*Review* **Towards the Next Decade of Industrie 4.0 – Current State in Research and Adoption and Promising Development Paths from a German Perspective**

**Johannes Winter , Anna Frey and Jan Biehler \***

acatech—National Academy of Science and Engineering, 80333 Munich, Germany **\*** Correspondence: biehler@acatech.de

**Abstract:** Considering the first ten years of Industrie 4.0 in Germany—the digital transformation of industry towards the goal of increased manufacturing productivity and mass customization significant progress has been achieved. However, future efforts are required. This review first evaluates the status quo of implementation and research in Germany and finds that large-scale companies have proceeded faster than small- and middle-sized enterprises. Currently, regardless of their size, companies have in common a shortage of qualified specialists, coupled with a lack of adequate base technologies for Industrie 4.0 and an insufficient digital mindset. The creation of platform-based digital business models is particularly lagging behind, despite high research interest. This review subsequently identifies three research-driven fields of action that are particularly important for the future of Industrie 4.0: (1) resilience of value networks in the strategic area of sovereignty, (2) Open-Source as a driver for the strategic area of interoperability, and (3) the strategic combination of digitalization and sustainability as a basis for sustainable business models in the strategic area of sustainability.

**Keywords:** Industrie 4.0; smart manufacturing; smart factory; digital transformation; industry; sustainability; sovereignty; interoperability; mass customization

### **1. Introduction**

Companies have always been interested in meeting customer needs, increasing customer loyalty, improving manufacturing efficiency and flexibility, and increasing sales through improved and novel products and services [1,2]. Ten years ago, researchers from computer science, engineering, economics, and related scientific disciplines, as well as representatives from industry and the service sector, presented a concept for the future of industrial value creation and customer-specific mass production that resonated globally. Under the leadership of Prof. Henning Kagermann, Prof. Wolfgang Wahlster, and Prof. Wolf-Dieter Lukas, a conceptual framework for the fourth industrial revolution was presented at the Hannover Fair 2011 [3], which has had a lasting impact on research in the areas of product development and systems engineering, production technology, and production systems, as well as in business process management and business model innovation [4]. Companies stepped up their efforts to automate and network production systems and value chains to advance the goal of higher productivity through a smart factory approach and to progress toward mass customization through digitally enhanced, tailor-made products and services at the price of a mass-produced product [5].

From a strategic and global perspective, significant progress has been achieved in this regard [6]. However, to date, it largely remains unclear how far research and adoption has proceeded country-wise and which focus points are set in the second decade of Industrie 4.0. This also holds for Germany, where the industrial sector plays a pivotal role, and where Industrie 4.0 had been developed as a concept. This review fills this research gap

**Citation:** Winter, J.; Frey, A.; Biehler, J. Towards the Next Decade of Industrie 4.0 – Current State in Research and Adoption and Promising Development Paths from a German Perspective. *Sci* **2022**, *4*, 31. https://doi.org/10.3390/sci4030031

Academic Editors: Luis Norberto López De Lacalle, Sharifu Ura and Claus Jacob

Received: 25 May 2022 Accepted: 9 August 2022 Published: 11 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 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 (https:// creativecommons.org/licenses/by/ 4.0/).

by following this guiding question: What is the status quo of adoption and research in Industrie 4.0 in Germany, and which fields of action require future attention?

We find that German industry has proceeded significantly in adapting to Industrie 4.0 whereby large-scale enterprises are more mature than small- and medium-scale enterprises. Regarding research and Research and Development (R&D) across the four key themes *Value Creation Scenarios for Industrie 4.0*, *Prospective Technological Trends*, *New Methods and Tools for Industrie 4.0*, and *Work and Society,* significant insights have been gained. However, in the face of increasing volatility and rapid changes in the development of Industrie 4.0, more interdisciplinary research is required across all research fields. Regarding the three strategic areas defined by German government for the next decade of Industrie 4.0 in Germany, we find that (1) resilience of value networks in the strategic area of sovereignty, (2) Open-Source as a driver for the strategic area of interoperability, and (3) the strategic combinations of digitalization and sustainability as a basis for sustainable business models in the strategic area of sustainability deserve attention in the fields of industry and research to deepen the industrial transformation and to stay competitive from the perspective of German researchers and company representatives.

