The Proceedings of the 2023 Conference on Systems Engineering Research

The systems engineering community is pushing toward the adoption of digital engineering to improve the design process and resultant systems across the life cycle. This shift depends on the ability to produce useful digital twins. Systems Modeling Language (SysML) version 1.x and its implementing tools do not contain truly open interfaces or otherwise enable data exchange in formats to integrate systems models with a variety of data analysis and simulation tools. Lacking data interoperability, SysML version 1.x models struggle to enable a systems engineering digital engineering vision. SysML v2 corrects many of these shortfalls with specification of a text-based language and a RESTful application programming interface (API). This chapter introduces PySysML2, a prototype software application to integrate SysML v2 models with the Python-based ecosystem of analysis tools. This work demonstrates the functionality and utility of PySysML2 by describing how to read into, manipulate within, and use SysML v2 models in a Python environment. Additionally, we demonstrate SysML v2 model serialization into a portable JSON (JavaScript Object Notation) format that supports interoperability with a wide range of commonly used data analysis tools. We close with a recommendation for continued open-source exploration of the pathfinder work presented in this chapter.

In this chapter, the authors outline an approach to formally model verification strategies using Systems Modeling Language (SysML) in a way that enables the automatic generation of the corresponding Bayesian network. The approach includes the development of a verification metamodel that can be represented as a SysML profile. A notional example is included, in which a CubeSat verification strategy is produced in accordance with the SysML profile and a representative Bayesian network is created. Results from the Bayesian update are presented, and the impact on the SysML model is discussed. Further work will focus on the continued development of this metamodel, the integration of the plug-in to automatically generate the corresponding Bayesian network, and more detailed case studies.

During systems design, systems engineers would like to reason about systems feasibility as early in the process as possible. In this chapter, we present a model-based systems engineering (MBSE) method to support design decisions early in the process of systems development by quantitative information about important system qualities. We focus on productivity and propose a method based on system workflows that provide customer value. We annotate the workflows with quantitative information and resources being used and define a formal semantics for these workflows to enable analysis by means of simulation. The approach reuses constructs of the MBSE method Arcadia and is supported by prototype tooling in the form of an add-on of the tool Capella. We visualize simulation results using Gantt charts, with several viewing possibilities and critical path analysis. Finally, the add-on includes design space exploration functionality, which supports specification of parameter ranges and automatic simulation of all combinations. This integrates early design space exploration with MBSE.

With the emergence of large-scale constellations and commercial services to supplement government programs, space systems engineering must draw on new approaches to improve standardized information exchange across organization boundaries. This chapter models distributed space systems in the Tradespace Analysis Tool for Constellations (TAT-C) using JavaScript Object Notation (JSON) Schema to improve interoperability among disparate systems engineering analysis tools. JSON Schema is a standardized vocabulary to define object schemas using the JavaScript Object Notation (JSON) encoding. TAT-C object schemas define four types of orbits, observing instruments, standalone satellites, and two constellation configurations (train and Walker). The proposed schema has been implemented in TAT-C to structure inputs and outputs to analysis functions using a web-based Representational State Transfer (REST) Hypertext Transfer Protocol (HTTP) interface specified by OpenAPI. An example JSON request document demonstrates how the object schema encodes and formats inputs to analyze coverage statistics over the United States for a proposed space mission with five satellites.

Organizational adoption of digital engineering (DE) and model-based systems engineering (MBSE) requires a strong foundation in systems engineering (SE) and a multiyear organizational digital transformation strategy. For the last 5 years, the Systems Engineering Research Center (SERC) has conducted a sustained series of research tasks to evaluate and develop a model for DE/MBSE adoption. This model is organized across three categories: organizational design, organizational enablers/barriers, and organizational change management. This chapter summarizes the results of each stage of this research, presents the derived model of enterprise adoption factors, and then outlines an adoption strategy using lessons learned and the 12 highest-impact adoption factors. The body of research summarized here provides justification and initiates a framework for organizations wanting to undergo digital transformation of their engineering practice.

A key part of digital transformation is the application of digital engineering and model-based systems engineering (MBSE) tools in mission engineering (ME). However, the current digital engineering practices and standards lack maturity, and the current guides are often too broad for the intention of reusability and a wide range of applications. Additionally, the diversity of system-of-system constituents deployed for missions as well as their complex system behavior and stakeholder interactions pose complex challenges to their successful application. We propose a general framework that builds on the existing guidelines to better organize and facilitate the digital mission engineering process. The methodology is categorized into multiple abstraction levels to demonstrate the existing layers of information produced and identify the relevant stakeholder viewpoints of data to improve communication and collaboration. A pilot implementation demonstrates the applicability of the framework to construct a cislunar reusable mission, and the benefits were determined based on digital engineering reference qualitative metrics, observations, and perceptions. The benefits of a digital-enabled framework include increased traceability and reusability, enhanced support for users, and improved communication and knowledge. Additional work remains to develop the early works of the proposed framework and address the pain points of academia and industry applications.

