I/IMHP A PORSCHE COMPANY

INDUSTRY 4.0 BAROMETER 2025

Data-Driven Production

Sponsors & Experts

Sponsor Dr. Christina Reich MHP christina.reich@mhp.com

Expert Dr. Walter Heibey MHP walter.heibey@mhp.com

Project Manager Julius Peters MHP julius.peters@mhp.com

Sponsor
Dr. Christian Fiebig
MHP
christian.fiebig@mhp.com
Sponsor
Prof. Dr. Johann Kranz
LMU
Head of the Chair for Digital
Services and Sustainability
kranz@lmu.de
Expert
Dr. Oliver Kelkar
MHP
oliver.kelkar@mhp.com

Authors

Christoph Unger MHP

Emina Delalic MHP

Muriel Herf MHP

Lukas Meyer MHP

Bennet Becker MHP
Niclas Maasackers MHP

Content

Foreword 4
Summary 6
Key Findings 8
1. Introducing the Industry 4.0 Barometer 2025 12
1.1. Focus 15
1.2. Evaluation Method 16
1.3. Interviews and Success Stories 16
1.4. Participants 16
2. Results of the Survey 20
2.1. Status Quo of Industry 4.0 23
Success Story Seamless Data Flow Between IT and OT: Digitalization with
“integrate_it” and “Sounce” 36
Interview Schaeffler Group 40
2.2. Data-Driven Production—Use of Data in Production 44
Interview TRUMPF SE + Co. KG 52
Success Story TRUMPF Data Integration Platform 60
Interview Department of Cyber and Information Technology, BMVg 64
2.3. Role of the CIO 69
Success Story Next-Level Order-Monitoring @ Porsche AG 70
Success Story Technology Company ZF 76
3. Conclusion 79
Recommendations for Action 81
Contact International 83
Figures 84

Dear Readers,

In recent years, numerous global economic developments have reshaped the framework conditions for our companies and customers—with lasting effects. Many sectors are still grappling with the consequences of the COVID-19 pandemic, as well as supply chain disruptions caused by political, economic, and social instabilities and conflicts. Yet, new major challenges are already on the horizon. The future direction of the U. S. government regarding trade relations and agreements could lead to fundamental changes in global economic structures. Ongoing tensions between the world’s two largest economies—the United States and China—are also intensifying, once again impacting supply chains, regulations, energy supply, and inflation— particularly in the technology sector. These economic and political developments underscore the continuous transformation of our economic environment, making a forward-looking and flexible corporate strategy more essential than ever.

Last year’s Industry 4.0 Barometer, which focused on artificial intelligence in manufacturing, revealed a key insight: despite noticeable progress in Industry 4.0, data silos and a lack of consistent data exchange continue to hinder the adoption of further Industry 4.0 technologies. Without this foundation, the full potential of artificial intelligence remains untapped, limiting the opportunities in a data-driven economy.

Building on these findings, this year’s Industry 4.0 Barometer is dedicated to the fundamental cornerstone of every successful digital transformation: data. With the Industry 4.0 Barometer 2025, we not only provide a comprehensive overview of the current status of Industry 4.0 activities, but also offer valuable insights from success stories and expert interviews, illustrating how companies are advancing their data strategies to prepare for a data-driven future.

We therefore invite you to discover with us the important role of data as a catalyst for digital transformation—and thus take a decisive step toward future resilience.

Before wishing you an insightful read, I would like to extend my gratitude to Professor Dr. Johann Kranz of Ludwig Maximilian University of Munich. This marks our seventh collaboration on the Industry 4.0 Barometer. A special thanks also to the more than 800 respondents and industry experts who contributed to our study. Together, we are committed to pioneering solutions that offer a resilient response to crises and shape a digital and better future. Exactly according to our purpose: Enabling You to Shape a Better Tomorrow.

I wish you all the best for 2025.

Yours,

Markus Wambach

Group COO

MHP Management- und IT-Beratung GmbH

The Industry 4.0 Barometer 2025 provides a comprehensive overview of the current state of Industry 4.0 across various industries in 2024. Companies from the so-called DACH region (Germany, Austria, and Switzerland), the United Kingdom (U.K.), the United States (USA), and China were surveyed on their digitalization initiatives and progress. In addition to the annually considered topics of technology, IT integration, strategy and goals, as well as obstacles, this year’s focus is on Data-Driven Production.

As in the previous year, businesses in 2024 faced significant challenges: geopolitical tensions and uncertainties, looming trade restrictions, rising energy costs, the effects of advancing climate change, the persistent shortage of specialists, and the lasting impact of the COVID-19 pandemic have all determined the economic framework. On the one hand, companies must ensure economic stability and minimize expenses, while on the other hand, they must continue to invest in innovations and digitalization to remain competitive. Despite these challenging conditions, developments in Industry 4.0 continue, particularly in the areas of automation, autonomous systems, and digital twins.

The results reveal that many companies, particularly in the DACH region, have only just begun their digital transformation. An international comparison clearly shows that the U.S. and China are advancing at a significantly higher pace, thereby solidifying their leading positions in Industry 4.0. While these countries benefit from innovationpromoting regulations and targeted investments, companies in the DACH region and the U.K. continue to struggle with structural barriers. Outdated IT infrastructures, a shortage of skilled professionals, and a lack of prioritization by management remain the biggest obstacles. This is particularly evident in the automotive sector and among smaller enterprises.

One of the central findings of the study is that many companies, while possessing a data foundation, do not leverage it effectively to support data-driven decision-making or drive innovation. Most companies still lack a comprehensive data strategy, which is, however, an essential prerequisite for progress in key future technologies such as artificial intelligence or digital twins. Companies in the U.S. and China are significantly more advanced in this area, using data operationally to optimize industrial processes. In contrast, companies in the DACH region face a considerable need for advancement, as the digitalization of many processes is hindered by data silos and a lack of data exchange. At the same time, limited budgets and a focus on existing production processes often complicate the implementation of modern technologies. However, medium-sized enterprises frequently emerge as pioneers, surpassing large corporations thanks to flatter hierarchies and a pragmatic approach.

Furthermore, it becomes evident that foundational investments in data management, IT infrastructure, and data literacy are essential to remain competitive. Additionally, targeted training programs, collaboration with external experts, and the development of scalable IT solutions are crucial. A particularly noteworthy finding is the central role of the CIO. Companies where the CIO holds an active role in executive leadership demonstrate a 32 percent higher performance in technical data analysis capabilities. A stronger integration of CIOs into leadership roles and the consistent promotion of data-driven production processes can therefore further accelerate transformation.

The successful implementation and utilization of Industry 4.0 technologies is a crucial factor in securing the economy’s global competitiveness. To drive this transformation forward, targeted and holistic political frameworks are required. By reducing bureaucracy and simplifying regulatory requirements, significant barriers to operationalization can be mitigated. Comprehensive skill development programs counteract the shortage of skilled professionals and capacity constraints, for example, by making STEM subjects in the education system more attractive and practice-oriented. Small and medium-sized enterprises require tax incentives and simplified access to funding to strengthen their innovative capacity. Finally, increased international collaboration is essential to benefit from best practices and reduce the technological gap with leading countries such as the U.S. and China. A synergistic approach, combining corporate initiatives and targeted political measures, is the key to seizing the potential of Industry 4.0 and ensuring long-term global competitiveness.

Summary

Key Findings

GENERAL

The U.S. and China as drivers of the digital transformation in manufacturing— the gap with the DACH region widens:

The DACH region faces significant challenges in data analysis capabilities and automation. A rigid focus on cost efficiency and risk minimization can hinder the flexibility and willingness to experiment that are essential for digital transformation. In contrast, China and the U.S. are increasingly adopting an agile approach that facilitates innovation. With strategic focus and an experimental corporate culture, they are breaking new ground and actively shaping the digital transition.

Digital Twin as a growth driver—logistics as a pioneer:

The Digital Twin shows the strongest development among the surveyed technologies and significantly contributes to efficiency gains and transparency. Particularly substantial progress has been made in the logistics sector. In the DACH region, however, the full potential of the digital twin remains underutilized, especially in production.

Supply chain transparency—quality as the key to cost reduction:

Real-time tracking and sensor technology not only increase agility and resilience but also enable early detection of quality issues, preventing high downstream costs. China serves as a front-runner, showcasing the advantages of these technologies.

Legacy IT and data silos as innovation blockers for small enterprises:

Outdated IT infrastructures and isolated data silos remain a major barrier— especially for small businesses with fewer than 100 employees. Limited investment capabilities exacerbate these challenges.

Barriers decrease—skilled labor shortages and legacy systems remain challenges:

Industry 4.0 obstacles are increasingly being overcome with regard to global development. However, skilled labor shortages and outdated systems remain significant hurdles, particularly in the DACH region. Outdated IT infrastructures and a lack of management attention are major barriers to progress. In contrast, China is successfully advancing digitalization through more flexible structures and the elimination of data silos.

Key Findings

DATA-DRIVEN PRODUCTION

Skilled labor shortages and lack of training as growth barriers:

The skilled labor shortage in the DACH region is severely hindering digital transformation. Additionally, there is a lag in training in data literacy and data-driven decision-making. Without targeted education and training programs, companies risk falling even further behind internationally.

Defensive data strategies dominate—innovation is neglected:

While the DACH region focuses on compliance, risk minimization, and process optimization, the U.S. and China take a more aggressive approach. They leverage data to develop new services, enter new markets, and generate additional revenue. By prioritizing security, the DACH region risks losing competitiveness and missing out on growth opportunities.

CIO as a game-changer— digitalization requires leadership:

The results clearly show that an actively involved CIO not only enables more efficient IT governance but also plays a crucial role in breaking down data silos and scaling IT architectures—a key lever for the successful implementation of Industry 4.0. Particularly in the context of data-driven production, embedding the CIO role at the board level is becoming an absolute necessity.

Data governance— decentralized vs. centralized. China as a role model:

China demonstrates how decentralized approaches can foster flexibility and innovation, while the DACH region remains committed to centralized structures that hinder progress. The U.S. and the U.K. achieve better results through hybrid models. To remain competitive and drive innovation, the DACH region must adopt a more flexible approach to data governance.

Data-driven economy— seizing new opportunities:

Data-driven decision-making holds potential for cost reduction, process efficiency, and quality improvements. However, companies in the DACH region, particularly in the automotive industry, have yet to fully leverage data as a strategic asset. Traditional structures and a limited focus on digital transformation slow down innovation and the adoption of data-driven business models, resulting in competitive disadvantages. Investments in training, infrastructure, and data-driven processes are crucial to combining quality assurance with cost reduction.

Introducing the Industry 4.0 Barometer 2025

Digital transformation is a management task and cannot be delegated. In addition to a data strategy with a clear technological vision, this also includes the roadmap from data silos and an often outdated IT infrastructure to a continuous exchange of data (data-driven at its best). The quality of the data must have the same importance for management as other classic KPIs (e.g. the OEE).

Dr. Christian Fiebig

Partner

MHP

Digital Factory & Supply Chain

Chapter 1 defines the fundamental framework for this study. It outlines the thematic focus of this year’s edi - tion, supplemented by an explanation of the applied evaluation methodology. Additionally, it presents the selection of interview partners, relevant success sto - ries, and an overview of the survey participants. The goal is to establish a solid foundation for the subse - quent analyses and results.

1.1. Focus

The adoption of Industry 4.0 continues to progress steadily and has already become a lived reality in many companies. The Industry 4.0 Barometer captures how this reality takes shape across industries and regions, what understanding of Industry 4.0 exists, and how mature the various technologies of the fourth indus - trial revolution are in practice. It provides valuable in - sights into existing gaps and opportunities within In - dustry 4.0. Additionally, it underscores how companies overcome these gaps, seize opportunities, and further strengthen their competitive position. To give compa - nies a comprehensive overview, MHP, in collaboration with Ludwig-Maximilians-University (LMU) Munich, has examined the status quo of Industry 4.0 imple - mentations for the seventh time. The results of this annual benchmark study offer insights into the state of Industry 4.0 activities at a given point in time across companies in the DACH region (Germany, Austria, Switzerland), the United Kingdom (U.K.), the United States (USA), and China.

The questionnaire used each year covers four key topic clusters:

1. Technology:

Efficient use of Industry 4.0 technologies (supply chain transparency, Digital Twin, automation, and autonomous systems)

2. IT Integration:

Enhancing the performance of internal IT infrastructure (data analytics and IT security)

Strategy and Goals: Strategic focus on Industry 4.0 activities

4. Obstacles:

Factors with a negative impact on the implementation of Industry 4.0 technologies

Each year, current digitalization trends are incorporat - ed into the study, ensuring it remains up to date. The Industry 4.0 Barometer 2025 focuses on Data-Driven Production, deliberately emphasizing a key aspect of digitalization: data. It becomes evident that data is not a new discovery but has always been the foun - dation for innovation and modern technologies. The historical development of the Industry 4.0 Barometer under-lines this point: While previous editions focused on Industrial AI (2024) and Shopfloor Automation & Sus-tainable Operations (2023), this year’s focus topic builds directly on these foundations—data.

In the context of data-driven production, the prop - er handling of data is especially crucial. The effective use of high-quality and relevant data is the key to future-oriented developments in production and lo - gistics.

The study defines data-driven production as an approach that, through precise data processing and interpretation, enables smart, sustainable, and resilient processes. The goal is to generate actionable insights that optimize decision-mak - ing, improve operational workflows, and in - crease added value. This data-driven approach fosters continuous innovation and efficiency, supports strategic objectives, and creates a sus - tainable competitive advantage.

1.2. Evaluation Method

The Industry 4.0 Barometer 2025 provides a detailed insight into the development status of companies from various industries in the field of Industry 4.0 in 2024. The study was based on a comprehensive questionnaire that included various five- and seven-point Likert scales to capture participants’ responses in a differentiated manner. The results were categorized into topic clusters and converted into percentage values—so-called Barometer results—which were calculated using the weighted arithmetic mean of the responses.

