Work

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

Year: 2022

Type: article

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.

Source: Proceedings of the Design Society

DOI: 10.1017/pds.2022.168

Authors: J. Mehlstäubl, F. Braun, M. Denk, R. Kraul, K. Paetzold

Institutions: Universität der Bundeswehr München, MAN (Germany), Technische Universität Dresden

Language: English

Topic: Product Development and Customization

Subfield: Management of Technology and Innovation

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid:

Funder: Bayerische Forschungsstiftung