When working a project, the one thing we can be confident about, is that there will be uncertainties and unknowns. It's hard enough to plan a car journey and guarantee an arrival time. Complex or novel projects will only be more challenging. On a project, late change can impact budgets and schedules. A Systems Engineer will consider the Known‐Knowns and the Known‐Unknowns. These are things they know. But what about the Unknown‐Knowns and the Unknown‐Unknowns? How can we determine what we don't know we don't know? Are there ways to improve their detection or protect ourselves from them? This paper looks at a range of studies to uncover the sources of Unknowns. Then it shall explore ways to improve the detection of them. Based on a study of over five hundred projects, we can begin to predict a likely level of unknowns. A project planner or budget owner can then protect themselves with Reserve for a Plausible Worst‐Case outcome. Detect what you can and protect yourself from what you don't yet know.
Florida State University partnered with Taras Shevchenko National University of Kyiv (KNU) to host a five‐day online Hackathon for students aimed at rapidly developing, cross‐disciplinary, and business savvy responses to the rise of natural and human‐caused disasters in the 21st century. The multinational student team codenamed ClearSight developed an innovative system‐of‐systems for assessing urban building damage after disasters or conflicts. Leveraging systems engineering techniques such as requirements analysis, concepts of operations (CONOPs), cost estimation, and more, their solution provides rapid and accurate damage assessments to enhance recovery efforts in affected areas. This paper outlines ClearSight's system concept, hardware/software integration, and collaborative development process, highlighting how it relies on real‐world data and computer vision for precise cost estimation. Key elements include drone deployment strategies and scalable, adaptable tools for stakeholders, emphasizing the solution's potential for significant real‐world impact. Despite the tight five‐day timeline, the team successfully created a comprehensive system concept and software deliverables, demonstrating that effective collaboration and systems engineering principles can drive meaningful, rapid innovation. The paper explores the role of systems engineering in leading the team's technical achievements, collaborative dynamics, competition experience, and broader lessons learned, demonstrating how ClearSight's victory validated its approach and underscored the power of expedited, globally relevant solutions to address critical societal challenges.
Modern cyber-physical systems are becoming increasingly complex, as they integrate digital and physical components to achieve higher levels of efficiency and connectivity. This complexity introduces new vulnerabilities and operational threats to the systems that are hard to predict or deal with. Resilience Engineering (RE) has emerged as a field that focuses on the ability of systems to withstand, adapt to, or recover from disruption or unexpected events, as opposed to traditional risk management methods that rely on reducing variability and uncertainty. However, RE approaches are very diverse and lack consistency, hindering the chances for more adoption, which ultimately impacts the ability to manage complex systems efficiently. This paper explores the use of Bifurcation Analysis to identify resilience capabilities in critical infrastructures. We apply a Bifurcation Analysis for resilience framework to the IEEE 9-Bus power system to analyze its resilience and operational condition under different levels of loading stress. Eigenvalue and continuation analysis are performed to address system stability and to measure absorptive, adaptive, and recovery capabilities. The contributions are the demonstration of the application of the framework to a power system, identifying critical points, and an expansion of the framework by recommending different metrics for different resilient capabilities. The results confirm the utility of the framework to understand and enhance resiliency in critical infrastructure. Future work can be done to address the scalability to larger systems, add operations, and increase simulation complexity.
Model-Based Systems Engineering (MBSE) is being adopted and integrated in industrial settings. Through MBSE several capabilities are unlocked and made available during development, especially in the earlier stages of development where there is historical use of document-based artifacts. A notable capability enabled by a primarily model-based development is Validation and Verification (V&V). Earlier V&V is potentially a strong enabler for improving development efficiency, particularly through reducing the need for later prototyping stages. In fact, through MBSE virtual prototyping can be enabled through the early and continuous use of models. While virtual prototyping provides a significant value proposition, it is often confirmed to be challenging to adopt MBSE and integrate approaches in specific industrial contexts. In this article, we analyze existing approaches to understand what made them successful when applied in industry. From these findings we develop a set of criteria that positively influences the integration process, and reason about how to concretely achieve successful industrial integration. From the identified criteria a discussion on existing barriers is also provided, reasoning why these successful factors are missing from a majority of the disseminated MBSE work. Our final contribution is a set of recommendations that could steer research towards successful application of early V&V in industry through various incentives.
