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Article

Analysis of Directional Activities for Industry 4.0 in the Example of Poland and Germany

by
Tomasz Jałowiec
and
Henryk Wojtaszek
*
Faculty of Management and Command, War Studies University, 00-910 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(7), 3848; https://doi.org/10.3390/su14073848
Submission received: 16 February 2022 / Revised: 14 March 2022 / Accepted: 16 March 2022 / Published: 24 March 2022
(This article belongs to the Special Issue Industry 4.0 Impacts on Lean Production Systems: Sustainable Practices)
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Versions Notes

Abstract

:
An analysis of directional activities in Poland and Germany towards the implementation of Industry 4.0 was carried out by comparing the common sustainable development features. The value of production sold along with the benefits of its implementation are presented. The transformation map was characterized along with development areas and potential directions of automation and robotization. Technological possibilities were assessed, considering the production of robots. The execution of activities aimed at implementing solutions in the field of Industry 4.0 in Poland was indicated. The key information gleaned in this study is the awareness of the implemented features proving the fulfillment of conditions relating to Industry 4.0. Action towards the sustainable replacement of machines that require repair or regeneration is significantly related to thinking towards rationalizing the actions taken and assessing the financial capabilities of companies so as not to lead to their collapse. The article presents original research on the characteristics of selected production companies in Poland and Germany striving for digital maturity and the results of our hypotheses. The key direction should be activities aimed at developing a coherent strategy, the proper selection and evaluation of managers, focusing on communication, and the pursuit of intelligent products by creating appropriate integration standards that facilitate the implementation of an innovative process generating modern technologies.

1. Introduction

Industry 4.0 is an important concept in the development of European states in response to the strong expansion of the USA and China into the global market. The enterprises of the Old Continent are stimulated to develop towards advanced automation and robotization in order to remain competitive in the global circulation of goods and services. In Poland, Industry 4.0 is the basic direction of transformation of Polish enterprises [1]. The need for change is key in development, innovation [2], and the ability to achieve success [3]. This allows for the creation of products individually tailored to the customer [4], flexible functioning in the market, and enhanced decision-making procedures. Growing shortages and the associated price increases for resources and social change in the context of ecological aspects [5] require a more intensive focus on sustainable development in industrial contexts. This goal is the economic and ecological increase in productivity [6]. The pursuit of digitization towards sustainable development becomes of key importance [7]. It seems natural that this action is also subject to risk or may indicate some kind of uncertainty [8,9].
The key to producing affordable, high-quality goods is the use of an advanced process control structure and their full control. This means that all sensors, machines, and robots in technology are connected by a network to the IT layer, where data are collected [10]. Then, they are transferred to the computing cloud, where they are subject to another form of digital processing. This is how flexible production is created.
It is worth emphasizing that people are irreplaceable in the manufacturing industry, if only because of the changing preferences of customers, which is associated with the need to constantly adapt the offer to the customer’s needs [11]. Profit maximization in an enterprise is based on competitive activities [12,13,14].
The following research problems were set:
(1)
What are the directional measures for Industry 4.0, taking the example of Poland and Germany?
(2)
What are the areas of development based on the transformation map and potential directions of automation and robotization?
(3)
What is a reliable source indicating technological development? Do automation and robotization indicate development in this area?
(4)
What is the awareness of the implemented features that confirm the fulfillment of conditions relating to Industry 4.0?
(5)
Are there any activities aimed at the sustainable replacement of machines that require repair or regeneration related to rationalization of undertaken activities and assessment of the financial possibilities of companies so as not to lead to their collapse?

