Using Network Science to Estimate the Cost of Architectural Growth

Between 1997 and 2009, 47 major defense acquisition programs experienced cost overruns of at least 15% or 30% over their current or original baseline estimates, respectively (GAO, 2011, p. 1). Known formally as a Nunn-McCurdy breach (GAO, 2011, p. 1), the reasons for this excessive growth are myriad, although nearly 70% of the cases identified engineering and design issues as a contributing factor (GAO, 2011, p. 5). Accordingly, Congress legislatively acknowledged the need for change in 2009 with the passage of the Weapon Systems Acquisition Reform Act (WSARA, 2009), which mandated additional rigor and accountability in early life cycle (or Pre-Milestone A) cost estimation. Consistent with this effort, the Department of Defense has recently required more system specification earlier in the life cycle, notably the submission of detailed architectural models, and this has created opportunities for new approaches. In this dissertation, I describe my effort to transform one such model (or view), namely the SV-3, into computational knowledge that can be leveraged in Pre-Milestone A cost estimation and risk analysis. The principal contribution of my work is Algorithm 3-a novel, network science-based method for estimating the cost of unforeseen architectural growth in defense programs. Specifically, using number theory, network science, simulation, and statistical analysis, I simultaneously find the best fitting probability mass functions and strengths of preferential attachment for an incoming subsystem's interfaces, and I apply blockmodeling to find the SV-3's globally optimal macrostructure. Leveraging these inputs, I use Monte Carlo simulation and the Constructive Systems Engineering Cost Model to estimate the systems engineering effort required to connect a new subsystem to the existing architecture. This effort is chronicled by the five articles given in Appendices A through C, and it is summarized in Chapter 2.In addition to Algorithm 3, there are several important, tangential outcomes of this work, including: an explicit connection between Model Based System Engineering and parametric cost modeling, a general procedure for organizations to improve the measurement reliability of their early life cycle cost estimates, and several exact and heuristic methods for the blockmodeling of one-, two-, and mixed-mode networks. More generally, this research highlights the benefits of applying network science to systems engineering, and it reinforces the value of viewing architectural models as computational objects.Dissertation not available (per author's request