INCOSE Systems Vision 2035 outlines the need for greater understanding of both complex system dynamics and efficient systems. Energy efficiency and coordination in multi-agent systems is a key focal point of current research. Finding solutions to group movement that balance energy savings and group cohesion provide unique benefits to the system. While current approaches focus on improving the efficiency of individual swarm members (a reductionist approach), we approach this challenge through using biologically inspired design to harness emergence. Thus, the hypothesis examine is: “ If rolling swarm inspired movement is applied to ground swarms encountering obstacles, then the system's overall energy efficiency will improve because cooperation in both climbing and moving reduces the energy required to operate in these environments. ” In this article a method for moving groups of agents together as a team is presented, inspired by the behaviors of millipede swarms. The individual agents represent robots that are abstracted into uniform sliding blocks. Using simple behaviors based on individual knowledge, rather than global information sharing, we present a new efficient way to travel in groups. Data shows up to a 96% decrease in energy spent in certain scenarios for systems using the proposed MilliSwarm movement design. Additionally, this algorithm proves to be increasingly efficient at larger swarm sizes, while not requiring additional computing resources for coordination. Future work in this project will work on applying these functions to 3D environments, real-world case studies, and additional non-physical system implementation.
The urgent global need to reduce greenhouse gas emissions has intensified efforts to develop sustainable transportation solutions. This study examines the environmental impacts of two transit bus technologies, battery-electric and 100% biodiesel (B100) internal combustion engine (ICE), within Oahu, Hawaii, a region heavily dependent on imported fossil fuels with unique environmental and logistical challenges. Employing a comprehensive Life Cycle Assessment (LCA) methodology, the study evaluates both technologies across multiple environmental impact categories, including energy consumption, water use, and greenhouse gas emissions, over a vehicle's lifespan. Data was modeled using the GREET model tailored to Hawaii's regional characteristics, including local biodiesel production and the island's specific electricity grid mix. Results indicate that B100 biodiesel ICE buses currently have a lower environmental impact compared to battery-electric buses in terms of energy and water consumption, as well as greenhouse gas emissions, which are primarily driven by the high fossil fuel content in Hawaii's electricity grid and the lifecycle impact of lithium-ion batteries. This analysis suggests that integrating locally sourced B100 biodiesel into Hawaii's public transportation fleet may be a more immediate, sustainable option. In contrast, battery-electric buses could become more viable as Hawaii transitions to a renewable energy grid. The study underscores the importance of a regionalized, systemslevel approach in evaluating sustainable transportation technologies and providing strategic recommendations to inform policy decisions. However, limitations of the study include reliance on generic component data and the exclusion of infrastructure impacts, highlighting areas for refined data in future analyses.
This manuscript presents a novel application of Model-Based Systems Engineering (MBSE) to define and execute a campaign of research aimed at addressing an ambitious system design end state. Unlike traditional approaches that begin with fixed requirements, the methodology adopts an evolutionary approach to investigate the “art of the possible.” Starting with a high-level description of the end state, a series of derived research questions are defined that must be sequentially answered to achieve the final goal. These questions serve as the foundation for defining research activities, each articulated to address one or more questions while incrementally advancing understanding of the solution space. Leveraging MBSE ensures traceability between research questions, research activities, and evolving system architectures, as part of a robust framework for managing complexity. Each research activity is conducted using a specific prototype system. The architecture of that system is developed to address the scope of the research questions being asked. Initial research activities focus on simpler questions and employ straightforward architectures, establishing baseline knowledge and proof-of-concept capabilities. Subsequent research activities build on these foundations, progressively increasing in complexity as they address more challenging research questions. This incremental, evolutionary approach enables the systematic refinement of design concepts and technologies, while reducing risk and uncertainty as system design progresses towards the desired end state. The methodology departs from traditional verification and validation (V&V) practices by focusing on exploratory research rather than verifying predetermined requirements. Instead, the aim is to probe the feasibility and potential of emerging ideas and technologies. A case study demonstrates the effectiveness of this framework, highlighting how an applied MBSE approach can guide complex research campaigns with clarity and precision. The results emphasise the value of this approach in system development for ambitious use cases that may otherwise be unattainable.
Verification and Validation (V&V) are critical processes of systems engineering that ensure alignment between stakeholder needs and system realization. As industry transitions from document-centric to model-based systems engineering (MBSE), limited guidance exists for implementing V&V in a modelbased environment, particularly for early lifecycle activities. This paper presents an adaptable methodology leveraging Cameo Systems Modeler and a SysML-derived Meta-Model to perform V&V across needs, requirements, design, and system levels. By aligning with the INCOSE Needs and Requirements Manual (NRM) (INCOSE Needs and Requirements Manual, Needs, Requirements, Verification, Validation across the Lifecycle, 2024), the proposed approach is split into two major processes: needs and requirements V&V and design and system V&V. Needs and requirements V&V integrates automated and manual methods for ensuring need and requirement sets are written in compliance with the INCOSE NRM through the instantiation of structured attributes and text validation rules. Design and system V&V uses a custom profile to define system V&V attributes and activities used for verification and validation planning in order to ensure that the system design and realized system meets the intent of the requirements and needs. This modelbased framework enhances the efficiency and accuracy of both needs and requirements V&V and system and design V&V activities throughout the system development lifecycle.
