Insight最新文献

Systems engineering has numerous guiding propositions scattered across various publications and classified under different schema, leading to confusion and inconsistency. This paper presents a framework for understanding the origin and evolution of any guiding proposition and developing such a guiding proposition into a principle to meet the challenges of Industry 4.0 and Society 5.0. We argue that following this process will enhance the elegance and transdisciplinary value of systems engineering principles and aid in solving complex problems.

National security challenges require a new approach to collaborative problem solving to address emergent challenges or opportunities. To effectively address these challenges, development of artificial intelligence (AI) technologies including machine learning (ML) and deep learning (DL), is underway. Advancing AI/ML capabilities requires transdisciplinary research encompassing the fusion of technology and emergent scientific discovery. Achieving this requires a departure from traditional research and development (R&D) methods. New development processes need to support the understanding that research progresses iteratively technology insertion is incremental, and the final capability is evolutionary. We propose a novel systems engineering/research model called the vortical model. The vortical model introduces an iterative framework through which emerging advances in research outcomes are effectively demonstrated and validated for integration, as new capabilities, at varying technology insertion points. Our goal is to facilitate the transfer of knowledge from emerging research for swift, effective integration into the organization's mission capabilities.

The future value of systems engineering may well be measured by its contribution to INCOSE's vision of “a better world through a systems approach.” To stay relevant, systems engineering must expand its scope beyond the technical realm by addressing today's most pressing and complex problems, which span technical, social, and ecological domains. This paper builds on our previous work on the evolving architecture of the systems engineering discipline, detailing how it can maintain its value by effectively engaging in eco-socio-technical challenges. We propose collaborative strategies with other disciplines to enhance and broaden its foundational base, which will be crucial for realizing its potential as a transdisciplinary field in an increasingly complex world.

In an increasingly complex landscape of advanced technologies, the question of how systems engineering can retain its relevance is more pertinent than ever. Originating with a pragmatic focus on achieving technical objectives, systems engineering has shifted towards process and methodology. We argue, however, that it's time for this discipline to return to its roots and embrace its nature as a transdiscipline. Transdisciplinarity is not just a characteristic of systems engineering; it's necessary for devising elegant solutions to today's complex challenges. In this article, we present a comprehensive framework around the nature of systems engineering, detailing its principles, methods, and purposes. This framework demonstrates how systems engineering is linked to numerous disciplines and social institutions, showcasing its multifaceted impact. By understanding and using this framework as a lens on the discipline, we can foster a common recognition of systems engineering's value, ensuring its continued significance in a rapidly evolving world.

This article offers insights from five INCOSE Fellows on the evolution and significance of transdisciplinarity in systems engineering. Michael Pennotti reviews the origins of systems engineering, emphasizing its inherent transdisciplinary nature and the need for continuous evolution. Azad Madni considers transdisciplinarity as systems engineering's true calling, crucial for the 21st century, and highlights his TRASEE™ education paradigm that underpins the Systems Architecting and Engineering program that he directs at the University of Southern California as pivotal for systems engineering's advancement. Hillary Sillitto sees the climate crisis as systems engineering's most critical and complex challenge, asserting transdisciplinarity's crucial role in addressing it. David Rousseau examines the cultural and scientific underpinnings of transdisciplinarity, presenting systems engineering as a prime example. Peter Brook envisions the joint evolution of systems sciences and systems engineering to confront future challenges, advocating for transdisciplinarity as an essential role in systems engineering leadership for addressing global challenges.

In the early stages of systems development, systems engineers will typically evaluate alternatives based on performance, cost, risk, and schedule to evaluate the solution space of alternatives. While these criteria have proven to be successful, there is growing interest in the analysis of carbon costs as well to contribute to the decision making. These decision criteria are very good to help the decision maker select the best alternative within the solution space in which to develop a system concept. We offer another criterion for consideration to account for carbon expenditure throughout the systems engineering lifecycle. We believe that including this dimension can influence decision makers to evaluate a richer portion of the solution space. This approach is developed and exercised with a notional example.

Industry 4.0, the Internet of Things, and large-scale system-to-system interactions are driving digital transformation in the industry. Model-based systems engineering (MBSE) is one of the core paradigms behind this transformation. MBSE practices are widely applied to enterprise (including system of systems and mission) architectures, which become a crucial part of successful digital transformation. The core challenge today is not only how digital continuity can be maintained by connecting different layers of models (such as system models to system-of-systems models), but also how to perform detailed analysis and simulation at the enterprise level model. This paper studies Systems Modeling Language (SysML®) as the standard language to model systems, Unified Architecture Framework (UAF) as the framework, Unified Architecture Framework Modeling Language (UAFML) as the language to model enterprise architectures and proposes an approach for end-to-end co-execution of the integrated enterprise model.