepted: 3 April 2020; Published: 15 April 2020

**Abstract:** Industry 4.0 (I4.0) approaches, frameworks, and technologies have gained an increasing relevance in order to gain sustainable and competitive advantages for industrial enterprises and for small and medium enterprises (SMEs), as well. Contrary to previous studies, which are mainly focused on companies, we conducted a questionnaire-based survey on inhabitants, in an attempt to examine general awareness about I4.0 concepts, in the region of South India. Our findings revealed a rather poor informational level of I4.0 concept and its components, which consequently leads to inadequate future actions and expectations. Moreover, respondents with prior information about I4.0 framework tend to have rather positive opinions and expectations of possible future trends. We emphasize that insufficient knowledge of the potential workforce regarding I4.0 concepts, especially in a region with ascending demographic development, can be considered as one of the main barriers for a successful and sustainable future development towards the 4th industrial revolution.

**Keywords:** Industry 4.0; Internet of Things; India; SMEs; awareness

#### **1. Introduction**

In recent years, as a consequence of business and social evolution, a multitude of modern topics emerged, and therefore gained tremendous attention, in the areas of manufacturing, logistics, and organizational development of small and medium enterprises (SMEs), as a consequence of an ongoing business and social evolution. Megatrends, such as globalization, demographics' dynamics, mass customization, technological progress, or climate change, lead to tremendous challenges for both society and the business sector. As a reaction to this very complex and volatile business environment, various strategic initiatives have taken place all over the world, in order to keep pace with the exponential technological development and to achieve sustainable future growth, for example, the "Made in China 2025", Germany's "High Tech Strategy 2020", or the US's "Industrial Internet Consortium" [1]. The aim of these projects is to develop and implement future concepts, frameworks, and technologies (e.g., Internet of Things or Industry 4.0) in order to make industries more effective, competitive, sustainable, and to produce higher value added [2] while minimizing negative impact on environment. Considering the costs, Industry 4.0 initiatives could also improve enterprises' costs management [3–5].

In this study, the authors focus on Industry 4.0 (I4.0) as the most promising concept from both social and manufacturing–orientated point of view. Over the past years, we faced a strong advance of technology among almost all industrial sectors. New business propositions and applications within the business systems were enabled given the new technologies. As Thestrup et al. [6] stated, the systematic gathering of physical and virtual data from users, sensors, or devices, emerged. The so called "Internet of Things" (IoT) [7] was defined as a world-wide network of such objects communicating and operating through standardized communication protocols. However, IoT was recognized after the ITU1 report [8], describing IoT as ability of connecting everyday objects, meaning that people will be able to communicate with objects, the same as objects will be able to communicate among themselves. Prerequisite to such communication is advanced wireless technology (identification technologies and sensors). Logically, IoT can be diversified to Industrial IoT and Commercial IoT, while I4.0 expects all those parts to be interconnected and communicating.

To simplify, the goal of IoT infrastructure is to enable participants (people and objects) to be more flexible, to react appropriately and autonomously, thanks to an information sharing network. Harbor Research [9] suggests that two major strands of technological development emerged in the beginning of the 21st century; first is the mentioned IoT, and secondly, "Internet of People" (IoP, or social networking). These interconnected devices, processes, machines, products, etc., will have significant impact on enterprise's life cycle, efficiency, functioning, and consequently, on the broader economy [10].

To conclude, Sundmaeker et al. [11] defines the IoT as an integrated part of 'Future Internet', or a "dynamic global network infrastructure with self-configuring capabilities based on standard and interoperable communication protocols where physical and virtual 'things' have identities, physical attributes, and virtual personalities and use intelligent interfaces, and are seamlessly integrated into the information network". Internet of Things, as an essential part of I4.0 concept, is already partially adopted by households, with aim of creating a "smart house", despite the fact that not every gadget is appropriately connectable yet [12]. The same problem can be observed among enterprises, especially in the area of SMEs. It assumed that the main barriers for becoming "smart" are insufficient education and little knowledge [10].