A growing, increasingly urban population with a high ecological footprint is raising the threat of global food insecurity. While sustainable food systems such as mobile plant cultivation units (MPCUs) combined with new digital technologies such as digital twins provide a promising mitigation strategy, cities often differ significantly in terms of available resources as well as climate and infrastructure, making a one-size-fits-all design solution insufficient. A set of digital twin use case scenarios and reference architectures for understanding how to design, manufacture, service, and retire MPCUs in different city contexts provides the first step toward using data-driven digital twins to help address these challenges and promote sustainable cities. This chapter contributes to this effort by advancing a use case scenario where a digital twin is used to automatically grow selected plants in MPCUs by monitoring relevant physical conditions; based on this scenario, a first-level functional architecture for the digital twin is proposed. This proposed architecture shows promise in the light of existing reference architectures for digital twins created for a different domain. Research is continuing on how to design and operate a digital twin in conjunction with an MPCU.

The US Aerospace and Defense industries are in the midst of a transformation to a digital paradigm. As an example, the US Department of Defense (USDOD) released a strategy for implementation of digital engineering (DE) in 2018. The strategy defined key focus areas of DE, one of which was focused on culture and workforce development. To meet the need of development of the USDOD workforce, we have created the Simulation Training Environment for Digital Engineering (STEDE). A subset of STEDE is focused on digital acquisition artifacts, which are expected contribute to acceleration of the acquisition process. Rather than existing as static, largely textual documents, the acquisition artifacts are digitally transformed to become a dynamic set of data and models in a digital ecosystem, interconnected through a digital continuum. The Systems Engineering Research Center (SERC), to which our research group belongs, has a number of projects that advance knowledge of digital acquisition artifacts, such as the digital test and evaluation master plan (d-TEMP), which is expected to complement the pursuit of educational research presented in this article. However, we have specifically selected to focus this article on the digital systems engineering plan (d-SEP). While we do not share the curriculum in this article, we use this article to provide insights that we have uncovered during our pursuit of the basis for DE education.

In order to achieve the full benefit of the Modular Open Systems Approach (MOSA), products must be both open and modular. In conjunction with openness, a strong focus on modularity brings benefits in the form of reduced development and integration costs, by scoping products that are optimally reusable and easily integrable. This chapter discusses the concepts and benefits of systematically finding well-scoped groupings of functionality, which are designed to be integrated efficiently and to maximize potential reusability – all in the pursuit of achieving modularity. This methodology can not only guide the decision-making for program office and procurement managers, but, perhaps more importantly, it can also provide an objective and traceable justification for their decisions.

Dynamic reconfiguration is challenging, especially for a system of systems (SoS), although it is necessary given the nature and amount of change these systems undergo. SoS has certain specifics, such as independent development and the so-called emergent behavior, which need to be handled through new approaches. We investigate the possibilities of using modeling to provide some guarantees for such complex SoS in the presence of change. Our modeling language of choice is Systems Machine Language version 2 (SysML v2) and its textual representation in particular because it leads to more formal and precise representation and promotes the utilization of tools. We focus on the automation that tools can achieve as the SoS evolves during its life cycle. Our approach uses selected sets of models for each constituent system and a higher-level integration that ensures that all combinations can work at run time. We also explore the effects on SoS when adding, removing, and updating a constituent system. A protocol is proposed for handling dynamic reconfiguration in a distributed manner.

The National Aeronautics and Space Administration (NASA) has evolved multiple spacesuit systems for performing extravehicular activity (EVA) or space walks. These spacesuit systems include the Apollo Extravehicular Mobility Unit (EMU), Space Shuttle and International Space Station (ISS) EMU, and Exploration EMU (xEMU). Each spacesuit system is like the other for functionality. However, each spacesuit system is different in configuration based on the technology infused into the system associated with the purpose of the mission. Each spacesuit system is made up of many components, and the integrated environment targeted for operations leads to an integrated system that is complex. Since Apollo, NASA has invested in multiple technologies that make up these spacesuit systems in different iterations. The Apollo EMU was designed in the 1960s with a focus to facilitate the first human to walk on the Moon. The Space Shuttle EMU was designed in the 1970s for reusable microgravity operations that began in the early 1980s. The Space Shuttle EMU was enhanced to facilitate extended operation on the ISS. Over the last 15 years, NASA has been designing, developing, and testing a new spacesuit system, the xEMU which is considered a design, verification, and test unit. NASA is planning to land the first woman and first person of color on the Moon. NASA recently engaged industry through a new contractual arrangement to provide EVA services needed to return to the Moon and to continue operations on the ISS. Spacesuit systems are complex. Understanding the requirements, operational environment, the necessary technologies, and the integrated spacesuit system is paramount. In addition, understanding the technology infusion process to meet the mission objectives is critical. This paper will review the spacesuit systems for EVA and several component functions within the spacesuits, along with a system comparison of those technologies from Apollo to xEMU.

