Many companies design families of products based on product platforms that enable economies of scale and scope while satisfying a variety of market applications. Product family design is a difficult and challenging task, and a variety of methods and tools have been created to support this platform-based product development. Unfortunately, many of these methods and tools have been developed-and consequently exist-in isolation from one other. In this paper, we introduce an approach to integrate several of these disparate tools into a framework to translate user needs and requirements into commonality specifications during product family design. The novelty of the approach lies in how we integrate the market segmentation grid, Generational Variety Index (GVI), Design Structure Matrix (DSM), commonality indices, mathematical modeling and optimization, and multi-dimensional data visualization tools to identify what to make common, what to make unique, and what parameter settings are best for each component and/or subsystem in the product family. The design of a family of unmanned ground vehicles (UGVs) demonstrates the proposed approach and highlights its benefits and limitations.

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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.