Applying Model-Based Systems Engineering to architecture optimization and selection during system acquisition

Abstract

The architecture selection process early in a major system acquisition is a critical step in determining the overall affordability and technical performance success of a program. There are recognized deficiencies that frequently occur in this step such as poor transparency into the final selection decision and excessive focus on lowest cost, which is not necessarily the best value for all of the stakeholders. This research investigates improvements to the architecture selection process by integrating Model-Based Systems Engineering (MBSE) techniques, enforcing rigorous, quantitative evaluation metrics with a corresponding understanding of uncertainties, and stakeholder feedback in order to generate an architecture that is more optimized and trusted to provide better value for the stakeholders. Three case studies were analyzed to demonstrate this proposed process. The first focused on a satellite communications System of Systems (SoS) acquisition to demonstrate the overall feasibility and applicability of the process. The second investigated an electro-optical remote sensing satellite system to compare this proposed process to a current architecture selection process typified by the United States Department of Defense (U.S. DoD) Analysis of Alternatives (AoA). The third case study analyzed the evaluation of a service-oriented architecture (SOA) providing satellite command and control with cyber security protections in order to demonstrate rigorous accounting of uncertainty through the architecture evaluation and selection. These case studies serve to define and demonstrate a new, more transparent and trusted architecture selection process that consistently provides better value for the stakeholders of a major system acquisition. While the examples in this research focused on U.S. DoD and other major acquisitions, the methodology developed is broadly applicable to other domains where this is a need for optimization of enterprise architectures as the basis for effective system acquisition. The results from the three case studies showed the new process outperformed the current methodology for conducting architecture evaluations in nearly all criteria considered and in particular selects architectures of better value, provides greater visibility into the actual decision making, and improves trust in the decision through a robust understanding of uncertainty. The primary contribution of this research then is improved information support to an architecture selection in the early phases of a system acquisition program. The proposed methodology presents a decision authority with an integrated assessment of each alternative, traceable to the concerns of the system's stakeholders, and thus enables a more informed and objective selection of the preferred alternative. It is recommended that the methodology proposed in this work is considered for future architecture evaluations.