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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, [email protected]

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

References

Abdi, H. and Valentin, D. (2007), “Multiple correspondence analysis”, Encyclopedia of Measurement and Statistics, Vol. 2 No. 4, pp. 651–657.Google Scholar
Blessing, L.T.M. and Chakrabarti, A. (2009), DRM, a Design Research Methodology, DRM, a Design Research Methodology, Springer, https://dx.doi.org/10.1007/978-1-84882-587-1.CrossRefGoogle Scholar
Braun, F., Kreimeyer, M., Kopal, B. and Paetzold, K. (2017), “Herausforderungen in der Validierung der Variantenbeschreibung komplexer Produkte”, DFX 2017: Proceedings of the 28th Symposium Design for X, pp. 61–73.Google Scholar
Chan, K.Y., Kwong, C.K. and Hu, B.Q. (2012), “Market segmentation and ideal point identification for new product design using fuzzy data compression and fuzzy clustering methods”, Applied Soft Computing Journal, Vol. 12 No. 4, pp. 1371–1378, https://dx.doi.org/10.1016/j.asoc.2011.11.026.CrossRefGoogle Scholar
Davies, D.L. and Bouldin, D.W. (1979), “A cluster separation measure”, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE, No. 2, pp. 224–227.CrossRefGoogle Scholar
DIN 199-1. (2002), “Technische Produktdokumentation-CAD-Modelle, Zeichnungen und Stücklisten-Teil 1: Begriffe”, Beuth Berlin.Google Scholar
Géron, A. (2017), Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media.Google Scholar
Greisel, M., Kissel, M., Spinola, B. and Kreimeyer, M. (2013), “Design for adaptability in multi-variant product families”, Proceedings of the International Conference on Engineering Design, ICED, Vol. 4 DS75-04 No. August, pp. 179–188.Google Scholar
Han, J., Pei, J. and Tong, H. (2012), Data Mining: Concepts and Techniques, doi: https://doi.org/10.1016/C2009-0-61819-5.CrossRefGoogle Scholar
Hancock, J.T. and Khoshgoftaar, T.M. (2020), “Survey on categorical data for neural networks”, Journal of Big Data, SpringerOpen, Vol. 7 No. 1, pp. 1–41.Google Scholar
Hochdörffer, J., Laule, C. and Lanza, G. (2018), “Product variety management using data-mining methods - Reducing planning complexity by applying clustering analysis on product portfolios”, IEEE International Conference on Industrial Engineering and Engineering Management, Vol. 2017-Decem, pp. 593–597, https://dx.doi.org/10.1109/IEEM.2017.8289960.CrossRefGoogle Scholar
Hu, S.J., Zhu, X., Wang, H. and Koren, Y. (2008), “Product variety and manufacturing complexity in assembly systems and supply chains”, CIRP Annals - Manufacturing Technology, Vol. 57 No. 1, pp. 45–48, https://dx.doi.org/10.1016/j.cirp.2008.03.138.CrossRefGoogle Scholar
Jonas, H. (2013), Eine Methode Zur Strategischen Planung Modularer Produktprogramme, Technische Universität Hamburg-Harburg.Google Scholar
Kissel, M.P. (2014), “Mustererkennung in komplexen Produktportfolios”, p. 212.Google Scholar
Krause, D. and Gebhardt, N. (2018), Methoden Zur Entwicklung Modularer Produktfamilien, Methodische Entwicklung Modularer Produktfamilien, Vol. №3, https://dx.doi.org/10.1007/978-3-662-53040-5_6.CrossRefGoogle Scholar
Kreimeyer, M., Baumberger, C., Deubzer, F. and Ziethen, D. (2016), “An integrated product information model for variant design in commercial vehicle development”, Proceedings of International Design Conference, DESIGN, Vol. DS 84 No. 1, pp. 707–716.Google Scholar
Kreimeyer, M., Förg, A. and Lienkamp, M. (2013), “Mehrstufige modulorientierte Baukastenentwicklung für Nutzfahrzeuge”, VDI-Berichte, No. 2186, pp. 99–112.Google Scholar
Kubat, M. (2021), An Introduction to Machine Learning, https://dx.doi.org/10.1007/9783030819354.CrossRefGoogle Scholar
Kusiak, A., Smith, M.R. and Song, Z. (2007), “Planning product configurations based on sales data”, IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, Vol. 37 No. 4, pp. 602–609, https://dx.doi.org/10.1109/TSMCC.2007.897503.CrossRefGoogle Scholar