These descriptive findings add to country-specific research on Industrie 4.0 in Germany and are a valuable source for cross-country comparisons of the status quo of adoption and research. For practitioners, the findings help to identify future fields of actions and to deepen adoption of Industrie 4.0. Thereby, the review contributes to the academic and practical discussion of Industrie 4.0 not only in Germany, but also globally.

#### **2. Methodology**

By analyzing what is needed for the further dissemination and implementation of Industrie 4.0, this review firstly details the current status quo in implementation, scientific research, and R&D across the four key themes of Industrie 4.0 in German research: *Value Creation Scenarios for Industrie 4.0*, *Prospective Technological Trends*, *New Methods and Tools for Industrie 4.0*, and *Work and Society* [7].

Therefore, we rely on an efficient literature review of sources distributed across different academic disciplines and popular literature [8,9]. To cover relevant publications across engineering, production, and management, we relied on three publication databases (EBSCOhost, Emerald Insight and Google Scholar). We were searching for the terms "Industrie 4.0" and "Industry 4.0" in combination with "Germany" and "Deutschland". The title, abstracts, and keywords of the first 50 results for each search term were analyzed by two researchers independently to ensure reliability. Only those search results with a clear reference to "Industrie 4.0" were considered as relevant. For these, a forward and backward search was conducted, focusing on whether new research items tackled the implementation status of Industrie 4.0 in Germany and/or one or some of the four key themes of research, and excluding those research items that did not deliver knowledge to this research interest.

In addition, we applied a qualitative discourse analysis [10] of primary and secondary data from expert interviews originating from research projects conducted with the participation of the authors on Industrie 4.0 and in adjacent topic areas, such as Artificial Intelligence, Smart Services, or Advanced Systems Engineering, among others, to identify current and future research issues and to derive evidence from these expert interviews regarding adoption and the four aforementioned key themes of research on Industrie 4.0.

Secondly, building on the matrix method of literature review [11], we discussed one field of action each in the three strategic areas that will drive the next decade of Industrie 4.0: *sustainability* ("digitainabilized" platform-based business models), *sovereignty* (resilient value networks) and *interoperability* (Open-Source) [12].

Here, given the three strategic areas of the vision 2030 of the German Plattform Industrie 4.0, building on the efficient literature review from the first step, we searched for the terms "sustainability", "sovereignty", and "interoperability", with two researchers working independently in three publication databases (EBSCOhost, Emerald Insight, and Google Scholar) to identify fields of action requiring specific attention to drive Industrie 4.0 to success. Therefore, implications for research and practice raised in the research items received keywords and a prioritization. The two researchers then aggregated the assigned keywords and prioritization and discussed those cases with discrepancies. The three aforementioned fields of action with the highest prioritization were identified, and evidence acquired in the literature review was matched on these fields of action to round off this review on the status and outlook for Industrie 4.0 adoption and research in Germany.

However, the focused scope of the review should be taken into account when reading the review. The key themes of the research on Industrie 4.0 [7] and the strategic areas in the vision 2030 [12] were applied to structure the review. On the one hand, this approach may lead to (international) developments outside these guidelines being disregarded. On the other hand, this review gives evidence for researchers and practitioners to develop measures to strengthen Industrie 4.0 in accordance with the politically set strategy in Germany.

#### **3. Status Quo: 10 Years of Industrie 4.0 in Practice and Research in Germany**

The industry sector—predominantly comprising the automotive industry, the electrical and chemical industry, mechanical and plant engineering, and metal production—plays a pivotal role for the German economy. With a contribution to German gross value added (GVA) of 20.2% in 2021 [13], its contribution is almost twice as high as that of competitors on the world market, such as France, the United Kingdom, or the United States and is only matched among the biggest economies of the world, such as Japan, China, and South Korea [14,15]. Industry is a significant driver of innovation, with almost 60% of total German Research & Development (R&D) spending invested in this sector [16,17]. This indicates the relevance of the industrial sector for the German economy.

