The purpose of this paper is to investigate if early stage function models of design can be used to predict the market-value of a commercial product. In previous research, several metrics of complexity of graph-based product models have been proposed and suitably chosen combinations of these metrics have been shown to predict the time required in assembling commercial products. By extension, this research investigates if this approach, using new sets of combinations of complexity metrics, can predict market-value. To this end, the complexity values of function structures for eighteen products from the Design Repository are determined from their function structure graphs, while their market values are procured from different vendor quotes in the open market. The complexity and value information for fourteen samples are used to train a neural net program to define a predictive mapping scheme. This program is then used to predict the value of the final four products. The results of this approach demonstrate that complexity metrics can be used as inputs to neural networks to establish an accurate mapping from function structure design representations to market values to within the distribution of values for products of similar type.
- Design Engineering Division and Computers and Information in Engineering Division
Complexity as a Surrogate Mapping Between Function Models and Market Value
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Mathieson, JL, Shanthakumar, A, Sen, C, Arlitt, R, Summers, JD, & Stone, R. "Complexity as a Surrogate Mapping Between Function Models and Market Value." Proceedings of the ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 9: 23rd International Conference on Design Theory and Methodology; 16th Design for Manufacturing and the Life Cycle Conference. Washington, DC, USA. August 28–31, 2011. pp. 55-64. ASME. https://doi.org/10.1115/DETC2011-47481
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