Research Papers: Design Theory and Methodology

Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test

[+] Author and Article Information
Christopher McComb

Department of Mechanical Engineering,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: ccm@cmu.edu

Jonathan Cagan

Fellow ASME
Department of Mechanical Engineering,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: cagan@cmu.edu

Kenneth Kotovsky

Department of Psychology,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: kotovsky@cmu.edu

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received July 9, 2016; final manuscript received January 10, 2017; published online February 6, 2017. Editor: Irem Tumer.

J. Mech. Des 139(4), 041101 (Feb 06, 2017) (12 pages) Paper No: MD-16-1497; doi: 10.1115/1.4035793 History: Received July 09, 2016; Revised January 10, 2017

The performance of a team with the right characteristics can exceed the mere sum of the constituent members' individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is vital that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of configuration design problems can be leveraged to select the best values for team characteristics (specifically team size and interaction frequency). A computational model of design teams which has been shown to effectively emulate human team behavior is employed to pinpoint optimized team characteristics for solving a variety of configuration design problems. These configuration design problems are characterized with respect to the local and global structure of the design space, the alignment between objectives, and the resources allotted for solving the problem. Regression analysis is then used to create equations for predicting optimized values for team characteristics based on problem properties. These equations achieve moderate to high accuracy, making it possible to design teams based on those problem properties. Further analysis reveals hypotheses about how the problem properties can influence a team's search for solutions. This work also conducts a cognitive study on a different problem to test the predictive equations. For a configuration problem of moderate size, the model predicts that zero interaction between team members should lead to the best outcome. A cognitive study of human teams verifies this surprising prediction, offering partial validation of the predictive theory.

Copyright © 2017 by ASME
Topics: Design , Teams
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Grahic Jump Location
Fig. 1

Example solutions to structural design problems, showing required loads and supports: (a) narrow-base tower layout, (b) wide-base tower layout, (c) single-span bridge layout, and (d) double-span bridge layout

Grahic Jump Location
Fig. 2

Example solutions to fluid channel design problems, showing pressures at required inlets and outlets: (a) concentric water distribution network, (b) eccentric water distribution network, (c) concentric oil distribution network, and (d) eccentric oil distribution network

Grahic Jump Location
Fig. 8

Contribution to final model for case B, main effects + interactions

Grahic Jump Location
Fig. 7

Contribution to final model for case B, main effects only

Grahic Jump Location
Fig. 6

Contribution to final model for case A, main effects + interactions

Grahic Jump Location
Fig. 5

Contribution to final model for case A, main effects only

Grahic Jump Location
Fig. 4

Determining the optimal team characteristics for (a) case A and (b) case B

Grahic Jump Location
Fig. 3

Random walk example

Grahic Jump Location
Fig. 9

Quality of best solutions with respect to total cost. Error bars indicate ±1 standard error.




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