#### *3.1. Development of German Industry since 2011 and Outlook on Industry Strategy until 2030*

Since Industrie 4.0 was propagated as the vision for the digital transformation of German industry in 2011—aiming at connecting resources, services, and humans in real time throughout production on the basis of Cyber Physical Systems (CPS) and the Internet of Things (IoT) [3]—the gross value added of German industry increased in absolute terms, while its proportion relative to the GVA of German economy remained stable, in comparison to a decline in other countries [15,18]. German industry even experienced an internationally unmatched phase of "reindustrialization" until 2016, with increasing employee numbers, an increasing proportion of GVA, and higher productivity gains compared to other sectors, before the COVID-19 crisis and interrelated supply chain problems initiated a phase of deterioration in German industry, which was also due to its high dependency from world markets originating in its export orientation [19].

The deepening adaption towards Industrie 4.0 correlated with the continuing strength of industry as a value driver of the German economy, but could not prevent the phase of downturn during the pandemic. This phase is deemed to be temporary, due to two factors: First, extensive investments made by companies in the development of digital technologies and competencies in the first ten years of Industrie 4.0 have not amortized yet, because they are not easily quantifiable in terms of return on investment, as it is mostly intangible assets and human capital that have been targets of investment so far [19] (pp. 195–197). Second, after these significant digitalization steps in the first phase of Industrie 4.0, industry has appeared to "take a breath" regarding innovative value-driving projects in the run-up to the next wave of industrial digitalization [20], which will likely focus on industrial artificial intelligence (AI) and the creation of digital platform-based business models [6,21,22].

Put together, in the first ten years, German industry invested in shaping the breeding ground of Industrie 4.0, the base layer of digital infrastructure, architecture, products, and competences. To exploit this breeding ground—the cumulated value creation potential of Industrie 4.0 for German industry is estimated at 425 billion Euros until 2025 [23]—the German government set the ambitious goal in its Industrial Strategy 2030 of increasing the contribution of industry to GVA to 25% in 2030 (an increase of roughly five percentage points compared to 2021) [16]. After ten years of Industrie 4.0, the German government's goals thus incentivize an acceleration of research and adoption of the changes to strengthen value creation, scalation, and sustainable long-term orientation of German industry.

#### *3.2. Status Quo: Adoption of Industrie 4.0 in German Industry*

To assess whether German industry is in a position to achieve these strategic goals by deepening its digital transformation, this section provides a status quo overview on the adoption of Industrie 4.0.

In practice, Industrie 4.0 is increasingly perceived as a competitive advantage by companies in the industrial sector [7]. The call for the digital transformation of industry towards the paradigm of smart factory has received significant attention in economic circles and has also holistically incentivized research [6,7]. Capitalizing on the global market leadership of German industry, its good network with customers, high competencies in creating individual solutions, and the strength and flexibility of small and medium-scale enterprises, many companies have made significant progress regarding digitalization in the last ten years [6,24]. However, there are industry-wide internal and external barriers to the implementation of Industrie 4.0. These barriers contribute to a slanted picture regarding the current adoption of Industrie 4.0 by German industry enterprises [24,25].

One dividing line occurs between large-scale enterprises and small and mediumscale enterprises (SMEs), whose digital transformation has proceeded at a slower rate. A recent study found that some SMEs in a sound financial position are not incentivized by competitive pressure to initiate digitalization (laziness–success paradox). Correspondingly, they operate with a lack of strategic expertise and a lack of digital enterprise culture, which preempts the drive toward digitalization of core processes [24]. Cost pressure is also higher for smaller companies. It nudges them to reject digital solutions if they are not perceived as cost-effective improvements over the analogous status quo. Digital projects are among those abandoned first if SMEs face financial problems; those projects do not contribute immediately to value creation, and when they are implemented, they often serve an immediate goal in the production chain rather than strengthening basic industrial infrastructure.

As a consequence, compared with large-scale enterprises, SMEs are worse off regarding data management, which is a prerequisite for the next decade of Industrie 4.0. If SMEs apply AI solutions, they do so primarily for their products and services, and only very seldomly for improving internal processes [26]. This is due to a lack of know-how and an insufficient data base regarding internal processes, resulting from rudimental data management. Thus, in the current adoption rates, SMEs are lagging behind large-scale enterprises as industry proceeds into the second decade of Industrie 4.0.

