Value creation is mainly generated through projects. Project work extends beyond the boundaries of individual companies, and cross-company and cross-border networks are formed for this purpose. This poses major challenges in terms of cooperation and the exchange of information and data. SMEs in particular face additional technical and resource-related challenges. This paper presents a concept for collaborative data spaces to support project work in European networks. This concept is developed from a process-, human-, and technology-oriented perspective in order to enable a comprehensive socio-technical solution.

The medical technology (MedTech) sector is characterized by the primacy of patient safety, the regulatory frameworks designed to ensure it, and the technological innovation that must navigate both. Prior research addresses innovation management, data-driven methods, artificial intelligence, and regulatory processes in MedTech largely as separate streams, providing limited insight into how data are systematically used to support innovation management decisions across the product lifecycle. This paper presents a systematic literature review (SLR) of 51 peer-reviewed publications published between 2012 and 2026, identified through a structured search in Scopus, to examine how data are employed to inform innovation management decisions in medical technology. The review synthesizes the literature along three analytical dimensions: the data types and analytical methods employed in MedTech innovation, the mechanisms through which regulatory frameworks shape data requirements and decision points, and the frameworks proposed to integrate data use with innovation management. The findings reveal a systematic asymmetry in the literature: analytically advanced approaches from general industry lack regulatory embeddedness, while MedTech-specific research remains analytically underdeveloped. To address this structural disconnect, a conceptual model is proposed that positions data as a regulation-embedded decision resource spanning from early ideation through post-market surveillance. A critical assessment reveals that the primary impediments to data-driven MedTech innovation reside not at the analytical but at the organizational level, specifically in data governance, cross-functional coordination, and the systematic reuse of regulatory data for innovation purposes.

The European Health Data Space (EHDS) introduces new obligations for the primary use of electronic health data, including citizen access, data portability, restriction of access, and transparency of data usage. While interoperability standards and patient access solutions provide essential building blocks, there is a lack of reference architectures that systematically translate validated business scenarios and user-centred requirements into architectural design. This paper presents a requirements-driven reference architecture for health data portability. The architecture is derived through a structured methodology combining Business Use Cases, representative personas, and user stories, consolidated into requirements, and mapped to architectural responsibilities through explicit traceability. The resulting reference architecture is a logical, technology-agnostic model that addresses core EHDS obligations by design. The approach was validated across 8 EU Adoption Sites demonstrating how structured co-creation artefacts can be effectively used to ground architectural design in validated user and regulatory needs, contributing a reusable reference model for EHDS-compliant health data portability.

Engineering organizations increasingly struggle with inconsistencies in product development processes, particularly as digital transformation introduces heterogeneous tool landscapes, fragmented data flows and complex interdisciplinary collaboration. Existing approaches primarily focus on documenting inconsistencies but offer limited guidance on how to systematically derive and implement effective improvement measures. This work proposes a reference procedure that integrates structured inconsistency documentation with a method-based problem solving framework to support organizations in developing inconsistency resilient processes. Building on an established inconsistency description template and the SPALTEN methodology, the procedure provides a transparent sequence of steps for diagnosing inconsistency mechanisms, identifying potential measures and prioritizing them based on organizational needs.

The procedure was exploratively applied in a six month case study within an aerospace company during an ongoing PDM integration initiative. In total, thirty seven inconsistency situations were documented, clustered into recurring patterns and evaluated to derive eleven improvement measures. The application demonstrated that the procedure strengthens organizational clarity, supports systematic decision making and reveals improvement opportunities in communication, data handling and tool integration. While only the first four steps could be validated due to project constraints, the results indicate that structured inconsistency management can serve as an effective enabler for digital transformation. The study concludes with a critical reflection of methodological limitations and outlines future research needs including complete validation, deeper diagnostic models and integrated economic assessment.