Additionally, participants were able to distribute 100 points across different options in prioritization questions. The original Likert scale values were transformed into metric scales (0–5 or 0–7) and combined with the relative frequencies of responses to calculate Barometer results ranging from 0 to 100 percent. These Barometer results serve as benchmarks, enabling comparisons of results based on different characteristics of participants and their companies. The survey was conducted anonymously to ensure valid and representative insights.

1.3. Interviews and Success Stories

In addition to analyzing the survey results, the Industry 4.0 Barometer also includes interviews with leading industry experts, as well as MHP Success Stories that highlight the practical application of Industry 4.0 technologies. The interviewed experts not only provide insights into the focus topic of data-driven production, but also share their personal assessments of the progress of digital transformation in the industry. Furthermore, they were asked about specific use cases and digitalization projects within their own organizations.

The following experts were interviewed:

Thomas Speck, CIO (TRUMPF SE + Co. KG)

Alexander B. Wurst, Vice President Process Development & Digitalization Division Bearings & Industrial Solutions (Schaeffler AG)

Frank Endler, Colonel (GS) (Dept. CIT, BMVg)

The MHP Success Stories present practical applications in the field of data-driven production, focusing on the implementation of data-based solutions. In addition to the initial challenges faced by companies, the process of implementing the solution and the resulting outcomes are particularly highlighted. One example is the IoT transformation in product development at ZF, where a central measurement data platform was introduced. This platform enables cross-site collection, exploration, and automated analysis of measurement data. Another example is the Data Integration Platform (DIP) at TRUMPF, which accelerates the digitalization of the innovation process and serves as a foundation for generating new value streams. The MHP Success Story “Next-Level Order-Monitoring” at Porsche describes the introduction of a modern analytics and reporting architecture to improve order management. By using an event-driven, modular architecture on AWS, which enables fast response times and real-time data consistency, the digital transformation of production processes is supported and optimized for future scalability. The MHP Success Story “integrate_it” presents an intelligent middleware solution developed by MHP, already in use for quality assurance at a German automotive manufacturer. integrate_it not only collects, transforms, and distributes machine data, but also links sensor and machine data, paving the way for AI applications, among other use cases.

1.4. Participants

The results of the Industry 4.0 Barometer 2025 are based on feedback from a total of 823 participants from different regions. This includes 216 participants from the DACH region, 201 from the United Kingdom (U.K.), 201 from the United States (U.S.), and 205 from China (Figure 1).

The analysis of company sizes among the participants presents a diverse picture: 67 percent of respondents work in small and medium-sized enterprises (SMEs) with fewer than 1,000 employees, 22 percent are employed in companies with 1,000 to 10,000 employees, and 11 percent work in companies with more than 10,000 employees (Figure 2).

The survey included participants from all hierarchical levels, from operational staff to top management. The majority of participants can be classified up to the third level below executive management. The information and communication technology sector is the most represented industry at 24 percent, followed by construction and healthcare (each at 9 percent). The automotive industry (OEMs and suppliers combined)

Figure 1: Distribution of participants by region

Figure 2: Distribution of participants by size of business

accounts for only 4 percent of respondents (Figure 3). The most frequently represented departments are IT (30 percent) and production (13 percent) (Figure 4). This aligns with the focus areas of the Industry 4.0 Barometer.

Results of the Survey

Industrial companies will only gain a data-related competitive advantage if data is analyzed and understood organically in the context of their own production processes. Although IT is the primary technological enabler of data-driven production, data competence must be recognized as an overall entrepreneurial challenge that also affects the departments to the same extent. This applies in particular to the DACH region, which has the greatest need for action internationally.

Dr. Walter Heibey

Partner

Porsche Group | Production

MHP

At the beginning of the chapter, an overview of the In - dustry 4.0 status quo is provided, supplemented by an analysis of the results categorized into clusters: tech - nology, IT integration, strategy and goals, as well as obstacles. Subsequently, specific findings on the focus topic of data-driven production are presented. Finally, the role of the Chief Information Officer (CIO) and its significance for Industry 4.0 activities are analyzed in greater depth. This chapter thus provides a structured foundation for interpreting and discussing the study’s findings.

2.1. Status Quo of Industry 4.0

The global economy is caught between the poles of digital transformation, increasing connectivity and au - tomation, sustainable practices and global trade links. The term Industry 4.0 was originally coined in 2012 to describe the vision of a fourth industrial revolution driven by the integration of cyberphysical systems, the Internet of Things (IoT), and intelligent automa - tion. Today, the challenges have evolved: technologies such as Artificial Intelligence (AI), big data, and cloud computing are driving innovation, while geopolitical uncertainties and market volatility shape the broader landscape. Since 2018, the Industry 4.0 Barometer has annually examined current topics such as industrial AI, shopfloor automation, and sustainable production, reflecting the ongoing transformation. Several key trends are emerging for 2025, including the increased use of generative AI for automation, improvements in cybersecurity, and the optimization of digitized supply chains. These developments are supported by techno - logical advances such as the Internet of Things, Machine Learning (ML), and cloud and edge computing. At the core of these key technologies are data and data-driven approaches. These approaches prioritize the extensive use of data to improve decision-making processes, operational procedures, and strategic ob - jectives. The United States and China are often consid - ered pioneers in this regard.

After focusing on specific topics such as shopfloor au - tomation and artificial intelligence in previous years, the Industry 4.0 Barometer 2025 deliberately returns to the fundamentals: data. Our cross-industry project experience increasingly indicates that many companies face significant hurdles in implementing and applying advanced technologies. The reasons include the lack of a suitable data infrastructure, a well-thought-out data utilization strategy, or a data-driven corporate culture, which is an essential foundation for the fur - ther deployment of technologies and developments. These issues need to be examined and verified through the study’s findings. Given the importance attributed to data, this year’s Industry 4.0 Barometer focuses on “Data-Driven Production” with the aim of providing readers with a fact-based evaluation of the topic and offering practical solutions through selected interviews and success stories.

The analysis of historical Barometer results from 2022 to 2025 in the current study covers the topic clusters Technology, IT Integration, Strategy and Goals, and Obstacles. Previous studies are not considered due to a lack of comparability. In the Technology cluster, the ar - eas of supply chain transparency, digital twin, and au - tomation & autonomous systems are examined. The IT Integration Cluster focuses on IT architecture, IT secu - rity, IT system scalability, and data analytics capabilities. The Strategy and Goals Cluster presents an overview of … The Obstacles Cluster investigates key resources and prerequisites for the implementation of Industry 4.0 technologies, covering both hindering factors such as a skilled labor shortage, capacity constraints, supply chain issues, and uncertainties regarding ROI, as well as enabling conditions such as the elimination of data silos and the management of legacy systems.

The Total Barometer result is determined as the av - erage of the Barometer results across the three topic clusters per year. The Barometer results of the Obsta - cles Cluster have been inverted to reflect an opposite scale, thus adequately depicting progress in overcom - ing these hurdles.

The results indicate positive developments in the ex - amined areas over the analysis period. From 2022 to 2025, the Total Barometer result increased significant - ly from 48 percent to 64 percent, representing a 33 percent growth (Figure 5). In the subsequent survey results, this increase is explored in greater detail within each cluster.

Total Barometer value in comparison of topic clusters over the years

Development of the IT Integration Cluster over the Years

* Barometer value: Weighted arithmetic mean as a percentage value

Topic Cluster 1: Technology

Supply Chain Transparency

This topic cluster analyzes the implementation status of product traceability along the entire value chain, as well as the collection and transmission of environmental and condition data through sensor-based systems in production, warehousing, and logistics.

The precise tracking of products and resources can significantly improve efficiency and transparency in supply chains. Through real-time tracking, companies can accurately determine the location of their goods, ensure resilience in volatile markets, and respond more flexibly to disruptions and unforeseen events. Transparency is increasingly seen as a strategic element to enhance agility and resilience across the entire supply chain. Accordingly, companies are investing more in measures to improve supply chain transparency.

In the survey, 60 percent of participants state that their company is partially or fully capable of tracking both individual components and end products. This represents an improvement of six percentage points compared to the previous year. The Barometer result further highlights this progress: it increased from 49 percent (2023) to 60 percent (2024) and reached 64 percent in the current survey. This represents the highest value within the examined Technology Cluster. A similar trend is observed in the field of sensor technology. Here, the Barometer result rose from 44 percent (2023) to 53 percent (2024) and ultimately to 60 percent in 2025 (Figure 6).

Our production facilities, systems, warehouse, and logistics operations are equipped with sensors to capture and transmit environmental parameters and condition data.
In my company, all individual components and final products can be tracked and traced across the entire value chain.

In international comparison, China leads in the use of tracking technologies. 71 percent of respondents there indicate that their company is partially or fully capable of tracking individual components or end products. In the DACH region, this figure is only 47 percent, while it is 59 percent in the U.S. and 65 percent in the U.K. (Figure 7). Notably, the proportion of companies without tracking technologies is highest in the DACH region at 18 percent, whereas nearly all respondents in China state that they at least have implementation plans. China is also ahead in the field of sensor technology: 65 percent of participating companies partially or fully capture and transmit environmental parameters and condition data. In the DACH region, this figure stands at 44 percent, compared to 57 percent in the U.S. and 52 percent in the U.K.. A significant gap is evident in the DACH region: 22 percent of surveyed companies do not comprehensively use production facility sensors to collect and transmit environmental parameters and condition data across production, products, and logistics (Figure 8). By contrast, all surveyed Chinese companies report using sensors—clearly highlighting the significant gap and the urgent need for the DACH region to catch up.

Figure 7: Supply chain transparency by region

Figure 8: Deployment of sensor technology in production, warehousing, and logistics by region

The increasing willingness of companies to invest more heavily in the digitalization of their supply chains is primarily reflected in the increased use of digital twins for simulation, control, and optimization. This trend is also evident in the rise of the Barometer result for product usage, which increases from 51 percent in 2024 to 58 percent in 2025. The use of digital twins in production facilities increased from 48 percent (2024) to 54 percent (2025), and in logistics from 52 percent (2024) to 61 percent (2025) (Figure 9). The intensified use of digital twins, particularly in logistics, can likely be explained by the fact that logistics plays a central role in maintaining production processes and overall operational continuity.

Implementing digital twins enhances transparency in the supply chain, leading to more efficient operations and better control of logistics processes. Consequently, companies currently perceive the greatest benefits of digital twins in logistics. The high acceptance and increasingly prioritized use of these technologies indicate that companies are recognizing and leveraging the value digital twins offer—particularly in terms of transparency, flexibility, and efficiency improvements. Despite widespread use in logistics, the potential of this technology, especially in the optimization of production and production facilities, has not yet been fully realized.

* Barometer value: Weighted arithmetic mean as a percentage value
Figure 9: Distribution of the Digital Twin

In my company, there is a digital replica that captures process and condition data for simulation, control, and optimization of our production facilities.

Figure 10: Distribution of the Digital Twin by region

An international comparison shows that China is leading in this field: 67 percent of participating Chinese companies use digital replicas in their production facilities either partially or fully. In contrast, the share in the German-speaking DACH region is only 41 percent. This difference becomes even more pronounced when looking at companies that do not use digital replicas at all: In the DACH region, 30 percent of respondents have not yet adopted these technologies, the highest percentage compared to other regions. The U.S. and the U.K. have better values, with 18 percent and 25 percent, respectively. In

China, however, only 5 percent of companies do not use digital replicas (Figure 10). The described trends in the use of digital twins in production facilities are also reflected in other areas, such as product optimization and logistics. The comprehensive application of the digital twin in China suggests a holistic digitalization of the industry, whereas the DACH region still has room for improvement in systematically deploying this technology. This discrepancy can largely be attributed to differing digitalization strategies and government initiatives, with China more actively promoting the adoption of new technologies.

The use of automation and autonomous systems is slightly below the level of supply chain transparency and digital twins (Figure 11). This lower frequency of adoption could indicate that companies are more hesitant to implement automation solutions than technologies like the digital twin, which primarily enable real-time transparency and control. A possible reason for this is the more complex implementation processes and higher initial investments associated with introducing fully automated and autonomous systems. These systems not only re-quire technological adjustments but also necessitate a deep restructuring of operational processes, which slows down the adoption speed. Uncertainties regarding longterm profitability and technical integration may also lead companies to proceed cautiously in this area.

The international comparison shows that China is leading in machine-to-machine communication (M2M): 71 percent of surveyed Chinese companies report that their systems exchange information in real-time and automatically, whereas this figure is only 56 percent in the DACH region. The rise in the Barometer result from 57 to 63 percent indicates growing global interest and increased investments in real-time machine communication.

The Chinese government views technological progress as the primary driver of economic development. By promoting digitalization through reduced regu-

* Barometer value: Weighted arithmetic mean as a percentage value
Figure 11: Maturity level of automation and autonomous systems

Our facilities, devices, and systems automatically and autonomously exchange information in real time (Machine-to-Machine Communication).

We use machines and robots that
can act autonomously, self-regulate, or
improve themselves (e.g., autonomous transport systems).

Figure 12: Maturity level of automation and autonomous systems by region

lations, companies can adopt and implement new technologies more quickly. In the DACH region, 16 percent of surveyed companies report that they do not use systems with real-time information exchange, while this share is only 1 percent in China (Figure 12). The lower prevalence of automated and autonomous systems in the DACH region can be attributed to several factors. First, stricter regulatory requirements and safety standards hinder faster implementation. Second, cultural differences also play a role: companies in the DACH region tend to be more risk-averse and rely on long-established technologies. This also explains the slow adoption of automated guided vehicle (AGV) systems, which only 35 percent of respondents from the DACH region report using partially or fully, compared to 59 percent in China.

Overall, the development of AGV system usage is positive. The Barometer result increases from 38 percent in 2023 to 46 percent in 2024 and reaches 50 percent in the current observation period. The reluctance in the DACH region to introduce AGV systems may also be linked to concerns about job losses and the complex organizational changes that such technology entails.

Limited financial resources and the sometimes long amortization periods of autonomous systems present significant barriers, particularly for small and medium-sized enterprises, preventing them from investing in this area.

