Deep learning based cost estimation of circuit boards: a case study in the automotive industry

ABSTRACT

Early cost estimation is a decisive value driver in the product development process in manufacturing industries. Machine learning offers new intelligent methods to support traditional cost calculation processes. While traditional research on intelligent cost estimation focuses on machine learning regression or classification models, we propose a new approach based on interlocking deep learning methods. In this paper we investigate the applicability of deep learning techniques, focusing on image recognition and deep learning regression as well as autoencoding to estimate product costs of circuit boards to be purchased. We create and evaluate deep learning models using real-world data from an original equipment manufacturer (OEM). Our findings suggest that deep learning models can streamline cost calculation and estimation processes while deep learning object recognition-based cost estimation outperforms autoencoding techniques. This research is designed to be transferable to other cost estimation projects.

KEYWORDS:

• Cost estimation
• deep learning
• object recognition
• autoencoding
• automotive industry

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data available within the article or its supplementary materials.

Additional information

Notes on contributors

Frank Bodendorf

Frank Bodendorf graduated in 2017 from the School of Engineering at the University of Erlangen-Nuremberg, Germany, with a master's degree in industrial engineering. Subsequently he has been research assistant at the Institute for Factory Automation and Production Systems. Actually he holds a leading position at the BMW Group, focusing on data governance and digital transformation. He is responsible for numerous digitalisation projects in areas such as cost engineering, purchasing, and supply chain management.

Stefan Merbele

Stefan Merbele graduated with a master`s degree in mechanical engineering in 2020 at the University of Erlangen-Nuremberg. During his studies, he was engaged in numerous projects in robotics and software engineering. Actually he is working as a cost engineer and data scientist at the BMW Group.

Jörg Franke

Prof. Dr.-Ing. Jörg Franke graduated in 1989 with a diploma degree in production engineering. After holding several top management positions in industry he is director of the Institute for Factory Automation and Production Systems at the University of Erlangen-Nuremberg since 2009.