2. Materials and Methods

The first part of the article presents the characteristics of the industry, presenting individual countries with reference to the value of product sales. The study concerned two countries, Poland and Germany, and their implementation of Industry 4.0 compared to other countries of the European Union. A comparison of common features of the two countries was performed—namely, Industry 4.0 and sustainable development. We examined the beginning of the industry from 2.0 and 3.0 and the benefits of development tools of Industry 4.0, along with the achievable model of digital maturity.
We also developed a graphical presentation of the value of industrial production sold in selected countries in 2020. The key common features related to Industry 4.0 and sustainable development were compared.
In this paper, we discuss Industry 4.0 along with the benefits of its implementation. The transformation map is presented as a road map to Industry 4.0 striving for digital maturity of the enterprise, along with the characteristics of development areas and potential directions of automation and robotization.
The technological possibilities were analyzed taking into account a 10-year period. The already fulfilled technological innovations in the form of robotic production have been considered key and reliable. The data are presented on a global scale and broken down into selected countries, along with a forecast of an upward trend until 2024. The execution of activities aimed at implementing solutions in the field of Industry 4.0 in Poland was assessed.
The next stage was the implementation of original research presenting the characteristics of selected production companies striving for digital maturity in Poland and Germany, and the verification of the hypotheses along with the results.
On the basis of the research problems posed, the following hypotheses were composed:
Hypothesis 1 (H1).
There are significant differences in how employees perceive the development of industry 2.0, 3.0, and 4.0.
Hypothesis 2 (H2).
Polish manufacturing companies are less focused on industrial evolution than German companies.
Hypothesis 3 (H3).
A German manager shows instructions to employees more often than in Poland.
Hypothesis 4 (H4).
A German manager sets goals at work more often than in Poland.
Hypothesis 5 (H5).
Polish and German managers define the rules of work to an equal extent.
Hypothesis 6 (H6).
The linear production process applies to both Poland and Germany.
Hypothesis 7 (H7).
Sub-suppliers in the supply chain are implemented to a greater extent in Germany than in Poland.
Hypothesis 8 (H8).
Algorithms making autonomous decisions in Germany are more complex than in Poland.
Hypothesis 9 (H9).
Decision making by employees in manufacturing companies is at the same level in both analyzed countries.
Hypothesis 10 (H10).
The role of the IT system in the decisions taken is realized to a lesser extent in Germany than in Poland.
Hypothesis 11 (H11).
The realization of the finished product is comparable in both analyzed countries.
Hypothesis 12 (H12).
In both analyzed countries, it is possible to obtain a finished product with its usefulness.
Hypothesis 13 (H13).
Employees are jointly responsible for making decisions at a uniform level in both analyzed countries.
Hypothesis 14 (H14).
Employees improving algorithms implement these activities to a greater extent in Germany than in Poland.

Characteristics of the Industry and Its Development in the Context of Industry 4.0

The chart below presents the share of the value of production sold by individual EU member states in 2020. Six countries generated three-quarters of the value of marketed production—around 80%. Germany had the highest value of sold production (30% of the European Union), followed by Italy with around 20%, France and Spain with slightly more than and less than 10%, respectively, Poland with 5%, and The Netherlands with less than 3%. The remaining 21 EU member states contributed less than The Netherlands.
The value of sold production of industry according to selected countries is presented in Figure 1.
When analyzing the production sectors, we noted that Slovakia relied on transport activities, which accounted for 53% of the total value of production sold in the country in 2020, followed by Greece (43%), Hungary and the Czech Republic (30% each), The Netherlands (36%), and Spain (30%), and then Bulgaria, Croatia, France, and Belgium with around 30% each.
The values of sold production of the Czech Republic and Slovakia are only 10% and 6%, respectively. Germany is a significant producer (almost 30%). The key common features relating to Industry 4.0 and sustainable development were compared. The main features of sustainable development are employees, along with efficiency (mainly economic, and to a lesser extent, environmental) (Figure 2).
The key analyzed function is Big Data, which is least related to the concept of sustainable development. It seems obvious that technical key functions such as Big Data, Internet of Things, and cyber-physical systems occupy lower percentages.
The basis for applying for financing is the Industry 4.0 [17] roadmap. It should include a diagnosis of administrative and production processes, business models, and areas of potential for transformation [18]. A roadmap is nothing more than a plan of changes that must be implemented in the enterprise to achieve digital maturity and obtain advanced production methods [19]. The full potential of Industry 4.0 comes to life when its development tools are used together, as shown in Figure 3 below.
Industry 4.0 is based on nine technological pillars [22]. These innovations connect the physical and digital worlds and enable the creation of intelligent [23] and autonomous systems [24]. Businesses and supply chains [25] already take advantage of some of these advanced technologies [26].
The benefits of implementing Industry 4.0 are presented in Figure 4.
The road map to Industry 4.0 is about transformation. It defines the digital maturity of a company along with the characteristics of development areas and potential directions of automation and robotization [38].
The tool was built taking into account the key Industry 4.0 aspects of enterprise development. It enables the identification of the stage of a company’s development in several dimensions based on three pillars: organization, processes, and technologies. Figure 5 reflects Industry 4.0 in terms of the digital maturity model [39].
The role of model study is to determine the current state of digital maturity of an enterprise and show the challenges and the scope of necessary changes, the implementation of which will allow the company to become an organization compliant with Industry 4.0 standards [42].