A few decades ago, digital health was primarily limited to business-to-customer-facing wellness apps. The trajectory of the digital health transformation was significantly boosted and bolstered by significant growth in venture capital funding following the COVID-19 pandemic. Digital health venture capital funding mirrored that of siloed biomedical venture capital growth. However, the value of digital health transformation in healthcare delivery (a system of systems) goes beyond the focus on technology. For digital health transformation to realize its unique opportunity to address the fragmentation of healthcare delivery systems and to truly build digital health systems that address interoperability for healthcare systems and patients, there needs to be a systemic (systems thinking) approach to digital health transformation. It's important to note that digital health, as discussed here, includes Artificial Intelligence (AI). While this paper focuses on digital transformation in healthcare, it also acknowledges the significance of digital engineering within systems engineering. And yet, there has been minimal integration of systems engineering and systems thinking into digital health transformation. Through two illustrative examples, we apply systems thinking to analyze the systemic focus of digital health companies to illustrate by juxtaposition the differences between technology-focused and system-focused digital health companies. In doing so, we highlight the pressing need to integrate systems thinking and systems engineering into a fast-paced digital health transformation climate at this critical stage of growth.
This research proposes a novel methodology for the modular design of maritime radar systems for autonomous ships, with a particular focus on X-band and S-band radars. In the face of the mounting intricacy of contemporary systems, this study adopts a system engineering perspective to address the issue of product modularity. The primary objective of this initiative is to formulate efficient modular designs, with a particular emphasis on system architecture characteristics and performance requirements. The research utilizes the Capella modeling tool for system decomposition. The initial step in the research process is to identify 17 key components of radar systems. A comprehensive evaluation framework was subsequently developed, incorporating four metrics: design inner difficulty, design outer difficulty, design gap, and design conflict. The present study introduces a novel approach to systematically assess modular designs based on quantitative metrics. This approach addresses the critical gap in evaluating design trade-offs and feasibility. This approach is distinct from existing modularization methods that often lack a formal evaluation step. These metrics are designed to assess a variety of factors, including module relationships, interface complexities, performance variations, and alignment with existing radar designs. The methodology integrates random module generation with Non-dominated Sorting Genetic Algorithm III (NSGA-III) for optimization, evaluating 5,000 potential designs to identify optimal modular configurations. The resultant designs for both X-band and S-band radars feature two distinct module types: shared modules for minimizing system complexity and customizable modules for performance variation. A total of four modules are identified for X-band radars, with 22 effective variations. In contrast, S-band radars are composed of three modules and 24 variations. The research demonstrates that the modularization of transmitter components enhances customization capabilities while managing system complexity. The proposed designs demonstrate a substantial degree of congruence with existing radar architectures, thereby reducing implementation costs and alterations. The study validates the effectiveness of the methodology in creating practical modular designs that balance customization with system complexity. However, further research is needed to address software modularization and expand performance variations.
Swedish industry perceives a need for systems engineers due to the useful skillset they develop. However, teaching and providing educational paths for undergraduate and graduate students is a challenge in the Systems Engineering (SE) field. There are several structural and practical challenges to enable education in SE, related to the wide scope and emphasis on thinking as opposed to doing. Additionally, managing broad engineering programs from a faculty perspective is challenging due to the multi-disciplinary nature required in teaching. In this article we discuss the nuances of providing educational opportunities for SE based on our experiences in implementing a 5-year Integrated Master of Science in Engineering Program in Sweden. We provide our experiences and lessons learned from managing the SE program, while providing an overview of the program and its rationale. These findings can be used to strengthen future educational initiatives to support the development of SE knowledge while transferring experiences from the Swedish system to a wider audience. We discuss our findings through the lens of the future education of systems engineering, emphasizing what changes are expected to meet the needs of the future and the principles we will strive for going forward.
