Visual Knowledge Management Tools (VKMTs) have gained attention for their ability to support knowledge capture, organization, and communication in fields such as software engineering, process design, and interdisciplinary collaboration. Despite their theoretical advantages, adoption across academia and industry remains inconsistent. This paper explores the barriers hindering VKMT adoption through a systematic literature review of 38 peer-reviewed studies published between 2015 and 2024. Key challenges were identified and thematically grouped into five categories: technological, organizational, human, economic, and cultural. The findings reveal that while many barriers are common to general technology adoption, others—such as visual complexity, lack of interoperability, and visual literacy gaps—are more specific to VKMTs. To support further research and practice, we provide a descriptive summary of the literature base, outline the methodological approach, and offer a structured framework of adoption barriers. Practical recommendations are offered for stakeholders seeking to implement VKMTs, along with suggestions for future empirical work to bridge the gap between potential and application.

Although the strategic importance of identifying, understanding and mitigating risks are known, many organisations fail to thoroughly put it in practice. However, living in a vulnerable, uncertain, complex and ambiguous world where many risks cannot be forecasted is no valid excuse to ignore risk management altogether. A McKinsey study found that many organizations agree as senior executives aim to adopt pro-active risk management. To make that possible we propose a multi-perspective approach to consulting that addresses why and where to look, what to analyze, and how to analyze it and derives suitable mitigation strategies. We introduce the multi-perspective consulting in the context of analyzing sustainability risks. The approach guides users from trend to risk identification, then towards derived (sustainability) goals and mitigation strategies and their implementation. We propose an according IT-tool support for this process, in order to provide consultants with additional features to better guide, consult and customize the approach to their clients' needs. The smart process design in the DIME low-code modelling development environment is envisioned to enable consultants to define, design and customize the process, the (multi-) perspectives of interest and the methods of use with their clients.

Abstract With the heterogeneity of the industry 4.0 world, and more generally of the Cyberphysical Systems realm, the quest towards a platform approach to solve the interoperability problem is front and centre to any system and system-of-systems project. Traditional approaches cover individual aspects, like data exchange formats and published interfaces. They may adhere to some standard, however they hardly cover the production of the integration layer, which is implemented as bespoke glue code that is hard to produce and even harder to maintain. Therefore, the traditional integration approach often leads to poor code quality, further increasing the time and cost and reducing the agility, and a high reliance on the individual development skills. We are instead tackling the interoperability challenge by building a model driven/low-code Digital Thread platform that 1) systematizes the integration methodology, 2) provides methods and techniques for the individual integrations based on a layered Domain Specific Languages (DSL) approach, 3) through the DSLs it covers the integration space domain by domain, technology by technology, and is thus highly generalizable and reusable, 4) showcases a first collection of examples from the domains of robotics, IoT, data analytics, AI/ML and web applications, 5) brings cohesiveness to the aforementioned heterogeneous platform, and 6) is easier to understand and maintain, even by not specialized programmers. We showcase the power, versatility and the potential of the Digital Thread platform on four interoperability case studies: the generic extension to REST services, to robotics through the UR family of robots, to the integration of various external databases (for data integration) and to the provision of data analytics capabilities in R.