Moreover, interconnected objects and subjects are just one prerequisite for the so called "4th industrial revolution", where cyber and physical levels should merge [3]. The term Industry 4.0 points to the 4th industrial revolution, and was firstly presented on Hannover-Messe (one of the biggest international trade fairs, oriented on new and smart technologies) in 2011, while it also indicates the initiative of the German government to improve the environment in the manufacturing sector using new technologies (information regarding the concept was brought up in 2014 at the World Economic Forum in Davos [13]). According to BITKOM (Germany's digital association, founded in 1999 as a merger of individual industry associations in Berlin, representing more than 2500 companies in the digital economy, among them 1000 SMEs, all 400 start-ups), the 4th industrial revolution should allow control over the entire life cycle of the product and value stream, therefore redefine organization entirely. With respect to efficiency oriented on cost-savings and complexity reduction, Modrak and Bednar [14,15] conclude that I4.0 environment will initiate mass customization, mainly because of the ability of each entity throughout the value stream to communicate and identify itself. All these visions and concepts are meant to be environmentally, economically, but mainly socially sustainable. Thestrup et al. [6] also report to the importance of value creation and value constellations for developing the sustainable business models in industrial services. Sustainable development towards I4.0 is however a very complex issue. Leaving a now technical standpoint, we emphasize non-technical aspects of proposed changes within the industries.

As Slusarczyk [16] suggests, the 4th revolution differs from previous revolutions, because it will apply to all aspects of everyday lives, as a consequence of environment, where information will be exchanged between objects, between people, and between people and objects. In other words, real-time data exchange and horizontal and vertical integration of production systems are the main pillars of I4.0 [17], along with cyber security, autonomous systems, capability of analyzing large data sets, virtual reality, and cloud computing. Undoubtedly, such changes would require managerial decisions firstly, due to inevitable initial costs linked to such new technological equipment. Schröder et al. [18] leaves an open question, whether it is even worth implementing I4.0, especially for SMEs, despite the consensus we find among authors describing reduced costs and more efficient processes and environment as a consequence of I4.0. We argue that such dynamics within the industries should be examined deeply, and various elements of sustainable development, not only the economic point of view, should be

evaluated [19,20]. The opposite to mentioned cost-saving and cost-reducing is initial need of significant financial expenditures, that are in many occasions out of reach for companies, especially SMEs.

Another very important social aspect of such smart environment is how the intelligent machines will affect the labor market [20–24]. This topic has basically been examined from two perspectives; firstly, by describing requirements towards workers in I4.0 [20–24]; secondly, by examining the standpoint of workers and their outlook or current state of mind [20,25]. Regarding the I4.0 environment, the authors can conclude that there is a growing trend in the literature highlighting the importance of sustainable development and management [26]. We argue that unless reasonable level of awareness and basic knowledge of Industry 4.0 related concepts, parts, and inevitable parts is reached, it will be hard to successfully move towards smart environment, especially in case of less developed regions. Insufficient information base of the eligible work force represents an obstacle for potential employers oriented towards I4.0. Inadequate information and knowledge could also lead potential employees towards wrong or misjudged conclusions or attitudes. Probably the most crowded thought is that bringing in the intelligent machines would steal jobs, again, especially in less developed regions with less qualified, manually involved workforce. Consequently, a lack of awareness and affinity towards any modernization steps could hold the required transformation processes. However, according to Statista [27], countries without any problems with unemployment (e.g., Germany, USA, Japan) report the highest numbers of installed industrial robots per 10,000 employees.