…the technical infrastructure for advanced data analytics.

much worse

worse

same

better

much better

Topic Cluster 2: IT Integration

Data Analytics Capabilities

Companies are increasingly recognizing that a data-driven approach across the entire value chain plays a crucial role in optimizing their processes. However, despite significant progress in various areas of Industry 4.0 in recent years, this year’s results indicate that progress in data analytics capabilities is significantly lower compared to the previous year. As a logical consequence, this year’s focus is on “Data-Driven Production.” The question arises as to how companies can utilize their data assets more effectively along the value chain to optimize and sustain their production.

The greatest progress in data analytics capabilities has been observed in production processes involving partially and fully automated decisions, such as through the use of artificial intelligence and machine learning methods. While the Barometer result was still at 62 percent in 2024, this year’s study shows a rise to 66 percent, indicating the growing implementation of such technologies in production processes (Figure 13). Despite this increase, this area still has the lowest maturity level within the field of data analytics capabilities and is rated most critically compared to other areas, underscoring a continued need for action.

Please assess your company’s data analytics capabilities compared to your direct competitors, with regard to…

…the skills and competencies of your personnel in advanced data analytics methods.

* Barometer value: Weighted arithmetic mean as a percentage value Figure 13: Maturity level of data analytics capabilities

The continuous expansion of technical infrastructure and the targeted development of relevant data analytics capabilities have enabled companies in recent years to establish the foundations necessary to systematically leverage the benefits of automated decision-making processes. Looking at overall progress since the beginning of the survey, companies have seen the greatest increase in this area (Figure 13). The Barometer result in the first survey in 2022 was only 36 percent. This underscores the central importance that automated decision-making processes now have in modern value creation.

A closer look at the results of an international comparison reveals significant regional differences. Companies were asked to assess their data analytics capabilities relative to their competition. The DACH region, in particular, significantly lags behind. While 78 percent of participants in the U.S. rate their capabilities as superior to their competitors, this is only true for 61 percent in the DACH region (Figure 14). Reasons for this include lower investments in training programs and a delayed implementation of advanced analytical methods. A targeted expansion of data capabilities is therefore essential to close the international gap and secure future competitive advantages.

IT Security

In recent years, a consistent trend has emerged: IT security is firmly embedded within companies and treated as a priority. Once again, organizations have made progress in all areas of IT security, particularly in implementing uniform authorization concepts for company data this year. In this area, the maturity level has increased from 76 to 80 percent—the highest score among all surveyed IT security topics. A total of 86 percent of respondents indicate that they have a uniform authorization framework in place. In international comparison, the DACH region and the U.K. hold leading positions, although all regions demonstrate high levels of maturity. Within the context of this year’s focus topic, data-driven production, access to company data is especially critical. Only through structured

much	worse	same	better	much
worse				better

Please assess your company’s data analytics capabilities compared to your direct competitors, with regard to the skills and competencies of your personnel in advanced data analytics methods.

Figure 14: Data analytics capabilities by region

access management can stakeholders retrieve the necessary data to make informed decisions and control automation processes.

In the other two areas of IT security—assigning high importance to IT security management within the company and possessing comprehensive and sufficient expertise to defend against cyberattacks—the

U.K. and the U.S. lead the rankings, followed by China, while the DACH region lags behind. The regional differences are particularly evident in terms of cyberattack defense: while 90 percent of surveyed companies in the United States agree that they have sufficient expertise, this figure is only 76 percent in the DACH region (Figure 15).

Figure 15: Status of IT security by region

Strategic Industry 4.0 focus of the surveyed companies

Figure 16: Strategic Industry 4.0 focus of the surveyed companies
Participants could allocate a total of 100 points. The results shown here represent the average score for each response option.

Topic Cluster 3: Strategy and Goals

As part of the Strategy and Goals Cluster, respondents were asked to distribute 100 points across five strategic objectives of their companies. The results show that increasing profitability is considered the most important goal of Industry 4.0, receiving an average of 30 points. This highlights that cost efficiency and profitability are central motivations. In second place is improving product and production quality, which receives an average of 23 points, emphasizing that quality assurance and enhancement are also key drivers. Ranked closely together are the development of new services (17 points) and the adaptation of flexibility (16 points). At the lower end of the priority list is expansion into new market and customer segments, which receives an average of 14 points (Figure 16).

A regional comparison reveals significant differences in the prioritization of goals. In the DACH region, increasing profitability is particularly emphasized, receiving 34 out of 100 points, while this value is only 25 points in China. A possible reason for this could be the higher labor costs in the DACH region compared to China. As a result, a stronger focus on cost efficiency is necessary to remain competitive. Companies in the DACH region face greater pressure to reduce operational costs and achieve efficiency improvements in order to secure their margins.

It is particularly noteworthy that the information and communication technology industry prioritizes profitability as the top strategic goal in all the regions considered—DACH, the USA, the U.K., and China. In this industry, in-creasing profitability is ten times more important than it is for automobile manufacturers (OEMs).

Success Story Seamless Data Flow Between IT and OT: Digitalization with “integrate_it” and “Sounce”

Project Description

The middleware solution “integrate_it” enables the capture, transformation, and distribution of machine data. This functionality not only ensures optimal control of industrial equipment but also facilitates a seamless data flow between IT and OT (Operational Technology, encompassing hardware and software for controlling industrial equipment). This comprehensive data integration paves the way for additional applications from the MHP Industrial Cloud Solutions (ICS) portfolio, such as the AI-based anomaly detection software “Sounce.” This represents a prime example of Industry 4.0 technologies that play a critical role in connectivity, standardization, and—specifically in this use case—quality assurance.

Initial Situation and Challenges

Companies and production managers are aware of the benefits of digital processes—real-time insights from the shop floor improve machine availability and performance, enhance manufacturing quality, and boost productivity. However, businesses often fail to ensure the necessary connectivity. The primary obstacle lies in heterogeneous shop floor environments. Assets (including tools, machines, and equipment) vary widely in age and manufacturer, each employing a range of technologies and proprietary protocols. Also, a binding standard for data exchange is lacking, as is support for common machine and IT protocols. And finally, establishing cloud connectivity remains challenging.

Approach and Functionality

A German automotive manufacturer was faced with a highly heterogeneous IT and OT environment while striving for improved data integration. To enable seamless digitalization of the shop floor, MHP implemented the industrial cloud solution “integrate_it”

as part of an ongoing project. Serving as middleware, the solution facilitated data exchange across common proprietary and open protocols. This enabled the capturing of data from nearly all machines and equipment, transforming it as needed and transmitting it in the appropriate protocol to other shop floor assets as well as downstream IT and OT systems.

A key success factor is that “integrate_it” supports common machine protocols such as OPC UA, RFC 1006, and others, as well as widely used IT protocols like Kafka, MQTT, and REST. Additionally, this SaaS solution can be directly connected to Amazon Web Services (AWS), Microsoft Azure, or any private cloud of the user company. A further benefit is that the solution converts captured machine data into the human-readable JSON (JavaScript Object Notation)

format, enriches it, and maps it to freely configurable data models. A rule engine enables initial rulebased manipulation of the data, allowing for immediate insights to inform latency-critical decisions.

Results and Outlook

A notable impact of “integrate_it” at the automotive manufacturer’s plant will be the consolidation of machine and sensor data to generate precise insights and predictions—particularly in Quality Assurance. The industrial cloud solution “Sounce” has been in use in this area since 2021.

With its AI-based capabilities, the solution enables the manufacturer to detect anomalies in components and products, respond rapidly, and optimize manufacturing and assembly processes. In the quality assurance setup, a minimally invasive acoustic sensor captures sound within test objects. The “Sounce” software converts the sound into spectrograms and compares those to stored pattern data. Deviations are immediately detected and interpreted. Deeper insights will become possible once sensor data is linked with machine data through “integrate_it.”

This will allow inspectors not only to be alerted to automatically detected quality deviations but also to identify the root causes in the manufacturing machines and equipment. A significant benefit of “Sounce” is that a deep learning algorithm supports numerous additional application scenarios. Moreover, fault detection enables early identification of issues even during development processes.

“Sounce” software in action

More on this: https://www.mhp.com/en/solutions/ industrial-cloud-solutions

Topic Cluster 4: Obstacles

In the current survey, the skilled labor shortage shows the greatest decrease. While in 2024, 52 percent of participants reported that difficulties in recruiting qualified employees delayed the implementation of Industry 4.0 technologies, this figure drops to 41 percent in 2025 (Figure 17). This could be attributed to targeted training initiatives, improved recruitment strategies, and the increased use of automation technologies, as well as a change in the composition of the surveyed companies.

The impact of supply chain issues as an obstacle to the implementation of Industry 4.0 has also decreased significantly. Only 31 percent of respondents cite this as a hurdle, representing a decrease of 10 percentage points compared to the previous year. This suggests that companies have developed more resilient supply chains to better respond to recurring crises. The increasing adoption of resilience strategies, such as supplier diversification and the use of digital tools for real-time supply chain monitoring, is likely playing a key role here.

The implementation of Industry 4.0 technologies is delayed in our company due to…

…historically grown legacy systems.

…insufficient attention from management.

…the shortage of skilled labor.

…integration into day-to-day operations.
…uncertain ROI.
…lack of data exchange with partners.

…data silos.

…uncertainty in scaling.

…supply chain issues.

Figure 17: Obstacles to the implementation of Industry 4.0 technologies

Despite these positive developments, the integration into existing legacy systems remains the biggest challenge. Once again this year, around 48 percent of respondents cite outdated IT infrastructures as the main reason for delays in the adoption of Industry 4.0 technologies. This issue is particularly relevant in the DACH region, high-lighting a structural challenge that is more common in historically developed companies, particularly in industrial sectors. Industry comparisons show that legacy systems are a central obstacle, especially in the automotive industry, which has traditionally been heavily dependent on long-established IT systems. The effort and complexity of modernizing outdated systems could significantly slow down progress in this sector. In addition to technical hurdles, the lack of attention to Industry 4.0 at the management level has also been identified as a significant barrier. 43 percent of respondents state that strategic prioritization within the company is insufficient to drive the necessary investments and changes. A noteworthy development is the reduction of data silos in China. They are increasingly perceived as less significant obstacles. The share of Chinese respondents citing this challenge has dropped from 57 percent in the previous year to 41 percent in the current study (Figure 18). This trend suggests improved data integration and accelerated digitalization of processes. While China is setting new standards in digitalization, the DACH region is still struggling with outdated systems. The key lies not only in technology but also in the ability to forge new paths.

strongly disagree	disagree	somewhat disagree	neutral	somewhat agree	agree	strongly agree

The introduction of Industry 4.0 technologies is delayed in our company because data silos hinder the implementation of cross-functional solutions.
The introduction of Industry 4.0 technologies is delayed in our company because there is no seamless data exchange with partners along the value chain.

Figure 18: Obstacles to the implementation of Industry 4.0 technologies by region

Schaeffler Group

SCHAEFFLER

Schaeffler – Brief Profile

Schaeffler Group – We pioneer motion

For over 75 years, the Schaeffler Group has been driving groundbreaking innovations and developments in motion engineering. With cutting-edge technologies, products, and services in the fields of electromobility, CO -efficient powertrains, chassis solutions, and renewable energy, the company is a trusted partner in making movement more efficient, intelligent, and sustainable—across the entire lifecycle. Schaeffler presents its comprehensive product and service portfolio within the mobility ecosystem through eight product families, ranging from bearing solutions and linear guides of all kinds to repair and monitoring services. With approximately 120,000 employees at more than 250 locations in 55 countries, Schaeffler is one of the world’s largest familyowned enterprises and ranks among Germany’s most innovative companies.

Alexander B. Wurst – Brief Biography

Alexander B. Wurst is a passionate mechanical engineer with extensive expertise in production, digitalization, and process optimization. As Process Development & Digitalization Vice President at the Division for Bearings & Industrial Solutions at Schaeffler Group, he has successfully implemented an endto-end digitalization strategy. At Schaeffler, he drives comprehensive restructuring processes, enhances efficiency, and optimizes cost structures. Recognized as a true leader and digital ambassador, he actively promotes market-oriented business models and innovations in the field of Industry 4.0.

Participants: Alexander B. Wurst (DTO and Digital Ambassador at Schaeffler), Thomas Klüe (MHP – Senior Manager Digital Factory and Supply Chain), Tobias Schreiber (MHP – Senior Consultant Digital Factory and Supply Chain)

Thomas Klüe: Can you give us a brief overview of your role at Schaeffler and your experience in the field of digitalization and IT thus far?

Alexander B. Wurst: As part of the Division for Bearings & Industrial Solutions, we set the strategic framework for our industrial business. Our portfolio includes rolling bearing solutions, linear solutions, condition monitoring solutions for bearings, and innovative products in the field of robotics. Within this division, I serve as the Digital Transformation Owner (DTO) and am responsible for digitalization. My team and I focus on strategic alignment, developing the roadmap, and ensuring its global implementation in collaboration with our four regions. At its core, our role is to define the direction and establish a structured, coordinated approach. This involves leveraging synergies and aligning digitalization efforts with business objectives and divisional goals in a targeted manner. Additionally, I am responsible for Process Development. To ensure optimal alignment between processes and digitalization, we have consolidated the two within a single organizational unit. This allows us to create digital yet lean, stable, and robust processes while driving a major transformation in the ERP landscape. Another key aspect of my role is overseeing the division’s IT portfolio. Together with my team, I determine which IT systems, applications, and tools will be used across our regions. The focus here is on scaling existing solutions, establishing standards, and eliminating redundancy to optimize resource efficiency and ensure a more targeted approach.

Alexander B. Wurst DTO and Digital Ambassador (Schaeffler)

Thomas Klüe: In your perspective, how relevant is data in 2025?

