Recommender Systems for Variant Management in the Automotive Industry

DS 119: Proceedings of the 33rd Symposium Design for X (DFX2022)

Year: 2022
Editor: Dieter Krause, Kristin Paetzold, Sandro Wartzack
Author: Thorsten Schmidt, Anastasia Marbach, Frank Mantwill
Series: DfX
Institution: Institute of Machine Elements and Computer Aided Product Design (MRP), Helmut Schmidt University Hamburg
Page(s): 10
DOI number: 10.35199/dfx2022.13


This paper transfers some state-of-the-art methods of recommender systems for an application in the product development process of variant rich products in the automotive industry. Therefore, an introduction into the characteristics of the rule-based description of variant-rich products is given, followed by a presentation of three selected recommendation approaches, namely Collaborative Filtering, Association Rule Mining and Bayesian Networks. The presented approaches are then evaluated against the background of the variant-rich product configuration. Advantages and disadvantages of the methods in regard of this special use-case are highlighted and possible applications and limitations are discussed. In conclusion, further research needs for future implementation are identified.

Keywords: recommender systems, variant-rich product description, automotive industry


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