2016 SE Doctoral Dissertation Showcase – Exemplary Recognition
A framework for creating value robust systems in the face of changing value perceptions during the architecture and design of systems is proposed. Both unarticulated value, that which is not explicitly communicated to system designers, and dynamic value, that which changes over time, are used to motivate the dynamic Multi-Attribute Tradespace Exploration (MATE) process. Value can be represented as decision maker perceived attributes, which can be classified according to the ease by which the system can display them. The attribute class spectrum from least to most costly ranges from articulated, class 0 attributes, to inaccessible value, class 4 attributes. Supporting the value-adding approach, the system property concepts of flexibility, adaptability, rigidity, robustness, scalability, and modifiability are proposed to be different aspects of the same concept: changeability.
A quantification of changeability is shown to be the Filtered Outdegree of a design within a networked tradespace formed through explicit consideration of transition paths between design instantiations. A focus on designing not only for value, but for changeability as well, leads to the concept of path enabling variables, whose purpose is to increase change paths or decrease cost for change. Value robustness is shown to be achieved through either passive or active means. Passive value robustness can be quantified as the Pareto Trace number of a design, reflecting the number of contexts within which a particular design is determined to be best value at a given level of resource expenditure. Active value robustness is achieved through a strategy of pursuing designs with increased changeability and accessibility to likely high value regions of a tradespace. Supporting the process, the Design-Value Matrix and the Rule-Effects Matrix help system designers visualize the key factors for creating dynamic value-generating systems by capturing the important relationships between decision makers, design variables, attributes, path enablers, and resources.
The dynamic MATE process is applied to two real system cases including the Joint Direct Attack Munition (JDAM) and the Terrestrial Planet Finder (TPF). The framework is shown to be applicable at both quantitative and qualitative levels, giving insight into assessing and designing for changeability and value robustness for systems.
Adam Ross received the PhD in Engineering Systems, MIT, an SM in Aeronautics and Astronautics, MIT, an SM in Technology and Policy, MIT, and an AB in Physics and Astronomy and Astrophysics, Harvard University. His Research Areas are managing unarticulated value, designing for changeability and value robustness, dynamic tradespace exploration for complex systems, visual analytics, and game-based media for systems education. <Please complete> Dr. Adam Ross is a research scientist in the Engineering Systems Division at the Massachusetts Institute of Technology. He is co-founder and lead research scientist for MIT’s Systems Engineering Advancement Research Initiative (SEAri), a research group focused on advancing the theories, methods, and effective practice of systems engineering applied to complex socio-technical systems through collaborative research with industry and government. Dr. Ross has professional experience working with government, industry, and academia. He holds a dual bachelor degree in Physics and Astrophysics from Harvard University, two masters degrees in Aerospace Engineering and Technology & Policy, as well as a doctoral degree in Engineering Systems from MIT. Dr. Ross has research interests and advises students in ongoing research projects in advanced systems design and selection methods, tradespace exploration, managing unarticulated value, designing for changeability, value-based decision analysis, and systems-of-systems engineering. He has received numerous paper awards, including the Systems Engineering 2008 Outstanding Journal Paper of the Year. Dr. Ross has published over 80 papers in the area of space systems design, systems engineering, and tradespace exploration. He has led ten years of research and development of novel systems engineering methods and techniques for evaluating and valuing system tradespaces and the “ilities” across alternative futures during early phase design. His approach is trans?disciplinary, leveraging techniques from engineering design, operations research, behavioral economics, and data visualization. He serves on technical committees with both AIAA and IEEE, and is recognized as a leading expert in system tradespace exploration and change-related “ilities.”
Adam Michael Ross, Research Scientist, Systems Engineering Advancement Research Initiative, Engineering Systems Division, Massachusetts Institute of Technology
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