Decision making in conceptual engineering design: An empirical investigation

Abstract

This paper describes the aims, methodology and results of an empirical research project. The aim was to gather new insight about how collaborative decision-making processes in the conceptual engineering design phase of the product development process takes place. The insight contributes to bridging the gap between the theoretical development of new decision-making methods in academia and the needs of practitioners. The researchers observed, recorded and transcribed three workshops involving groups that were engaged in the collaborative decision-making processes. Through analysing the transcripts, a set of decision-making related activities was identi®ed. Each activity's time consumption was quanti®ed, and a number of decision-making process models at di erent levels of detail were developed. During this analysis, various observations on particular process characteristics were made. It was observed that generating formal structures and documentation was bene®cial to the decision-making process. This was particularly true with respect to developing problem understanding and consistency. The outcomes should be valuable for the design and development of improved decision-support systems.

Selecting system architecture: What a single industrial experiment can tell us about the traps to avoid when choosing selection criteria

“…Most empirical studies focus on the decision-making process when a set of selection criteria is already given (Kihlander, 2011), while very few studies are dedicated to the process of defining, evaluating, and selecting criteria in product development, particularly in preliminary design. Yet the study of Girod et al (2003) suggests that the process of choosing the criteria, according to which the alternatives are evaluated, does not seem to be considered as an important point: in three different groups of students and experts aiming at selecting a concept, a maximum of 10% of selection time is dedicated to definition and weighting of criteria. In return, it seems that concept selection causes many problems in practical cases (Weiss & Hari, 1997) and results in a waste of time and increase of costs due to the rework resulting from wrong decisions (Ullman, 2006).…”

A Scientific Foundation of Collaborative Engineering

“…Communication tools with networking and server-client database accessing functions support the stakeholders in declaring, sharing, and modifying their design prospective. Girod et al [57] conducted an empirical study into how collaborative decision-making processes in conceptual design phase take place. Wang et al [53] presented a comprehensive literature review of existing research projects dealing with collaborative engineering at the concept design stage.…”

Pugh matrix and aggregated by extent analysis using trapezoidal fuzzy number for assessing conceptual designs

“…Decision making in the conceptual phase of engineering design usually involves an evaluation of the design alternatives based on the identified and grouped design features and subfeatures respectively (Green & Mamtani, 2004;Renzi et al, 2015). Two tasks that are usually done by design experts and decision-makers are assigning weights to the relative importance of the design features in the optimal design and assigning weights to the sub-features in order to ascertain and quantify their contributions to the performance of the design features (Girod et al, 2003;Arjun Raj & Vinodh, 2016;Chakraborty et al, 2017). Design expert decision for establishing weight of design features in optimal design has been a long-term source of information for creating comparison among design features and sub-features when trying to select an optimal design from a set of alternative design concepts (Derelöv, 2009;Hambali et al, 2009;Hambali et al, 2011).…”