3. Analysis of Technological Possibilities in the World

An analysis of technological possibilities on a global scale was made, taking into account a 10-year period. The already fulfilled technological innovations in the form of robotic production have been considered key and reliable.
Figure 6 presents the operational stock of industrial robots in the world and Figure 7 presents annual installations of industrial robots.
Figure 6 shows an upward trend in the operational stock of industrial robots worldwide even in the time of the COVID-19 pandemic. A similar situation (although to a much lesser extent) applies to annual installations of industrial robots worldwide (Figure 7). The period of the pandemic allows for the conclusion that the use of innovative solutions based on the technology of Industry 4.0 robots will not lead to an increase in the unit costs of an employee and will not lead to a situation in which this cost should be counted as a loss. It can be unequivocally stated that despite the lack of widespread functionality of robots, there are noticeable actions supported by facts presenting an upward trend, despite the uncertain situation caused by the pandemic, risk, and changes in the attitude of potential users of modern technologies. We intentionally compiled the analysis referring to the whole world because we next present individual countries in the form of aggregate data for 2020.
Figure 8 presents the aggregate data in the group of 10,000 robotic installations by individual countries in the world in 2020.
In Figure 8 above, there are countries that notably outperform the average score by at least 30%. The most was recorded in Korea (as many as 932 robots installed). Singapore had over 300 fewer robots than Korea, with 605 total, putting it in second place. It is worth presenting two more, Japan with 390 robots, and Germany, with 371.
According to Figure 9, the forecasts indicate that by 2024, production (the use of robots) is to increase by at least 1.5 times.
Figure 10 shows the number of robot installations by country. Japan is in the lead, then the United States, Korea, and finally Germany.
Future research directions may also be highlighted. Germany, Singapore, and South Korea are at the forefront of industrial digitization. High-ranking country strategies can be replicated in newly digitizing countries [46]. The manufacturing, energy, logistics, and mining sectors will become more efficient as a result of this digitization—a process that is attracting interest in countries around the world. Europe and Asia are global leaders. Of the top 10 scorers, four were European countries and four were Asian countries. The highly rated countries have innovative policies, investment programs, and training programs that can be replicated [47].
The key is the opinion of representatives of manufacturing companies from Poland on Industry 4.0 and technology, as well as their needs and challenges. PSI Polska conducted a market study that covered large- and medium-sized manufacturing companies. As part of the study, called “Readiness of manufacturing companies to implement Industry 4.0 solutions” [48], PSI Polska conducted telephone interviews using the CATI technique with decision makers from 228 enterprises operating in four sectors: machinery and equipment, cars and transport equipment, furniture, and metal products.
The aim of the study was to determine the current state of digital maturity of an enterprise and show the challenges and the scope of necessary changes [49], the implementation of which will allow the company to become an organization compliant with Industry 4.0 standards (Table 1).
Based on the market research conducted by PSI Polska and our own research, a result (descriptive) model was designed towards the necessary activities related to the implementation of Industry 4.0 (Figure 11).
The model indicates a project team that should respect the current industrial needs and use career development paths (human resources) through proper planning, production processes, and the supply chain. Project teams should benefit from expertise and training. Knowledge can be acquired through experience and external experts. Training can also be arranged thanks to external experts. It is assumed that the training will be delivered through an innovative training methodology towards a knowledge-based training program with a long-term strategy capable of meeting current and future goals [62].
Communication is one of the most important elements for an advanced structure to centrally manage by processes. Systems can be implemented and analyzed in real time and can be combined with autonomous decisions. The processes can also be carried out by employees according to defined procedures without the use of advanced IT systems. They can also be used by advanced IT systems.
European utilities are at the forefront of digital innovation. Due to the pressure of regulators and investors to decarbonize energy companies, digitization has become a central element of their strategy. Utility providers use digital technologies and workforce innovation to lower operating costs, find new revenue streams, increase security, and support net zero goals. Popular technologies include digital power plant twins, network inspection drones, and artificial intelligence for energy management. We ranked the 10 largest publicly traded energy companies based on five metrics: management, talent, investment, technology, and adoption. Firms’ performance levels are measured against each other; this does not indicate how well they are performing in absolute terms. The results highlight the strengths and weaknesses of companies in terms of digital innovation. Utility companies are creative with their strategies [63]. The assessment of the largest enterprises in terms of management, talent, investment, technology, and adoption showed that technology was always one of the present, but not the key, elements in this respect [64]. The analysis took into account factors such as management, talent, investment, technology, and adoption.

