Set‐based design: The state‐of‐practice and research opportunities

Product development efforts now more than ever are in need of methodologies that can address the challenges of increased system complexities, shortening time to market, increased demands in mass customization, market instabilities, geographical barriers, improved innovation, and adaptability to emerging technologies. To address these challenges most companies will need to make key decisions early in the product development life-cycle. In this early phase there are high levels of information uncertainty and information ambiguity. Under these circumstances many companies will converge too early to a point design (Point Based Design-PBD) which will lead to increased cost and schedule delays due to reworking the design later in the product development life cycle. To overcome these challenges many researchers have proposed the Set-Based Design (SBD) methodology. However, there has been limited guidance on how to define, reason, and narrow sets while improving the level of abstraction of the design.To address such concerns, a literature review was conducted. The contributions of this research include: (1) aggregated literature from over 100 sources on quantitative methods (QM) that has not been considered SBD but does support set-based thinking,(2) consolidated body of knowledge on QM to help industrial practitioners implement SBD, (3) defined gaps and opportunities for future research, and (4) defined strengths and limitations of QM and techniques to define, reason and narrow sets.

Section: Previous Literature Reviews On Sbdmentioning

confidence: 99%

Survey on set‐based design (SBD) quantitative methods

Dullen

Verma

Blackburn

et al. 2021

“…Unfortunately, there exist no methodologies within literature describing how to coordinate these decisions in the quantitative SBD process, or describe the potential benefits of such coordination in regards to design convergence. 5 This research presents a comprehensive framework and methodology, facilitating value of information (VOI) enabled design convergence.…”

Section: Research Description Primary Research Contributionmentioning

confidence: 99%

“…Our methodology addresses SBD methodology gaps regarding decision processes, uncertainty reduction, MBE, and program management. 5 This paper builds upon research regarding VOI analysis in support of uncertainty reduction decisions, 6 and program management decision making in quantitative SBD, 1 providing process refinements to each.…”

Section: Research Description Primary Research Contributionmentioning

confidence: 99%

“…Fortunately, quantitative SBD is a growing research area, providing beneficial contributions to the state-of-practice. 5 Many existing quantitative SBD methodologies employ techniques like tradespace analysis using Monte Carlo simulation, 13 dynamic programing, 14 and other optimization methods. 15 However, these methodologies tend to focus on early conceptual design during Phase I, or have limitations regarding design scalability and computational complexity.…”

Section: Quantitative Sbdmentioning

confidence: 99%

See 1 more Smart Citation

Using value of information in quantitative set‐based design

Shallcross

Parnell

Pohl

et al. 2021

Self Cite

Increasing system complexity requires that engineers, systems analysts, and program managers use comprehensive design methodologies to deliver affordable and resilient designs. One method is set-based design (SBD), a product development and managerial process distinctly suited for developing complex systems under uncertainty. SBD simultaneously develops, analyzes, and matures numerous potential design sets, enabling the identification of high-value, affordable, and resilient designs. Published SBD research is a rich source of both qualitative and quantitative methods. This research specifically focuses on quantitative SBD methods to apply a value of information (VOI) methodology enabling design convergence and selection. We build upon previous SBD research to enable design maturation and uncertainty reduction. Our methodology integrates design maturation and multiobjective VOI analysis into a comprehensive quantitative SBD process to guide system development from initial design concepts to the pre-production design decision. In doing so, we also provide refinements and process improvements to existing quantitative SBD methods. We demonstrate our methodology with a model-based UAV design case study using an integrated suite of system, value, and cost models. Our case study specifically focuses on the design maturation and model selection decisions enabling design space convergence.We compare our current results with those from a previous UAV case study, achieving a 41% reduction in required computation time for design space convergence. These results highlight the methodology's ability to reduce program risk and potential to improve SBD convergence efficiency.

“…Unfortunately, limited research exists regarding design maturation decisions in SBD, specifically in regards to the value of collecting additional information in support of these decisions. 5 During system development, program managers and system analysts may wonder if the design space and remaining design sets are sufficiently mature, what are the critical drivers of uncertainty, and what information gathering activities offer the greatest potential to identify and reduce existing design uncertainty? In the absence of established methodologies, design maturation may become an ad hoc and inefficient process leading to premature design decisions, resulting in potentially suboptimal or infeasible system designs.…”

Section: Introductionmentioning

confidence: 99%

A value of information methodology for multiobjective decisions in quantitative set‐based design

Shallcross

Parnell

Pohl

et al. 2021

Self Cite

Engineering complex systems is an exercise in sequential multiobjective decision making under uncertainty. One method for handling this complexity and uncertainty is set-based design (SBD). SBD is a concurrent engineering and management methodology that develops, analyzes, and matures numerous design options, reducing risk and delivering higher value to the stakeholders and end users. SBD accomplishes this through controlled design space convergence which reduces uncertainty and prevents premature design decisions. While SBD has been the subject of numerous scholarly articles, there is limited research providing quantitative methodologies that inform decisions enabling design maturation and convergence. We present a value of information (VOI) based methodology for multiobjective decision problems, and demonstrate its applicability for SBD decisions. We apply Bayesian decision models and information value to inform multiobjective modeling and design maturation decisions. Research contributions include: 1) a framework integrating VOI into the SBD process, 2) a multiobjective VOI method assessing a higher-resolution model's ability to reduce uncertainty, and 3) a means of informing modeling decisions by comparing multiple high resolutions models, given their usage cost and their potential to deliver information value.Finally, we demonstrate the inherent issues associated with premature decisions and traditional point-based design approaches which run the risk of selecting an alternative that later proves infeasible.