# Quantitative Set-Based Design to Inform Design Teams
## 4 Insights from Quantitative Set-Based Design
### Providing Design Decisions Insights to Design Teams  
SBD requires greater initial analytical effort than traditional PBD methods. The added effort is justified by the potential to more fully explore the tradespace and develop better system designs. Performing quantitative SBD provides the opportunity to better inform system and sub-system design teams and help them recommend better designs.  
To illustrate this, consider the SBD implementation method found in Figure 6, and the refined UAV model that produced 1165 feasible designs from 100,000 design alternatives with an illustrative team. After developing an integrated model and uniformly developing alternatives, system analysts can consider each individual design decision. They then evaluate the tradespace and model output in order to present the results to the relevant system architects, system engineers, and subsystem designers.  
Figure 13 shows an example tradespace for the refined UAV case study, which uses the EO sensor width choice as the set driver. The analyst presents this tradespace to the corresponding concept architects and EO sensor designers. From the tradespace review, the engineers decide to remove the EO sensor width choices 7, 8, and 9 since these EO sensors contain zero feasible designs. Additionally, they decide to remove EO width choice 6 since its 13 feasible designs provide lower values at a higher cost compared with other EO sensor width choices.  
The system analyst performs this analysis with each design decision and has a similar conversation with the appropriate engineering teams. The analyst uses dominance and infeasibility to provide insights to these teams. The outcomes of these conversations remove the electric engine type, operating altitudes 600 to 1000, and infrared (IR) sensor width choices 6, 7, 8, and 9, in addition to the EO sensor width options.  
The system analyst then updates the integrated model by changing the decision options appropriately and uniformly redeveloping the alternatives. For example, this process removes the 50,000 designs that use the electric engine and then creates 100,000 designs that use the piston engine. The result of this process produces a new revised tradespace, seen in Figure 14. System architects, systems engineers, and subsystem designers have similar conversations as before. This time the EO sensor design team removes EO sensor width choices 1 and 5 since EO sensor width choices 2, 3, and 4 provide equal to greater value at an equal or lower cost. In addition to these EO sensor width options, the other  
Figure 13: Original UAV Tradespace with Electro-Optical Sensor Width Choice as Set Driver.  
engineering teams remove IR sensor width choices 1 and 5, EO sensor field of view 15, and IR sensor field of view 15.  
The system analyst repeats this process of discussing, removing, and recreating alternatives until all teams agree with the final tradespace to propose to the decision authority. Project managers and system analysts will need to balance this uncertainty resolution technique with time.  
We found the example UAV final tradespace after repeating this process three times, shown in Figure 15 and summarized in Table 3. Table 4 summarizes the overall impact on the tradespace by this SBD refinement process. This method of tradespace refinement increased the number of feasible designs by 3626% (1165 to 43,414). This is due to the removal of infeasible or dominated decision options, which allowed the exploration of other combinations of feasible decision options. This creates denser sets, which provides higher confidence for those decision options due to the increase in information.  
Figure 14: First Revised UAV Tradespace with Electro-Optical Sensor Width Choice as Set Driver.  
Figure 15: Final UAV Tradespace with Electro-Optical Sensor Width Choice as Set Driver.