Modularity is recognized as a general and effective solution to tackle the complexity of system of systems in modern society. Despite the value and benefits of modularity, it has been a known challenge to measure and evaluate the modularity of complex systems in real life, especially early in the development life cycle such as in the requirement engineering phase. This paper proposes a framework that enables early assessment of the modular structure of a system based on system requirements. The framework combines (1) a Natural Language Processing for Requirements Engineering (NLP4RE) application developed together with the Systems Engineering Research Center (SERC), (2) a Decoupling Level metric, and (3) a novel Ablation procedure to quantify the decoupling and coupling effects of each design element in a system. We conduct a case study on an unmanned aircraft system (UAS) from SERC, for which the third and fourth authors of this paper have in-depth knowledge about. From this case study, we yielded interesting findings that align with expert’s intuitive understanding of the UAS. The case study indicates that our framework can provide early and quantitative assessment of the modular structure of a system based on the requirements. It has the potential to assist the architect in reasoning and designing a modular structure by reviewing the decoupling and coupling effects of system requirements and key terms.

In the discipline and practice of Systems Engineering, we typically derive verification models based on implicit, heuristic assumptions rather than science-based approach. For example, when we define verification requirements, we may optionally define a desired fidelity of a verification model as to its representativeness to a final product in the qualitative terms of high, medium, or low fidelity. Is this fidelity ever confirmed? Or is the fidelity simply assumed? Given that the intent of verification is to infer adherence of a system design to its corresponding system requirements; if the fidelity is assumed, then there exists an uncharacterized challenge to verification. Furthermore, in the pursuit of theoretical foundations of Systems Engineering, the derivation of verification models is an unexplored research territory. This chapter summarizes the findings from a research dissertation on the study of equivalence in Systems Engineering within the frame of verification. The dissertation leveraged systems theory principles to explore the relationships necessary and sufficient to define verification models based on framing between the verification artifacts: system requirements, system design, verification requirements, and verification models.

There has been increasing interest in developing numerical methods to inform the design of verification strategies in recent years. Existing work has been applied to toy problems that significantly simplify the complexity of verification in real-life applications, both in terms of the size of the problem and the interdependencies between the different elements of a verification strategy. To our knowledge, there is no publicly available evidence of the complexity of the verification “problem” in terms of requirements that need to be verified, verification activities that are conducted, and their interrelationships. In this chapter, we use knowledge graphs to visualize the size of the verification problem for two industrial projects, as captured in their verification artifacts, which include requirements traceability matrices and verification matrices.

With any mature systems engineering approach, a defined problem statement is required. The present workplace and workforce are continuously evolving. Prior to the COVID pandemic, workforce turnover averaged a mean of 1.8 years. With the prevalence of COVID-19 and restrictions, more than 20 million jobs have turned to fully remote work. “Between 2019 and 2021, the number of people primarily working from home tripled from 5.7% (roughly 9 million people) to 17.9% (27.6 million people), according to new 2021 American Community Survey (ACS) 1-year estimates released today by the U.S. Census Bureau.” 1 According to the U.S. Bureau of Labor Statistics, the average employee turnover rate in 2021 was 47.2%.2 After COVID-19, 92% of people surveyed expect to work from home at least 1 day per week and 80% expected to work at least 3 days from home per week.3 Forty-seven percent of millennials are planning to leave their jobs within 2 years, and Gen Zers report a comparable number. With high turnover (e.g., the so-called “Great Resignation” with 47 million Americans voluntarily quitting in 20216 and an estimated 48 million in 202216) and isolated employees, finding good one-on-one mentors for employees is increasingly difficult. Wisdom gained from years of experience from senior mentors in a specific field is often not transferred and so is lost when the older employees retire. This is particularly true of our critical utilities, construction, and transportation infrastructures. Consequently, it is imperative to find a way to capture historical systems engineering lessons learned and enhance the knowledge of current and future employees. Henry Ford quoted, “The philosophy of life indicates that our principal business on this planet is the gaining of experience.”17 Thirty-seven percent of businesses and organizations currently employ artificial intelligence (AI). Research suggests that AI has the potential to boost employee productivity by approximately 40% by 2035. Ninety percent of data is unstructured, meaning that without technology to process the big data, companies are unable to focus on important data points. The direct motivation for this chapter is combining traditional systems engineering with cognitive science, meta-analytics, meta-algorithmics, and AI results in new SE constructs, focused around metacognition. The concept to aid with this introduced in the chapter is known as “Knowledge Intelligence Transduction” (KIT). This special issue will focus on the KIT concept and how it can be used to positively impact the future workforce and build resiliency of knowledge across generations and challenges.

The term “technical debt” (TD) is no longer limited to software engineering but can be applied to the full product development life cycle. TD is particularly relevant to systems engineers because it impacts product development as well as program execution, resulting in lower productivity and increased risk. Although TD has its benefits, leading indicators (LIs) have traditionally been used in systems engineering to help prevent surprises during system development by providing timely information about potential problems, improve cost estimating by providing more accurate information about the system under development, and provide information about which activities are most likely to impact the schedule.The use of LI supports the effective management of systems engineering by enabling predictions of expected project performance and potential future states. Moreover, LI aid leadership in delivering value to customers and end users while facilitating interventions and actions to avoid rework and wasted effort (Roedler G, Rhodes DH, Schimmoller H, Jones C, Systems engineering leading indicators guide. Developed and Published by Members of, 2010).This paper examines how different TD types can be linked to LI in systems engineering. It also provides a simple introduction of TD to systems engineers and technical program managers, presenting LI in systems engineering as an established methodology with relevant metrics.