4. Result

Characteristics of Original Research Aimed at Digital Maturity of Selected Production Companies in Poland and Germany

Here, we present original research on the characteristics of selected production companies in Poland and Germany striving for digital maturity. It has been assumed that not all distinctive features are related to Industry 2.0, 3.0, and 4.0. It may turn out that enterprises are not ready to change, or not all characteristic changes have been introduced. The established hypotheses H1–H14 were verified.
We analyzed 638 respondents from manufacturing companies with an active status in Poland and Germany in the period from 2020 to 2021.
These enterprises came from two sections: D, industrial processing (321 observations), and I, transport and warehouse management (317 observations), presented in Figure 12 and Figure 13.
The ratios of surveyed industrial processing, transport, and warehouse management enterprises were similar in Poland and Germany. The subjects of the study were employees of selected production enterprises in Poland and Germany.
Based on the available literature on the subject, model features characterizing individual types of industry 2.0, 3.0, and 4.0 were established, as presented in Table 2.
Questions for respondents were intentionally mixed in so as not to suggest an answer. Thanks to this, we can obtain greater credibility of the respondents.
When carrying out the analysis, Wilks’ lambda initial research method was used to assess the condition of companies’ preparation for the implementation of Industry 4.0, and to verify whether the other elements were implemented. The statistical calculations are presented in the Table 3 below [65].
Signs from a to k were adopted, where a—manager giving the instruction, b— rule-modeling manager, c—detailed instructions, d—employees co-responsible for making decisions, e—employee correcting algorithms, f—linear production process, g—sub-suppliers in the supply chain, h—algorithms making autonomous decisions, i—decision-making workers, j- system role of decision IT, and k—finished product and final parameters of the operation. Figure 14, Figure 15, Figure 16, Figure 17, Figure 18, Figure 19 and Figure 20 present the results.
Managers giving precise instructions are a feature of Industry 2.0. The present research indicates that Germany is closer to execution in over 80%, while Poland has less than 50%.
Ultimately, each product is ready and has final parameters for operation. There is an 8% difference between Poland and Germany, with Germany being dominant. This means that in Germany, the pursuit of the assumed goal is more noticeable.
Sub-suppliers in the supply chain present an 18% difference, with the advantage of Germany in relation to Poland. It turns out that Germany lacked 8% to complete the set goal, and Poland was three times more. The orientation of sub-suppliers in the supply chain is popular in Industry 3.0.
Employee correcting algorithms are classified into Industry 4.0. Both in Poland and Germany, they are at a high level, with a difference of 8% indicating the advantage of Germany, which is missing 6% in total, and Poland is 4 times more short of it.
The linear production process is attributed to Industry 2.0, where both in Poland and Germany, it is at a similar level, with a difference of 1%, where in Poland, 34% is required to implement the full assumption, and in Germany, 1% less.
Employees making decisions on their own are basically forced without adequate remuneration (they have imposed competences) into Industry 2.0.
The term “decision-making workers” defines people who do not have competences and are often delegated tasks (responsibility is imposed on employees for the implementation of most tasks). Poland has 18% more delegated tasks than Germany.
On the other hand, 8% is missing in the case of complete delegation of tasks.
Employees co-responsible for making decisions are generally assigned to Industry 3.0, and this situation occurs to the greatest extent in Poland, with less than 2% to full implementation, and in Germany, with as much as 24%.