Ma, J. and Kim, H.M. (2016), “Product family architecture design with predictive, data-driven product family design method”, Research in Engineering Design, Springer London, Vol. 27 No. 1, pp. 5–21, https://dx.doi.org/10.1007/s00163-015-0201-4.CrossRefGoogle Scholar
Mehlstäubl, J., Braun, F., Denk, M., Kraul, R. and Paetzold, K. (2022), “Using Machine Learning for Product Portfolio Management: A Methodical Approach to Predict Values of Product Attributes for Multi-Variant Product Portfolios”, Proceedings of the Design Society, Vol. 2, pp. 1659–1668, https://dx.doi.org/10.1017/pds.2022.168.CrossRefGoogle Scholar
Mehlstäubl, J., Braun, F., Gadzo, E. and Paetzold, K. (2023), “Machine Learning to generate Knowledge for Decision-making Processes in Product Portfolio and Variety Management”, 9th International Conference on Research Into Design.CrossRefGoogle Scholar
Mehlstäubl, J., Braun, F. and Paetzold, K. (2021), “Data Mining in Product Portfolio and Variety Management – Literature Review on Use Cases and Research Potentials”, 2021 IEEE Technology & Engineering Management Conference-Europe (TEMSCON-EUR), pp. 442–447.CrossRefGoogle Scholar
Mehlstäubl, J., Gadzo, E., Atzberger, A. and Paetzold, K. (2022), “Herausforderungen datengetriebener Methoden in der Produktentwicklung/Challenges of data-driven methods in product development”, Konstruktion, Vol. 74 No. 06, pp. 60–66, https://dx.doi.org/10.37544/0720-5953-2022-06-60.CrossRefGoogle Scholar
Mitchell, T.M. (1997), Machine Learning, Vol. 1, McGraw-hill New York.Google Scholar
Mortensen, N.H., Yu, B., Skovgaard, H. and Harlou, U. (2000), “Conceptual modeling of product families in configuration projects”, Workshop at the 14th European Conference on Artificial Intelligence, Berlin, Germany, pp. 68–73.Google Scholar
Murphy, K.P. (2012), Machine Learning: A Probabilistic Perspective, MIT press, https://dx.doi.org/10.1109/pes.2005.1489456.Google Scholar
Neis, J. (2015), Analyse Der Produktportfoliokomplexität Unter Anwendung von Verfahren Des Data Mining, Shaker Verlag.Google Scholar
Romanowski, C.J. and Nagi, R. (2004), “A data mining approach to forming generic bills of materials in support of variant design activities”, Journal of Computing and Information Science in Engineering, Vol. 4 No. 4, pp. 316–328, https://dx.doi.org/10.1115/1.1812556.CrossRefGoogle Scholar
Rousseeuw, P.J. (1987), “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis”, Journal of Computational and Applied Mathematics, Elsevier, Vol. 20, pp. 53–65.CrossRefGoogle Scholar
Samuel, A.L. (1959), “Some Studies in Machine Learning Using the Game of Checkers”, IBM Journal of Research and Development, Vol. 3 No. 3, pp. 210–229, https://dx.doi.org/10.1147/rd.33.0210.CrossRefGoogle Scholar
Schmieder, M. and Thomas, S. (2005), Plattformstrategien Und Modularisierung in Der Automobilentwicklung, Shaker.Google Scholar
Schuh, G., Riesener, M. and Jank, M.-H. (2018), “Managing Customized and Profitable Product Portfolios Using Advanced Analytics”, Customization 4.0, pp. 203–216, https://dx.doi.org/10.1007/978-3-319-77556-2_13.CrossRefGoogle Scholar
Tucker, C.S., Kim, H.M., Barker, D.E. and Zhang, Y. (2010), “A ReliefF attribute weighting and X-means clustering methodology for top-down product family optimization”, Engineering Optimization, Vol. 42 No. 7, pp. 593–616, https://dx.doi.org/10.1080/03052150903353328.CrossRefGoogle Scholar
Wirth, R. and Hipp, J. (2000), “CRISP-DM: Towards a Standard Process Model for Data Mining”, Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining, Vol. 1, Springer-Verlag London, UK, pp. 29–39.Google Scholar
Zhang, Y., Jiao, J. and Ma, Y. (2007), “Market, segmentation for product family positioning based on fuzzy clustering”, Journal of Engineering Design, Vol. 18 No. 3, pp. 227–241, https://dx.doi.org/10.1080/09544820600752781.CrossRefGoogle Scholar