As the global defense landscape continues to evolve, Norway's ability to orchestrate an ecosystem that takes advantage of its innovation capabilities will be essential in addressing both traditional and emerging security threats - startups and Small and Medium Enterprises operating in the technological trajectory plays an important role in achieving that goal. The defense landscape cannot thrive on public investments alone, as consequence, we need a deeper understanding of how to orchestrate the ecosystem. Due to the complexity of the issue at hand, a qualitative approach was taken, in form of in-depth interviews with relevant actors within the ecosystem, to get a better understanding of the current situation. The findings indicate that the Norwegian defense ecosystem could benefit from improving its ability to identify and take advantage of disruptive technologies developed outside the traditional defense boundaries and there seems to be challenges related to scalability for unconventional technologies which makes this difficult and should be investigated further, in addition to trust barriers, rigid procurement structures, and the need for better interaction between organizations within the ecosystem.

In most systems of systems ecosystems, there is a significant number of challenges in the digital information streams between organizations and within organizations. This paper gives a retrospective analysis on the current situation to identify the major bottlenecks. It limits itself to diagnosing the current situation with the purpose that future research can address a roadmap to address the current problems.

The growth of artificial intelligence and cloud services drives the need for new large data centers, which significantly raises electricity demand in the regions where companies build and operate them. This paper develops a research design to investigate how the planned 230 MW Stargate Norway AI data center in Narvik may affect electricity prices for end-consumers in the NO4 pricing area of Northern Norway. The purpose of this paper is not to present final results, but to establish a methodological approach. The study proposes a mixed-methods approach that combines quantitative analysis of electricity price data and system information from the Nordic power market with a household survey on expectations, price concerns, and potential changes in electricity use. Scenariobased calculations explore how adding a large new baseload could affect average price, peak-season prices, and short-term volatility. The main goal is to understand how such a data center might reshape regional electricity systems and household electricity costs in the context of renewable energy.

ABSTRACT The industry aims to adopt data‐driven methods to improve system availability and reliability. A critical challenge in this process is achieving data sensemaking for practitioners, including maintenance personnel and decision‐makers, product developers, and consultants. This article replicates a data framework that combines conceptual modeling and data analysis to achieve data sensemaking. The main author previously developed and exemplified the framework through a case study of an automated parking system in a small‐and‐medium‐sized enterprise. This study implemented the framework in a more complex context: the Norwegian maintenance Metro public transportation department within a large enterprise, using Oslo Metro. Applying the framework assisted the maintenance department in closing the internal maintenance feedback loop. In this context, we collected and analyzed exploratively internal failure data and external weather data spanning 6 years. In parallel, we developed several conceptual models co‐created with various stakeholders. These models facilitated a shared understanding of the maintenance processes and guided exploratory data analysis, with data analysis, in turn, supporting conceptual modeling. This study implemented and evaluated the framework using on‐site and participant observations, interviews, subject matter experts’ feedback, and co‐creation sessions. Findings from the observations emphasize the complementary nature of conceptual modeling and data analysis in achieving data sensemaking, leading to a deeper understanding of Oslo Metro behavior. This understanding aids in improving maintenance processes and their effectiveness, enhancing Oslo Metro availability and reliability. Despite the framework implementation is context‐dependent, findings indicate its effectiveness, motivating practitioners to implement it in other contexts.