In general, large-scale enterprises are significantly more mature regarding their progress in digitalization [25]. Only a few have not yet started digitalization, often due to a lack of enthusiasm of top management in initiating the transformation, as qualitative focus interviews indicate [24]. However, these companies are endangering their competitiveness and are exceptions. Large-scale enterprises rather face other barriers occurring in later stages in the process of digitalization; legal restrictions, data security and privacy concerns, and uncertainty regarding the expansion of digitalization activities hinder a deepening of digitalization [24].

These factors are likely to also affect SMEs in the later stages of digitalization and complement other general factors inhibiting digitalization, irrespective of company size. The insufficient digital infrastructure in Germany—Germany has the fourth-lowest ITinvestment to GDP ratio within the EU [27]—and structural disadvantages in Northern and Eastern Germany are major barriers already hindering the initiation of digital projects [19].

If these internal and external barriers do not hinder digitalization ambitions, the disproportionate shortage of qualified employees in German industry does [15,28]. Without the required knowledge, successful digitalization projects that create network effects and attract follow-up investments are less likely [29,30]. Additionally, asymmetry in industrial value chains—especially in the automotive industry—hinders proactive and targeted digitalization. Companies that are highly dependent on bigger suppliers or customers in the value chain have little control over measures implemented and need to react to the digitalization requirements of those partners. This aggravates digitalization barriers, especially for SMEs who have fewer resources to facilitate such responses.

Brought together, just as industry is proceeding into the second decade of Industrie 4.0, SMEs are lagging behind large-scale enterprises (see reasons in Table 1). In general, digitalization expertise that does not meet the standards of domain expertise in German industry, insufficient external digital infrastructure, lacking creativity and courage for thinking in new business models and problems in becoming agile [24,31] (p. 22) often inhibit the adoption of Industrie 4.0 in Germany.

**Table 1.** Hindering factors of Industrie 4.0 in Germany per company type (Own Illustration).


#### *3.3. Status Quo: Industrie 4.0 in Germany from a Research Perspective*

Hence, evidence-based targeted improvements in the four key themes of Industrie 4.0 [7] are required. Therefore, a more detailed assessment of the status quo setting a focus on the need for research and development in Germany is required.

#### 3.3.1. Research Theme: Value Creation Scenarios

Regarding *Value Creation Scenarios* for Industrie 4.0, platform-based business models for industry are considered as among the most promising fields of action for Industrie 4.0 [32]. Until now, especially in the B2C sector, they have not been widely adapted [33]. Especially for SMEs, market penetration seems to be inhibited by high investment costs, challenges in determining concrete benefits, and insufficient data quality originating from the aforementioned rudimentary data management [25]. Thus, only the first steps towards digital business models have been made in many domains so far [25,32].

Correspondingly, industry-wide innovation environments that build the base layer for platform-based business models are correspondingly under-researched [24]. Developing global digital ecosystems compliant with European data protection standards that could be based on Open-Source architecture promise to drive research and implementation in this area of action [34,35]. However, to develop and further analyze value creation scenarios for Industrie 4.0, the basic technologies and infrastructure need to be put into practice.

#### 3.3.2. Research Theme: Prospective Technological Trends

Considering *Prospective Technological Trends*, blind spots in implementation und domestic production can be identified relating to technologies with the potential to strengthen the sovereignty of German industry [24,36,37]. Considering the volatile geo-economic and geopolitical status quo, resilient ecosystems ensuring safe, secure control and selfdetermination for companies with regard to their data require more attention. This includes trialing certification, norming, and standardization, with the aim of creating a resilient basic infrastructure. In addition, it is important to ensure the availability of key technologies for

Industrie 4.0 (e.g., 5G) to safeguard and stabilize value chains [38]. Such a resilient infrastructure is required for the next evolutive step of Industrie 4.0 towards the smart factory, where machines, components, materials, and people exchange information seamlessly, in an automated fashion, and in real time in order to adapt production optimally to changing conditions in a self-learning manner [39,40].