5. Discussion

Preparatory activities towards Industry 4.0 are based on the performance of an audit, analysis of existing processes, analysis of existing resources, and analysis of existing business systems.
Below is a descriptive model of the transformation to Industry 4.0 (Figure 21).
Reactivation of machines’ functioning for digitization 4.0, retrofitting of older machines, installation and connection of video cameras and heat sensors, modernization and replacement of older resources, and assessment of the profitability of Industry 4.0 implementation are carried out thanks to solid Big Data. Moreover, assessment of return on investment, integration of further business areas, time of transformation to Industry 4.0, gradual nature of changes, no disruptions in business activities, business analysis, and digital transformation roadmaps are made possible. It is worth adding that Industry 4.0 solutions can be implemented almost immediately [67,68,69,70,71,72,73,74].
Employee qualifications trigger their creativity and innovative thinking by replacing repetitive work with innovation and business enriched with digital activities. Optimizing activities towards mobile devices is carried out through an intuitive system and optimized towards mobile devices [75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91].
Figure 22 below characterizes the intensification of the degree of implementation of individual decision-making features in Poland and Germany.
The intensity of the degree of implementation of individual decision-making features is characterized by the colors: light blue—a smaller degree, dark blue—a higher degree, magenta—the highest degree. The algorithms that are designed to improve teamwork in both analyzed countries have a slight degree of intensity. Sub-suppliers in the supply chain are implemented to a greater extent in Poland than in Germany of the same intensity.
The decisions made by the IT system and the modeling of requirements by superiors are largely carried out in Germany. Implementation to a small degree occurs mainly in Poland.
Figure 23 clearly shows that Germany surpasses Poland in terms of linear production processes, algorithms making autonomous decisions, decision-making workers, IT systems’ role in decisions, finished products, and final parameters of the operation.
The linear production process, by definition, should be ordered in time as a sequence of changes and states occurring one after another, the result of which is the obtained product. Figure 24 presents the characteristics of the realized characteristics in relation to linear production.
Features striving for process maturity that have been implemented include employees co-responsible for making decisions, as well as the finished product and final parameters of the operation (blue line—Germany, red line—Poland).
Figure 25 presents a summary analysis of Poland and Germany in terms of the assumed hypotheses.
The analysis of the factors determining the industry phase 2.0, 3.0, or 4.0 allowed us to confirm or reject the declared research hypotheses.
The first hypothesis was confirmed, indicating that there are significant differences in employees’ perception of the advancement of industry 2.0, 3.0, and 4.0, as was the second hypothesis stating that Polish manufacturing companies are less focused on industrial evolution than German companies. The third hypothesis was also confirmed, where German managers almost twice more often indicate instructions to their employees than in Poland, as was the fourth one stating that German managers more often than in Poland define goals at work (almost a twofold difference).
The fifth hypothesis, that Polish and German managers define the rules at work equally, was rejected—there are twice as many rules modeled by managers in Germany as in Poland. The sixth hypothesis was confirmed, stating that the linear production process applies equally to Poland and Germany. The seventh hypothesis was confirmed, specifying a third more sub-suppliers in the supply chain in Germany than in Poland. The eighth hypothesis was confirmed, indicating that the algorithms that make autonomous decisions are more complex in Germany than in Poland. The ninth hypothesis, that the decision making of employees in manufacturing enterprises is at the same level in both analyzed countries, was rejected—decisions are made to a greater extent in Poland than in Germany. The tenth hypothesis was confirmed, indicating that the IT system and the role of the decisions made are realized to a greater extent in Germany than in Poland. The eleventh hypothesis was rejected, indicating that the finished product is possibly comparable in both analyzed countries, but with the advantage of Germany by a third. The eleventh hypothesis was confirmed. In both analyzed countries, it is possible to obtain the finished product and the final parameters of the operation, but with the advantage of Germany (8%). The hypothesis that employees are jointly responsible for making decisions at a uniform level in both analyzed countries was rejected. The hypothesis indicating that employees who correct algorithms perform these activities to a greater extent in Germany than in Poland—because in Poland, algorithms are modified (corrected) to a greater extent, which in turn may indicate a greater scale of errors.

