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Authors: Oliver Böhme and Tobias Meisen

Affiliation: Chair for Technologies and Management of Digital Transformation, Bergische Universität Wuppertal, Rainer-Gruenter-Str. 21, Wuppertal, Germany

Keyword(s): Machine Learning, Classification, Prediction, Deep Neural Networks, MLP, LSTM, Multivariate, Automotive, R&D, Projects Progressions, Project Life Cycle, Comparative Analysis.

Abstract: The increasing complexity in automotive product development is forcing traditional manufacturers to fundamentally rethink. As a result, many companies are already investing in the development of methods to increase the controllability of their development processes. The use of data-driven approaches is a promising way to provide an early prediction of potential problems in the course of a project by learning from the past. In vehicle development, projects can be divided into two basic categories: new vehicle launches and model enhancement projects. The course of projects according to the above-mentioned categories can be based on different influencing factors. To verify this hypothesis and to determine the extent of the differences in the data, we carry out a data-driven classification of the project category. In contrast to the recognition of other time-dependent data (e.g., univariate sensor data courses), we use multivariate project information from the automotive industry. With t his paper, which is of an application nature, we prove that a multivariate classification of automotive projects can be realized based on the underlying project’s progression. (More)

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Paper citation in several formats:
Böhme, O. and Meisen, T. (2021). Applied Feature-oriented Project Life Cycle Classification. In Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA, ISBN 978-989-758-521-0; ISSN 2184-285X, pages 285-291. DOI: 10.5220/0010578402850291

@conference{data21,
author={Oliver Böhme. and Tobias Meisen.},
title={Applied Feature-oriented Project Life Cycle Classification},
booktitle={Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA,},
year={2021},
pages={285-291},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010578402850291},
isbn={978-989-758-521-0},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA,
TI - Applied Feature-oriented Project Life Cycle Classification
SN - 978-989-758-521-0
IS - 2184-285X
AU - Böhme, O.
AU - Meisen, T.
PY - 2021
SP - 285
EP - 291
DO - 10.5220/0010578402850291