The aim of conducted research is to investigate and analyze general awareness and knowledge regarding Industry 4.0 within the area of South India. Based on an initial analysis, questionnaire-based surveys in the literature show a lack of covering focus on knowledge and attitudes of potential employees within the examined region and in general, as well. This paper concerns the crucial perspective of the potential workforce in the way to find a sustainable process of implementing Industry 4.0 in less developed regions. The questionnaire was created on the basis of previous industry visits and consultations with students, employers, and entrepreneurs in the investigated region. Issues addressed the most by respondents are highlighted and summarized.

The article is further organized as follows: Section 2 provides a problems description, where issues necessary for further research are stated. Section 3 provides a methodology description, while in Section 4, we present obtained results. Section 5 concludes the findings of this research study.

#### **2. Problems Description**

The emerging economies should leverage their advantages, such as huge markets, attractive conditions for manufacturing, fast growing economies, and mainly larger labor force with more favorable demography [28]. Admitting that Industry 4.0 will primarily affect manufacturing sector, we face significant discrepancies among countries and regions. Despite the estimate that India will be the world's fastest growing economy in following years [29] with manufacturing sector that could hit 1 trillion US\$ in 2025 [30], we doubt the ability of successful transformation towards Industry 4.0, hence we find India and its regions important to examine with respect to the 4th industrial revolution [31]. For example, having Germany—a technology and manufacturing leader, however, with an aging population and lack of labor force—and on the other hand, an emerging country like India—suffering from technological gaps, which put India to a level of Industry 2.0 as Iyer [28] concludes, on the contrary, with strong demography.

There is also a political will to spur manufacturing sector, translated into initiatives such as "Digital India" [32], "Skill India" [33], or "Make in India", with aims to (among others) create sufficient skill sets within the urban poor and rural migrants for inclusive growth, or to increase technological depth in manufacturing in order to increase domestic value addition. In addition, there is the mentioned demographic factor—India has the best demographic dynamics, with approximately 60% of population in age between 15 and 59 [34]. The open question remains, are citizens and workers ready for such development in the foreseeable future?

We accept that within such a huge country, significant disparities among particular regions exist, hence we applied our research only in southern part of India (authors were physically present in the state of Tamil Nadu during data collection). South Indian region includes several states and union territories (Kerala, Andaman and Nicobar Islands, Tamil Nadu, Pondicherry, Andra Pradesh, Karnataka, Telangana, Lakshadweep Islands), which combined accounts for 19.31% of the geographical area of the whole of India. With over 250 million people, South India represents around 20% of the country's population [35]. As of 2016, economic growth of South India was around 17%, compared to 8% growth of the whole of India, while GDP of South India accounted for 30% of total Indian's GDP. Some specific industries are even more important from overall perspectives, such as cotton production (48% of India's entire cotton production comes from South India) or agricultural production (36% of whole state's production comes from South India).

Same as for other countries and regions, the main employers are SMEs. Unfortunately, as Iyer [28] states for India's industrial policy in general, it is old, and lacking critical technology. Many enterprises in this area are old and have long lasting traditions. Despite the established reputation and customers created, they are equipped with insufficient and old devices or machines. Internet access and computer equipment within industries in this region is also rather poor. Since the majority of the research has been conducted in the field of needed modernization, especially with respect to the SMEs in order to successfully transform towards Industry 4.0, we would rather point at the necessity of having a potential labor force ready for such a transformation. It is therefore considered that awareness of I4.0 needs to be continuously expanded and promoted, as confirmed by several authors [10,36–38]. Even if obtaining new machines and gadgets would be economically viable, will there be enough sufficiently educated workers? Throughout the literature, we find papers addressing similar problems within different regions, e.g., concluding that qualified specialists are often not satisfied with the salary, which causes their outflow in favor of richer economic regions, leaving almost no people able to operate such modern machines [39]. We argue that unless some basic level of knowledge regarding the addressed issues is reached within the population, the ability of forming a sustainable path to become competitive in an Industry 4.0 environment is rather limited.

