期刊
RESEARCH IN ENGINEERING DESIGN
卷 27, 期 1, 页码 5-21出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s00163-015-0201-4
关键词
Product family design; Clustering-based approach; Market-driven approach; Prediction intervals; Predictive design analytics
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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