Alexander B. Wurst: In my opinion, data holds immense relevance. At the end of the day, data is one of the production factors, alongside capital, labor, and land. Companies that manage to use data better will have a competitive advantage. It’s not just about owning data, but about having the right data available in sufficient quality, as well as having the appropriate control processes for data management. And if we can achieve all of this as a company, then we are ready to apply AI, automate processes correctly, and trust that automation. Then a company is capable of providing advanced and reliable forecasts. But this is only possible if companies carefully manage, maintain, and control their data. For me, data is not only the key foundation, but also an indispensable prerequisite for improving processes and gaining an advantage in global competition.

Thomas Klüe: What essential prerequisites, in your opinion, still need to be met in order to successfully implement profitable data-driven production?

Alexander B. Wurst: The relevance of data is strongly linked to the mindset of the individuals involved. It’s not about the technical prerequisites needed to work with data perfectly or better. The real question is how we handle the data. Do I understand the relevance of data? Do I see data as the differentiating factor? If we realize that our daily interactions with a system enable a use case or bring efficiency to another part of the organization, then we’ve succeeded. Simply put, if fields are just being filled in for the sake of filling them, it does nothing for the company.

Tobias Schreiber: When you think of the decision-making processes at Schaeffler: To what extent can you rely on meaningful and valid data, and where do you still see challenges or potential for improvement in the data foundation for decisions?

Alexander B. Wurst: For top management level decision-making and reporting, we are already using automated and interactive reports. For example, we were able to reduce the process duration for creating quotes for standard products to less than one day by using curated and cleansed data.

However, the optimization doesn’t stop there; it is an iterative process. After all, we don’t just want to digitize the current state; that’s merely a milestone on the way. Additionally, there is an ongoing influx of data that we must aggregate for even better decision-making. For instance, when creating quotes, the responsible employees don’t just want to know the price for a standard product at a batch size of 100 units. They also want to know which subregion the quote is being created for, which phase of the market cycle is currently prevailing, what the stock level is, and whether it’s a buyer’s or seller’s market. Market conditions are becoming more demanding, and the half-life of information is becoming shorter. Therefore, we must react more frequently, and much more quickly, to be able to prepare and support the right decisions.

Thomas Klüe: What long-term goals does Schaeffler pursue in the field of digital production, and how are these goals to be achieved? What role does the “Production Traceability” initiative, in which MHP has collaborated with Schaeffler, play in the overall picture, and where do you currently stand in this transformation process?

Alexander B. Wurst: Our product portfolio is basically divided into two areas: our core business, which includes both the rolling bearing and linear business, and our growth business, with which we are investing into the future. This includes mechatronic products such as robotics, condition monitoring solutions, and actuators. The Production Traceability initiative primarily targets our core business, with a particular focus on the rolling bearing business, as these products will remain mechanical for the majority of applications. Some of this is due to space constraints, but there are also environmental factors which preclude a mechanically excellent product being further “sensorized” or “mechatronized.” However, we can significantly expand and enhance our core business with digital services, making it easier for the customer to digitally integrate our products into their processes. With the help of digitalization, we can also achieve significant improvements along the production chain. This is the internal dimension of the Production Traceability initiative: collecting data from the manufacturing steps, correlating them, and not optimizing individual machines or individual production steps, but considering the entire chain, so that interactions become visible and the optimum progression along the value chain can be identified.

Our product portfolio is complex and includes various manufacturing equipment and value-adding stages, some of which involve partners and suppliers. The Production Traceability initiative aims to manage this complexity and translate it into productivity improvements. The goal is to optimize manufacturing with the data we collect and make available within this initiative, and to provide digital services that offer the customer unmatched added value. Even when a disruption or complaint occurs, we want to be able to provide meaningful insights and showcase how the product was manufactured, highlighting correlations so that the problem does not recur in the future. At Schaeffler, we have the vision of making manufacturing automated, autonomous, flexible, and sustainable for the future. For all four dimensions, reliable data is needed to help run a machine automatically. Additionally, these are all topics that the Production Traceability initiative contributes to.

Tobias Schreiber: According to our research, companies from the USA and China are considered to be more “experimental,” for example in terms of the acceptance of a failure culture in the area of innovations—how do you evaluate this aspect?

Alexander B. Wurst: I believe that it is the interplay in a global initiative that brings out the individual strengths of our international setup. I wouldn’t classify one region as more “experimental” than another. Viewing complex challenges from different technical and cultural perspectives is a strength. That’s why, from the very beginning, we didn’t limit our initiative to the DACH region, which in many cases would seem more convenient and tangible, but set it up globally. We involve our plants in China, America, and emerging countries like India, so it’s not a “headquarter ivory tower initiative” that can’t be implemented in practice. The initiative aims to be a blueprint and modularly adaptable at different levels, and applicable in all our plants. We also take regional preferences into account. In China, for example, there is a stronger mobile approach, and with Alibaba Cloud or the WeChat ecosystem, the IT infrastructure conditions are completely different. However, the key is to strike the right balance. We are a premium manufacturer delivering excellent products with a very high level of quality to provide our customers with durable and application-specific products. We hold the same standard for our digital solutions. And we can only meet this standard if our data structure is excellent in advance.

Tobias Schreiber: What advice would you give to other companies that want to embark on the path to data-driven production?

Alexander B. Wurst: As every company is somewhat unique, it is never just about the IT perspective when it comes to data, but always about the product, the business model, the respective market, and, above all, the specific corporate culture. Nevertheless, I would always recommend incorporating business relevance from the very beginning. In our initiative, we focused on the data we need for our divisional objectives. Which data supports profitable growth, the strategic expansion of the product portfolio, or customer added value? This consistency is the longest-lasting common thread in any business field. We did not accumulate the data for the sake of IT systems alone; the purpose and goals of the company are clearly customer-oriented.

A second piece of advice could be “embrace the gaps.” Unlike what was assumed by many a few years ago, it has been shown that it is not necessary to collect all data. If you have 20 out of 100 data points and they are the right ones and of high quality, then you’ve done the job 100% right, even if 80 data objects were not captured, or not captured digitally. One should trust that they have understood their business properly and know which data is actually needed and is relevant both internally and for the customer.

Thomas Klüe: What trends or developments in the field of data do you consider particularly relevant, and what goals has Schaeffler set for the future? Specifically, we are referring to the Catena-X project of the DLR, in which Schaeffler is involved. Are there any concrete ideas for promoting this platform concept with suppliers?

Alexander B. Wurst: Business relationships will likely become even more intertwined, with companies interacting much more closely, and also being more connected in terms of systems than is currently the case. In the exchange of information and communication, for example, via emails, the human component will recede into the background and be replaced by system-to-system connections. For companies, this means they must know and master their own business processes and the associated data, so they are capable of keeping up with partners, customers, and suppliers in the increasingly complex networks, while also fulfilling regulations regarding climate and environmental protection more efficiently.

Moreover, in my opinion, there is a global trend in which process excellence and increased productivity can improve the efficiency of areas of manufacturing. Here, digitalization is the key component to making production more automated and autonomous. And even with the major issues of our time—sustainability, both from a resource and control perspective— digitalization is the key.

Many companies start with a central department that is supposed to turn the company inside out and offer only digital business processes and everything “as a service.” In a second and third wave, it becomes clear that a much more federalized approach is necessary, meaning that a strong IT backbone must be in place and the digitalization units must simultaneously be close to the business. What is highly relevant for the customer and ultimately decisive in the market is when we, for example, take the capacity information from our manufacturing and enrich it with market data and sales information, so that our salespeople can be even more insightful and respond on a caseby-case basis. With this networking of individual (data) silos, a tangible added value is created for the customer; however, this networking also presents a challenge. We have already proven, though, that we are capable of mastering it.

Regarding Catena-X: We are an integral part and partner of Catena-X, and we are shaping the exchange of data across company boundaries to ensure that businesses maintain control over their data. We also transfer our experiences to other business areas, although outside the automotive sector the supplier and customer structures are much more heterogeneous. Smaller suppliers often lack the resources to become part of such an ecosystem, which leads us to carefully consider when and in which use case we participate. Despite these challenges, we are technologically prepared and closely involved in the developments.

2.2. Data-Driven Production— Use of Data in Production

In Industry 4.0, an increasing amount of data is being generated with ever finer granularity. The targeted use of these data assets within a company helps to optimize and automate, as well as to plan production processes. However, before companies can fully leverage the productivity potential of data-driven production, they must systematically and purposefully manage the fundamental resource—data. Data is the foundation upon which technologies such as Artificial Intelligence, Machine Learning, and the Internet of Things (IoT) are built.

Companies that approach the topic of data comprehensively—considering requirements, usage, and application—face a variety of technical, organizational, operational, and strategic questions. We are examining these in detail this year, as our experience shows that many companies still have room for improvement in these areas.

A key aspect is interoperability, as companies often work with a variety of different systems and technologies that do not always communicate seamlessly with one another. Standardized interfaces and uniform protocols are needed to overcome these barriers. Companies face the challenge of not only capturing large amounts of data but also analyzing and utilizing it effectively. This results in challenges such as integrating heterogeneous systems, ensuring data quality, and managing security and privacy issues.

Another obstacle is converting the collected data into actionable insights. Advanced analytical tools and technologies, such as machine Learning, are crucial in this regard to recognize patterns and enable informed decision-making. The ability to generate strategic value from data is increasingly becoming a competitive advantage.

However, successful data utilization requires not only the technical means for data collection but also the development of a clear data strategy that covers the entire data lifecycle—from collection to processing and analytics. Given the growing volumes of data and changing business requirements, it is crucial to design this strategy in a flexible and forward-looking manner. Data-driven companies cannot only increase their efficiency but also enhance their flexibility and respond more quickly to disruptions or fluctuations. The ability to strategically and securely deploy data thus becomes a critical success factor in global competition.

Data Strategy

A mature data strategy helps companies to use data strategically as a valuable resource. It cannot only promote more informed decision-making and optimize business processes but also strengthen innovation and ultimately improve a company’s competitiveness. The high importance of data and necessary data management was defined by the British mathematician Clive Humby back in 2006 with his comparison “Data is the new oil.”1 The true value of data, just like oil, only becomes apparent through its processing and analysis.

Overall, it shows that a clear majority of the surveyed companies pursue a data strategy in relation to data-driven production: 76 percent have implemented a clear data strategy and corresponding measures to ensure high data quality and availability. Data strategies have become a central element of business development for many companies (Figure 19). Only a very small percentage of respondents still see gaps or missing strategies in this area. Additionally, 74 percent of companies have developed concrete roadmaps for utilizing data, highlighting that the potential of data for optimizing business processes is already recognized in many companies. Companies have adopted a clear and positive stance on the strategic use of data. In fact, 80 percent of the respondents treat data as valuable assets (resources) for generating insights within their organizations. In terms of overall data strategies, the development of roadmaps, and the significance of data, the DACH region shows notable room for improvement in an international context. In the USA, the U.K., and China, the implementation of clearly defined data strategies is more advanced (Figure 20). The targeted use of data and its high importance are more strongly considered and hold greater relevance.

Figure 19: General data strategies in companies

In general, a distinction can be made between a defensive and an offensive data strategy. A defensive data strategy focuses on ensuring the security and protection of data. The primary emphasis is on measures to minimize risks, such as data breaches and unauthorized access. The goal is to reduce potential threats and maintain data integrity. Participants were asked to what extent defensive data strategies help achieve key business goals such as reducing operating costs, meeting regulatory requirements, or improving data quality. Overall, the results show that most companies effectively implement the defensive components of their data strategy (Figure 21).

My company has a clear plan for how data is managed to ensure high data quality and availability.

My company has a clear roadmap outlining when which business cases are to be solved with the help of defined and subsequently available data.

My company treats data as a strategic asset to generate valuable information across the organization.

Figure 20: International comparison of general data strategy

In contrast to the defensive data strategy, the offensive data strategy aims to maximize the value of data and optimize its use to achieve business advantages. With an offensive strategy, the focus is on gaining insights and fostering innovation. The goal is to derive strategic advantages from available data and enhance competitiveness. Participants were asked to what extent the offensive data strategy is considered helpful in generating additional revenue, creating new products and services, or responding more quickly to market changes. Overall, the elements of an offensive data strategy are largely viewed positively. 75 percent of participants generate additional revenue through active data use with existing products and services, highlighting the economic benefits of an offensive data strategy (Figure 22). 73 percent consider the strategy

Figure 21: Corporate defensive data strategy

My company’s offensive data strategy helps to...

Figure 22: Corporate offensive data strategy

helpful in developing new products and services, as well as in reacting quickly to competition and market changes. 72 percent also rate it as supportive in utilizing new data sources. An offensive data strategy not only enables companies to generate additional revenue with existing products and services but also fosters innovation and responsiveness to market changes. However, when it comes to the direct monetization of corporate data, only 50 percent of respondents agree. A possible reason for this could be the increasing awareness of the actual monetization potential of data. Particularly in the manufacturing sector, the idea persisted that data from machines could be sold directly as additional assets. This assumption has proven to be unrealistic, as data only becomes valuable in the context of the entire production process. It shows that companies are not willing to pay twice—for equipment and for its data. A well-thought-out data strategy lays the foundation for the targeted use of data to optimize business processes and foster innovation. It is closely linked to the ability to realize data-driven competitive advantages. The next section will show how companies can strengthen their market position and achieve economic success through the strategic use of data.

When comparing defensive and offensive data strategies, it is noticeable that most companies adopt a defensive approach. This is particularly evident in the DACH region, where the focus is primarily on risk and cost reduction, as well as compliance with regulatory requirements. However, such a one-dimensional focus leaves potential value creation and innovation opportunities untapped. Chinese companies take a more balanced approach, combining the economic opportunities of an offensive data strategy with appropriate risk and cost management.

Data-Related Competitive Advantage

A data-related competitive advantage arises when companies strategically leverage data to make better decisions, increase efficiency, drive innovation, and respond more quickly to market changes. This can involve generating additional revenue from existing products and services, developing new products and offerings, unlocking new data sources, and monetizing company data. Participants were asked to what extent their companies perceive a data-driven competitive advantage in relation to their competitors. The study examined whether the use of data leads to economic success, faster and more reliable decision-making, increased market and production responsiveness, and valuable insights into core processes.