#### **3. Methodology**

Research was conducted in the area of South India, where different industries operate in several segments, with a majority representation of SMEs. The aim of this survey-based study is to examine level of awareness and general consciousness of Industry 4.0 among South Indian students, workers, entrepreneurs, in other words, a broad spectrum of citizens. We expect that proper analysis of the gathered responses could provide us with unique and valuable knowledge of the current state of mind of local citizens, along with their current level of internet/connection requiring gadgets/platforms, and further serve as guidance for finding a suitable implementing strategy for new technologies in such areas.

Results presented in this paper concern opinions and knowledge of inhabitants living, studying, working, or doing business in the previously described area. For obtaining responses, a questionnaire was used, and data collection took place from December 2019 to February 2020. As advised by several authors [40,41], we used fixed-choice questions, in order to maintain time efficiency and difficulty of evaluation. Questionnaire was distributed within several traceable ways during the stay of authors in Tamil Nadu (see Table 1). Sample contains 564 unique responses (after removing incomplete and inappropriately filled responses—respondents' answers were checked to confirm all required questions had been answered in a prescribed manner). Respondents were notified in advance that providing answers to this questionnaire is anonymous. All answers provided will serve only for research purposes, and no personal details will be required or stored.


**Table 1.** Profile of respondents.

Note: \*Union territory; Source: Prepared by authors.

We divided the questionnaire into four main parts (Figure 1). In the first part, we focused on the social status of the respondent, education, and the place where respondent currently works, studies, or stays. In the second part, we were interested in respondents' basic internet communication and usage of social communication applications. In the third and main part, we looked at the awareness of Industry 4.0 in general among respondents. We asked about key terms such as cloud solutions, mass customization, Internet of Things, Industry 4.0, smart manufacturing, smart cities, etc. In the fourth part, we intended to examine what the I4.0 could bring to the South Indian region from the responders' perspective.

**Figure 1.** Stages of survey. Note: \* For all types of questions, please see Supplementary Materials.

Several scales were used due to substance of the question (full text of the questionnaire and scales of answers is provided in Supplementary Materials). Questions addressing previous experience and general awareness about key terms were scaled binomially (yes/no). Other questions addressing South India region were scaled as 5 levels: Not at all important/no—1; slightly important/rather no—2; no opinion (due to lack of information/knowledge, referring also to "I do not know")—3; fairly important/rather yes—4; very important/yes—5. Supplementary questions regarding usage of social media, email, or e-commerce had specific scales examining frequency of usage.

To analyze responses, we used tables of counts and percentages for the joint distribution of two (severe combinations) categorical variables. We used custom and contingency tables, statistical testing, and generated bar graphs for easier data presentation. Pearson's chi-square test was performed to test the independence between the row and column variables. Pearson's chi-square test requires a large sample. The main rule regarding the sample size is that not more than 20% of expected cells should be less than 5, and none of the expected cells should be less than 1 [42,43]. If the relationship was significant, consequently we used z-test to compare the proportion of column pairs to each other (adjusted by Bonferroni correction) according to the social variables and variables reported by Industry 4.0 areas. For 2 × 2 tables, we used Fisher's Exact test. The column proportions test shows whether the ratio in one column is significantly different from the ratio in the other column. The test assigns a letter key (A, B, C) to each category reported in column variables. The definition of each comparison of column proportions is discussed in the following section. All statistical outputs were processed in the IBM SPSS Statistics v25.0.

In this study we try to identify the main drivers and barriers of Industry 4.0 readiness in South India. Among the literature, we follow several research questions to investigate their importance on Industry 4.0 readiness in this region. Accordingly, these research issues can be concluded:


Further, we will concentrate on presenting the most attention grabbing outcomes and dependences from responses that were statistically proven as significant.

#### **4. Results & Discussion**

In order not to confuse respondents and avoid misinterpretations, we provided short descriptions of possibly unknown terms related to our scope (presented in Supplementary Materials). The questionnaire, in its actual form, is composed by twenty-seven questions divided into four main areas, mentioned above.

