technical landscape assessment

This work proposes a systematic approach to derive task effectiveness requirements in the absence of explicit user needs. The method deconstructs task intent into context, functionality, constraints, critical dimensions, performance attributes, and architectural solutions, and introduces a task complexity factor to quantify the impact of external challenges and technology maturity. By integrating Best-Worst Scaling, it prioritizes critical dimensions based on stakeholder judgments. Through task decomposition modeling and quantitative complexity analysis, the framework supports integration with UAF/SysML artifacts and establishes a traceable mechanism for generating Tier 1 and Tier 2 requirements. The approach is validated using a close air support mission case study, effectively addressing a critical gap in requirements engineering when clear initial inputs are unavailable.

To address interdisciplinary interoperability, variant configuration governance, end-to-end traceability, and cross-organizational collaboration challenges arising from the networked evolution of Systems of Systems (SoS), this paper proposes a lifecycle management framework for Network-Centric Development (NCD). Methodologically, it grounds the framework in Model-Based Systems Engineering (MBSE) semantics and integrates Product Lifecycle Management (PLM) governance, CAD-CAE model synchronization, and closed-loop digital thread/digital twin capabilities. Its core contributions are four foundational principles: (1) reference architecture with a unified data model; (2) end-to-end configuration sovereignty; (3) review-driven model gating; and (4) quantifiable value contribution assessment. Empirical validation across transportation, healthcare, and public-sector domains demonstrates significant improvements in change robustness and model reuse rate, reduced delivery cycles, and enhanced support for sustainability-oriented decision-making.

This study addresses the persistent challenge of translating European academic research into industrial impact, particularly in light of Industry 5.0’s demands for technical depth, sustainability, and human-centric design—requirements inadequately met by traditional doctoral training. To bridge this gap, the project proposes a dual-layer competence framework guided by four design principles: modularity, practical relevance, robust mentorship, and cross-domain applicability. Through expert interviews, co-design workshops, and a multi-method analytical framework, the approach systematically integrates academic rigor with industrial needs, yielding a scalable and modular developmental pathway for early-career researchers. This model effectively narrows the translational divide between scholarly output and real-world industrial application, offering an innovative paradigm for cultivating research talent aligned with the ethos and exigencies of Industry 5.0.

This work addresses the challenge of quantifying the academic impact of commercial engineering software such as Ansys Granta, which is hindered by inconsistent citation practices and rapidly growing publication volumes. We propose the first reproducible, semi-automated framework that integrates DOI and citation parsing, expert annotation, and a relational database (Ansys Granta MI Enterprise) to transform heterogeneous usage evidence into a structured knowledge base. As of September 2025, the framework has compiled a multi-source literature repository comprising over 1,100 manually verified records, enabling rapid retrieval, systematic review reproduction, and technology landscape scanning. The resulting knowledge base reveals dominant application domains, key contributing institutions, and integration patterns within CAD/CAE/FEM environments, thereby facilitating systematic tracking and analysis of the long-term technical influence of commercial engineering software.

Existing software architecture frameworks inadequately model machine learning (ML) systems, as they overlook the needs of emerging stakeholders—such as data scientists and data engineers—and lack expressive support for ML-specific characteristics, including component uncertainty, heterogeneity, and collaborative behavior. Method: Through an empirical study involving interviews and surveys with 61 domain experts from 25 organizations across 10 countries, we systematically identified ML-relevant stakeholders and their concerns for the first time. Contribution/Results: We propose novel, ML-adapted architectural viewpoints and views, extending traditional frameworks to enable unified modeling of both ML and non-ML components. This yields the *ML-Enhanced Systems Architecture Framework Extension Guide*, which has been preliminarily adopted in industry for intelligent system architecture governance. Our work bridges a critical theoretical and practical gap in stakeholder modeling and viewpoint systematization for ML system architecture design.

This study addresses the persistent challenges faced by User Experience Research (UXR) teams—namely, stakeholder bias, reactive engagement, and fragmented insights—that hinder their ability to exert strategic influence. To overcome these limitations, the authors innovatively integrate structured strategic thinking into UXR function development, proposing an organizational maturity model grounded in a UXR Point-of-View (POV) framework. Complementing this model is a practical playbook that combines “offensive” and “defensive” strategies to guide implementation. This integrated approach systematically enables UXR teams to transition from tactical execution to strategic impact, significantly enhancing their capacity to forge strategic partnerships, generate actionable insights, and contribute meaningfully to long-term corporate strategy formulation.

This study addresses the challenge of transforming stakeholder requirements into product requirements in software-driven automotive systems. Leveraging a dataset of 8,082 stakeholder requirements and 5,870 product requirements provided by Infineon, the research employs a hybrid methodology integrating structural statistics, decision modeling, traceability mining, textual analysis, and hardware-software linkage to systematically analyze the requirement refinement process. It reveals, for the first time, that requirement complexity primarily stems from ambiguous architectural scope and missing contextual information rather than linguistic redundancy. The work establishes a classification framework for mapping stakeholder to product requirements, identifies systematic differences across abstraction levels, and proposes key improvements in requirement validation, deviation management, and contextual tooling to support efficient and reusable automotive development.

This study addresses the urgent need for multinational enterprises to align digital efficiency with environmental responsibility amid concurrent green and digital transitions. Integrating Technology Roadmapping (TRM) with the ITU ICT Innovation Ecosystem Toolkit, and complemented by bibliometric analysis and a stakeholder canvas, the authors develop a sociotechnical framework tailored to dual transformation. They propose a novel “sustainable intelligence” paradigm, positioning Global Business Services (GBS) as an operational airlock that bridges macro-level policy pressures and micro-level AI-native workflows. The research further highlights the potential of “intermediate power” hubs—such as Poland, Portugal, and Malaysia—to offer a “third way” within global value chains. The resulting data-driven design approach advances practical pathways for Industry 5.0 in a multipolar digital economy, facilitating coordinated flows of talent and supply chains.