The vision of the Army Installations Strategy (AIS) and the Installations of the Future (IotF) program is to enhance mission effectiveness and resilience in a prudent, efficient, and forward-thinking manner, including Army installations and contingency bases, energy, and environmental programs. Modernization of the nation’s installation portfolio has many challenges. One challenge is due to the lack of a framework for acquiring and transmitting data to and from government networks and systems for implementing modernization efforts. This research effort supports AIS and IotF by contributing to the modernization of installation decision-making processes by providing a data-driven platform for applying complex computational analytics and high-performance computing assets within the Virtual Testbed for Installations Mission Effectiveness (VTIME) integrated platform. This approach supports installation decision-makers for making holistic tactical and operational decisions. Current decision processes are predominantly manual in nature, require extensive human interactions, and lack relevant data. This research integrates various analytical methods, such as machine learning (ML) in conjunction with real-time data, to power a decision dashboard that can more effectively communicate the impact of risks to an installation. The desired outcome will create a sustainable and translational data-driven decision framework that will inform leadership for the installation operations decision process. This paper explores the ML analytics applied to historical data that provide insight to support the modernization of the weather-impacted installation operations decision flow process.

Measurement may seem a rudimentary concept: temperature can be measured in degrees using a thermometer; length can be measured in distance using a ruler. Measurement for system design is more complex, given that measures are implicitly intended to assess the “goodness” of a design. This research proposes that measures in systems design can direct a design away from its goals through measurement challenges. Three types of measurement challenges are considered: challenges from the measures themselves, challenges from weighting the measures, and challenges from the use of the measures. This research illustrates measurement challenges for system design mathematically, graphically, and through real-world case study examples. The goal of this research is to demonstrate through multiple forms of evidence that measurement can misdirect system design. Establishing that measures in system design can influence a design away from its goals enables future research to investigate why these measurement challenges occur and how to prevent them.

While there are numerous products on the market to help prevent flat tires from occurring, none have proven to be 100% effective. Airless-tire products have been proposed in the past, and the research behind such technologies is ongoing. However, the envisioned product designed, discussed, and analyzed in this paper (an airless-tire insert for mountain bikes) addresses the issue with a radically different perspective and proposes a potential solution that not only can eliminate flat tires from occurring altogether but can also improve ride feel. The contents of this paper include the underlying derivations used to model a “Multi-Spring Model” (MSM) of an airless mountain bike tire, benchmark force versus displacement testing data of pneumatic tires inflated to varying pressures, and physical test data obtained from the team’s patent pending experimental prototype. While this paper provides a technical overview of the product, it also addresses the product development process and verifiable testing procedures through the system engineer’s perspective. In addition, this paper also discusses the feasibility of using shape-memory alloy (SMA) material (used by NASA on the wheels of the Mars rover) as the primary structural component for a tire insert.

Research into critical infrastructure network architecture design faces two significant challenges. First, real-world network performance data is often not available due to being proprietary. Second, many efforts focus on analyzing the structure of an infrastructure network at a single point in time, while real-world networks are constantly evolving. In this chapter, these two gaps (need for more data and for time-series data) are examined by utilizing a new data source: the video game Factorio. Factorio is a manufacturing simulator. Utilizing publicly available recordings of players’ networks in game, a shared end point, and completion time stamps allows the examination of different network strategies. The key research question examined in this work is how does network evolution change when comparing ten expert and ten novice designers? This chapter provides two key contributions. First, a qualitative and quantitative analysis of how ten different structural graph theory metrics evolve when comparing expert and novice designers is provided. The expert dataset has a narrower distribution, indicating common strategies, and focuses on critical path manufacturing early in the network’s evolution. The second contribution is a set of time-series network data that can be used for additional studies. By examining the differences in network evolution between experts and novices, this article performs a critical first step toward using in situ graph theory metrics as a decision aid for designers during infrastructure evolution.

This chapter presents a method for analyzing a multi-model system of analysis in a graph representation to verify well-formed construction. It utilizes the Digital Engineering Framework for Integration and Interoperability to establish a system of analysis and presents methods for defining well-formed construction of those systems and verifying the well-formedness of the systems based on the definitions. The chapter explores both consistency checks and closed-world reasoning-based rules to build a robust foundation for a context-dependent definition of well-formed construction and uses the SPARQL query language for traversing the graph representation of the system to determine the system’s adherence to the proposed definition. It utilizes a domain neutral case study to construct a manually verified well-formed system of analysis plus variations of the model with seeded faults to validate the verification process.

Gaining a deep insight into the factors that influence product competition is essential for a company to maintain its competitiveness in the market. While many studies have been conducted on competition analysis of various products, existing work often has oversight of market heterogeneity. This makes the analysis of product competition less accurate, which could significantly influence many downstream product design decisions. To address this issue, this paper presents a network mining approach to support product competition analysis for engineering design. The approach investigates product competition (represented by co-consideration relations) networks at three different levels, including macro (competition within the entire market), meso (competitions happening between a small group of products), and micro (competitiveness of individual products) levels. In this approach, we first develop a network motif-based representation of individual products’ competitiveness. Then, we use the Exponential Random Graph Model (ERGM) to study how the inclusion of such competitiveness measurement would influence products’ co-consideration relations and improve the model’s goodness-of-fit. This network mining approach is demonstrated in a case study on the household vacuum cleaner market, where heterogeneous customer preferences are pervasive. A multilevel network analysis of product competition provides a new way to quantify the competitiveness of a product in a heterogeneous market. It also helps quantify the importance of different competitive roles (e.g., competition within a brand or across brands) in forming co-consideration relations in the market.