The digital transformation of systems engineering requires converting legacy documentation into standardized models, but manual conversion remains time-intensive and error-prone. The recent development of Large Language Models (LLMs) offers an exciting but unexplored solution. This paper conducts a mathematical analysis to show that LLMs can scale in a trivially parallelizable manner with O(n^2) time and cost complexity, proving that in general, LLM-based approaches can theoretically scale up to large systems. Then, the paper introduces a novel automated end to end pipeline that transforms unstructured documentation into SysML Block Definition Diagrams using LLMs, a Groovy script to import the diagrams into Cameo 2022x, and graph theory-based invariants to improve the output. The system employs proposition-based retrieval-augmented generation with smart transitive reduction for relationship refinement, achieving F1 scores of 0.95 for element identification and 0.85 for relationship extraction on test documents. This work represents a significant step toward automating the transition from document-based to model-based systems engineering, potentially reducing the time and effort required for digital transformation of systems engineering processes.
The rise of generative AI large language models (LLMs), such as ChatGPT, has sparked legal and ethical debates over copyright infringement. Artists argue that these systems exploit their intellectual property (IP), while developers maintain their methods are non-expressive, thus avoiding direct legal violations. However, the training of AI on human-generated media patterns raises fundamental questions about the ownership of artistic algorithms and patterns – the cognitive frameworks – that define an artist's style and creative fingerprint. These patterns, the result of years of practice and innovation, are appropriated by AI systems without proper recognition or compensation. This paper employs a systems approach to investigate these issues, analyzing the interplay of sociotechnical factors—technological, legal, and social—underpinning the tension between generative AI and artist IP rights. By framing the problem as a system with interdependent components, this research explores the pathways through which artists can assert ownership, developers can adopt fair practices, and legal frameworks can evolve to protect artistic integrity.
Missions and research objectives at the National Aeronautics and Space Administration (NASA) continue to increase in scope and complexity while under austere schedule and budget constraints. Digital transformation is a key enabler for NASA to do more with less. But as many large and storied organizations are experiencing, the rate of digital transformation is as much a social problem as a technical one. Contributing social factors include the distribution of the inherent willingness of individuals to adopt new technologies and the natural tendencies of like-minded individuals to succumb to groupthink within their communities of practice. A team of NASA systems engineers recently attempted to identify and expose those tendencies and kick-start more productive dialog in the area of digital engineering by leading a group model building session using community-based system dynamics approaches at the 2024 NASA Systems Engineering Workshop, which included over 400 participants. This paper captures the approach, results, and findings of this ambitious experiment.
Systems engineering methodologies are complex and interconnected, reflecting various philosophies and problem-solving approaches. Practitioners often question their suitability for all contexts, especially complex adaptive systems. This has led to the need for meta-methodologies to help select appropriate methodologies. Michael C. Jackson's “Critical Systems Thinking: A Practitioners Guide” introduces a more pluralistic approach, exploring different systemic perspectives to inform the selection and combination of methodologies, replacing the term “Meta-methodology” with the term “Practice” to imply Critical Systems Practice (CSP). Taking the lead from this, this paper reviews a set of similarly constructed publications, termed Systems Practice Frameworks (SPF), that all seek to aid the reader in characterizing their unique problem and proposing pointers to tools, techniques, heuristics or methodologies. It compares and contrasts these approaches to assess their suitability, guides readers in selecting suitable approaches to address complex challenges and identifies recommendations for improving SPFs. It concludes that SPFs are increasingly pivotal in helping Systems Engineers face their unique, complex challenges. CSP is identified as the most useful SPF, but the other SPFs indicate improvements that can be made, such as simplifying the advice and pointing towards a more comprehensible and accessible range of practices.
This paper presents the current model-based systems engineering efforts at Toshiba Corporation. It describes the background that has led us to these efforts as well as the approach that we have adopted on Design Configurator and Model Based Systems Engineering (MBSE) in the development of engineer-to-order (ETO) products. To improve the efficiency of engineering process and to construct a more proposalbased sales process (sales process that proposes high-value product systems to customers by verifying performance feasibility at the customer requirements inquiry and quotation phase), we proposed a standard process which integrate top-level design process with low level design process targeting ETO systems with high design loads. To implement the proposed standard process, we have built a configurator enhanced with mode-based development (MBD) interface to meet large scale customization (requiring major change to design) such as customization involving design mold modification. We have added to the configurator functions to optimize key design parameters in collaboration with external simulators as well as optimization tools, addressing the major change requirements from customers. This configurator enhancement applied a flexible concept of modular design to propose an approximate configuration as a baseline to accommodate higher levels of customization and estimate costs more accurately. Sequentially, proposed baseline configurations' design parameters are transferred swiftly to verification and validation(V&V) phase with smooth integration between the configurator and Model-based V&V tools for optimization, to ensure the optimal customization that meets customers' requirements. The preliminary evaluation with a Proof-of-concept for pump systems' motor design showed that the proposed process and tools return substantial improvement in engineering lead time, design cost and design quality.
