Abstract. Tradeoff studies are a critical tool to provide information to support decision making for discipline engineers, systems engineers, and program managers throughout the system life cycle. Unfortunately, the quality of trade studies is inconsistent between organizations and within organizations. This paper reports on part of an INCOSE effort to improve tradeoff studies and discusses a proposed INCOSE Decision Management Process aligned with ISO/IEC 15288. The proposed process discussed in this paper integrates decision analysis best practices with systems engineering activities to create a baseline from which future papers can explore possible innovations to further enhance tradeoff study quality. The process enables enterprises to develop an in-depth understanding of the complex relationship between requirements, the design choices made to address each requirement, and the system level consequences of the sum of design choices across the full set of performance requirements as well as other elements of stakeholder value to include cost and schedule. Through data visualization techniques, decision makers can quickly understand and crisply communicate a complex trade-space and converge on recommendations that are robust in the presence of uncertainty.

The U.S. Department of Defense (DoD) has recently revised the defense acquisition system to address suspected root causes hindering higher success rates. This article applies two systems thinking methodologies in a uniquely integrated fashion to provide an in‐depth review and compelling interpretation of the revised defense acquisition system as put forth in January 7, 2015 DoDI 5000.02. Changes from the previous defense acquisition system are significant and may be cause for some cautious optimism in the United States. This article describes how the architects of the revised defense acquisition system have increased emphasis on systems engineering activities applied early in the lifecycle so that meaningful trade‐offs between capability requirements and lifecycle costs can be explored as requirements are being written to ensure realistic program baselines are established such that associated lifecycle costs will likely fit within future budgets. Expressed as emerging systems engineering research questions, this article identifies several gaps that are likely to emerge as the defense acquisition community attempts to execute the new acquisition system.

The DoD and Engineered Resilient Systems (ERS) community seek to leverage the capabilities of model‐based engineering (MBE) early in the design process to improve decision making in AoAs. Traditional tradespace exploration with point‐based design often converges quickly on a solution and engineering changes are required after this selection. Set‐based design considers sets of all possible solutions and enables down‐selecting possibilities to converge at a final solution. Using an Army case study and an open source excel add‐in called SIPMath, this research develops an integrated MBE model and example that simultaneously generates numerous designs through physics models into the value and cost tradespace allowing exploration for set‐based design analysis and producing a better efficient frontier than traditional point‐based design AoAs. Grouping design decisions into sets based on their characteristic decision, and simultaneously evaluating the value and cost tradespace, allows for a set‐based design approach that provides insight into the design decisions.

The Engineered Resilient Systems research program seeks to improve decision making in the Analysis of Alternatives process by leveraging model-based engineering (MBE) early in the design process to develop more resilient systems. Traditional tradespace exploration using point-based design often converges quickly to an initial baseline design concept with subsequent engineering changes to modify the design. However, this process can lead to significant cost growth if the initial concept is not able to meet requirements or if the revised design is not affordable. Enabled by MBE, set-based design (SBD) considers sets of all possible design concepts and down-selects design concepts to converge to a final design using insights into design trade-off analysis, modeling and simulation, and test data. Using a notional unmanned aerial vehicle case study with low-fidelity physics-based models and an open source Excel® add-in called SIPmath©, this research implements an integrated MBE trade-off analytics framework that simultaneously generates numerous SBDs using parametric performance and cost models and evaluates the designs in the value and cost tradespace. In addition, this research explores incorporating resilience quantification and uncertainty into SBD trade-off analysis. Future research is needed to validate the use of SBD with low-fidelity models for tradespace exploration in early system design.

Recently, efforts to model and assess a system's resilience to disruptions due to environmental and adversarial threats have increased substantially. Researchers have investigated resilience in many disciplines, including sociology, psychology, computer networks, and engineering systems, to name a few. When assessing engineering system resilience, the resilience assessment typically considers a single performance measure, a disruption, a loss of performance, the time required to recover, or a combination of these elements. We define and use a resilient engineered system definition that separates system resilience into platform and mission resilience. Most complex systems have multiple performance measures; this research proposes using multiple objective decision analysis to assess system resilience for systems with multiple performance measures using two distinct methods. The first method quantifies platform resilience and includes resilience and other "ilities" directly in the value hierarchy, while the second method quantifies mission resilience and uses the "ilities" in the calculation of the expected mission performance for every performance measure in the value hierarchy. We illustrate the mission resilience method using a transportation systems-of-systems network with varying levels of resilience due to the level of connectivity and autonomy of the vehicles and platform resilience by using a notional military example. Our analysis found that it is necessary to quantify performance in context with specific mission(s) and scenario(s) under specific threat(s) and then use modeling and simulation to help determine the resilience of a system for a given set of conditions. The example demonstrates how incorporating system mission resilience can improve performance for some performance measures while negatively affecting others.