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Research Papers: Design Automation

A Multidisciplinary Framework to Model Complex Team-Based Product Development

[+] Author and Article Information
Shun Takai

Department of Technology,
Northern Illinois University,
DeKalb, IL 60115
e-mail: stakai@niu.edu

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 27, 2015; final manuscript received March 4, 2016; published online April 15, 2016. Assoc. Editor: Christopher Mattson.

J. Mech. Des 138(6), 061402 (Apr 15, 2016) (12 pages) Paper No: MD-15-1670; doi: 10.1115/1.4033038 History: Received September 27, 2015; Revised March 04, 2016

This paper investigates a multidisciplinary framework that simulates design decisions in a complex team-based product development in which engineers simultaneously work on a team project and individual projects. The proposed framework integrates collaborative design with (1) equilibrium analysis, (2) uncertainty modeling based on behavioral game-theory results, and (3) noncooperative decision making using decision analysis. In the proposed framework, noncooperative decision making is used to simulate engineers’ decisions about team-project commitment and to analyze potential free riding. Collaborative design is used to model design outcomes when engineers commit to the team project. Equilibrium analysis and behavioral game-theory results are used to infer uncertainty about other engineers’ decisions. Decision analysis is used to calculate expected values of decision alternatives. The proposed framework and the design decision making model are illustrated using a pressure vessel design as a team project conducted by two engineers: a design engineer and a materials engineer.

Copyright © 2016 by ASME
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References

Figures

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Fig. 1

Framework for two-engineer project

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Fig. 2

Both engineers commit to team project

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Fig. 3

Only engineer 1 commits to team project

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Fig. 4

Schematics of model in a two-engineer project

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Fig. 5

Decision flow, modeling and analysis flow, and relevant disciplines

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Fig. 7

Engineers’ values

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Fig. 8

Design engineer’s decision tree

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Fig. 9

Comparison of design engineer’s expectations for different values of e

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Fig. 10

Nash equilibrium (Ο: design engineer’s values in Nash equilibrium, Δ: materials engineer’s values in Nash equilibrium). (a) Unique Nash equilibrium, (b) dominant Nash equilibrium, (c) multiple nondominant Nash equilibria, and (d) prisoner’s dilemma.

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Fig. 11

Games with and without dominant equilibrium. (a) With dominant equilibrium and (b) without dominant equilibrium.

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Fig. 12

Inference on Pr(C) for different values of e

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Fig. 13

Design engineer’s expectations with inference on Pr(C)

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Fig. 14

Comparison of design engineer’s expectations for different values of ed and em

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Fig. 15

Inference on Pr(C) for different values of ed and em

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Fig. 16

Design engineer’s expectations with inference on Pr(C) for different values of ed and em

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Fig. 17

Design engineer’s expectations when ed = 0

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Fig. 18

Design engineer’s expectations when ed = 0.5

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Fig. 19

Design engineer’s expectations when ed = 1

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Fig. 20

Design engineer’s decisions for different values of ed and em

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Fig. 21

Design engineer’s expected value for different values of ed and em

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