The Future of Systems Engineering (FuSE) is an INCOSE‐led multiorganizational collaborative initiative pursuing INCOSE's Vision 2025 and beyond. To accomplish this the FuSE initiative encompasses a number of topic areas with active projects to shape the future of systems engineering. This paper addresses the FuSE Security topic area and provides a roadmap of eleven foundational concepts for building the security vision. The purpose of this paper is to instigate and inspire thinking and involvement in the development and practice of the foundational concepts.
, provides linkages to the historical roots and technical underpinnings of this framework, and outlines a catalog of component models for populating multi-level models. This includes a description of the "systems movement," a summary of philosophical underpinnings, a review of seminal concepts, an overview of complex systems, discussion of complex adaptive systems, and contrasts of a range of systems approaches. Alternative modeling frameworks, including multi-level modeling frameworks, problem structuring methods, and computational representations, are also addressed. A proposed framework is presented for multi-level modeling of socio-technical systems, including discussion of the phenomena typically associated with each level, as well as a wide range of models of human behavior and performance. A comparison is provided of multi-level representations of the domains of healthcare delivery, energy consumption, and military operations. An illustrative example is presented focused on counterfeit parts in the military supply chain, in terms of both the consequences of such parts and interdicting the motivations to counterfeit. Finally, a wide range of fundamental research issues underlying multi-level modeling of complex systems is summarized. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 66 19a. NAME OF RESPONSIBLE PERSON a. REPORT unclassified b. ABSTRACT unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98)
Model-based systems engineering (MBSE) is an increasingly accepted practice in the Systems Engineering (SE) community, however, little has been done to empirically show that MBSE provides value. Furthermore, as the industry continues in the direction of digital transformation, MBSE will become a critical component of the larger Digital Engineering (DE) approach. This paper presents a measurement framework for selecting and developing appropriate metrics to assess the value/benefits of MBSE and subsequently DE. Utilizing expected benefits identified in a review of MBSE literature, a causal map was hypothesized to show how expected benefits (potential metrics) influence and relate to each other. This was done in order to systematically determine which benefits would be the most impactful to measure. The hypothesized causal model was presented for feedback to subject-matter experts from a working group developing the first DE measurement framework. This group is a joint effort with industry, academia, and the USA government to develop DE metric standards. Once the causal map was finalized, a case study was used to partially validate the causal model.Based on the causal map and subsequent analysis, we can recommend the first metrics to be employed for DE/MBSE based on the most influential nodes of the causal model.The potential metric candidates include: system quality, defects, time, rework, ease of making changes, system understanding, Effort, accessibility of information, collaboration, project methods/processes, and use of DE/MBSE tools. We believe a concerted effort across the industry to focus on measuring these variables is the most effective way to establish proof of the value of MBSE and DE.
Systems engineering has been considered for a long time both an art and a science. In fact, previous work has shown that principles and practices of systems engineering are exhibited in the creation of a film original score, a major artistic endeavor, and that some of the concepts employed in artistic painting to convey beauty are also used by system architects to reduce complexity and achieve elegance. Reflecting on the authors' experience as practitioners and educators of systems thinking and systems architecting, this paper discusses a common linkage between art and systems engineering through a third discipline, systems thinking, which has a unique role in helping us understand the dynamic nature of complex systems. Then we elaborate a learning model that relates art, systems thinking, and systems architecture, which results in a number of propositions to improve the systems thinking skills of systems architects via learning aesthetic interpretation of art.
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