Overall, the majority of respondents perceive the improved use of data as a clear advantage for companies (Figure 23). It is particularly noteworthy that 80 percent of participants identify access to deeper insights into core processes as a key benefit. 76 percent of respondents consider the ability to make faster and more reliable decisions as a key advantage of data-driven processes. In addition, 74 percent regard increased responsiveness in both the market and production as an important competitive edge. This demonstrates that data-driven technologies offer significant benefits at both the operational and strategic levels. Furthermore, 76 percent of respondents agree that targeted use of data contributes to economic success. These findings highlight how data-driven approaches can enhance operational efficiency and agility.

Data Governance

Data governance in companies refers to the strategic management and oversight of data administration to en-sure that data is used reliably, securely, and efficiently while also complying with legal requirements and internal policies. To meet these requirements, clearly defined roles and responsibilities are essential. The survey shows that, in terms of the highest level of agreement decision-making authorities—such as roles (e.g., Chief Data Officer, Data Owner, Data Steward, Data Custodian, Data Producer, Data Consumer) and committees (e.g., Data Governance Council, Data Stewardship Team, Data Owner Committee)—are clearly defined in only about 20 percent of companies (Figure 24). However, without clearly established role and committee concepts, there is a risk that data will not be managed and used consistently and systematically across the company. Efficient, company-wide decision-making processes that align IT and business strategies are crucial for improving data-driven production processes. By contrast, companies demonstrate greater success in assigning specific decision-making authorities and tasks, even though these roles and responsibilities are clearly defined in only about 20 percent of companies.

Companies can assign decision-making authority and responsibilities regarding data either centralized or decentralized. A central data governance approach is characterized by centralized control of data policies and process-es, ensuring high data quality and consistency, but potentially limiting flexibility and decision-making speed. In contrast, a decentralized approach places responsibility within individual business units, allowing for greater adaptability and faster decisions, but potentially leading to data inconsistencies.

Vbuoniaaahesie

aabesie

1eumaaoaaabesie

enetr

teumaaeraaeee

aaebe

buinigaibe

Figure 23: Data usage compared to competitors

The results indicate that centralized approaches are more common in companies than decentralized ones (Figure 25). In an international comparison, the DACH region stands out with a particularly strong tendency toward centralization. This suggests an attempt to transfer existing hierarchical structures from the physical world into the digital realm. It raises the question of whether this tendency reflects a management culture focused on control and security. Here, the role of the CIO could help counterbalance this approach and foster greater trust in data handling within the company, as the analysis on (see p. 69 et seq.) demonstrates. In summary, the findings indicate that data governance in companies is not yet fully mature. Responsibilities in the form of roles and committees need further refinement to ensure that corporate data is systematically and comprehensively collected, managed, and utilized.

Data Management

Data management in companies is crucial for systematically collecting, storing, and analyzing information, enabling well-founded decisions and securing longterm competitive advantages. In this context, companies were surveyed on aspects such as data quality, data availability, documentation of management processes, and metadata maintenance. Regarding data quality, 76 percent of respondents stated that they can work adequately with the quality of their data (Figure 26). In an international comparison, the USA, the U.K., and China show similar levels of data quality (75–80 percent), whereas the DACH region significantly lags behind at 65 percent. The differences in data quality assessments across these regions point to several influencing factors. Possible reasons include a lack of clear data ownership and insufficient data governance in some companies, which leads to lower data quality. Additionally, regulatory requirements, such as stricter data protection laws in certain regions, may restrict access to and processing of data, thereby affecting its quality. A relatively new and increasingly cited factor influencing data quality is the varying speed of artificial intelligence (AI) implementation. AI has the capability to clean, harmonize, and detect anomalies in data. Companies that invest less in AI may struggle to improve their data quality. Furthermore, a company’s system landscape and data storage can provide insights into the overall quality of its data. This is also reflected in the interviews conducted. For example, Thomas Speck, CIO of TRUMPF SE + Co. KG, noted that decentralized data silos and an unclear tool-set negatively impact data quality and hinder the comprehensive implementation of data-driven production.

strongly disagree

disagree

somewhat disagree

neutral

somewhat agree agree

strongly agree

Figure 24: Corporate data governance

more centralized than decentralized	more decentralized than centralized	decentralized approach	centralized approach

How does your company assign responsibilities and decision-making authority regarding the management and utilization of process data from production?

Figure 25: Responsibilities and decision rights for the management and use of process data

Interview

TRUMPF SE + Co. KG

TRUMPF

TRUMPF – Brief Profile

TRUMPF has a history spanning over 100 years. Today, it is known for high-quality machine tools, lasers, electronics, and innovative system solutions that support industrial companies worldwide. The company is headquartered in Ditzingen, northwest of Stuttgart, and employs around 19,000 people, approximately half of whom work in Germany. TRUMPF offers customized solutions and machines for high-performance production lines across various industries, including automotive, construction, healthcare, science, energy, and aerospace. In the field of laser technology, TRUMPF is a global leader, operating in over 70 countries. As a driver of innovation, TRUMPF is also actively embracing digital transformation. The company not only optimizes internal processes but also develops new data-driven and customer-centric business models to meet the demands of modern industry.

Thomas Speck – Brief Biography

Thomas Speck is a business informatics specialist with more than 20 years of professional experience, and he has been the Chief Information Officer (CIO) of the German family-owned company TRUMPF SE + Co. KG since June 2021. With a strong background in IT strategy, software development, and digitalization, he plays a key role in driving the company’s digital transformation.

Participants: Thomas Speck (TRUMPF – CIO), Julius Peters (MHP – Manager Digital Factory and Supply Chain), Johanna Veit (MHP – Senior Sales Director)

Julius Peters: Can you give us a brief insight into your role and responsibilities at TRUMPF, particularly in light of your previous experience in IT and digitalization?

Thomas Speck: As CIO, I am responsible for global IT at TRUMPF, overseeing all IT-related topics—from business process management systems and classic infrastructure to digitalization topics such as big data, software development, and AI development. A key focus of my work is transforming IT into a service-oriented function with a stronger emphasis on business value creation. I originally studied computer science and have a solid background as a software developer and IT program manager. Digitalization and IT transformation have been central themes throughout my career.

Julius Peters: Generally speaking, how relevant do you think data will be in 2025?

Thomas Speck: Data plays a crucial role for companies, but more important than mere availability is the ability to leverage data effectively and establish the right foundations within the organization. Even 20 years ago, I was working with central data warehouses and professional data management. Today, the key challenge is to collect and store data in a standardized and efficient way while ensuring high data quality to create real added value and enable scalability.

Thomas Speck CIO (TRUMPF)

Julius Peters: Many companies face challenges in implementing AI effectively. What internal and external challenges do you see in this regard?

Thomas Speck: Setting realistic expectations is crucial. In my experience, the main challenge lies in establishing a consistent data architecture and governance framework. For this purpose, we at TRUMPF have created a data integration platform as a central data marketplace that bundles data from business process management systems, unstructured sources, and IoT information.

Ensuring data integrity and quality remains the biggest challenge. Responsibilities for data, data governance, and the traceability of data flows are also key factors. AI alone will struggle to resolve issues related to poor data quality or unstructured data management.

Additionally, external regulatory requirements pose a significant challenge. Different data protection and compliance regulations in various markets necessitate a flexible yet robust data strategy to comply with legal frameworks.

Julius Peters: You have emphasized the importance of a consistent data architecture and data quality. Given this, how would you assess the current state of the data infrastructure at TRUMPF, particularly in terms of efficiency and cost-effectiveness?

Thomas Speck: Our platform has evolved over the years and is now highly efficient, serving as a crucial lever for implementing our digitalization initiatives. We have developed smart strategies to actively manage cloud costs by optimizing cloud consumption and deciding which data to access and which to archive.

For our end customers, we offer digital solutions in the form of software products and process optimizations based on data, enabling more efficient production and reduced waste. A key factor—both internally and for our customers—is the networking of our systems. This allows us to automate production processes, increase machine availability, and ultimately enhance efficiency on the shop floor.

Furthermore, we expect additional performance improvements through the standardization of our business processes within our large-scale transformation programs, such as S4.

Julius Peters: How advanced is process mining at TRUMPF in this context?

Thomas Speck: We have explored the topic, but currently, the S4 transformation takes top priority. Once we have completed the transition to S4, process mining could become relevant—especially to assess whether standard SAP processes are being followed or if new workarounds emerge. Overall, however, I think that you do not necessarily have to solve this with process mining. In my experience, a Business Process Management Organization (BPMO) with process owners, process managers, and consultants can also effectively manage this.

Julius Peters: We briefly touched on the topic of cloud computing earlier—data security and server location are often debated. What is your take on this?

Thomas Speck: In my experience, hyperscalers already offer a high level of security and continuously introduce new measures to improve data security. Additionally, we can also ensure a corresponding level of security of our systems with our own resources. While internal misuse and data theft can never be completely ruled out, we have a tightly monitored security system in place. Overall, we consider our data storage and processing security to be at a high level.

Julius Peters: High security standards are essential, but at the same time, connectivity opens up new opportunities. How does TRUMPF leverage these to optimize machines and data usage?

Thomas Speck: We aim to invest in our products, machine connectivity, IoT, and data engineering to efficiently connect our machines, extract valuable information, and assess it using AI. Classic use cases where we have already made significant progress include machine condition monitoring, predictive maintenance, and digital twins.

Johanna Veit: The topics you just mentioned are very machine- and production-oriented. How is your IT department involved in this area?

Thomas Speck: IT provides central self-service plat - forms that allow users to develop applications in - dependently, while also offering support through consulting and technological expertise. In close col - laboration with business units, IT develops tailored software solutions and ensures their ongoing oper - ation.

Julius Peters: To succeed as a data-driven company, deep collaboration across all areas and levels is cru - cial. The term “change management” often comes up in this context. What approaches do you take to ensure active employee engagement alongside their daily tasks?

Thomas Speck: We anchor responsibility for data objects directly within business units through our BPMO structure—business process owners, managers, and key users ensure compliance with data quality and governance standards. Additionally, we promote knowledge sharing on digital topics through working groups, workshops, and town halls, and provide tar - geted training on digital skills via HR.

Julius Peters: Our research suggests that companies in the US and China are generally more experimental in terms of innovation and have a culture that em - braces failure. How do you view this aspect?

Thomas Speck: Mistakes are part of the process— the key is to make them quickly, learn from them, and avoid repeating them. In our current AI initia - tive, we have many use cases, and it is clear that not all will deliver long-term economic value. Ideally, we quickly determine whether a business case is viable so that we can promptly pivot and try again.

Julius Peters: You were named CIO of the Year 2023. What advice would you give to companies still in the early stages of transitioning to a data-driven production model?

Thomas Speck: The path we have taken at TRUMPF over the past six years could serve as a good refer - ence. As a first step, we developed a data strategy and realized that we had too many decentralized data pools and BI tools. Therefore, we first consoli - dated the BI front-end landscape and set up a central big data environment in which all data objects are made available. Close alignment with business units was particularly important. Together, we agreed on a reference architecture to implement development projects within a structured framework and create synergies. This was a key success factor. It enables us as IT to act quickly and implement new digital solu - tions efficiently.

If you are interested in the details, you can find out more in our white paper on the reference architecture.

Figure 26: Data quality by region

Data Infrastructure and Technical Capabilities

Shifting the focus from the organizational level of data management to the technological level in the context of data-driven production, participants were first asked to assess their company’s technological data infrastructure and capabilities. The results indicate that the technological data infrastructure is generally viewed positively, with 71 percent of respondents expressing agreement. This suggests that the majority of surveyed companies have the necessary technological foundations for implementing data-driven production. Real-time access to production data is particularly noteworthy, with the highest agreement rate at 77 percent. This indicates that many companies are capable of capturing production data in real time—an essential factor for efficient and flexible manufacturing. Standards ensuring interoperability were also rated positively, with 74 percent agreement, suggesting that many companies have the necessary frameworks to seamlessly integrate various systems and technologies. Obtaining production data of an appropriate quality and granularity was also viewed positively by 74 percent of respondents, which shows that many companies are able to collect high-quality data. However, integrating external data and seamlessly consolidating it into a common repository2 received lower agreement rates of 67 percent and 68 percent, respectively. This may be due to differences in data formats that hinder smooth integration. These areas still offer room for improvement (Figure 27). Companies in the DACH region may still need to take steps to improve their technological data infrastructure and remain competitive. However, the survey overall indicates that many companies already have a solid technological foundation to support data-driven production. The positive feedback from respondents can likely be attributed to a combination of technological advancements, a stronger data-driven corporate culture, and improved data infra-structure.

Figure 27: Data technology as the foundation for data-driven production

Trends such as the IoT, cloud technology, data quality, and interoperability standards could enable companies to utilize data more effectively and create better-integrated systems. Additionally, ongoing digitalization and strong management support may further drive improvements in data availability and utilization—as reflected in the survey’s positive ratings.

Data Culture

A company’s data culture defines its attitude and practices regarding data-driven decision-making and innovation. A strong data culture is characterized by the seamless integration of data-based decisions, the recognition of data as a valuable asset, and the cross-departmental sharing of information. Additionally, a culture of experimentation is encouraged to generate new insights and drive tangible change. Alongside the previously mentioned organizational foundations, a well-established data culture is a key prerequisite for data-driven production.

With an agreement rate of 81 percent, the recognition of data as a valuable asset is particularly well received (Figure 28). This indicates a strong awareness of data as a strategic resource. Furthermore, the majority of employees and works councils have a positive attitude toward using data to achieve company objectives. However, a regional comparison reveals significant differences between the DACH region and other countries. This discrepancy may be attributed to varying regulations, as the U.K., the USA, and China tend to have more favorable conditions for data utilization and innovation. In contrast, the DACH region performs noticeably worse and lags behind the developments seen in other regions regarding data culture.