Engineering complex systems requires integrating multiple perspectives to create a cohesive whole without overlooking critical elements. In the early stages of concept development, systems engineers often work independently, with limited time and minimal stakeholder input. This constraint increases the likelihood of missing essential system components, leading to models that lack completeness and detail due to cognitive limitations. This work presents an approach for traversing a knowledge graph to extract semantically related concepts from words used in diagrams. The graph search strategy is based on exploration and refinement activities as defined in the systems lifecycle management standard, ISO/IEC/IEEE 24748-1:2024. The weighted concepts are provided as semantic cues to aid word retrieval, optimizing the use of working memory capacity. The proposed method is implemented as a plugin for the diagramming tool Draw.io, utilizing the semantic network, ConceptNet. The plugin dynamically extracts related words and their relationship strengths, visualizing them as word clouds to enhance conceptual modeling. The approach was evaluated using 25 Systemigrams from the literature for measuring its ability to highlight critical features and reduce ambiguity in system representation. Results indicate that the approach helps identify 24.15% more critical features and provides detailed elaboration on 15.5% of them, aligning with key conclusions drawn from the Systemigrams. Additionally, findings suggest that the tool’s effectiveness depends on its ability to provide domain-specific vocabulary, particularly in scenarios requiring scientific and engineering comprehension.

Informal soft system methodologies hold a significant role in developing complex systems. They bridge system knowledge and sensemaking among heterogeneous stakeholders. This article investigates the application of conceptual models to support such communication and understanding among transdisciplinary stakeholders, ensuring the translation of customer requirements and needs into suitable engineered systems. This article presents a case study incorporating observations, interviews, and a review of conceptual models utilized by an aerospace and defense case company for the development of future Manned–Unmanned Systems. It explores how practitioners employ conceptual modeling to support the Human Systems Integration (HSI) aspects of technological, organizational, and human elements of Manned–Unmanned Teaming (MUM-T) systems. The results indicate that practitioners utilize a mix of informal and formal types of conceptual models when developing Human Systems Integration aspects of the system. Formal models, such as sequence diagrams, requirement overviews, and functional flow models, are applied when addressing technology-focused aspects. Organization-centered modeling leverages representations like stakeholder maps and swimlane diagrams, while people-centered aspects rely more on informal techniques such as storytelling and user personas. The findings suggest a potential underestimation by practitioners of the value of quantification in conceptual modeling for Manned–Unmanned Systems development. This study highlights the important role that conceptual modeling methods play, particularly focusing on the informal aspects. These methods are instrumental in enhancing effective communication and understanding among transdisciplinary stakeholders. Furthermore, they facilitate mutual understanding, which is essential for fostering collaboration and shared vision in the development of complex systems. This facilitates deeper insights and reasoning into HSI for MUM-T applications.

This study explores the effects of ChatGPT on higher education in systems engineering. It focuses on how large language models influenced student learning and academic honesty before and after the introduction of ChatGPT 3,5. Comprehensive research is limited in the literature. This research uses surveys, experiments, and case studies to understand the role of AI in the Systems-Engineering Master's course at the University. . Most students used available AI tools in their homework, which helped improve the grades of their semester papers. The use of language models brings issues like plagiarism, the need for critical thinking, and low effort to write the term paper. This paper emphasizes the need for clear guidelines to ensure responsible use of AI and support ongoing skill development. The new guidelines can assist students and teachers in their learning and teaching processes, promoting ethical usage of AI and efficiency in their professional undertakings.

This study examines how a Concept of Operations model integrated into a prefabrication process addresses the challenges of project cost and delivery time in plumbing operations from a technical contractor's perspective. First, we analyzed the process flow to identify the pain points faced in the plumbing process and developed an As-Is Concept of operation model. We identified the factors affecting prefabrication of cost, labor, time, and logistics as provided by state-of-the-art. Second, we mapped out the workflow and proposed an improved model that addresses the challenges, with findings showing improvements in both time and cost savings for projects. Finally, we supported the findings with a cost model and evaluated the proposed Concept of the operations model with industry experts. These experts foresee the proposed model as a good recommendation for rethinking the process and setting up a streamlined prefabrication line. With the proposed model, we made an estimation for a selected building site, which revealed time saving of 1911 hours translating to 573 KNOK. To verify our results, we suggest additional testing before proceeding with full-scale implementation on a construction project.