Firstly, we asked our respondents if they ever heard about key terms related to 4th industrial revolution. As presented in Figure 2, the term "mass customization" is not known by almost 60% of respondents, while, more importantly, the term "Industry 4.0" is unknown to 49.6% of respondents. Rather than focusing only on simple percentage points-presentations of answers observed, we examined and focused mainly on dependences between key answers on a statistically significant basis, as presented further in this chapter.

**Figure 2.** Awareness in general on Industry 4.0.

Before examining key aspects of this survey, we took the first step by examining dependence between age, education, and such awareness. In all tables below, Chi-square statistic (X2) and *p*-value (Sig.) are presented for each row question, as inevitable assumptions for further column proportions comparison. X<sup>2</sup> refers to Pearson Chi-Square statistic value, obtained by Chi-square test in SPSS, which tests the hypothesis that two variables (row and column) are independent. Sig. refers to significance value, which has the information we are looking for. The lower the Sig., the less likely it is that two variables are unrelated. When the significance value is less than 0.05, we can conclude that there is a relationship between two variables. To understand the relationship between row and column variables, we examine the crosstabulation tables with results of the column proportions tests. As we mentioned in previous section, the column proportions test shows whether the proportion in one column is significantly different from the proportion in the other column. The test assigns a letter key (A, B, C) to each category reported in column variables. We used three significance levels: 0.05 \*; 0.01 \*\*; 0.001 \*\*\*. Column proportion tests are performed by z-test, and tests are adjusted for all pairwise comparisons within a row of each innermost sub-table using the Bonferroni correction (see Sedgwick [50]). Below we provide Table 2, where statistically significant relationship between answers "No" to above mentioned general awareness questions and education "Higher Secondary and below" can be observed. We find this in line with basic logic that ongoing and deeper education opens possibilities and provides information about new approaches and cutting-edge trends. Similarly, we find a logical relationship within our answers, that higher education (Doctorate, Medical, or Law degree or higher) goes with higher age of the respondent. However, we consider the fact that 46.4% (45.3%) of the group "Higher Secondary and below" answered "No" when asked about "Cloud solutions" ("Internet of Things"), as a result of teaching plans that are not updated sufficiently, not the respondents' inability to learn about possibilities linked to I4.0.



Results significant pair, key category proportion appears category larger proportion. refers to Chi-square statistic. Sig. refers to the two-sided asymptotic significance of the chi-square statistic. Significance level for upper case letters (A, B, C): 0.05 \*; 0.01 \*\*; 0.001 \*\*\*. Tests are adjusted for all pairwise comparisons within a row of each innermost sub-table using the Bonferroni correction; Source: Prepared by authors.

In Table 2, the column proportions test assigns a letter key, (A) or (B), to each category of question Q10–Q17. (A) refers to the answers "No" and (B) to the answers "Yes". The row variables are "Age" and "Education", which have four categories of answers. The two-sided asymptotic significance of chi-square statistics adjusted by Bonferroni correction is less than 0.05 \* in all comparisons, except comparison between "Age" and "Mass Customization" (Sig. 0.100). The Sig. value (0.000 \*\*\*) is less than 0.001, therefore statistically significant. For the column proportions test associated with the age group "25 or below" and the answers to question Q10, the B key appears in the column "No".

Thus, we can conclude that the proportion of respondents aged "25 or below", who answered the question Q10 about cloud solutions negatively, is greater than the proportion of respondents that answered the question Q10 positively (aged "25 or below"). The same results are listed between the respondents aged "25 or below" and other questions, except the Q11 about mass customization. For the tests associated with "Education", the results indicate the same in the case of "Higher Secondary and below" education for all questions Q10–Q17.