The weakest aspect of data culture is the active demand for data-driven decisions by leadership. Only 57 percent of respondents agree, highlighting that many decisions are still made intuitively. Survey results on fostering a culture of experimentation with data show similar findings, with only 64 percent of respondents agreeing. This may not only be due to a lack of time to develop new ideas as a result of dayto-day business but also to economic uncertainties. In an environment where stability and resource preservation are the primary focus, experimental approaches often take a backseat. These factors may also explain international differences in the adoption of an experimentation culture. However, consciously creating space for experimentation—especially through management support—remains a key lever in strengthening a data-driven culture.

Data Competency

In order to use data for better decision-making and leverage new potential, employees need basic knowledge and skills for using data and interpreting results. The study analyzes employees’ data competency across five key areas. The skills examined include identifying and accessing data, assessing data quality, data management, data analytics, and evaluating and interpreting analytical results.

The results show that less than a quarter of the companies achieve the maximum value in all the competencies considered (Figure 29). The evaluations of competencies in each area are largely similar, with only minimal variations. Alarmingly, around 11 percent of respondents indicate that their company lacks fundamental knowledge and skills in data competency. While the assessment of data quality received a slightly lower agreement rate of 76 percent compared to other data competency areas, it still remains at a generally good level.

Notably, the U.S. once again leads in all areas (Figure 30). With an agreement rate of 83 percent, it is slightly ahead of China at 80 percent and the U.K. at 77 percent, while the DACH region lags significantly behind at 65 percent. Similar trends are observed across all aspects of data competency. The higher data competency levels among employees in the U.S. can likely be attributed to several factors. First, the technological infrastructure in the U.S. is often more advanced, enabling companies to integrate data-driven processes more efficiently and involve employees more extensively in their use. Second, American companies benefit from better data availability, supported by open market structures and a higher degree of digitalization. In contrast, the DACH region faces stricter data protection regulations and an ongoing shortage of skilled professionals, making it more difficult to build comprehensive data competencies. These challenges particularly hinder the ability to assess data quality and limit overall data usage.

Training and Development

To identify the root causes of missing data competencies and analyze the general foundation for achieving data literacy in companies, participants were surveyed about training and development opportunities within their organizations. Education and training in data literacy, data management, data analytics, and their practical applications play a crucial role in a company’s success. This study examines whether companies provide employees with sufficient time and resources for professional development. Additionally, it evaluates whether these training programs are mandatory, offer practical and relevant content, and promote continuous learning.

The survey results indicate that companies generally provide their employees with sufficient training and development opportunities, including specialized training for daily tasks, while also allocating adequate resources for these initiatives. The relevance of these training programs received the highest agreement rate at 75 percent, suggesting that companies are actively addressing the increasing demands for data competency. However, in all surveyed aspects of training and development, the U.S. and China outperform the DACH region (Figure 31).This finding is further supported by the BIDT study, in which participants from Germany evaluated their company’s training and development offerings. Depending on company size, between 11 and 17 percent rated these opportunities as very poor, highlighting the insufficient availability of training programs in many organizations.3

Success Story TRUMPF Data Integration Platform

The successful implementation of a central data platform accelerates the digitalization of the innovation process and serves as the foundation for new value streams.

Initial Situation and Challenges

TRUMPF SE + Co. KG, a high-tech company with over 100 years of experience, has already successfully navigated several transformations. Examples include the introduction of combined punching-laser machines in 1979, the development of laser-based 3D printing machines in 2003, the market launch of the EUV high-power pulsed laser for the chip industry in 2017, and the development of the world’s first laser-driven neutron source for industrial applications in 2024.

Despite past successes, competition in the machinery market has not been idle and has steadily increased over the past decades. Efficiency gains and sustainability are well-known marketing terms and no longer provide a unique selling proposition in this highly competitive market. Additional challenges arise from weaker supply chains and rising inflation, which put pressure on contribution margins.

In this dynamic market environment, adaptability is crucial. New value streams through digital and AI-based services are intended to secure TRUMPF’s market leadership. A central data management approach in the form of a Data Integration Platform (DIP), which provides consolidated, high-quality machine and business data, is the prerequisite for developing new digital services and disruptive business models.

In 2020, the successful go-live of DIP 2.0 and the use of cloud-native services was reported in our MHP Success Stories. After another three years of development, it is time to look back and reflect. The DIP provides data for customer services such as condition monitoring and the pay-per-part model, and TRUMPF’s customers can automatically access the DIP via the MyTRUMPF customer portal.

To develop and operate these services, the DIP had to offer more self-services, become more efficient in providing cloud-based services, and scale into additional business areas. These developments are fundamental to ensuring TRUMPF’s adaptability, innovative strength, and market leadership in the future.

Approach and Functionality

After the go-live of DIP 2.0 and before further expansion, a phase of optimization and consolidation was essential to avoid operational rigidity. In this phase, existing data pipelines were consolidated and merged into reusable components. This enabled a separation of generic code and job configurations, reducing the complexity of all data applications. Thanks to these measures, the development team was able to complete the hypercare phase more quickly, and the Application Management Service (AMS) team was able to take over operations earlier, leading to lower operational costs.

The freed-up development capacities were utilized for TRUMPF’s further digital ambitions. The mindset was clear: A central platform must not become a bottleneck for innovation. To ensure this, requirements analyses were conducted to develop an understanding of TRUMPF’s innovation cycle. The resulting platform functionalities were collected and prioritized based on requirements.

Over time, the findings were used to define reusable generic self-services, and their provision was implemented cost-effectively through Infrastructure as Code (IaC). This accelerated adoption in additional business areas. The services were tailored for various target groups (business analysts, analytics power users, as well as data engineers and scientists) and the maturity level of the use cases and integrated into a governance framework.

Business analysts, for example, can directly access business and machine data from the DIP without additional resources and unlock new potential. These insights can be published in standard reports made available to specific user groups. The process begins with a data release request, which is reviewed by the respective data owners.

When new data sources are required, a standardized onboarding process starts, which ensures that the data is loaded reliably and quality assured at least once a day and operated by the MHP AMS team. The responsible data owner is also defined in this process to enable other user groups to benefit from the new datasets immediately.

For digital services and projects with larger teams, consisting of data engineers and data scientists, a layered DevOps concept is used.

This concept enables the provision of dedicated workspaces in the cloud, offering teams not only a higher degree of freedom but also responsibility. The direct allocation of cloud costs to projects and departments regulates resource consumption in the teams’ own interest, which is particularly effective for machine learning use cases.

Results and Outlook

With over 1,200 daily data pipeline executions from more than 20 business applications, over 2.5 million new machine files daily, and around 4,000 direct and indirect TRUMPF users on the DIP, the success and acceptance of the platform are clearly demonstrated. The shutdown of numerous legacy systems and prototypes has reduced the complexity and cost of the IT landscape and fostered cross-project exchange, while consistently ensuring high data quality, which has led to robust business decisions.

However, the question of whether the goal has already been reached must still be answered with a clear no. As part of the Global IT initiative, the regional IT departments of subsidiaries are being more closely integrated into the headquarters in Ditzingen and trained as DIP ambassadors. Through dedicated workspaces for each region, local IT staff can meet the response times expected by the business and provide local requirements on the central platform.

Additionally, it has proven beneficial to monitor the maintenance and deployment costs of individual services to plan corresponding code refactoring phases into the roadmap. This helps periodically reduce complexity and thereby operational costs, as these initially increase with each new service deployment. Continuous IT service portfolio management must be consistently pursued to integrate new IT service functionalities requested by business units into the existing service landscape.

One example of this is the integration of the High Performance Computing Center (HLRS) at the University of Stuttgart to expand the existing DIP service “Machine Learning Bundle” with more cost-effective computing power and prepare for future quantum computing applications.

Through its service-oriented and customer-centric IT service offering, TRUMPF considers itself well equipped to meet the challenges of the market and to evolve from a cost center to a profit center in the future.

strongly disagree

disagree

somewhat disagree

neutral

somewhat agree

agree

stronglyagree

The employees in my company have basic knowledge and skills necessary to...

strongly disagree

disagree

somewhat disagree

neutral

somewhat agree

agree

strongly agree

The employees in my company have basic knowledge and skills necessary to assess data quality.

Figure 30: Data competency and data quality in international comparison

Interview

Department of Cyber and Information Technology, BMVg

BMVg – Brief Profile

The Federal Ministry of Defense (BMVg) is the highest federal authority of the Federal Republic of Germany responsible for directing defense policy and overseeing the Bundeswehr. Among its core tasks are strategic planning, military leadership, and international cooperation, particularly with NATO and the EU. Key responsibilities include ensuring defense readiness, crisis management, and cybersecurity. The BMVg is also actively advancing digitalization and leveraging data-driven approaches for logistics, maintenance, as well as cyber and information protection. In doing so, it is shaping the future of national security in a digitalized world.

Colonel in the General Staff Service (Colonel GS) Dipl.-Inf. (Univ.) Frank Endler M. A. Brief Biography

Colonel (GS) Frank Endler has been the Head of Department CIT I 4 since September 2024 and is therefore responsible for strategic management of digitalization, artificial intelligence (AI), data governance, and the Bundeswehr’s digitalization platform within the Federal Ministry of Defense (BMVg). Colonel (GS) Frank Endler has over two decades of operational experience as a signal officer in the Army, including international assignments and two deployments abroad. He has held multiple ministerial positions in security policy, research & technology, innovation, multinational capability planning, and cyber/IT. In his previous role, he served as Head of Steering at the Bundeswehr Digitalization Center (ZDigBw) and was involved in capability development for the Cyber and Information Domain Service (CIR).

Participants: Colonel (GS) Frank Endler (Dept. CIT, BMVg), Government Director Dr. Maral Kashgar (BMVg), Dr. Christian Fiebig (MHP – Partner Digital Factory and Supply Chain), Julius Peters (MHP – Manager Digital Factory and Supply Chain), Bennet Becker (MHP – Consultant Digital Factory and Supply Chain)

Julius Peters: Generally speaking, how relevant do you consider data in the year 2025?

Colonel (GS) Frank Endler: With the initial release of ChatGPT in 2022 and the now widely adopted applications using generative AI, this topic is currently gaining tremendous momentum and attention. The fundamental principle applies: no AI without data. The Bundeswehr’s data strategy, which has been in place since 2021, is currently being implemented— yet with the recent boom in generative AI, what was previously a specialist topic has become visible to everyone, and the associated opportunities are clearer than ever. Generative AI also signals the start of a new era in data management.

Colonel (GS) Frank Endler (Dept. CIT, BMVg)

Julius Peters: Where do you see the biggest challenges and areas for improvement that have yet to be addressed in this context, as well as the most significant levers for change?

Colonel (GS) Frank Endler: Especially in the context of the rise of AI, the continued implementation of the Bundeswehr’s data strategy is a strategic issue that must be addressed at all leadership levels in the future. The imminent seamless integration of AI into daily operations requires a general level of data literacy among all personnel. To achieve this, we need to establish foundational structures within the Bundeswehr and provide the necessary tools. This calls for a strong strategic framework, with the data strategy serving as the foundation, which is then realized through a Bundeswehr-wide implementation strategy.

For me, establishing a value-driven data management approach is of crucial importance. To realize this value, concrete use cases and prototypes are necessary— some of which we have already developed in recent years. The challenge lies in delivering these benefits under time pressure in the current critical security environment. This requires a significant acceleration of innovation across all areas and processes. We must establish data as a key driver of innovation. Wherever quick successes and tangible benefits become visible, people are willing to acquire the necessary skills. A great example of this is generative AI-powered chatbots, which are currently being developed and deployed by many. This creates a new experiential environment for users, allowing them to recognize and directly experience the added value of high-quality data. They also come to understand and appreciate the effort required to ensure data quality. One such example is that the BMVg is currently developing its own AI application with a specialized search function to support information retrieval in staff work.

Julius Peters: As a consulting firm, we also focus on the American and Chinese markets as well as the respective industrial companies on-site. These two countries are considered pioneers in terms of innovation. How do you assess Germany in a global comparison?

Colonel (GS) Frank Endler: Both countries have recognized that a long-term innovation strategy is a key success factor. China is catching up in this regard, while the U.S. has been fostering this innovative strength since the invention of the internet. Military research has played a crucial role in the U.S.: The Defense Advanced Research Projects Agency (DARPA) has been the origin of many innovations over the past decades, from the internet and GPS to speech recognition. This illustrates that innovations originating in the military sector can also provide significant societal and economic benefits.

For this reason, we have established a comprehensive innovation landscape within the Bundeswehr. This ranges from the Cyber Agency, which focuses on longterm cybersecurity research, to research institutes at the Bundeswehr universities, such as the CODE Research Institute and the Bundeswehr Center for Digitalization and Technology Research (dtec.bw). Additionally, our defense technology agencies conduct research and technology projects, while the Cyber Innovation Hub of the Bundeswehr (CIHBw) collaborates closely with military units and startups to drive innovation. As the Bundeswehr, we have developed diverse and strong competencies in the field of innovation.

The big question is how to quickly bring this innovation into action. In this regard, we have made significant progress both in internal process design and in the strategic digitalization as part of the turning point. Now, it will be essential for us to sustain the acceleration within the Bundeswehr and further expand the digitalization platform we have created. In light of the war in Ukraine, we will need to intensify our efforts to fulfill our defense mission. Over the past three years, Ukraine has demonstrated remarkable innovation speed, which has been crucial in maintaining its stability during the war. Given the geopolitical challenges, it is of critical importance to develop this innovation speed within the Bundeswehr as well. Therefore, innovation speed must replace combat experience.

Julius Peters: The overarching collaboration between partners such as politics, the BMVg, the Bundeswehr, and representatives from the defense industry is becoming increasingly important. When considering projects like the European defense project for the development of a connected air combat system FCAS (Future Combat Air System), what specific prerequisites and challenges do you see regarding the exchange of information and the secure transfer of data between the involved countries and organizations?

Colonel (GS) Frank Endler: FCAS is a good example of a large data project in the international arena. IT infrastructure and data architecture are key topics that we must develop with our allies according to common standards. Here, the joint NATO data architecture and the development of national and allied clouds will play a decisive role. In order to conduct Multi-Domain Operations, a Multi-Domain Combat Cloud is re - quired, which covers the entire operational area and accelerates the command and effects chain through the added value of data and AI.