This paper investigated the effect of automation processes in an industrial company engineering complex cyber-physical systems. The authors used an industry-as-laboratory approach as the research method, exploring an ongoing development project. The automation efforts focused on four areas: (1) test setup, (2) test execution, (3) test result analysis, and (4) documentation. All four areas showed promising results on increased effectiveness and/or efficiency. In particular, the automation of test result analysis will help the industrial company, KONGSBERG, reduce their main bottleneck in the test process, as well as reduce the risk of costly project delays. An automated system integration test process, facilitating iterative regression testing, will leverage the efficiency of the verification test process.

Industrial companies developing complex systems have face challenges with undesired unforeseen system behavior emerging in late development stages or during the system’s operational use. This paper proposes a systematic approach from a Systems Engineering perspective to overcome these challenges. Combining Design of Experiments with regression analysis while conveying a beneficial human vs machine task balance enables us to shift the focus from individual requirements to the overall system design for system testing without overwhelming efforts. We aim to keep the specific performance stated through requirements while ensuring a minimum performance throughout the parameter space. Actively using measurements during the development enables monitoring of the system performance throughout the parameter space facilitating detection and subsequent elimination or reduction of inherent detrimental emergent behavior. The proposed procedure also gives a solid rationale for the case company in what to test and not.

This study uses systems thinking as a duplicated research methodology to define and validate a case study early. This case study is a part of a complex sociotechnical research project. We use a systemigram to visualize the case study, including its different aspects, also called embedded units of analysis. This visualization aids in sharing, understanding, and stimulating discussion, explanation, and communication among heterogeneous stakeholders from industry and academia. We support the systemigram as a conceptual model with other systems thinking tools, including a context diagram, and customers, actors, transformation, worldview, owner, and environment (CATWOE) analysis. In addition, we applied other tools, such as workflow analysis and stakeholder analysis. We found that using systems thinking and its tools, mainly systemigram, aids researchers in well-defining, understanding, validating, and communicating the case study, its context, its aspects, its goals, and its relations among the heterogeneous stakeholders.

This paper focuses on the design, implementation, and assessment of the visual Concept of Operations (ConOps) as an informal visualization technique employed for early solution validation in Small and Medium-sized Enterprises (SMEs). SMEs face significant challenges in early solution validation due to the complex nature of modern systems and the constantly changing market demands. These challenges may be further intensified by immature leadership and ineffective communication within the organization. By applying an industry-as-laboratory approach in an SME industry case, this study aims to reduce the negative impacts of miscommunication between internal and external stakeholders and contribute to needs elicitation and system validation process. The results show that visual ConOps can effectively support the need elicitation process, which is crucial for early validation, however, it may not independently serve as a comprehensive communication tool between the developer team and stakeholders. It is essential to supplement visual ConOps with complementary tools to effectively convey stakeholder input to the developer team.

This paper presents a comprehensive research on the application of the Design of Experiments approach to explore engineered complex systems to reveal potential detrimental weak emergent behavior. The proposed methodology utilizes orthogonal arrays in combination with regression analysis to systematically explore the parameter space of a specific system function. By screening and investigating system boundaries and potential pain points, this research introduces a novel use of orthogonal arrays to effectively detect and map areas within a system's parameter space that does not comply with defined functional acceptance criteria. The findings demonstrate that this approach enables a systematic exploration of engineered complex systems to reveal inherent detrimental weak emergent behavior, thereby enhancing test coverage, expanding system knowledge, and facilitating mitigation efforts.

In this reflective paper, the authors present how systems thinking approaches could mitigate the broad challenges related to the decision-making process within the climate crisis we observe a few major roles: experts, who analyze and propose plans and actions; decision-makers, for instance, politicians with power; and implementers, who carry out the actions. How do these roles relate and interact? The paper concludes that there is a need for transdisciplinary competence. Systems thinking at societal level, which inherently is a rather complex systems of systems, is proposed to fulfill this need. Research is required to address this need for transdisciplinary competence.

