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Validating the Generational Variety Index (GVI) Through Product Family Optimization: A Preliminary Study

[+] Author Affiliations
Aaron Bobuk, Laura A. Slingerland, Timothy W. Simpson, Ben Donaldson

The Pennsylvania State University, University Park, PA

Karl Reichard

Applied Research Laboratory, State College, PA

Paper No. DETC2010-28573, pp. 879-889; 11 pages
  • ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1: 36th Design Automation Conference, Parts A and B
  • Montreal, Quebec, Canada, August 15–18, 2010
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4409-0 | eISBN: 978-0-7918-3881-5
  • Copyright © 2010 by ASME


Effective product platforms must strike an optimal balance between commonality and variety. Increasing commonality can reduce costs by improving economies of scale while increasing variety can improve market performance, or in our robot family example, satisfy various robot missions. Two metrics that have been developed to help resolve this tradeoff are the Generational Variety Index (GVI) and the Product Family Penalty Function (PFPF). GVI provides a metric to measure the amount of product redesign that is required for subsequent product offerings, whereas PFPF measures the dissimilarity or lack of commonality between design (input) parameters during product family optimization. GVI is examined because it is the most widely used metric applicable during conceptual development to determine platform components. PFPF is used to validate GVI because of its ease of implement for parametric variety, as used in this case. This paper describes a product family trade study that has been performed using GVI for a robot product family and compares the results to those obtained by optimizing the same family using PFPF. This work provides a first attempt to validate the output of GVI by using a complementary set of results obtained from optimization. The results of this study indicate that while there are sometimes similarities between the results of GVI and optimization using PFPF, there is not necessarily a direct correlation between these two metrics. Moreover, the platform recommended by GVI is not necessarily the most performance-optimized platform, but it can help improve commonality. In the same regard, PFPF may miss certain opportunities for commonality. The benefits of integrating the two approaches are also discussed.

Copyright © 2010 by ASME
Topics: Optimization



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