The key to success within NATO is the secure exchange of data. Trust in data management within the alliance is essential. Important developments in this regard in - clude new security concepts from NATO, such as Zero Trust and Data-Centric Security. Both the military and the industry have a strong interest in being able to pro - vide data securely and for specific purposes. In order to be interoperable and collaborate well with partners, it is crucial to have the ability to share data for a de - fined purpose in a limited manner. Only in this way can the level of trust be adequately represented.

This issue concerns not only the military but also in - dustry and academia. It is crucial that we focus togeth - er on solving this challenge in order to fully exploit the potential of new opportunities. The necessary resourc - es should not be viewed merely as “defense costs.” They are investments in the overall societal digital eco - nomic development—this shift in perspective is essen - tial for the development of our defense capabilities.

Julius Peters: One topic that concerns our clients is Product Lifecycle Management (PLM). If you could give us a brief insight: How is data from combat operations currently captured, and how accessible is this data, for example, to suppliers of the Bundeswehr, to continu - ously improve products and equipment—key phrase “data/information feedback loop”?

Colonel (GS) Frank Endler: The implementation of PLM is a key component of the Bundeswehr’s data strategy and an excellent example of value-oriented execution. PLM fundamentally aims to create digital continuity and a transparent flow of data and informa - tion throughout the entire lifecycle of (weapon) sys - tems, facilitating task fulfillment by all stakeholders. The effective collection, storage, and use of data ac - celerates and improves existing processes, creating ad - ditional value, such as through predictions and simula - tions that enhance the readiness of weapon systems. To this end, simulation models have been developed in a project over the past years for various systems in use, such as the Boxer, which allow for better planning and acceleration of maintenance or forecasting spare part requirements, thus improving availability. In this way, investment decisions in this area can be prepared based on data and evidence.

During the project, it became clear that the data from the Bundeswehr’s own systems alone are not suffi - cient, and additional data from the manufacturer and from the Army Maintenance Logistics (HIL) are need - ed. Therefore, we need both models for forecasting/ simulation and a data fabric layer, for example in a PLM system, to aggregate this data with other data from the Bundeswehr (e.g., SASPF ERP system) and make it usable. This initiative is currently developing dynamically, as we will have new possibilities to handle the data with AI-supported IT services, enabling us to generate new added value, such as improved material readiness for deployment.

With new (weapon) systems, the question arises whether to take the step of making digital twins for standardized use cases an integral part of procure - ment and usage for ships, aircraft, or land platforms. The key to success lies in the collaboration between the responsible parties within the organization—such as the process owner, the IT department providing the corresponding PLM IT services, and the industry. We need to carefully examine how standardized data can be exchanged. A collaborative change and configura - tion management process, taking into account cyber - security, classified information protection, intellectual property protection, as well as usage rights for data and information, can already be established during the contract design phase for the procurement of prod - ucts. We are in intensive discussions with all stakehold - ers, including the industry, to realize these and other added values in operational readiness through PLM.

Julius Peters: The next question links back to the be - ginning of our conversation: What changes in the han - dling of data or digitalization would you like to see?

Colonel (GS) Frank Endler: The first step is the wide - spread recognition that the use and added value of data, such as AI-driven or data-supported situational awareness, leadership, reconnaissance, and effects, are a conditio sine qua non for the successful com - pletion of missions in today’s crises and conflicts for the Bundeswehr. Innovation capability and skills in handling data and AI are crucial abilities that signifi - cantly contribute to survival and effectiveness on the battlefield. For all supporting processes, data is essen - tial for value creation. Therefore, data management has become a key leadership responsibility at all levels.

If we think one step further about where the Bundeswehr and industry need to continue collaborating, I would like to mention the topic of Software Defined Defense (SDD). This topic is currently gaining rapid momentum and provides an exciting framework for realizing the added value of data. Current conflicts show us that innovations are driven by software. The pace of hardware innovation cannot keep up with this and will be treated separately from now on. Therefore, SDD will become a guiding principle that will change the business model between industry and the public sector. We need to create programs instead of proj - ects, which we can continue to develop together for a long time, where the innovative power of software can be leveraged. An example of this is the Gepard self-propelled anti-aircraft gun, whose hardware from the 1970s is currently achieving a whole new sustain - able impact on the modern battlefield in protecting soldiers in Ukraine against drones, thanks to software.

The Bundeswehr and industry are successfully developing SDD together. This is crucial for the competi - tiveness of German industry in the international com - parison. If we can implement this Software Defined Defense (SDD) paradigm in the defense sector, per - haps we can apply it to many other areas as well. Un - der the common banner of Software Defined Defense, the Bundeswehr is working together with industry on ensuring Germany’s future viability.

Figure 31: Training relevance in international comparison

#

In terms of training and development opportunities in data understanding, data management, data analysis, and their practical application, the automotive industry performs 16% worse than the reference industry.

Figure 32: Training relevance in industry comparison

Another notable insight from the survey on training and development opportunities is the comparison between the automotive industry and reference industries. On average, the automotive industry performs 16 percent worse (Figure 32). Possible reasons for this gap include economic challenges, particularly in the DACH region, where significant budget cuts have reduced resources available for training programs. This stands in contrast to the industry’s strong technical expertise, which is already well-developed compared to sectors like the pharmaceutical industry. An interesting aspect is also the comparison based on company size regarding employee training and development. Mid-sized companies with 100 to 999 employees lead in this area, likely due to a combination of available resources, agility, and a strong focus on core competencies.

2.3. Role of the CIO

As part of the Industry 4.0 Barometer, the role of the Chief Information Officer (CIO) in driving Industry 4.0 within the company is also analyzed, with a particular focus on data-driven production this year. 74 percent of respondents indicate that the CIO is part of the executive management in their company (Figure 33). In an international comparison, Chinese companies again stand out as front-runners: 92 percent of participants report that a CIO is represented in the executive management or board of directors (Figure 34).

The results reveal a clear pattern: companies with a CIO in the executive management are leading in key areas of Industry 4.0 and data-driven production. Particularly noteworthy are their advances in data analysis capabilities, technologies, data culture, and their proactive data strategy. The difference in the assessment of data analytics capabilities compared to competitors is especially striking. Survey results clearly indicate that the role of the CIO is highly relevant to a company’s data competency. On average, companies with a CIO in the executive management score 32 percentage points better in meeting the fundamentals of data competency compared to those without a CIO in the executive team. These fundamentals include staff competencies, technological infrastructure, automated and AI-based decision-making, as well as data collection. Companies with a CIO in the executive management are 24 percent more likely to derive meaningful insights from their production data. In this context, the correct quality and granularity of data collection and usage are crucial. Supporting these results, participants from organizations with a CIO reported that external data can be easily integrated to improve production processes.

Success Story Next-Level Order-Monitoring @ Porsche AG

Initial Situation and Challenges

Porsche AG’s customer order management handles order planning and processing, seamlessly connecting vehicle production, sales, and aftersales operations.

Porsche AG is currently undergoing a digital transformation of its production processes, replacing legacy SAP systems with newly developed solutions on the AWS platform. This transformation is undertaken to optimize the underlying business processes and redesign these core functions. MHP has been supporting and guiding Porsche AG with comprehensive and diverse expertise—ranging from process design and architecture development to the implementation of the new systems.

Various analytics and reporting solutions are currently in use at different stages to monitor business processes. These solutions vary significantly in terms of implementation depth, scope of the respective subprocesses they cover, and their technical architecture. The existing solutions consist of different warehousing and BI systems, which are typically supplied with data from operational systems via overnight ETL procedures. As standalone solutions, they are only integrated with particular segments, making it difficult to gain comprehensive oversight of the overall process status and to ensure cross-system data consistency. In addition to these solutions, the operational order management systems offer various search functionalities that are used for analytical purposes. For special topics, ad-hoc data exports are processed further using Excel.

In recent years, increased product complexity and market dynamics have significantly reshaped these processes, leading to heightened frequency and complexity of operations. As a result, systems must enable faster response times and significantly shorter processing durations, such as those for planning cycles. These evolving conditions make it essential to replace existing analytics and reporting solutions. Successor systems must be tailored to meet these new challenges in order to optimally support the improved processes.

Approach and Functionality

MHP’s newly developed approach focuses on a holistic understanding of business processes, user groups, and the associated challenges. It was collaboratively designed by architects, business experts, and user research specialists to ensure all aspects of the project were properly addressed. The implementation was carried out in close coordination between all involved parties from both the customer and MHP, ensuring consistent alignment and proactively preventing miscommunication. As a first step, the architects defined guiding principles and architectural drivers to steer the further course of action. Additionally, an initial rough structuring was conducted, thoroughly identifying key areas of action. These were aligned with business processes and consolidate the requirements of various stakeholders and user groups into logical units. In collaboration with the customer’s domain experts, sponsors and stakeholders were designated for each area of action.

The areas of action were structured and analyzed in workshops together with the customer’s stakeholders. The workshop approach was guided by MHP’s “Double Diamond” design thinking model, enabling the abstraction of processes and user groups from existing systems. Initially, the focus is on exploring the problem space and achieving a comprehensive understanding. Only in the second phase, once a well-defined problem statement has been formulated, is a specific solution concept developed for the particular problem. The solutions generated through this process were documented as use cases, serving as key outcomes of the workshops. Each use case includes a self-contained user journey, user personas, and the corresponding functional and non-functional requirements.

Alongside this, the guiding principles were further detailed by MHP architects. To do so, the overall architectural situation at Porsche AG was analyzed more thoroughly. This analysis took into account Porsche AG’s data strategy as well as fundamental architectural decisions regarding key technologies and principles. From this analysis, architectural goals were derived, which, along with the guiding principles, formed the framework for concrete solution development. Within this defined framework, the functional and non-functional requirements from the workshops were translated into system capabilities. These capabilities were then grouped into logical building blocks and ultimately assembled into an overall architecture.

Results and Outlook

The developed architecture focuses on decoupling the calculation of key performance indicators (KPI) from the subsequent reporting. This separation allows for the integration of computed analytical data into the operational system to improve the user experience and support AI functionalities. Moreover, with event-driven calculations and data provisioning directly from the message bus of an operational system, seamless data consistency and real-time capability are ensured. This enables rapid response times despite high data complexity and accelerates planning cycles exponentially. The chosen AWS platform services allow for flexible scaling to continuously process increasing data volumes efficiently.

The calculated key performance indicators (KPIs) are treated as independent data products. They are made available to other users and reports in a decentralized manner within the framework of the Data Mesh. This allows for flexible scalability while maintaining the principle of a Single Source of Truth (SSoT).

The architecture is structured based on a building block design, defining system modules and interfaces. Each module encapsulates a specific capability, such as KPI calculation or event-driven communication with a message broker. The specification of a module includes its structure and capabilities, as well as key technologies for implementation and interfaces to other modules or external systems. For some of the modules, various configurations were developed to allow for optimal technology selection depending on the specific requirements of a given use case. For example, the business-level data store can be configured as an Amazon Redshift database for use cases with complex data models that have multiple aggregation levels. Alternatively, for simpler, flat data models, a more cost-effective configuration using Amazon Athena tables in Apache Iceberg format with storage in S3 can be used. For both cases, the interfaces to other modules are the same.

Overall, the modules form a flexible toolkit: the derived solution architectures include only the modules required for each specific use case, allowing them to be optimally tailored to the respective requirements and developed and deployed independently of one another. The implementation of the first use cases was carried out by an experienced and well-coordinated MHP project team using an agile approach, in close collaboration with the customer. The use cases were divided into verticals, which can be implemented independent of each other. Each vertical provides the customer with a directly perceivable added value. During a system test, a runtime of less than 5 seconds was demonstrated between a user action in the operational system and the display of the corresponding key performance indicators. The developed approach and building-block architecture offer excellent reusability for other areas of action and business units within Porsche AG. Furthermore, they can also be applied fully or partially in a segmented manner with other customers. The developed architecture serves as a reference architecture, demonstrating how analytics and reporting can be considered as two separate systems in the future and how they can be integrated into a fragmented, event-driven overarching system.

The presence of a CIO has a decisive impact on how data are handled within an organization. Participants from companies with a CIO confirm that cross-departmental data sharing is mandatory in their organization (34 percent). Notably, 77 percent of respondents with a CIO in the executive management agree that a culture of experimentation is encouraged. The survey highlights that a strong digital leadership role, a culture of trust, and the empowering of employees in data handling are critical factors. On average, the intensive use and strategic integration of data into business processes is 24 percentage points higher in companies with a CIO compared to those without one. The results underscore that a solid data strategy, supported by a central leadership figure like a CIO, is essential for success in data-driven production and business development. The most significant difference is observed in the development of new products and services, suggesting that companies with a dedicated data leadership role unlock innovation potential more efficiently.

In summary, the survey results clearly demonstrate that companies with a CIO in executive management are significantly better equipped to handle the challenges of data-driven production and Industry 4.0, gaining distinct advantages from it. These companies not only exhibit higher technological maturity but also a more developed data culture and a strategic approach to data management. The presence of a CIO at the executive level proves to be a critical factor in driving technological advancements, enhancing workforce data competency, and systematically leveraging data by integrating it into corporate strategies.

It also becomes evident that the influence of a CIO enables companies to overcome IT integration challenges more successfully. This observation is likely due to the extensive organizational adjustments required to effectively implement new technologies and processes. For a data-driven organization and production, IT infrastructure is an essential prerequisite for executing data strategies and, consequently, leveraging data effectively. Overall, the survey results suggest that the presence of a CIO in executive management is crucial for the long-term success and competitiveness of companies in an increasingly data-driven economy.

In 74% of the surveyed companies, the CIO is a member of the executive management or the board of directors.
Figure 33: CIO in the corporate governance: current status of respondents
Is the CIO/IT director a member of your company’s executive management or board of directors?

Figure 34: CIO in the corporate governance in regional comparison

In my company, the staff has the skills and competencies for advanced data analytics methods.

