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Research in Engineering Design

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09-09-2015 | Original Paper

Product family architecture design with predictive, data-driven product family design method

Authors: Jungmok Ma, Harrison M. Kim

Published in: Research in Engineering Design | Issue 1/2016

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Abstract

This article addresses the challenge of determining optimal product family architectures with customer preference data. The proposed model, predictive data-driven product family design (PDPFD), expands clustering-based approaches to incorporate a market-driven approach. The market-driven approach provides a profit model in the near future to determine the optimal position and number of product architectures among product architecture candidates generated by the k-means clustering algorithm. An extended market value prediction method is proposed to capture the trend of customer preferences and uncertainties in predictive modeling. A universal electric motors design example is used to demonstrate the implementation of the proposed framework in a hypothetical market. Finally, the comparative study with synthetic data shows that the PDPFD algorithm maximizes the expected profit, while clustering-based models do not consider market so that less profit can be achieved.

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Title: Product family architecture design with predictive, data-driven product family design method
Authors: Jungmok Ma
Harrison M. Kim
Publication date: 09-09-2015
Publisher: Springer London
Published in: Research in Engineering Design / Issue 1/2016
Print ISSN: 0934-9839
Electronic ISSN: 1435-6066
DOI: https://doi.org/10.1007/s00163-015-0201-4

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