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A Measure of Systems Engineering Effectiveness in Open Access

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This research presents an innovative means of gauging Systems Engineering effectiveness through a Systems Engineering Relative Effectiveness Index (SE REI) model. The SE REI model uses a Bayesian Belief Network to map causal relationships in government acquisitions of Complex Information Systems (CIS), enabling practitioners to identify and analyze Systems Engineering (SE) patterns and predict areas of potential SE performance risk. Although developed in the context of federal government acquisition of CIS, the SE REI model can be adapted to a broad spectrum of systems engineering domains through future research.Progress in Systems Engineering, in particular Systems-of-Systems Engineering, are driving the advance of Complex Information Systems with increasingly complicated constituent systems, components and integration points. As one of the world's largest purchasers of CIS, the federal government is at the forefront of addressing the challenges of acquiring and managing these CIS. Government, along with industry and academia, have invested enormous resources into evolving Systems Engineering (SE) practices to address these challenges and, over time, have integrated new and refined approaches and heuristics (Doskey, Mazzuchi, & Sarkani, 2013a). Although the measurement of SE effectiveness has its own rich history of research in terms of methods, process, and tools, it has not kept pace with the velocity of SE change. Current SE effectiveness measures provide guidance throughout the acquisition lifecycle, but tend to be descriptive instead of inferential in nature and therefore useful for retrospective reviews of programs, or as stage gates through SE processes. In response to the need for more prescriptive options, this research proposes a Systems Engineering Relative Effectiveness Index (SE REI) model. The SE REI model offers acquisition organizations - both government and industry - insights to address the challenges of integrating and evaluating complex programs. It provides predictive analysis of a project's SE challenges early in the acquisition lifecycle, enabling practitioners to assess and mitigate risks before investing significant resources and time in a program. To model a program or project's SE performance, the SE REI applies conditional probability through the use of a Bayesian Belief Network. While the SE REI model focuses on CIS projects with the high level of system integration commonly found in Systems-of-Systems Engineering projects, it is sufficiently extensible to adapt it to other project domains through recalculation of prior probabilities.

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