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Survey on set‐based design (SBD) quantitative methods
Author(s) -
Dullen Shawn,
Verma Dinesh,
Blackburn Mark,
Whitcomb Cliff
Publication year - 2021
Publication title -
systems engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.474
H-Index - 50
eISSN - 1520-6858
pISSN - 1098-1241
DOI - 10.1002/sys.21580
Subject(s) - new product development , computer science , set (abstract data type) , adaptability , ambiguity , risk analysis (engineering) , product (mathematics) , schedule , product design , mass customization , process management , management science , point (geometry) , systems engineering , operations research , engineering , personalization , business , marketing , economics , geometry , mathematics , management , world wide web , programming language , operating system
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.