Spec guidance for engineering design based on data mining and neural networks

Seyoung Park, Junegak Joung, Harrison Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Recently, many studies on product design have been utilizing online data. They analyze user-generated online data and draw design implications. However, most of them provide customers’ tendency for feature categories rather than spec ranges for sub-features, which are crucial in industrial applications. This paper proposes an approach based on data mining and neural networks to extract spec guidance for engineering design from online data. First, product sub-features are extracted from online data, and customer choice sets are constructed. Next, a neural network choice model is trained based on these choice sets. Finally, the model is interpreted by SHAP (SHapley Additive exPlanations). In the final stage, this study proposes a method for analyzing the obtained SHAP values to draw new design implications. The suggested approach was tested on smartphone review data, and the result provides a set of recommended spec values for each sub-feature. The resultant spec guidance can help companies design a product with spec configuration preferred by customers.

Original languageEnglish (US)
Article number103790
JournalComputers in Industry
Volume144
DOIs
StatePublished - Jan 2023

Keywords

  • Data mining
  • Explainable neural networks
  • Online reviews

ASJC Scopus subject areas

  • General Computer Science
  • General Engineering

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