In addition to the roll-out of digital twins, this requires, above all, the integration of artificial intelligence (AI) in the processes, products, and services of industry [41]. This would allow for the automation of production, the control of industrial drives (motion control), demand-driven maintenance of machinery, process optimization, and quality assurance through non-destructive testing, among other advances [42]. To implement AI industry-wide in factories, groundwork by research and in practice in the other key themes of Industrie 4.0 is required.

#### 3.3.3. Research Theme: New Methods and Tools for Industrie 4.0

Regarding *New Methods and Tools for Industrie 4.0*, models and simulations for the AI-driven smart factory are required. Although models and simulations meeting the needs of the cyber-physical matrix systems already exist (e.g., digital twins) [36], research has not made sufficient advancements regarding these for the highest expansion stage of the AI-driven smart factory [25,39]. Here, a lack of connection of different business models with data and data analytics hinders advancements. Consequently, on a higher complexity level, coupled, hybrid, and holistic models are required [43].

Regarding implementation, even digital twins require industry-wide roll-out [44]. The lack of clarity among companies regarding the quantification of the benefits of digital projects for business operations could explain why they have not been rolled out so far [24]. KPI-driven assessments measuring indirect and direct effects of digital projects on value creation that go beyond traditional cost-benefit analysis need to be developed by academic and practice-oriented research [24].

#### 3.3.4. Research Theme: Work and Society

Considering the fact that Industrie 4.0 is significantly interrelated with transformations in the workforce, *Work and Society* as the fourth key theme of Industrie 4.0 deserves attention as well. In the existing workforce—17% of German employees worked in the industrial sector in 2018 [19] (p. 193)—most of the employees are in wait-and-see mode and are cautious regarding Industrie 4.0. They are mostly indifferent, do not drive its implementation proactively, and are expected to react negatively toward the digital transformation of industry if its value added does not become visible, if employers do not communicate transparently about transformation steps, and if employees have the feeling that technology is being applied to control their work [29,30].

Digital transformation is accompanied by increased demand for qualified specialists with domain knowledge and information and communications technology (ICT) expertise. However, employees with the required skills to manage the transformation towards the smart factory are scarce. In 2021, more than every second company in industry sector is lacking qualified specialists in the long run [28]. Required soft skills include an interdisciplinary mindset, process know-how, leadership skills, a change and problem-solving mentality, and an orientation towards self-management. Hard skills are required in data analytics, processes, and customer-relationship-management, as well as abilities regarding IT security and IT business analytics and specific know-how about IT systems [29]. Additionally, AI-related competences are required (e.g., regarding human–machine interaction, basic knowledge about machine learning, and awareness regarding AI) [41,45].

In sum, our literature review indicates that the four key themes of Industrie 4.0 receive high attention in scientific research and in practice-oriented research, although more focus is required for interdisciplinary research in the face of accelerating technological progress [6]. The Research Council of the Plattform Industrie 4.0 (Forschungsbeirat der Plattform Industrie 4.0: advisory body of high-level experts from science and industry

advising the Plattform Industrie 4.0) is continuously monitoring research progress and potentials, and defines potentials for research projects in those areas deserving more attention. This promises an evidence-driven advancement of Industrie 4.0. Complemented with academic research on Industrie 4.0, the research community leverages innovation by creating incentives for companies to fund collaborative research and by providing guidance for (public) funding programs. However, there remains future research potential across all key themes, especially in the face of the next decade of Industrie 4.0 facing a future full of ambiguities, potentials, and threats in implementing Industrie 4.0 [46]. focus is required for interdisciplinary research in the face of accelerating technological progress [6]. The Research Council of the Plattform Industrie 4.0 (Forschungsbeirat der Plattform Industrie 4.0: advisory body of high-level experts from science and industry advising the Plattform Industrie 4.0) is continuously monitoring research progress and potentials, and defines potentials for research projects in those areas deserving more attention. This promises an evidence-driven advancement of Industrie 4.0. Complemented with academic research on Industrie 4.0, the research community leverages innovation by creating incentives for companies to fund collaborative research and by providing guidance for (public) funding programs. However, there remains future research potential

