Ideal Design Process characterization: the impact of preliminary decision-making tools in the consumption of resources and its uncertainty

AbstractThis study presents a set of definitions and properties that characterize the Ideal Design Process and the resources consumed during this process. As an application of both characterizations, the impact of preliminary decision-making tools in the consumption of resources and its uncertainty is introduced, deriving the conditions that a preliminary decision-making tool needs to satisfy to improve the design process. It is assumed the design process improves when the best design is obtained with a lower consumption of resources (time and money) and with a lower uncertainty, both concepts related to the acquired complexity and the risk. Axiomatic design is studied under this framework, showing evidences that indicate it satisfies this set of properties. Design scenarios where the preliminary decision-making tool deteriorates the design process are also found.

[1]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[2]  Mogens Myrup Andreasen,et al.  Integrated Product Development , 1987 .

[3]  Roman Žavbi,et al.  Synthesis of elementary product concepts based on knowledge twisting , 2010 .

[4]  Victor V. Kryssanov,et al.  Understanding design fundamentals: how synthesis and analysis drive creativity, resulting in emergence , 2001, Artif. Intell. Eng..

[5]  Efrén Moreno Benavides,et al.  Advanced Engineering Design , 2012 .

[6]  Steven M. Smith,et al.  Metrics for measuring ideation effectiveness , 2003 .

[7]  Belinda López-Mesa,et al.  A study of the use of concept selection methods from inside a company , 2011 .

[8]  K. Sudhakar,et al.  Optimized sequencing of analysis components in multidisciplinary systems , 2010 .

[9]  Yoram Reich,et al.  A critical review of General Design Theory , 1995 .

[10]  B. Rost,et al.  International Accounting Standards Board , 2010 .

[11]  Yoram Reich,et al.  Designing the process design process , 1997 .

[12]  Efrén Moreno Benavides,et al.  Advanced Engineering Design: An Integrated Approach , 2011 .

[13]  A. Osborn.  Applied imagination : principles and procedures of creative problem-solving , 1957 .

[14]  Brigitte Moench,et al.  Engineering Design A Systematic Approach , 2016 .

[15]  Nam P. Suh,et al.  principles in design , 1990 .

[16]  P. John Clarkson,et al.  Planning development processes for complex products , 2010 .

[17]  Stephen C.-Y. Lu,et al.  Complexity in design of technical systems , 2009 .

[18]  Clive L. Dym,et al.  Engineering Design: A Synthesis of Views , 1994 .

[19]  Yoram Reich,et al.  SOS – subjective objective system for generating optimal product concepts , 2005 .

[20]  R. H. Myers,et al.  Probability and Statistics for Engineers and Scientists , 1978 .

[21]  Cengiz Kahraman,et al.  Applications of axiomatic design principles: A literature review , 2010, Expert Syst. Appl..

[22]  Edwin T. Jaynes.  Prior Probabilities , 2010, Encyclopedia of Machine Learning.

[23]  S. Varughese,et al.  PRESENTATION OF FINANCIAL STATEMENTS , 2014, WILEY 2020 Interpretation and Application of IFRS® Standards.

[24]  Maria C. Yang,et al.  An approach to the extraction of preference-related information from design team language , 2011, Research in Engineering Design.

[25]  Jérémy Legardeur,et al.  Lessons learned from an empirical study of the early design phases of an unfulfilled innovation , 2010 .

[26]  Gregory M. Mocko,et al.  Engineering design complexity: an investigation of methods and measures , 2008 .

[27]  R.G. Weber,et al.  Conceptual design using a synergistically compatible morphological matrix , 1998, FIE '98. 28th Annual Frontiers in Education Conference. Moving from 'Teacher-Centered' to 'Learner-Centered' Education. Conference Proceedings (Cat. No.98CH36214).

[28]  Kristin L. Wood,et al.  Computations with Imprecise Parameters in Engineering Design: Background and Theory , 1989 .

[29]  Peter Matthews,et al.  Challenges to Bayesian decision support using morphological matrices for design: empirical evidence , 2011 .

[30]  E. Antonsson,et al.  The Method of Imprecision Compared to Utility Theory for Design Selection Problems , 1993 .

[31]  Suh Nam-pyo,et al.  Complexity: Theory and Applications , 2005 .

[32]  Yoram Reich,et al.  Managing product design quality under resource constraints , 2004 .

[33]  Cengiz Kahraman,et al.  A new multi-attribute decision making method: Hierarchical fuzzy axiomatic design , 2009, Expert Syst. Appl..

[34]  Nam P. Suh,et al.  Axiomatic Design: Advances and Applications , 2001 .