6. Conclusions

In this work, we examined activities that are focused on upgrading production systems to Industry 4.0. The industry framework presented here is Industry 4.0, which defines how various technologies at the intelligence level operate within the framework of automating three production systems. We note that the future of the current production is moving towards Industry 4.0.
Matching the offer to the customer’s needs is essential to achieve long-term success without complaints or risk, similar to sustainable development.
The way to produce inexpensive, high-quality goods is to use an advanced pilot process structure and its full control based on the ability to adapt and properly balance individual activities. It can be seen that the number of robots installed in highly developed countries is greater, which leads to the conclusion that people will be needed as a control resource. Production trends (robotic installations) are increasing and will continue to increase, especially until 2024.
The Digital Maturity Model Assessment Tool identifies screen elements that cannot be bypassed, significantly impacting processes, technologies, and organizations. It is worth emphasizing that in an organization, concerning leadership in the form of management, a specific strategy and cooperation are important. This is the direction of technological aspects in the field of connectivity, automation, and intelligent products. The processes are based on the integration of the product life cycle with the environment and the standardization of these processes. Determining the current state of digital maturity of an enterprise is important in assessing the necessary changes that increase the chances of that company implementing advanced technologies based on Industry 4.0.
Not only modern technologies contribute to the development of Industry 4.0. Adaptability, talent, and management are crucial. The implementation of changes based on the expansion of investments based on digital innovations is not enough to achieve success. Adaptive skills as well as talent and management should be radically linked to modern technologies, bearing in mind that basic management functions are an integral part of the management process, i.e., planning, organizing, motivating, and controlling.
A likely suitable step towards assessing the maturity of the process is to compare the global results with the unit results based on the robotic installation and purchase reports. It may be that the COVID-19 pandemic has contributed to the growth of efforts towards innovative technologies.
The key information is the awareness of the implemented features that prove that the Industry 4.0 condition is met. Activities for the sustainable replacement of machines that require repair or reconditioning are significantly related to thinking towards rationalizing the activities undertaken and assessing the financial capacity of companies so as not to lead to their collapse.
One of the first stages in the advancement of Industry 4.0 within companies should be implementing activities aimed at developing a coherent strategy, properly selecting and evaluating managers, focusing on communication, and pursuing intelligent products by developing appropriate integration standards and implementing an innovative process, ultimately generating modern technologies.