*Sci* **2022**, *4*, x FOR PEER REVIEW 7 of 17

is lacking qualified specialists in the long run [28]. Required soft skills include an interdisciplinary mindset, process know-how, leadership skills, a change and problem-solving mentality, and an orientation towards self-management. Hard skills are required in data analytics, processes, and customer-relationship-management, as well as abilities regarding IT security and IT business analytics and specific know-how about IT systems [29]. Additionally, AI-related competences are required (e.g., regarding human–machine interaction, basic knowledge about machine learning, and awareness regarding AI) [41,45]. In sum, our literature review indicates that the four key themes of Industrie 4.0 receive high attention in scientific research and in practice-oriented research, although more

across all key themes, especially in the face of the next decade of Industrie 4.0 facing a

#### **4. Fields of Action for the Next Decade of Industrie 4.0 from a German Perspective** future full of ambiguities, potentials, and threats in implementing Industrie 4.0 [46].

Against this background, German government, informed by the Research Council of the Plattform Industrie 4.0 and the Plattform Industrie 4.0 itself, has recognized the need for a strategic development of Industrie 4.0 for the next decade. It set the ambitious goal in its Industrial Strategy 2030 of increasing the share of German industry in gross value added in the economy to 25% by 2030—an increase of five percentage points [16]. Industrie 4.0 promises to be an important driver in this regard, with its target vision of the AI-driven smart factory and digital, platform-based business models. To leverage the potential of Industrie 4.0 in view of the economic, ecological, and geopolitical challenges of our time, and to follow a holistic approach for creating open digital ecosystems, strategic focus in Germany is set on three areas: sovereignty, interoperability, and sustainability [12]. **4. Fields of Action for the Next Decade of Industrie 4.0 from a German Perspective**  Against this background, German government, informed by the Research Council of the Plattform Industrie 4.0 and the Plattform Industrie 4.0 itself, has recognized the need for a strategic development of Industrie 4.0 for the next decade. It set the ambitious goal in its Industrial Strategy 2030 of increasing the share of German industry in gross value added in the economy to 25% by 2030—an increase of five percentage points [16]. Industrie 4.0 promises to be an important driver in this regard, with its target vision of the AIdriven smart factory and digital, platform-based business models. To leverage the potential of Industrie 4.0 in view of the economic, ecological, and geopolitical challenges of our time, and to follow a holistic approach for creating open digital ecosystems, strategic focus

Despite having been central to Industrie 4.0 already in the first ten years, German policy makers have recognized the need to intensify efforts in these areas in order to position German industry competitively in the market in the face of increasing political, ecological, and economic ambiguities [12]. Based on the matrix review assessment, one promising field of action was identified for each strategic area of the vision 2030 of the Plattform Industrie 4.0 (see descriptive Figure 1), as discussed in the following. For each field of action, an exemplary best case is presented that shows possible implementation paths. in Germany is set on three areas: sovereignty, interoperability, and sustainability [12]. Despite having been central to Industrie 4.0 already in the first ten years, German policy makers have recognized the need to intensify efforts in these areas in order to position German industry competitively in the market in the face of increasing political, ecological, and economic ambiguities [12]. Based on the matrix review assessment, one promising field of action was identified for each strategic area of the vision 2030 of the Plattform Industrie 4.0 (see descriptive Figure 1), as discussed in the following. For each field of action, an exemplary best case is presented that shows possible implementation paths.

**Figure 1.** Strategic Areas [8] and Promising Fields of Action for Industrie 4.0 (Own Illustration). **Figure 1.** Strategic Areas [8] and Promising Fields of Action for Industrie 4.0 (Own Illustration).

