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Using Machine Learning for Product Portfolio Management: A Methodical Approach to Predict Values of Product Attributes for Multi-Variant Product Portfolios

Published online by Cambridge University Press:  26 May 2022

J. Mehlstäubl ,
F. Braun ,
M. Denk ,
R. Kraul  and
K. Paetzold
J. Mehlstäubl*
Affiliation:
Universität der Bundeswehr München, Germany
F. Braun
Affiliation:
MAN Truck & Bus SE, Germany
M. Denk
Affiliation:
Universität der Bundeswehr München, Germany
R. Kraul
Affiliation:
MAN Truck & Bus SE, Germany
K. Paetzold
Affiliation:
Technische Universität Dresden, Germany
*
jan.mehlstaeubl@unibw.de

Abstract

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To satisfy customer needs in the best way, companies offer them an almost infinite number of product variants. Although, an identical product was not built before, the values of its attributes must be determined during the product configuration process. This paper introduces a methodical approach to predict the values of product attributes based on customer feature configurations using machine learning. Machine learning reduces the effort compared to rule-based expert systems and is both, more accurate and faster. The approach was validated by predicting vehicle weights using industrial data.

Keywords

machine learningportfolio managementproduct developmentartificial intelligence (AI)data-driven design
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1659 - 1668
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), 2022.

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