Author Contributions

Conceptualization, T.J. and H.W.; methodology, T.J. and H.W.; software, T.J. and H.W.; validation, T.J. and H.W.; formal analysis, T.J. and H.W.; investigation, T.J. and H.W.; resources, T.J. and H.W.; data curation, T.J. and H.W.; writing—original draft preparation, T.J. and H.W.; writing—review and editing, T.J. and H.W.; visualization, T.J. and H.W.; supervision, T.J. and H.W.; project administration, T.J. and H.W.; funding acquisition, T.J. and H.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The value of sold production of industry by selected countries, 2020 (%) [15].
Figure 1. The value of sold production of industry by selected countries, 2020 (%) [15].
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Figure 2. Sustainability density of key features [16].
Figure 2. Sustainability density of key features [16].
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Figure 3. Development tools of industry 2.0, 3.0, and 4.0 [20,21].
Figure 3. Development tools of industry 2.0, 3.0, and 4.0 [20,21].
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Figure 4. Benefits of Industry 4.0 [27,28,29,30,31,32,33,34,35,36,37].
Figure 4. Benefits of Industry 4.0 [27,28,29,30,31,32,33,34,35,36,37].
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Figure 5. Model of digital maturity [40,41].
Figure 5. Model of digital maturity [40,41].
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Figure 6. Operational stock of industrial robots—worldwide [43].
Figure 6. Operational stock of industrial robots—worldwide [43].
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Figure 7. Annual installations of industrial robots—worldwide [42].
Figure 7. Annual installations of industrial robots—worldwide [42].
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Figure 8. Robot density in the manufacturing industry, 2020 (robots installed per 10,000 employees) [43].
Figure 8. Robot density in the manufacturing industry, 2020 (robots installed per 10,000 employees) [43].
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Figure 9. Annual installations of industrial robots, 2015–2020 and 2021–2024 (0.000 of units) [44].
Figure 9. Annual installations of industrial robots, 2015–2020 and 2021–2024 (0.000 of units) [44].
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Figure 10. Annual installations of industrial robots (1000 units) [45].
Figure 10. Annual installations of industrial robots (1000 units) [45].
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Figure 11. Descriptive model of necessary actions towards readiness for Industry 4.0 [60,61].
Figure 11. Descriptive model of necessary actions towards readiness for Industry 4.0 [60,61].
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Figure 12. Characteristics of the research sample.
Figure 12. Characteristics of the research sample.
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Figure 13. Characteristics of the surveyed enterprises in Poland and Germany.
Figure 13. Characteristics of the surveyed enterprises in Poland and Germany.
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Figure 14. Manager giving the instruction.
Figure 14. Manager giving the instruction.
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Figure 15. Finished product and final parameters of the operation.
Figure 15. Finished product and final parameters of the operation.
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Figure 16. Sub-suppliers in the supply chain.
Figure 16. Sub-suppliers in the supply chain.
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Figure 17. Employee correcting algorithms.
Figure 17. Employee correcting algorithms.
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Figure 18. Linear production process.
Figure 18. Linear production process.
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Figure 19. Decision-making workers.
Figure 19. Decision-making workers.
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Figure 20. Employees co-responsible for making decisions.
Figure 20. Employees co-responsible for making decisions.
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Figure 21. Descriptive model of transformation to Industry 4.0 [66].
Figure 21. Descriptive model of transformation to Industry 4.0 [66].
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Figure 22. Characteristics of the intensity of the degree of implementation of individual decision-making features in Poland and Germany.
Figure 22. Characteristics of the intensity of the degree of implementation of individual decision-making features in Poland and Germany.