#### *4.1. Sovereignty 4.1. Sovereignty*

Globalization has caused high interdependencies in global value chains. German industry is highly dependent on raw materials, predominantly from the Far East (e.g. China). The digitalization of industry creates new dependencies, predominantly from the US, where most of the technology companies delivering services for German industry (e.g. Globalization has caused high interdependencies in global value chains. German industry is highly dependent on raw materials, predominantly from the Far East (e.g., China). The digitalization of industry creates new dependencies, predominantly from the US, where most of the technology companies delivering services for German industry (e.g., Microsoft with Azure, Amazon with AWS, or Salesforce and Oracle with its services) are located. Under the presidency of Donald J. Trump, the U.S. gradually turned away from Europe as its primary geostrategic and economic partner [47]. Even though it might have been a temporary phenomenon, it highlighted—together with the "trade war" between China and the US—the importance of technological sovereignty for the EU and Germany in terms of economic policy [48]. This has been recognized by policymakers, e.g., in the EU White Paper on Artificial Intelligence [49], which emphasizes the importance of economic sovereignty in general and in particular for Industrie 4.0 with the vision 2030 of the German Plattform Industrie 4.0 [12]. Whereas technological sovereignty demands government action to ensure independence on a macroeconomic level, on a microeconomic level, individual companies need to operationalize this strategic directive by increasing their ability "to cope with shocks of different sorts" [50] (p. 5) by becoming resilient.

The resilience of value networks requires specific attention as the COVID-19 pandemic has unveiled the dependency of German industry on global value chains [51]. Supply chain problems have led to a widespread production standstill causing a phase of deterioration in German industry. The shortages of semiconductor electronics and cable harnesses, as well as the Russian invasion in Ukraine causing oil and electricity price explosions, are affecting companies significantly. With these factors put together, German industry companies are not resilient in facing crises and volatility on the world market [52]. To increase the resilience of its value networks, German industry requires a strategy coupling robustness, to reduce the effects of volatility on company performance, with agility, in order to respond quickly to unpredictable disruptive events. Three measures to increase resilience can be identified in this regard: (1) the adoption of value creation networks, (2) safe and secure data integration, and (3) reliance on Industrie 4.0 technologies [53].

The paradigm of single sourcing coupled with just-in-time delivery has caused a production standstill during the COVID-19 pandemic due to the strict lockdowns in divergent parts of the world over time. German industry is still dependent on global value chains, as 43% of Germany's net exports are only generated due to its embedding in global value chains [54]. This dependency needs to be reduced.

A short-term measure in this regard is a mitigation of the just-in-time paradigm by maintaining larger inventories. In the event of a catastrophe—for example, in the event of economic lockdown measures in one production country—it would be possible to quickly switch to capacities in other production countries. This mitigates the supply-side risk by diversifying production capacities, but leads to higher costs.

A medium-term approach is a local-for-local strategy, i.e., locating production and the upstream value chain in the sales market. By synchronizing supply- and demandoriented risks in sales markets, only the respective regional clusters are affected in the event of a disaster. This concentration effect mitigates the risk of supply chain disruptions. At the same time, resources are duplicated to a much greater extent, causing efficiency losses. These solution approaches provide more resilience, but are not able to counter the increasing uncertainty and pressure for change in the market environment.

Thus, in the long run, the monolithic and highly optimized value chains of the present could be gradually replaced by resilient and adaptive value creation networks [53]: multiple sourcing based on regional local for local strategies, the creation of redundancies to allow for flexible reaction on crises, and a diversification and continuous evaluation of the supplier base as part of risk and sustainability management.

Based on the paradigm of co-creation, value creation networks require data infrastructure that allows companies to collaborate seamlessly in order to develop and strengthen resilient business models that, on a macroeconomic level, promise to increase digital sovereignty [21,55]. The German Plattform Industrie 4.0 drives such an attempt with its European Data Space Industrie 4.0, building a data infrastructure for companies from German industry and the ICT sector (see Figure 2).

These data spaces aim at not only increasing digital sovereignty, but also data sovereignty, enabling the secure, decentralized, and self-determined exchange, collection, analysis, and sharing of data. Thereby, they form the basis for new, highly scalable, data-driven, and platform-based business models—one of the focus points in the next decade of Industrie 4.0 [1–3,22].

Next to the establishment of value creation networks, data integration is a prerequisite for a resilient Industrie 4.0. Our analysis indicates that this is currently not achieved in practice, as SMEs especially face problems regarding data management [24,25,43]. They require support and innovative data ecosystem. Data integration comprises, in this regard, the use of platforms where standardized data exchange is enabled. Real-time monitoring of procurement, logistics, production, and sales processes allows for optimization of processes, and the created data could also be integrated into data exchange platforms and could train self-learning AI models for an industry-wide optimization of production.