This chapter analyses existing literature to identify an integration strategy suitable for a Norwegian defense contractor. Various types of unknowns cause uncertainties in the system design. These uncertainties manifest as problems discovered during later project phases. To mitigate such uncertainties, a criterion-driven integration strategy is suggested. Adding to this strategy, we recommend also identifying test-to-design areas. By doing so, uncertainties not directly captured by the chosen criterion may also be captured. Lastly, a research design with three iterations is recommended to validate the proposed integration strategy. This research shall be executed in Spring 2023, and the findings shall be published later.

The climate crisis threatens the sustainable development of our planet. Mitigating the complexity of the sustainable challenge needs a holistic and systematic perspective. Systems solutions, such as systems thinking and systems engineering, can help to mitigate such challenges. Systems engineering in particular has to assist in transdisciplinary development and cooperation. Methods, tools, and methodologies in systems engineering can be key enablers to align the present world condition towards sustainable trajectories. To align with the sustainable transition, industrial organizations need to integrate sustainability at their core: the system’s development. Realizing socio-technical systems that are sustainable is not a triviality. Based on industry interviews and a literature study, this article discusses these challenges and presents how systems thinking and systems engineering disciplines may support industries to mitigate the same. To realize sustainable systems this work suggests i) identifying sustainability as a quality of the system; ii) collecting environmentally sustainable (big) data; and iii) establishing a collaborative environment among stakeholders where to discuss challenges related to the system’s lifecycle.

This work presents how to automate emission accounting and analysis in the waste management industry. The methodology adopted is based on the combined use of Internet of Things (IoT) technology and a Systems Engineering approach. The presented methodology has been tested in an industrial case. In the case, there were multiple systems available to collect environmental data. However, the accessibility and the interpretability of this environmental data were observed as a challenge. After gathering the data in a centralized database, the automation of the Green House Gasses (GHG) emission management and accounting was performed. Findings show that the operational emissions of the industry partner mainly occur from energy and fuel consumption. By measuring and categorizing energy usage, the industry partner identified several potential improvements for reducing emissions. Lowering energy usage can consequently decrease the associated carbon footprint. Finally, the authors suggest some useful insights for companies with the aim of improving the effectiveness and efficiency of industrial GHG emissions accounting.

Rising levels of risk as cyber-attackers look to exploit system vulnerabilities threatens the Air Traffic Control industry. Attacks on Air Navigation Service Providers' communications systems may lead to airspace closure and even cause safety issues. This paper presents a novel Model-Based Systems Engineering method that enables systems engineers, in collaboration with system security and software engineers, to perform threat-modeling analysis of cyber-physical systems early in the system development process and incorporate mitigation strategies into the system design. The proposed model-based method covers few security concepts, including misuse cases, system assets, threats, risks, vulnerabilities, and security control identification. The study found that the proposed method is suitable for conducting security analysis for complex cyber-physical systems early in the system development process.

The complexity of delivering business value is increasing technically and socially. The increasing complexity triggers the need for an increase in systems competence in several roles within the technical domain. One of the core disciplines to focus on this competence is systems engineering, which gets increasing attention within the Dutch ecosystem to enhance individuals and organizations further in this competence. The challenge is a shortage of systems engineers and teachers in systems engineering. This study proposes a layered and integrated education offering with courses for depth and domain skills, multi-day programs with systems mindset and leadership capabilities, and tracks to broaden the knowledge to a broad variety of stakeholders. In addition, university colleges, universities, and other education providers have to cooperate in delivering cohesive education to all levels, e.g., bachelor, master, PhD, and lifelong learning.

This case study examines the effectiveness and industry relevance of a collaborative systems engineering master’s program in Kongsberg, Norway. Through close collaboration with industry partners, students gain practical experience and tackle real engineering challenges. The authors used statistical data, meeting notes, and an alumni survey to assess the program’s impact. The results indicate a high success rate of 87%, with alumni holding desirable positions in various engineering disciplines. The alumni expressed satisfaction with flexibility and teacher quality but desired more focus on leadership and soft skills. Strategic inputs highlight digitalization, sustainability, security, and progress in technology as critical topics for the industry, shaping the program’s evolution for continued relevance.