Product Competition Prediction in Engineering Design Using Graph Neural Networks
Name
aoje_1_011020.pdf
Description
Published version
Size
651.14 KB
Format
Adobe PDF
Checksum (MD5)
9dc645b541437d1effce47e609b8fd68
Author(s)
Ahmed, Faez
Cui, Yaxin
Fu, Yan
Chen, Wei
Date Issued
May 9, 2022
Journal
ASME Open Journal of Engineering
Publisher
ASME International
Citation
Ahmed, F., Cui, Y., Fu, Y., and Chen, W.Product Competition Prediction in Engineering Design Using Graph Neural Networks ASME Open J. Engineering ASME. January 2022 1 011020.
Version
Final published version
Abstract
Understanding relationships between different products in a market system and predicting how changes in design impact their market position can be instrumental for companies to create better products. We propose a graph neural network-based method for modeling relationships between products, where nodes in a network represent products and edges represent their relationships. Our modeling enables a systematic way to predict the relationship links between unseen products for future years. When applied to a Chinese car market case study, our method based on an inductive graph neural network approach, GraphSAGE, yields double the link prediction performance compared to an existing network modeling method—exponential random graph model-based method for predicting the car co-consideration relationships. Our work also overcomes scalability and multiple data type-related limitations of the traditional network modeling methods by modeling a larger number of attributes, mixed categorical and numerical attributes, and unseen products. While a vanilla GraphSAGE requires a partial network to make predictions, we augment it with an “adjacency prediction model” to circumvent the limitation of needing neighborhood information. Finally, we demonstrate how insights obtained from a permutation-based interpretability analysis can help a manufacturer understand how design attributes impact the predictions of product relationships. Overall, this work provides a systematic data-driven method to predict the relationships between products in a complex network such as the car market.
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1115/1.4054299
