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

Mehlstäubl J, Braun F, Denk M, Kraul R, Paetzold K (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Proceedings of the Design Society Cambridge University Press

Book Volume: 2

Pages Range: 1659-1668

DOI: 10.1017/pds.2022.168

Abstract

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.

Authors with CRIS profile

Martin Denk Chair of Engineering Design

Involved external institutions

MAN Truck & Bus DE Germany (DE) Technische Universität Dresden DE Germany (DE) Universität der Bundeswehr München DE Germany (DE)

How to cite

APA:

Mehlstäubl, J., Braun, F., Denk, M., Kraul, R., & 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, 2, 1659-1668. https://dx.doi.org/10.1017/pds.2022.168

MLA:

Mehlstäubl, Jan, et al. "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 2 (2022): 1659-1668.

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