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METHODICAL APPROACH TO CLUSTER CONFIGURATIONS OF PRODUCT VARIANTS OF COMPLEX PRODUCT PORTFOLIOS

Published online by Cambridge University Press:  19 June 2023

Jan Mehlstäubl ,
Christoph Pfeiffer ,
Ralf Kraul ,
Felix Braun  and
Kristin Paetzold-Byhain
Jan Mehlstäubl*
Affiliation:
Technische Universität Dresden;
Christoph Pfeiffer
Affiliation:
Technische Universität Dresden;
Ralf Kraul
Affiliation:
MAN Truck & Bus SE
Felix Braun
Affiliation:
MAN Truck & Bus SE
Kristin Paetzold-Byhain
Affiliation:
Technische Universität Dresden;
*
Mehlstäubl, Jan, University of the Bundeswehr Munich, Germany, jan.mehlstaeubl@unibw.de

Abstract

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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

ComplexityMachine learningPortfolio management
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2645 - 2654
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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