The study investigated how industrial systems can evolve autonomously within the context of Industry 4.0, focusing on the role of systems engineering in enabling such adaptability. Researchers first identified the research problem through a systematic literature review and a pre‑study that informed the development of a semi‑structured interview guide. The guide covered megatrends, future market performance, Industry 4.0, production system development, systems engineering, verification and validation, business systems engineering, continuous engineering, and technology transfer. Each interview lasted 90 minutes, involved at least three participants, and was recorded and transcribed. The data were coded and analyzed following Mayring’s qualitative content analysis, with iterative refinement of the code tree and validation through discussions with additional experts.

The technical findings highlight several key elements for designing autonomously adaptable Industry 4.0 systems. First, the study proposes a definition of such systems that emphasizes self‑modification capabilities, resilience to changing production demands, and integration of real‑time data analytics. Second, it identifies the critical engineering aspects that must be addressed: modular hardware design, flexible software architectures, robust communication protocols, and advanced simulation tools for rapid prototyping. Third, the research uncovers best‑practice examples from leading companies. For instance, a smart factory in tool manufacturing demonstrates how modular tooling and predictive maintenance can reduce downtime by up to 30 %. A Porsche production line illustrates the use of digital twins to optimize workflow and achieve a 15 % increase in throughput. Siemens’ matrix production model showcases how cross‑functional teams can coordinate complex product families, while the Data Factory NRW initiative demonstrates large‑scale data integration across multiple sites. Although the study does not report specific performance metrics for all cases, it consistently notes improvements in flexibility, resource utilization, and time‑to‑market.

The analysis also maps the influence of industry standards, guidelines, and norms on system design. Participants emphasized the importance of aligning with ISO 26262 for functional safety, IEC 61508 for functional safety of industrial automation, and the IEC 62443 series for cybersecurity. The research identifies key success factors such as early stakeholder involvement, iterative testing, and a culture that supports continuous learning. Future trends highlighted include the convergence of artificial intelligence with cyber‑physical systems, the rise of edge computing for real‑time decision making, and the increasing role of digital twins in end‑to‑end production management.

Collaboration in the project involved 18 experts from a diverse set of organizations across Germany and abroad. The partners represented tool manufacturing, medical technology, automotive, agricultural industry, food technology, small and medium enterprises, industry associations, and research institutes. Roles ranged from senior engineers and process specialists to chief technology officers and research directors. The Fraunhofer Institute for Industrial Engineering (IEM) provided the academic foundation and facilitated the integration of research findings with industry practice. The project’s timeline encompassed a pre‑study, interview phase, internal evaluation, and dissemination of results to business, policy, and scientific audiences. While the funding source is not explicitly stated in the report, the scope and institutional involvement suggest support from German research agencies focused on advancing Industry 4.0 and systems engineering.