We would like to highlight the relationship between the age group "25 or below" and answers "No" to general questions. In absolute terms, 56.0%, and 41.4%, respectively, of group "25 or below" answered "No" to questions addressing Industry 4.0 and IoT as a crucial stage of 4th industrial revolution, respectively. We consider this as a very poor informational level, especially within the young and flexible group of workers entering labor market. On the contrary, 79.8% (46.6%) of this group is using WhatsApp (Facebook) almost daily, therefore we cannot explain this level of awareness as a result of insufficient conditions for obtaining information or being digitally isolated. Motyl et al. [49] conducted a survey of more than 460 students at three different universities in Italy about the Industry 4.0 concept. The authors point out the importance of the digital behavior of the young people, whose relationship with digital world and services are very important for their further social, but also economic development, ultimately for the development of the region or country. We agree with the authors that in today's environment, it is important to empower a broader knowledge of the general I4.0 concepts and bring well-structured action plans into the educational process. These conclusions should emphasize, on the one hand, the role of education, and the SMEs on the other, which are dependent on an educated workforce in the terms of I4.0 and related IoT.

The second part contains information about the importance of several aspects of doing business from the perspective of respondents, considering SMEs. We present Figure 3 with questions Q18, Q21, Q22, and Q23. We observed a relatively high proportion of responds without any clear opinion regarding each question, while almost one quarter of respondents consider investing in training of workers as "Not at all important". Such attitudes we consider as negative, while on the other hand, almost 40% of respondents consider approaching smart manufacturing as very important.

**Figure 3.** The answers to the questions Q18, Q21–Q23.

In Table 3 below, each column refers to awareness questions mentioned above, and each row refers to questions regarding IoT, I4.0, smart manufacturing, e-commerce, and investing in workers' education.

We then expected the row questions (Q18, Q21–Q23) and column variables (Q10–Q17) would suggests some proportional relations. The fact is, that almost in all situations (where the questions Q18, Q21–Q23 were answered "No opinion", respectively "I don't know"), the proportion of respondents who answered the questions Q10–Q17 negatively is greater compared to the proportion of respondents who answered these questions positively. We argue that such statistical evidence of inability to form an opinion or express expectation stems from obvious lack of information. Stentoft et al. [45] also conclude that the lack of information and knowledge about Industry 4.0 and the lack of employee's readiness are among the main barriers for Industry 4.0. On the contrary, proportion of respondents who answered the questions Q10–Q17 positively is greater compared to the proportion of respondents with negative answers, if we are considering answers "very important" or "fairly important" regarding questions Q18 and Q21–Q23. A possible and logical explanation could be that respondents realize importance of successful transformation of the industries due to previous, at least basic, knowledge about questioned aspects. The most attention grabbing at this stage was, however, finding relations between answers "not at all important" to questions Q18, Q21–23, and questions Q10–Q17 that were answered as "No". The proportion of respondents answering questions Q12, Q14, and Q15 as "No" that also answered Q22 (regarding approaching smart manufacturing from SMEs perspective) as "Not at all important" was significantly higher than the proportion of respondents answering Q12, Q14, and Q15 as "Yes". In total, 10.3% of respondents answered Q22 as "Not at all important", 14.4% answered "Slightly important", and 24.1% answered "No opinion" (or "I don't know"), which makes together 48.8%. We can observe a similar relationship between answers "No" to Q12 and Q14 and answers "Not at all important" to question Q18, addressing importance of implementation of I4.0 from SMEs perspective. Additionally, the relationship between respondents answering Q13 regarding I4.0 as "No" and Q21 addressing investing in training workers answering as "Not at all important" is alarming. This could be seen as a lack of information about inevitable changes in the coming future translating into unclear thoughts about crucial education and training for current and potential employees. This is in contrast with consensus among the authors [46–48] that proper education and requalification is necessary, especially regarding current dynamics throughout the industries.