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Figure 23. Characteristics of decision variables for the implementation of Industry 4.0.
Figure 23. Characteristics of decision variables for the implementation of Industry 4.0.
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Figure 24. Characteristics of realized features in relation to linear production.
Figure 24. Characteristics of realized features in relation to linear production.
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Figure 25. Analysis of the assumed results for Poland and Germany.
Figure 25. Analysis of the assumed results for Poland and Germany.
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Table
Table 1. The status of activities aimed at implementing solutions in the field of Industry 4.0 in Poland.
Table 1. The status of activities aimed at implementing solutions in the field of Industry 4.0 in Poland.
The Course of ActionThe Implementation Process
Communication [50]Project teams rarely communicate with each other. Communication is only informal and uncoordinated. The role of communication channels through collaboration tools on separate/one-off tasks and projects has been reduced to one-time contacts.
Sales service
solution [51]		Sales communication with customers takes place via traditional (offline) channels, and the technological level does not allow for personalization of content, communication channels, offers, and products.
Solution implementation strategy [52,53]Building a strategy is not considered an important goal in the company’s current or future plans.
Scope of operation [54]A long-term strategy and an appropriately adjusted management model have been implemented in at least one area of activity.
Training [55]We do not train our employees. We train our selected employees when the situation in the market and the company require it. We have a training program focused on the continuous development of employees’ skills.
Knowledge [56]The Management Board does not have an established and full knowledge of the latest solutions and is unable to implement them effectively.
Processes [57]The processes are managed centrally and implemented by ad hoc methods by employees, without the use of advanced IT systems.
Planning [58]No resource planning and production processes.
Product life cycle [59]No product life cycle management.
Table
Table 2. Features indicating the pursuit of process maturity.
Table 2. Features indicating the pursuit of process maturity.
Feature of the Industrial SystemIndustry Symbol
Manager giving the instruction2.0
Manager who sets goals3.0
Rule-modeling manager4.0
Detailed instructions2.0
Employees co-responsible for making decisions3.0
Employee correcting algorithms4.0
Linear production process2.0
Sub-suppliers in the supply chain3.0
Algorithms making autonomous decisions4.0
Decision-making workers2.0
IT system role of decision3.0 and 4.0
Table
Table 3. Statistical calculations.
Table 3. Statistical calculations.
SymbolLMLRLaRaγ
a−0.000007−0.5097170.440196−8.4268.426((v1^(−0.000007))−1)/(−0.000007)
b0.000007−0.4636750.442439−8.3838.383((v2^(0.000007))−1)/(0.000007)
c−0.000060−0.1769120.101189−36.65436.654((v3^(−0.000060))−1)/(−0.000060)
d−0.000126−0.0791470.068855−53.86653.866((v4^(−0.000126))−1)/(−0.000126)
e−0.000409−0.4080310.010635−348.799−348.799((v5^(−0.000409))−1)/(−0.000409)
f−0.000048−0.1922450.153955−24.09124.091((v6^(−0.000048))−1)/(−0.000048)
g0.000046−0.1681550.150302−24.67624.676((v7^(0.000046))−1)/(0.000046)
h−0.000048−0.1922450.153955−24.09124.091((v8^(−0.000048))−1)/(−0.000048)
i0.000011−0.2861730.271334−13.66913.669((v9^(0.000011))−1)/(0.000011)
j−0.000018−0.2306220.283073−13.10213.102((v10^(−0.000018))−1)/(−0.000018)
k−0.000156−0.0416910.058960−62.90662.906((v11^(−0.000156))−1)/(−0.000156)
LM, L, R, R, La, Ra -individual calculation phases presented in the figures below. γ—the result of calculations for a given variant.
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Jałowiec, T.; Wojtaszek, H. Analysis of Directional Activities for Industry 4.0 in the Example of Poland and Germany. Sustainability 2022, 14, 3848. https://doi.org/10.3390/su14073848

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Jałowiec T, Wojtaszek H. Analysis of Directional Activities for Industry 4.0 in the Example of Poland and Germany. Sustainability. 2022; 14(7):3848. https://doi.org/10.3390/su14073848

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Jałowiec, Tomasz, and Henryk Wojtaszek. 2022. "Analysis of Directional Activities for Industry 4.0 in the Example of Poland and Germany" Sustainability 14, no. 7: 3848. https://doi.org/10.3390/su14073848

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