Artificial intelligence (AI) across the engineering domain has led to an increased discussion about the importance of creating, maintaining, and governing AI, and AI assurance has been discussed at multiple forums at times without specific steps to make the process actionable. This work proposes that AI assurance can be addressed through five intertwined components: ethics, transparency, compliance, safety, and certification. AI assurance’s importance comes from an increase in complex algorithms that learn from data corresponding to several studies and massive data banks that may exhibit bias and variance. In addition, the surge in AI has given rise to opportunities for the community to use systems engineering for AI assurance. This work develops a method to connect continuous integration/continuous deployment (CI/CD) pipelines and subject matter experts to address AI assurance in a systematic and semi-automated approach. The work demonstrates the method through a use case involving a relevant dataset.

Software reuse is a crucial external quality attribute targeted by open-source and commercial projects. Despite that software reuse has experienced an increased adoption throughout the years, little is known about what aspects of code reuse developers discuss. In this paper, we present an empirical study of 1409 posts to better understand the challenges developers face when reusing code. Our findings show that “visual studio” is the top occurring bigrams for question posts, and there are frequent design patterns utilized by developers for reuse. We envision our findings enabling researchers to develop guidelines to be utilized to foster software reuse.

Large-scale, Internet of Things projects often require significant change and adaptation in the project life cycle. This creates complexity when trying to effectively communicate information among multiple teams contributing to and interfacing within a project. This project uses agile project management and development while creating smart base software systems because of the limited and evolving stakeholder requirements, necessary collaboration with three other organizations, and undetermined methods. The agile process, however, creates unique challenges when applied to developing complex systems of systems with multiple stakeholders. Because agile management is a goal and not a predefined process, system developers need strategies to maintain the effectiveness of agile development. This research identifies challenges to system development and uses systems engineering (SE) principles to apply decision analysis and systems engineering methods (decision hierarchy, IDEF0, and swim lane diagram) to an agile project management framework. These methods facilitate the documentation of requirements, processes, ownership, and progress among multiple groups to enable a shared understanding and more effective progress reporting.

Smart transportation systems utilize a variety of Internet of Things (IoT) sensors, real-time data communication technologies, and advanced computing techniques to manage its services, resources, and infrastructure more efficiently. There is an increasing interest in transforming transportation systems into intelligent systems, but persistent concerns over privacy are slowing down the adoption of smart applications. This paper presents a knowledge elicitation methodology based on natural language processing and deep learning techniques to analyze privacy policies of several smart transportation IoT applications. Text similarity analysis is performed to identify the privacy functions that are less frequently addressed by IoT privacy policies. Initial results support decision-makers in understanding the contextual privacy characteristics of the smart transportation domain.

The endogenous evolution of intelligent systems exposes insufficiencies in some systems engineering activities that rely on exogenous properties of systems. One of these activities is product lining for manufacturing identical systems. In this paper, we argue that manufacturing intelligent systems using product lines engenders several problems with respect to systems engineering practices. As an alternative to product lining, we propose a new framework called product herding that recognizes and attempts to manage the tendency of intelligent systems to develop idiosyncratic features as they are fielded.

Requirements define the problem boundaries within which an engineering team tries to find acceptable solutions. Gaps in requirements formulation can lead to solutions that are not fit for purpose. However, the completeness of a set of requirements cannot be demonstrated; rather, completeness is an attempt, a best-effort pursuit. In current practice, where requirement gaps are frequent in system development, the human (engineer or team of engineers) becomes a major factor in the comprehensiveness of the resulting set of requirements. In this paper, we present an early implementation of a cognitive assistant that supports the (human) engineer in identifying gaps in the form of aSysML plugin for Magic Systems of Systems Architect.

Interactive dashboards are decision support tools that enable users to explore the relationships between their decisions and the consequences of those decisions. These dashboards in previous research have been proven to be most effective when customized to the specific context of the decision scenario. The objective of this project is to design an interactive dashboard using best practices in optimization and strategic decision-making, for the application of an artillery system derived from publicly available sources. Using Python’s dash library, the resulting dashboard enables users to explore design decisions, model mission success likelihood, optimize the design, explore Pareto-optimal trade-offs, trace performance improvement goals back to design parameters, and compare designs with existing systems. The dashboard is described in detail, and several use cases are put forward to illustrate the functionality and implementation scenarios for such an interactive dashboard for complex decision analysis.

