Methodical Approach to cluster Configurations of Product Variants of Complex Product Portfolios

DS 122: Proceedings of the Design Society: 24th International Conference on Engineering Design (ICED23)

Year: 2023
Editor: Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nadège Troussier
Author: Mehlstäubl, Jan (1); Pfeiffer, Christoph (1); Kraul, Ralf (2); Braun, Felix (2); Paetzold-Byhain, Kristin (1)
Series: ICED
Institution: 1: Technische Universität Dresden; 2: MAN Truck & Bus SE
Section: Design Methods
Page(s): 2645-2654
DOI number:


Companies are increasingly struggling to manage their complex product portfolios. Since they do not fully understand the complexity, intelligent solutions are required. Emerging technologies and tools offer new ways to deal with existing problems. With the help of clustering, similarities between product variants can be identified automatically, and complexity can be systematically reduced. This article aims to develop a methodological approach to identify correlations between product variants in complex product portfolios automatically by using clustering algorithms. The approach includes the systematic cleaning and transformation of product portfolio data. In addition, a guide for algorithm selection and evaluation of clustering results is presented. As the last step, the results are systematically analysed and visualised. To validate the methodical approach, it is applied to a real-world data set of a commercial vehicle manufacturer and the usefulness of the results is confirmed in an expert workshop.

Keywords: Complexity, Machine learning, Portfolio management

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