Figure 35: Influence of the CIO on Industry 4.0 and data-driven production with a focus on data analytics capabilities
In my company, production data is collected in the right quality and granularity to generate meaningful insights.

Figure 36: Influence of the CIO on Industry 4.0 and data-driven production with a focus on production data

Figure 37: Influence of the CIO on Industry 4.0 and data-driven production with a focus on data sharing
The data strategy of my company helps to respond quickly to competitors and market changes.

Figure 38: Influence of the CIO on Industry 4.0 and data-driven production with a focus on strategic data integration

For over a century, ZF, a global technology company, has been developing mobility systems for motor vehicles, public transportation, and industrial hardware. For new product development involving these technologies, it is crucial to collect measurement data in the pre-development phase. However, in the past, data was not always collected centrally, making data retrieval challenging and occasionally requiring repeated tests on software-defined vehicles (softwarecontrolled vehicles whose functions and characteristics are determined by software).

This situation was characterized by a lack of connectivity, inadequate harmonization of metadata, and insufficient standardization. As a result, measurement data from test vehicles and test benches were only made available locally and could only be used by a very limited number of users for specific validation tasks. Consequently, the true value of this data often remained untapped or was lost in a vast data space.

Moreover, no well-established link existed between simulation (keyword: Digital Twin) and real-world testing, preventing mutual use of results and hindering subsequent process optimization. Further challenges included diverse data sources, as test benches were often isolated from the internet and test drives took place in areas with poor or non-existent mobile coverage.

To avoid the storage of heterogeneous measurement data and improve the product development process, ZF created a centralized IoT-enabled measurement data platform. An IoT (Internet of Things) platform is a digital infrastructure that allows various connected devices to communicate, monitor, and control each other. This platform ensures the retention of critical measurement data in the course of the development process.

Measurement data, gathered during the test and validation phases of product development, contain valuable insights about product performance and functionality under different conditions. The systematic collection and analysis of this data can help ensure the quality and reliability of end products. Accordingly, ZF’s objective was to develop a solution that would enable the exploration and automatic analysis of measurement data.

Individual MVPs (Minimum Viable Products) were set up and gradually integrated, to connect systems such as test vehicles and test benches to the IoT platform. Each MVP incorporated a specific data source and extended core functionality. A data logger was installed with the first MVP to ensure precise data recording in test vehicles. An application was also developed to assist drivers in selecting the correct test data, which could be automatically and securely transferred to the IoT platform when a stable internet connection was available.

In the second MVP, a test bench was integrated as a data source. As it existed in a separate network with-

自 500+

out internet access, a gateway application was required that would ensure the secure transfer of measurement data to the IoT platform and allow testers to enrich the data with additional metadata before uploading. This metadata, detailing test bench configurations and settings, is essential for interpreting test results and selecting the correct measurement data for subsequent virtual simulations, such as simulation model testing.

The final MVP included functions for external integration and further processing of measurement data. An API (Application Programming Interface) enabled each data file to be searchable by filename, timestamp, measurement values within the file, and even aggregated statistics. This allows existing or newly developed analysis tools to interface with the IoT platform and analysis results to be re-integrated into the platform. Consequently, the platform not only stores raw measurement data with pre-aggregated statistics but also any additional analysis results derived from raw data. Linking analysis results and metadata with raw data facilitates search functionality, allowing users to locate information quickly and conduct further analysis based on initial findings.

Results and Outlook

The IoT platform laid the foundation for lean validation procedures by means of reusable components and actively supports the implementation of a Digital Twin in development processes. ZF has successfully established processes for virtual validation, virtual testing, and simulation through this new measurement data platform. Additional benefits illustrating the positive impact of the new IoT platform include:

1,000+ global users

connected test systems worldwide

Time Savings and Improved Efficiency

 A standardized data format and metadata management exist.

 Data is stored centrally and easily accessible.

 Automated evaluations are possible without manual effort.

 Global rollout saves time and resources.

Sustainability

 Test repetitions due to missing data are avoided thanks to centralized, mutually available test results.

Cost Reduction

 Data storage is cost-effective and audit-proof (up to 20 years audit security).

Centralization

 A global pool of harmonized and standardized data exists.

Homogeneous test data, access to a central storage location, and the simplified evaluation, comparability, and further utilization of information mean that product development at ZF greatly benefits from the new IoT measurement data platform. Employees can now reuse analysis results from the platform, which enhances collaboration and efficiency. The digitization, automation, and validation of vehicle testing saves costs in the long term and increases efficiency while reducing time and effort, significantly accelerating the market launch process.

19 percent stated in the MHP Mobility Study at the end of 2024 that AI improves efficiency and costs in production.4 Still 19 percent—I mean: ONLY 19 percent. Only one in five is definitely too little for competitiveness, for the necessary automation against the backdrop of a skilled worker shortage and priceaggressive global competition. And AI is only 10 percent “magic.” 90 percent is hard work for excellent data. To achieve this, commitment, willingness, and management attention can—no, must—increase significantly. There is no alternative to data-driven production.

Dr. Oliver Kelkar

MHP

Associated Partner

Strategic Investments & Labs

Conclusion

For this year’s Industry 4.0 Barometer, we were once again able to gather numerous survey participants, conduct interviews with experts, and compile MHP Success Stories. The results clearly demonstrate that digitalization in production and logistics continues to gain momentum despite various challenges.

A particularly notable advancement can be observed in the field of technology and IT integration, indicating a strategic prioritization of digitalization initiatives and a targeted approach to overcoming operational hurdles. Companies are increasingly recognizing the added value of innovative technologies, such as sensor technology, traceability, and digital twins. These developments not only enhance transparency and efficiency in production but also improve the agility and resilience of supply chains. However, a significant regional disparity remains: while China is emerging as a front-runner in this area, the DACH region continues to lag behind other regions in terms of implementation.

Data competency and data strategy remain key areas of action. While many companies have now established a clear data strategy, significant gaps still exist in the development of competencies and training opportunities. This highlights the importance of not only focusing on data from a technical perspective but also integrating it into organizational and cultural frameworks. The United States benefits from more advanced technological infrastructures and fewer regulatory restrictions, reinforcing its leading position in this field.

Another key finding relates to IT security. A uniform authorization concept for employees and resources are now considered a fundamental necessity for data-driven production. Companies in the DACH region and the U.K. are particularly well-positioned in this regard. However, integrating Industry 4.0 technologies into existing legacy systems remains one of the biggest challenges. Outdated IT infrastructures and a lack of strategic prioritization by management slow down progress.

Despite ongoing automation, the economic pressure on companies remains high. Efficiency improvements and cost reductions dominate strategic considerations, while innovation initiatives are often deprioritized. The risk of companies missing long-term opportunities due to an exclusive focus on short-term profitability is particularly evident in the DACH region.

From a broader perspective, it is essential to acknowledge that many companies, especially in the DACH region, face significant, and in some cases, existential challenges. These arise from global economic uncertainties, rising energy and raw material costs, persistent supply chain disruptions, and past miscalculations. As a result, the findings of this study should be interpreted within the context of these difficult conditions, as they significantly impact companies’ room for maneuver and willingness to invest. Considering individual starting points and industry-specific differences is crucial to providing a realistic picture of digital transformation and its obstacles.

In conclusion, the study highlights that global progress in implementing Industry 4.0 technologies is closely linked to the individual strategic, technological, and organizational conditions of companies. While digitalization offers significant potential, its successful implementation requires not only technological investments but also a clear vision, targeted competency development, and the willingness to drive transformative change. Companies should focus on strengthening digital competencies, modernizing existing IT infrastructures, and fully leveraging the potential of supporting technologies. These measures create a solid foundation for optimizing data-driven processes and unlocking new competitive opportunities.

Recommendations for Action

The digital transformation and data-driven production are bringing profound changes to businesses, political institutions, and society. The increasing use of automation, artificial intelligence, and interconnected systems demands a fundamental shift in traditional processes and business models. Successfully navigating these challenges and maintaining longterm competitiveness requires a holistic strategy that engages all stakeholders. The following recommendations focus on the three key players that are essential for the success of the data-driven transformation: companies, policymakers, and employees.

Companies should strategically integrate the CIO into executive management to ensure that technological and data-related decisions are optimally aligned with corporate objectives. This strengthens the strategic focus on data-driven value creation and enables faster adaptation to market changes. At the same time, companies should embed data strategies as a key driver of operational efficiency and innovation within their business processes. The CIO plays a crucial role in this by ensuring continuous improvements in data quality and making data accessible across all business units. Additionally, building modern, scalable IT infrastructures is essential, as they form the foundation for efficient data utilization and the implementation of innovative technologies such as artificial intelligence and machine learning.

Policymakers bear the responsibility of creating suitable legal and economic frameworks to drive innovation and ensure international competitiveness. Key measures include investments in expanding power and data networks, tax incentives for digital technologies, and funding programs for digital transformation initiatives. These actions can sustainably strengthen digital infrastructure and enhance Germany’s attractiveness as a business location.

Employees should actively engage on expanding their digital skills to secure their employability and support companies in their digital transformation. Participating in online courses, training programs, and targeted professional development helps to develop the necessary competencies for the future of work and meet the demands of an increasingly data-driven work environment.

In a global comparison, the DACH region lags behind the digital pioneers United States and China when it comes to implementing data-based approaches. However, the potential of data-driven production presents significant opportunities, particularly in this region and in the United Kingdom. Overcoming hurdles such as outdated IT infrastructure and fragmented data landscapes can drive critical progress in the field of Industry 4.0. Ultimately, it is primarily up to the companies themselves to leverage data-driven production to restore or secure their competitiveness—or, even better, to sustainably enhance it.

ENABLING YOU TO SHAPE A BETTER TOMORROW

About MHP

As a technology and business partner, MHP digitalizes its customers’ processes and products, guiding them through their IT transformations along the entire value chain. As a digitalization pioneer in the mobility and manufacturing sectors, MHP applies its expertise across various industries and serves as the premium partner for thought leaders on their journey toward a Better Tomorrow. Globally, MHP supports more than 300 clients, including leading corporations and innovative mid-sized companies. MHP provides both operational and strategic consulting, offering deep IT and technology expertise as well as industry-specific know-how. Operating as OneTeam, MHP has an international presence, with its headquarters in Germany and subsidiaries in the USA, the U.K., and China. For over 25 years, MHP has been shaping the future together with its clients. More than 5,000 MHP employees share a commitment to excellence and sustainable success—a drive that continues to propel MHP forward today, tomorrow, and in the future.

mhp.com/newsroom

GERMANY

CHINA

Federico Magno Group CEO federico.magno@mhp.com

Thomas Mooser CEO MHP China thomas.mooser@mhp.com

Markus Wambach Lei Yao Group COO Associated Partner markus.wambach@mhp.com Technology Consulting lei.yao@mhp.com

：

： . ： . . . . . . . C . . ? . . . ? . . . . . . . . ： . . . ： . ： .
. . .
. . ：
. . ：
. . . USA . . . . ： . . . . . . . . . . . . . . . . ： ? . . . C

Contact International

USA

Tobias Hoffmeister CEO MHP Americas tobias.hoffmeister@mhp.com

Greg Reynolds Sales Director greg.reynolds@mhp.com

Marcus Bohlemann Senior Account Executive marcus.bohlemann@mhp.com

UNITED KINGDOM

Bodo Philipp
CEO MHP UK
bodo.philipp@mhp.com

Figures

Figure 1: Distribution of participants by region 17
Figure 2: Distribution of participants by size of business 17
Figure 3: Distribution of participants by sector 18
Figure 4: Distribution of participants by department 19
Figure 5: Total Barometer comparison over the years 24
Figure 6: Technological equipment along the entire value chain 25
Figure 7: Supply chain transparency by region 26
Figure 8: Deployment of sensor technology in production, warehousing, and logistics by region 27
Figure 9: Distribution of the Digital Twin 28
Figure 10: Distribution of the Digital Twin by region 29
Figure 11: Maturity level of automation and autonomous systems 30
Figure 12: Maturity level of automation and autonomous systems by region 31
Figure 13: Maturity level of data analytics capabilities 32
Figure 14: Data analytics capabilities by region 33
Figure 15: Status of IT security by region 34
Figure 16: Strategic Industry 4.0 focus of the surveyed companies 35
Figure 17: Obstacles to the implementation of Industry 4.0 technologies 38
Figure 18: Obstacles to the implementation of Industry 4.0 technologies by region 39
Figure 19: General data strategies in companies 45
Figure 20: International comparison of general data strategy 46
Figure 21: Corporate defensive data strategy 47
Figure 22: Corporate offensive data strategy 48
Figure 23: Data usage compared to competitors 49
Figure 24: Corporate data governance 50
Figure 25: Responsibilities and decision rights for the management and use of process data 51
Figure 26: Data quality by region 56
Figure 27: Data technology as the foundation for data-driven production 57
Figure 28: Key findings of data culture 58
Figure 29: Five key areas of data competency 62
Figure 30: Data competency and data quality in international comparison 63
Figure 31: Training relevance in international comparison 68
Figure 32: Training relevance in industry comparison 69
Figure 33: CIO in the corporate governance: current status of respondents 72
Figure 34: CIO in the corporate governance in regional comparison 73
Figure 35: Influence of the CIO on Industry 4.0 and data-driven production with a
focus on data analytics capabilities 74
Figure 36: Influence of the CIO on Industry 4.0 and data-driven production with a
focus on production data 74
Figure 37: Influence of the CIO on Industry 4.0 and data-driven production with a
focus on data sharing 75
Figure 38: Influence of the CIO on Industry 4.0 and data-driven production with a
focus on strategic data integration 75

I/IMHP A PORSCHE COMPANY

LMU MAXIMILIANS- LUDWIG-UNIVERSITAT MONCHEN

The Industry 4.0 Barometer 2025 and the Executive Summary were published by: MHP Management- und IT-Beratung GmbH in cooperation with the Ludwig-Maximilians-University of Munich.

2025年工业4.0晴雨表

常见问题