The main goal of product line engineering is to build complex system architecture at the best quality, cost, and resource <Q,R,T> ratio. The return on investment (ROI) in terms of <Q,R,T> assessment is however not trivial, as systems to build rise in complexity. Moreover, the perspective of system of systems engineering that set up both historic and new systems in capabilities and the introduction of more and more autonomous systems in architectures make the anticipation of return on investment impossible to achieve without computer assistance. The necessary tools to assess <Q,R,T> as precisely as possible also involve a wide exploration of possibilities in an ever-changing context. The absence of mechanical or multi-physical aspects in SysML-based tools, either in its version 1.3 or 2.0, makes it inefficient in representing or simulating robotic systems or system of system engineering. This article explains the benefits of tackling a classic Multi-Objective Knapsack Problem (MOKP) to the UGV product line items selection using a seamless system architecting toolchain. The association of MBSE (Model-Based System Engineering), OR (Operation Research) and MBD (model-based design) that generated various designs is presented. Our results in system engineering in UGV presents a Pareto Front of trade-offs that can count as numerous possibilities that sole MBSE or separate MBD simulation could not have represented as the best in the sense of <Q,R,T>. The simultaneous variations in both hardware and mechanical design were entirely automated using standard tools with no redesign. This shows that seamless automations should pave the future of system engineering tools. With Operation research and Systems engineering tackling methods, our model upscale to real systems will shape system-driven engineering that will require new skills in system validation and verification and simulation analytics.

Many complex socio-technical systems enable the conduct of daily activities across the United States. These systems incorporate engineered systems, their human operators, processes, and the people, property, and environments the systems affect. Understanding these socio-technical systems and the interactions within them is difficult. The U.S. Army Corps of Engineers must allocate resources to operate and maintain complex socio-technical systems across multiple business lines, such as Flood Risk Management, in order to mitigate risk. This chapter presents a methodology to provide decision-makers with improved understanding of their complex socio-technical systems through the development of a risk-informed prioritization framework. Likelihood of facility and system degradation based on the condition of components is developed from subject matter expert initialized Bayesian networks. Designed simulation experiments with hydrological models provide estimates of flood consequences at the watershed level. By combining likelihood and consequence values, this methodology develops relative risk scores that are used as inputs to a mixed integer program that provides decision-makers a recommended set of investments given constrained resources. Two case study applications are provided.

Decision-based design often states an aspirational goal for value functions of achieving perfect ordinal consistency. How reliably such a standard can be achieved, however, is rarely addressed. Due to the multiple options available to an engineer regarding model form, model fitting procedures, training data, etc., there are often multiple value functions which could be developed for any particular problem. The extent to which those functions can be distinguished from one another depends on the exact validation procedures used to determine acceptability. This work utilizes a space launch vehicle simulation model to generate outcomes for the comparison of value functions. A training set of 12 outcomes are rank-ordered, and 250 models which produce the correct order of those 12 outcomes are generated. Relative preference of 2 separate alternatives is then compared across all 250 acceptable functions. This comparison is made with both certain and uncertain outcomes associated with the alternatives. In the base condition with certain outcomes, 64% of the models preferred Alternative A and 36% preferred Alternative B. With uncertain outcomes, relative preference depended on both the shape of the resulting distributions as well as the decision criterion used to characterize the distributions. These results demonstrate that functions which produce the same rank ordering of a training set are not guaranteed to have full ordinal consistency, highlighting the importance of validation procedures in engineering value modeling.

Wearable devices are able to sense, collect, and upload various physiological data. With the technological improvements and progress in power, computation complexity, and size for wearable devices, their application has rapidly expanded in various use scenarios in healthcare. The main challenge of utilizing healthcare data through wearable devices is data integrity. The accurate assessment of data integrity necessitates a comprehensive understanding of the contextual information of wearable usages in free-living conditions, such as user scenarios. To examine the effects of user scenarios on data integrity and validate the assessment method, we conducted a human-subject experiment. We collected raw acceleration and heart rate data from participants using the Apple Watch in the lab under the selected user scenarios. We implemented an anomaly detection method based on an ensemble of neural networks. Then, we associated those data compromises with user scenarios and tested if the selected scenarios influenced the data. This chapter demonstrates the ability of the proposed method to assess data integrity under different user scenarios and contributes to future work that quantifies the influence of user scenarios as potential root causes.

Residential electricity consumption is responsible for a significant portion of greenhouse gas emissions each year in the United States. Conserving energy and increasing demand for renewable energy sources are two ways individuals can help reduce the negative impacts of electricity consumption. Research on monetary incentives and fees has demonstrated their potential to encourage pro-environmental behaviors, but knowledge of their effects on sustainable energy behaviors in particular is incomplete. In this chapter, an investigation of three levels of incentives and fees framed to encourage either energy conservation or investment in renewable energy is conducted to determine their effect on consumer energy behavioral intentions. Through a survey that exposed participants to incentives and fees on bill graphics, participants’ perceptions and intended future behaviors were measured. Data were collected about consumers’ willingness to pay to participate in clean energy programs, invest in solar panels, and upgrade to efficient appliances. Results of the survey experiment show that exposure to low levels of incentives and fees significantly increased participants’ intentions to participate in pro-environmental behaviors and willingness to pay for solar panels when compared to a control group, while high incentive and fee values were no longer effective. Moderate and high incentive and fee levels were, however, effective in increasing participants’ perceived costs and benefits from participating in energy-efficient behaviors. Finally, the framing of the incentives and fees was not found to be significantly influential with respect to participant perceptions and intended energy behaviors.

