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

DS 116: Proceedings of the DESIGN2022 17th International Design Conference

Year: 2022
Editor: Mario Štorga, Stanko Škec, Tomislav Martinec, Dorian Marjanović
Author: Jan Mehlst
Series: DESIGN
Institution: 1: Universit
Section: Artificial Intelligence and Data-Driven Design
Page(s): 1659-1668
DOI number: https://doi.org/10.1017/pds.2022.168
ISSN: 2732-527X (Online)

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.

Keywords: machine learning, portfolio management, product development, artificial intelligence (AI), data-driven design