# Quantitative Set-Based Design to Inform Design Teams
## 2 Set-Based Design
### Quantitative Set-Based Design with System Design Teams  
Quantitative SBD implementation methodologies are an emerging area of research. [PERSON] et al. [38] provided an SBD scheme for product development that \"minimizes the impacts, by proactively considering the possibility of changes in the external factors and the implication of mid-course design changes.\" [PERSON] et al. [39] introduced and demonstrated an SBD tradespace exploration implementation process for early system design, as shown in Figure 3. They also demonstrated that SBD requires the use of MBE and an integrated framework in an early design to adequately explore the tradespace. Figure 3: System Analysts SBD Tradespace Exploration Process (Modified from Ref. [39]). Figure 2: SBD Conceptual Framework for System Design. Figure 3 starts by determining the mission needs and system requirements. It then requires the creation of an integrated model that connects these requirements to design decisions, and ultimately, to an affordability analysis. Instead of using optimization, the SBD process uses Monte Carlo simulation to uniformly develop the alternatives and evaluate the alternatives using desired response variables, such as system performance or cost, as illustrated in Figure 4 as an affordability analysis (system performance versus system cost). The integrated model assesses the feasibility of each alternative based on the requirements. If the number of feasible designs is unacceptable, the mission needs and system requirements are reevaluated, and the model is updated. The feasible points in the tradespace can be grouped into sets once the number of feasible designs is found to be acceptable by the decision authority. SBD's use of sets enables additional analyses, such as the one illustrated in Figure 5. These sets are evaluated to provide insights into the tradespace and system requirements. Analysts use dominance to eliminate sets. For example, Figure 5 demonstrates that engine type P dominates engine type E. Analysts then present the remaining sets of alternatives to the decision authority to carry forward to the next design phase. The process in Figure 3 provides an \"analyst\" perspective or the technical aspect of SBD, but it does not describe how to use that analysis to encourage interactions between subsystem design teams, i.e., the social aspect of SBD, to inform design decisions. The qualitative SBD literature uses a parallel approach, which requires interaction with subsystem design teams. This research uses the early design set-based design tradespace exploration  
Figure 4: SBD Feasible Tradespace Created by Monte Carlo Simulation. Figure 5: Tradespace Classified by Engine Type as Sets. process found in Figure 3 by using quantitative analysis to provide design teams with the ability to assess the impact of their proposed subsystem design on the system. Figure 6 depicts our proposed quantitative SBD implementation methodology with the roles and responsibilities described in Table 1. Many organizations might not have all of these roles or may have other names for them. The engineering manager or systems engineer may be expected to fill many of these roles. The implementation of this method requires technical and social skills. Implementers must be able to perform the required analysis, while managing the overall process and communicating with the right stakeholders at the right time in the most effective manner. As with any system design, the process begins with mission analysis. This opportunity or capability shortfall results in a need for a new system design. Following need validation and use case development, systems engineers define design concepts and request design models from the various system architects and subsystem design teams. Analysts then construct an integrated system model from these subsystem and system models, as well as other analytical models and simulations, which are consolidated in an MBE environment.