Fairly distributing the benefits and burdens of energy use and generation is at the heart of a just energy transition. Many end-use technologies in residential buildings can generate benefits to occupants (e.g., reduced energy costs and improved wellbeing) and the electric grid (e.g., shedding and shifting capabilities and reductions in peak demand), offering a lens through which to assess injustices in the sector. With agent-based models (ABMs) well positioned to analyze heterogeneous populations and diverse decision-making processes, we propose a sociotechnical framework that will support researchers and policymakers in developing agent-based models that account for dimensions of energy justice through technology adoption. The framework is designed to generate agent populations that better emulate the technology adoption dynamics of real-world households and holds strong potential to be utilized in the development of other agent-based models, such as those considering energy behaviors or improvements to building envelopes.

Network-based analyses have been shown to be effective in understanding customer preferences by modeling the interactions and relationships between customers and products as a complex network system. Certain network representations, such as bipartite networks, can capture customers’ two-stage (consideration-then-choice) decision-making processes by constructing either the “consideration” or “choice” links between the customer nodes and the product nodes. However, there is a dearth of research that examines network-based approaches to understanding complex heterogeneous customer preferences and the influence of product features in different market segments. This paper presents a market-segmented network modeling approach for understanding heterogeneous customer preferences in two-stage (consideration-then-choice) decision-making. Joint correspondence analysis is utilized to identify the correlations between product association networks and customer attributes, and then market segments are characterized by clustering customer attributes. We then construct bipartite customer–product networks and use the Exponential Random Graph Model to investigate factors that influence customer decision-making processes and how they vary among customer groups. A case study using real customer survey data for vacuum cleaners, a common household appliance with various product categories and a sizable market, serves to demonstrate the approach. The survey has been systematically designed and conducted on Cint platform to collect customer considerations and choices, product features, and customer attributes. Our findings reveal that customers are heterogeneous across different market segments, which can be clustered based on their demographic attributes, usage contexts, and personal viewpoints. Within the identified market segments based on these aforementioned customer attributes, customer preferences toward product attributes show heterogeneity in different stages of choice-making. Particularly, it is observed that the majority of the product design attributes receive more attention in the consideration stage than in the choice stage. Our study advances the use of network-based models for analyzing customer preference heterogeneity across different market segments and in different stages of customers’ decision-making.

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.

Systems thinking and teaming are two domains in health systems science. Health systems science is an emerging third science of health care education and has been recently adopted by the American Medical Association to prepare individuals for the challenges of twenty-first-century medicine. Given the complexity of health systems, a better understanding of systems thinking allows learners to see the “big picture” and how the parts in a system are related to the whole system. Teaming is a domain that teaches skills required for groups of interdependent individuals to achieve high reliability in the health care they provide. There is an opportunity to use systems thinking to better understand the relationships of teamwork competencies to health system outcomes. Team training is an effective means for increasing communication. The authors present a causal model that graphically illustrates relationships between teamwork competencies as well as relationships to other important health system factors such as preventable medical errors, and clinician and organizational outcomes. This model enables a holistic understanding of teamwork competencies and its expected effects.

Industry-based scientific research has become a critical enabler of business success and digital transformation as corporations have learned to appreciate the value of applied science and its impact on business performance. Yet, the chaotic nature of academic research, with which researchers often feel more comfortable, disrupts the alignment of goals, objectives, plans, and results of industry-based science teams with business objectives and corporate standards. The high costs and high stakes in science-driven technology, product, service, and operation development call for a robust approach to this problem. Surprisingly, the systems approach, a key success factor of enterprise operations, is consistently missing from studies of science team building and industry research, although industry research must be done in close connection with technologies, products, services, and operations because it directly impacts the content and value of such enterprise activities. This chapter introduces a systems approach as a means for creating and running systematic industry-based science enterprises that operate effectively and efficiently at scale, while aligning to stakeholder goals and objectives and interoperating with enterprise architectures. We base our science enterprise architecture on a digital systems engineering enterprise architecture, due to the significant similarities between the two ecosystems.

Urban water distribution networks have provided potable water to communities and households worldwide over the last century. Within the last two decades, there has been a rise in complications with water distribution systems meeting demands. Urban water distributions fail to meet demands due to increases in natural and man-made disturbances, population growth, and aging water distribution network structures. These issues have caused urban water distribution system designers and decision-makers to shift their interests from focusing solely on efficiency to designs capable of meeting customer potable water demands under normal operations and during disturbances. Ecology, specifically biological ecosystems, provides system resilience inspiration, taken from their structure and functioning that has survived disturbances over millions of years. The work here investigates mimicking the decentralization of food webs to improve network resilience by incorporating decentralized water storage tanks, using the established two loop network (TLN) as a case study. TLN is an introductory water network provided by the University of Exeter for system engineers and designers to test optimization and exploratory techniques. The case study was selected due to its simplistic design which allowed the authors to understand the effects of decentralizing the network toward improving its ability to handle disruptions. The findings suggest decentralization can improve the water network resilience a minimum of three times as much as the original network’s design. Furthermore, introducing decentralization was also found to increase the system’s ability to meet the demand for all nodes during disruptions, something the original case was unable to accomplish while simultaneously reducing the amount of freshwater consumed during disruptions.