**3.**Awarenessvs.importanceofthemodernenvironmentofdoingbusinessregardingSMEs.

Very

A

A

A

A

A

important



Note:category with the larger column proportion. X2 refers to Chi-square statistic. Sig. refers to the two-sided asymptotic significance of the chi-square statistic. Significance level for case letters (A, B, C): 0.05 \*; 0.01 \*\*; 0.001 \*\*\*. Tests are adjusted for all pairwise comparisons within a row of each innermost sub-table using the Bonferroni correction.l Source: Preparedby authors.

upper

Responds to the questions Q19, Q20, and Q25 are presented further in the Figure 4. The respondents were able to choose one of five options: "No"; "Rather no"; "No opinion" (referring also to "I don't know"); "Rather yes"; "Yes". In each of these questions, we can see a high proportion of respondents who replied to all questions with the "No opinion" ("I don't know"). In Q19, it was more than 32% of respondents, in Q20 more than 33%, and in Q25 more than 21%. This again points towards a lack of information resulting in an inability to form an opinion regarding the issue. On the other hand, the answers "No" and "Rather no" opened further questions that we attempted to examine. Within the age group "25 or below", more than 16% of respondents think that IoT concept will be ineffective for South India's SMEs. Almost 34% of the respondents within this group reported "No opinion". Examining performance of this group also on other questions, we observed nearly 17% of the respondents claiming the SMEs in South India are not ready to implement IoT and I4.0 concept, and as many as 36% of the respondents were unable to make a judgement. For more than 27% of the respondents aged "25 or below", the I4.0 concept is personally unimportant. More than 24% of respondents from the whole sample do not consider the IoT and I4.0 concept as important from a personal point of view.

**Figure 4.** The answers to the questions Q19, Q20, and Q25.

These results are further examined against general awareness in the Table 4. Similarly, we applied column proportions test. For each combination of testing, we also point to the value of the asymptotic significance statistic (Sig.), which in all cases is less than 0.05 \* level, and thus variables are related. This table includes also a comparison of the answers to question Q27, which is focused on whether respondents expect any Smart City in South India within the next 10 years. In proportional testing, we found that in three cases (Q12, Q14, and Q17), the Sig. value is higher than the confidence level 0.05 \* (0.100; 0.091; 0.234). In such cases, we consider these variables as independent.


**4.**Awarenessvs.questionsQ19,Q20,Q25,andQ27.

144

case letters (A, B, C): 0.05 \*; 0.01 \*\*; 0.001 \*\*\* a. Tests are adjusted for all pairwise comparisons within a row of each innermost sub-table using the Bonferroni correction; Source: Prepared

by authors.

*Sustainability* **2020**, *12*, 3207

We highlight the high portion of "No opinion" ("I don't know") answers observed within the set of questions Q19, Q20, and Q25, related to answers "No" (questions Q10–Q17). A similar pattern was observed and described in Table 2. One concern could be potential complexity or difficulty of questions Q19 and Q20, therefore forming a substantiated opinion could be harder for respondent. On the contrary, inability to take a personal stance towards I4.0 and related IoT, we explain as lack of sufficient information, as described previously. Additionally, on a personal level, implementation of I4.0 and IoT (Q25) is not important for respondents answering Q12 and Q13. In total, 24.3% (7.6%) of respondents answered "No" ("Rather no") to a question Q25.

Regarding question Q27, where respondents were asked if they see any Smart City within the region in the next 10 years, in total almost 74% answers were positive, which is rather overconfident, especially in contrast to other studies [28] examining current state of art in India. Putting this question in the context of questions Q10–Q17 results in similar outcomes as for previous sets of questions, where negative answer to Q10–Q17 are related to negative answer addressing Smart City. On the other hand, proportion of respondents answering Q27 positively, that answered also Q10–Q17 positively, is higher on the statistically significant basis than the proportion of those who answered Q10–Q17 negatively.

