# Quantitative Set-Based Design to Inform Design Teams
## 2 Set-Based Design  
### Point-Based versus Set-Based Design  
The design of complex systems is challenging and very time-consuming. Traditionally, designers and engineers use point-based design (PBD) methods that decompose the system into subsystems or components and use subsystem design expertise to begin preliminary design. In this process, a team of designers concurrently use techniques from their field of expertise to perform analyses. Additionally, subsystems and systems engineers use modeling, simulation, and optimization tools to find good solutions throughout the PBD decomposition process. Optimizing PBD decomposition to solve engineering design problems is not a new research field [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. These methods use system design variables or incorporate value to reflect stakeholder needs for decision making [15]. No matter the optimization method, the end result may be a few good solutions or several \"optimal\" solutions to investigate in further analyses. Of course, real-world complex systems often have a non-linear design space, which makes it difficult to find the true efficient frontier. These methods find \"good\" solutions but not necessarily solutions on the design space's actual Pareto Frontier. This occurs throughout the system design process until the decision-maker selects a single PBD solution for further development. Set-based design (SBD) builds upon the best practices of PBD. One key difference is that SBD considers a large number of alternatives grouped into sets and reduces the number of these sets by increasing the detail and analyses to determine feasibility. [PERSON] et al. [16] illustrate this difference between PBD and SBD to provide a motivation to use SBD. The illustration, seen in Figure 1, shows how SBD has a greater potential to find sets of solutions on the Paetro Frontier based upon its use of a larger number of alternatives. Figure 1: PBD (point-based design) and SBD (set-based design) Tradespace Comparison [16]. In 1993, [PERSON] and [PERSON] [13] published two SBD articles, which discussed a method to find the optimal design of a mechanical system by using sets of specifications. In 1995, [PERSON] et al. [17] used the phrase \"set-based concurrent engineering\" to describe Toyota's design approach, which included delaying decisions, communicating \"ambiguously,\" and producing a large number of alternatives. [PERSON] et al. [17] found from analyzing Toyota's processes that SBD increases communication, collaborators' trust, and parallelism, while reducing the number of meetings and enabling an improved search for a \"globally optimal design\". They further provided a five-step process to perform SBD: (1) define sets of alternatives at the system level; (2) define sets of alternatives at the subsystem level; (3) look at subsystems to find parallels to categorize the sets; (4) converge slowly to a single solution by using step 3 to determine subsystem specifications, and (5) maintain all decisions once they are made [17]. Later, [PERSON] et al. [18] provided three SBD tenets: (1) \"considers large number of designs\", (2) \"allows specialist to consider a design from their own perspective and use the intersection between individual sets to optimize a design\", and (3) \"establish feasibility before commitment\". The third tenet uses a slow set-narrowing process that includes increasing detail, a commitment to the selected set, and an uncertainty management process that uses process gates as elements to \"establish feasibility\". These steps and characteristics are consistent with the work of other researchers [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. However, [PERSON] et al. [36] performed a SBD literature search that demonstrated a lack of quantitative techniques in the previous SBD literature. Additionally, they introduced the need to use decision analysis to capture value and the concepts of set drivers and set modifiers to add greater meaning to the design set definition.