A microgrid is a localized energy grid that can disengage from the traditional grid and operate independently. Microgrids can be conceptualized as System of Systems: networked integration of constituent systems that together achieve novel capabilities. Improving resilience (the ability to survive and recover from disruptions) and reducing the cost of energy are critical considerations in microgrid design. However, microgrid resilience evaluation techniques require explicit disruption models – information that is not readily available in the early design stages. Therefore, these models cannot inform early-stage design decisions when changes can be made affordably. Recent research has indicated that Ecological Network Analysis is a promising tool for the design of resilient and affordable System of Systems. However, this approach has not yet been tested as a tool for microgrid design. This work provides an adapted Ecological Network Analysis framework that accounts for two unique architectural features of microgrids: (a) energy storage, and (b) integration of different types of energy generation technology. The Ecological Network Analysis-based assessment of microgrid architectures is compared against their resilience and cost of energy evaluations using a state-of-the-art tool. The results of the comparison provide support for the use of Ecological Network Analysis as a reliable early-stage decision-support tool for resilient microgrid design.

To help safeguard the environment and its inhabitant organisms, it is essential to incorporate algal systems into the remediation of nutrient pollution at the initial nutrient or pollution source by using the systems to grow algae and cyanobacteria. To maximize output and cut costs, it is crucial to understand the algal system of systems (A-SoS) with a focus on the meta-architecture of the photobioreactor while comparing system attributes to an open algal system. Algal systems are complex systems, and for qualitative reasons, it is important to comprehend how the pieces behave and how they interact to produce the behavior of the whole. For a photobioreactor focusing on the interactions of the parts together rather than their performance accessed separately can help to manage the system efficiently and optimally. These interfaces are collectively monitored by a cyber-physical system (CPS) in which the mechanism is controlled by computer algorithms called the system of systems (SoS) explorer. In this chapter, using the Engineering Management and Systems Engineering’s SoS Explorer tool the systems and characteristics of the algal system were accessed, noting the characteristics, capability, and key performance attributes. These factors further aided in achieving the optimal performance using the genetic algorithm coherently with the fuzzy assessor as a fitness function to obtain the best fit of the system to recognize improvements for future performances.

Despite the efforts to increase the pace of sustainable design adaptation in industries, several systemic barriers currently hinder this shift. The design for sustainability methods has been utilized in product design and development phases in many industries. However, they do not have a holistic approach that can capture these systemic drivers and barriers while considering all three pillars of sustainability: environmental, social, and economic. This research proposes a systems thinking approach toward sustainable design that can collectively consider different aspects of the production system in an attempt to resolve the multidimensional challenges within the design for sustainability. A reusable water bottle is selected as the case study to illustrate the applications and limitations of this approach. In addition, this case study also helped to define the boundaries and stakeholders involved in the system and reduce the abstractions. The results from this analysis are demonstrated as a causal loop diagram that could be implemented in a system dynamics model to quantitively identify the systematic forces and leverage points driving sustainable design in product development. The comprehensive understanding provided by this analysis revealed many improvement possibilities, trade-offs, and feedback loops within the system that can assist in realizing sustainable product design proliferation and associated positive sustainability outcomes.

Systems engineering 2050 is a project for planning a research agenda for systems engineering methods and tools for developing systems in 2050. It is uncommon to look far into the future in a fast-moving technological world and context and identify how systems would look like at that time and what we would need to develop them. Such a project is always placed in a context that determines its focus, budget, and, consequently, its outcome. We describe the methodology of this research that had to be developed for executing it and our insight about systems in 2050 and the challenges they create. Subsequently, we briefly describe creating a research agenda for systems engineering in 2050 that calls for a collaborative community effort.

Systems thinking is an evolving field, and the demand for engineers with a high systems thinking capacity is growing. Since different areas require specific systems thinking abilities, there is a need for tools to identify people with appropriate systems thinking characteristics for various fields and positions. In this study, we introduce a practical tool for identifying systems thinking abilities via factor analysis of answers to a questionnaire designed to provide insights into such capabilities. The study sample included 120 respondents who completed the questionnaire after a learning process in a seminar on systems thinking principles. A factor analysis divided the questions into five groups, each representing one characteristic of systems thinking. This division of the questionnaire allows for identifying applicants with a high capacity for systems thinking and assessing their specific systems thinking capabilities.

Digital engineering is a transformative strategy that leverages an integrated model-based approach to improve communication, decision making, design understanding, and acquisition efficiency of system development. As modern systems are derived from preexisting systems, harvesting expert knowledge from proven systems in a useful, model-based way will reduce the experiential learning and cognition required for new system development, contributing to a digital engineering transformation. Motivated by performance gains observed during a multiyear, sequential development activity, this survey reviews knowledge, architecture, and pattern literature to establish a framework for research of architectural methods for expert knowledge identification and description using model-based system engineering. The multiyear sequential development activity is offered as the experimental system of interest for this research. This work aims to enable a digital engineering strategy that improves concept phase decision making, accelerates knowledge acquisition from lessons learned repositories, and eases the burden of generational knowledge loss.

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.