ABSTRACT Successful Model‐Based Systems Engineering (MBSE) includes designing the system architecture and its traceable integration into the development process in order to align system realization. To this end, we propose a framework for reaching end‐to‐end traceability with a novel degree of automation powered by a cloud application. We explain the framework using the example of a testbench robot and outline its transferability to other systems.
Human-AI collaboration is a defining characteristic of AI-augmented systems and increasingly critical for organizational performance. However, small and medium-sized enterprises (SMEs) lack practical, validated methods to assess how effectively humans and artificial intelligence (AI) function together in their organization and to guide improvements in collaborative maturity. This paper extends and empirically validates the Human-AI Collaboration Maturity Model (HAIC-MM) as a systems engineering assessment framework designed to reflect the operational realities and resource constraints of SMEs. The study introduces a quantitative scoring methodology that incorporates novel balance-based scoring and dependency-aware capability progression to capture interdependencies between human and AI subsystems and their influence on system-level performance and behavior. The framework is operationalized through a web-based platform and evaluated through deployment with ten SME practitioners across diverse industries. Results indicate high practitioner satisfaction with assessment usability, strong alignment between outputs and organizational realities, and clear practical value of the generated reports. The novel balance-based scoring approach effectively revealed misalignments between human and AI capabilities that conventional averaging methods obscure, while dependency logic prevented artificially inflated maturity classifications. Overall, HAIC-MM is shown to be a usable, interpretable, and practically valuable tool for assessing and strengthening human-AI collaboration in SMEs. By empirically validating the model and introducing a novel structured scoring approach, this work advances systems engineering methods for evaluating and managing human-AI collaboration, supporting system integration and lifecycle monitoring of AI-augmented systems.
Evolving objectives and requirements, risk management processes, and external disruptions frequently drive change in engineered systems. Due to these pressures, system architects must anticipate and manage change throughout a system's lifecycle. Yet, managing change is challenging due to the risk of change propagation. Many existing change propagation analysis (CPA) methods provide insight into where change may travel but provide limited understanding of why change propagates. CPA methods also help identify which design elements should be "flexible," yet the meaning of flexibility at the element-level is rarely clarified. We address this gap by defining flexibility of system elements and quantitatively measuring them as local, context-dependent properties. Element flexibility provides designers with insight into the feasibility of modifying system elements, the impact of modifications on the ability to make future changes, and pathways for restoring feasibility after an infeasible modification is made. A high-powered rocket design case is introduced and leveraged to illustrate how tracking element flexibility could address these areas of inquiry in practice. The ability to quantify element flexibility also provides a foundation for future changeability research, serving as a structured approach for determining the specific modifications that propagate change.
This study presents a multi-criteria decision framework for quantitatively evaluating and enhancing the utilization efficiency of large-scale instruments (LSIs) within academic institutions. Facing the complex socio-technical challenge of balancing multi-stakeholder needs, including researchers, facility managers, and institutional administrators, we developed an integrated evaluation model based on the fuzzy Borda method. The model systematically prioritizes four key system performance indicators: Total operating hours, Basic management capacity, Testing revenue, and Research output. Deployed in a real-world case at Shanghai University involving 350 LSIs, indicators of Testing revenue and Research output show high sensitivity to Internet of Things (IoT) implementation in the presence of remote management and improved user access from the IoT-based technology. This further leads to non-uniform efficiency distributions under both part-time and full-time management regimes. Therefore, this framework was used to assess the impact of a pervasive IoT intervention. A significant 15.00% average improvement in overall system efficiency was achieved through implemented IoT. Monte Carlo simulation subsequently supports its robustness (between 14.87% and 14.98%). This work contributes a transferable decision-support methodology for the operational performance evaluation of complex shared infrastructure, highlighting how the core challenges in system sustainment and optimization could be addressed via systematic evaluation and digital integration. By separating the measurement methodology contribution (a fuzzy Borda-based evaluation framework) from the empirical IoT impact assessment, the study provides both a general-purpose evaluation tool and concrete evidence of IoT's efficacy.Significance and Practitioner Points This study contributes a validated multi-criteria decision framework by integrating the fuzzy Borda method with Monte Carlo-based robustness analysis, capable of quantifying the utilization efficiency of shared research infrastructure. The framework systematically translates multi-stakeholder preferences into weighted performance metrics, covering encompassing operational throughput, financial return, research output, and management quality; it also addresses inherent uncertainty and subjectivity in expert judgments. Empirical findings reveal that IoT-driven efficiency gains are significantly moderated by underlying management regimes (full-time versus part-time staffing), offering new insights into the nature of technology-enabled performance improvement in socio-technical systems. For practitioners, the framework offers a transparent and actionable tool to assess high-value shared assets. It converts initially ambiguous stakeholder priorities into clearly defined performance indicators, providing a solid basis for investment decisions and policy development. The Shanghai University case study illustrates how IoT deployment, including automated usage logging, QR-code access, and dual-sensor validation, can reduce administrative workload and improve instrument accessibility, thereby improving the utilization efficiency of shared research infrastructure. The framework can be adapted to various institutional settings, regardless of whether they operate under full-time or part-time management models, and allows stakeholder preferences to be updated periodically as institutional priorities shift over time.
The United States Department of War and industry are rapidly adopting digital engineering and Model-Based Systems Engineering to facilitate system design and acquisition using languages such as the Systems Modeling Language (SysML). These tools support the design of increasingly complex systems as they maintain system requirements, analysis, and system specification within a common toolset. The models provide a single source of truth to be shared across the design team, ideally improving knowledge sharing. Unfortunately, representation of human interaction with these tools has been limited. The current research defined a SysML extension and modeling method to support critical task analysis for user interface design of safety-critical systems. This language extension and method capture the required elements for critical task analysis as required in United States Department of War documentation, include associated information elements for each task, and provide a method to simulate the performance of these critical tasks to estimate the criticality of each information element to the user within a specific mission. The model provides a method to understand the information requirements during user interface design, as well as to include human task modeling to support simulation during mission engineering.