With the increasing complexity of commercial aircraft and the rapidly changing market demands, the system engineering development pattern extensively adopted by aircraft OEM has evolved from the traditional document-based systems engineering (DBSE) to model-based systems engineering (MBSE) and pattern-based systems engineering (PBSE). MBSE employs models to describe products, while PBSE builds upon MBSE by utilizing engineering patterns, which are validated in advance, to enhance the efficiency and quality of data production in both MBSE and DBSE. However, during PBSE engineering practices, we have observed certain challenges, such as the barriers to initializing product S* models and the low efficiency in generating instances. Artificial intelligence for systems engineering (AI4SE) is an emerging concept aimed at creating a more efficient and user-friendly systems engineering implementation environment through the integration of artificial intelligence (AI), machine learning (ML), and related technologies. This paper explores the application of AI4SE in real-world engineering projects by leveraging large language models (LLMs) to develop a methodology that reduces the deployment threshold of PBSE for enterprises and enhances the efficiency of instance generation.
This paper reviews the literature and addresses an important topic and a frequently asked question on the differences, relationships, and causality of system adaptability and system resilience. It discusses areas that are potentially unique to each concept, and the significant common areas. We depict the relationship through a comparison chart and a Venn Diagram that addresses both the unique areas and areas of synergy. We present different types and representative case studies/examples for both adaptive systems and resilient systems. This is a comprehensive study from the Resilient System working group and System Adaptability working group in INCOSE for answering the frequent question on the difference and relationship between adaptability and resilience and will be useful to help the systems engineering community to understand each concept and apply techniques accordingly. We also identify the value of applicable resilience and adaptability techniques for stakeholders and show directions for further investigations and subsequent studies.
In the realm of Model-Based Systems Engineering (MBSE), the effective framework is paramount for streamlining complex system development processes. Over the years, various frameworks and methodologies related to MBSE have been developed and adopted. Many of these originated with a focus on industry-specific needs, while others are closely tied to particular tools and systems engineering languages. As organizations transition into increasingly heterogeneous environments, characterized by a growing ecosystem of diverse stakeholders, it becomes imperative to establish a framework that is agnostic to tools, industry sectors, and languages while catering to System of Systems (SoS). The framework should be comprehensive, integrated across the entire system lifecycle, and aligned with industry-leading practices and standards. This research paper delves into the creation of a new MBSE framework viz. Universal Systems Engineering Lifecycle Framework (USELIFE) by conducting a comprehensive study and comparison of the most utilized frameworks and methods in the field, addressing their limitations, and introducing some new concepts. The proposed framework provides a structured approach to system modeling, based on key standards & guides such as the INCOSE Handbook, ISO/IEC/IEEE 15288:2023 (System Lifecycle Processes), and ISO/IEC/IEEE 29148:2018 (Requirements Engineering). It provides a common platform for stakeholder collaboration, focusing on model-centric end-to-end lifecycle modeling for consistency, traceability, and reusability of system information. The framework is presented through two intersecting perspectives: (a) providing a comprehensive overview of the interconnected aspects of the systems engineering lifecycle, and (b) a layered approach that analyzes and demystifies systems engineering challenges using multiple derivatives. This framework is demonstrated through a case study of an e-bike, facilitating the understanding of different perspectives and the process of building system models. It assists engineers in capturing and managing system requirements, design iterations, verification, validation, and operational factors throughout the system lifecycle.
With the advent of software-defined vehicles (SDVs) such as self-driving cars and battery electric vehicles (BEVs), the automotive industry faces the most drastic change over the last hundred (100) years. Over-the-Air (OTA) updates at high frequencies are expected to enhance SDVs' functionality and the connected car concept aims to create a new value by allowing cars to communicate with their external environment. On the other hand, the complexity of these systems is significantly increasing due to the number of software and the connected car concept required. To ensure the quality and enhance the agility of the SDV development, many automotive manufacturers have tried to introduce Model-Based Systems Engineering (MBSE) and Agile development approaches concurrently, but achieving a balance between these two approaches is a challenge. This paper proposes the methodology and practices to combine MBSE and Agile development by connecting models and simulation to verification planning seamlessly. We use the Systems Modeling Language (SysML) to model and analyze the requirements, logical architecture, test cases, and the System Architecture models to capture engineering information that reflects verification planning and actual verification. Since the requirements and needed verifications evolve continuously in Agile development, the proposed approach can ensure that the requirements, design, and test cases are updated alongside each iteration without sacrificing agility. The proposed approach allows high-level reuse of models and simulation to contribute to verification planning to achieve quality while maintaining focus on agility.