Thus, we find implementation of any I4.0 related features difficult from non-technical point of view, if respondents' expectations are negative towards companies. On the other hand, throughout each set of questions, we observe a significantly higher proportion of respondents expecting rather positive impacts within questioned aspects, that have previous knowledge or information (which should be also considered as a part of intellectual capital [51]) about key terms addressed in the first part compared to those without such information. To add on, respondents with previous experience with IoT and I4.0 expressed positive expectations with higher frequency compared to those without such experience. On the contrary, we consider some responds to questions addressing Smart cities in South India (Q27), or readiness of SMEs for implementing IoT and I4.0 (Q20), as rather over-confident, considering the current state of art not only in South India [28]. Such observations could however stem from possible drawbacks as sampling error. Other possible limitations could be that only main results are presented. The survey was prepared only in South India's region with high representation of people of state Tamil Nadu, which could lead to possible biasedness. Additionally, authors realize that a higher proportion of employees of SMEs in the survey could lead to more valuable outcomes. Thus, we recommend further examination of the mentioned region because of its huge demographic potential. Possible improvement of the conducted research should be expanding the sample, or expert surveys with representatives of employee associations and other social parties.

#### **5. Conclusions**

In this paper, we attempted to examine general awareness, opinions, and attitudes of South India's inhabitants towards Industry 4.0 and its features. Contrary to the majority of studies examining I4.0 readiness and implications, we oriented our research towards citizens rather than SMEs. Conducting a survey using a questionnaire, we gathered unique answers containing crucial information about current state of art regarding addressed issues, the same as future expectations. Besides simple counts of answers, we provided also testing of interdependences between general awareness questions, and several sets of questions addressing various issues.

The main findings suggest that general awareness is quite low (almost 50% of respondents have no prior information of Industry 4.0), which consequently leads to inability to form any opinion regarding effects of such new trends on working and personal life, the same as on living and business environment. Respondents with insufficient knowledge of I4.0 and related IoT then tend to answer negatively regarding questions about possible transformation of SMEs towards I4.0, or they are unable to form any opinion regarding addressed aspects. On the contrary, respondents possessing prior information or knowledge regarding I4.0 and related IoT as its crucial part expressed positive expectations in general. We argue that this approach provides us with important information described in detail in previous section, that should serve as steppingstone in forming a sustainable strategy of implementing I4.0 features for both large enterprises and SMEs. From the perspective of sustainability of the transformation process towards competitive and functional I4.0 environment, we therefore highlight the importance of proper skills, education, working conditions, and informational level, which we consider, to some extent, in line with other studies examining sustainable management and development as a part of I4.0 linked research [6,26,39]. To conclude, improving education and providing proper information is crucial from the authors' perspective.

Based on examined interdependences, we argue that proper education and relevant information dissemination is non-technical, however crucial, in order to achieve a sustainable transformation process of the current environment in South India towards Industry 4.0 and its features. Since we find this approach rather extraordinary, similar survey-based studies in other regions should provide comparable results, and consequently form a broader picture of inhabitants' preparedness and awareness of I4.0.

#### **Supplementary Materials:** The following are available online at http://www.mdpi.com/2071-1050/12/8/3207/s1.

**Author Contributions:** Conceptualization, L.S. and J.S.; methodology, L.S. and J.S.; validation, Darya Dancaková; formal analysis, J.S.; investigation, L.S. and J.S.; resources, L.S., J.S.; writing—original draft preparation, L.S., J.S. and D.D.; writing—review and editing, M.W.; editing and visualization, J.S. and D.D.; supervision, M.W.; project administration, M.W., L.S., and J.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Scientific Grant Agency of the Slovak Republic VEGA, grant number 1/0430/19. This research work is part of actual research activities in the project with acronym SME 4.0, and titled as "SME 4.0—Smart Manufacturing and Logistics for SMEs in an X-to-order and Mass Customization Environment", from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 734713.

**Acknowledgments:** Authors express gratitude for tremendous support and help to Prof. Naavendra Krishnan, Prof. Sudhakara Pandian, and all SACS MAVMM staff during authors' stay in Madurai, Tamil Nadu.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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