Therefore, the industry-wide implementation of Industrie 4.0 technologies is key. Software-based risk management is required for resilient and adaptive value creation. Big data analytics and artificial intelligence are the basic technologies for reaching the

paradigm of the *Smart Factory* [26]. Digital twins or digital shadows require industry-wide penetration [39,44]. Only if industry applies those technologies can it future-proof for the next decade of Industrie 4.0. *Sci* **2022**, *4*, x FOR PEER REVIEW 9 of 17

**Figure 2.** DataSpace Industrie 4.0—Initiative for a sovereign European industrial data ecosystem (own illustration based on Plattform Industrie 4.0 [56]). **Figure 2.** DataSpace Industrie 4.0—Initiative for a sovereign European industrial data ecosystem (own illustration based on Plattform Industrie 4.0 [56]).

#### These data spaces aim at not only increasing digital sovereignty, but also data sover-*4.2. Interoperability*

eignty, enabling the secure, decentralized, and self-determined exchange, collection, analysis, and sharing of data. Thereby, they form the basis for new, highly scalable, datadriven, and platform-based business models—one of the focus points in the next decade of Industrie 4.0 [1–3,22]. Next to the establishment of value creation networks, data integration is a prerequisite for a resilient Industrie 4.0. Our analysis indicates that this is currently not achieved in practice, as SMEs especially face problems regarding data management [24,25,43]. They require support and innovative data ecosystem. Data integration comprises, in this regard, the use of platforms where standardized data exchange is enabled. Real-time monitoring of procurement, logistics, production, and sales processes allows for optimization of processes, and the created data could also be integrated into data exchange platforms and could train self-learning AI models for an industry-wide optimization of production. Therefore, the industry-wide implementation of Industrie 4.0 technologies is key. Software-based risk management is required for resilient and adaptive value creation. Big Industrie 4.0, postulating the AI-driven smart factory and platform-based business models, requires the establishment of an innovative ecosystem where companies and customers interact seamlessly. To reduce transaction costs, interoperability is key. In the first ten years of Industrie 4.0, significant initiatives were started. The GAIA-X project aims at developing a safe and interconnected data infrastructure for a European digital ecosystem with industry sitting at its heart setting policy rules, guidelines, and a standard architecture framework contributing to cross-sector interoperability. The launch of RAMI 4.0 as an international industry-specific standardization and reference architecture for Industrie 4.0, the coordination of the standardization of Industrie 4.0 by the Standardization Council Industrie 4.0, the establishment of OPC UA as a standard for interoperable interfaces for AI-driven production, and the launch of the administration asset shell have improved industry-specific interoperability predominantly by means of standardization [57,58].

data analytics and artificial intelligence are the basic technologies for reaching the paradigm of the *Smart Factory* [26]. Digital twins or digital shadows require industry-wide penetration [39,44]. Only if industry applies those technologies can it future-proof for the next decade of Industrie 4.0. *4.2. Interoperability*  Industrie 4.0, postulating the AI-driven smart factory and platform-based business models, requires the establishment of an innovative ecosystem where companies and cus-For the next decade of Industrie 4.0, Open-Source will play a pivotal role in implementing and exploiting interoperability for the creation of new platform-based business models [34,35]. Already at the beginning of the 2020s, Open-Source Software (OSS) contributes between €65 billion to €95 billion to the EU's GDP (equaling EU-GDP of air and water transport combined) and promises significant growth opportunities in the future, especially for Industrie 4.0 [59]. OSS could even be applied to enhance interoperability, as demonstrated by BaSyx-middleware (see Figure 3). *Sci* **2022**, *4*, x FOR PEER REVIEW 10 of 17 contributes between €65 billion to €95 billion to the EU's GDP (equaling EU-GDP of air and water transport combined) and promises significant growth opportunities in the future, especially for Industrie 4.0 [59]. OSS could even be applied to enhance interoperability, as demonstrated by BaSyx-middleware (see Figure 3).

tomers interact seamlessly. To reduce transaction costs, interoperability is key. In the first

facing.
