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Authors: Oliver Böhme 1 and Tobias Meisen 2

Affiliations: 1 Chair for Technologies and Management of Digital Transformation, Bergische Universität Wuppertal, Rainer-Gruenter-Str. 21, Wuppertal, Germany ; 2 Department of Electrical Engineering, Information Technology and Media Technology, Bergische Universität Wuppertal, Rainer-Gruenter-Str. 21, Wuppertal, Germany

Keyword(s): Machine Learning, Input Features, Automotive, R&D, Project Success Indicators, Critical Success Factors, Employee Survey.

Abstract: Today project managers estimate time and other project relevant key performance indicators by using project management tools e.g. milestone trend analysis. We believe that predicting the project’s progress with traditional methods will soon reach its limitations due to the increasing complexity in vehicle development. Machine learning methods provide one possible solution. The vision is to predict the progress of development projects in the early stages of the project. In order to make this vision come true, we need to define measurable input features for machine learning models. In this paper, we focus on representing an approach to identify parameters that exert influence on the progress of development projects.

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Paper citation in several formats:
Böhme, O. and Meisen, T. (2021). Predicting the Progress of Vehicle Development Projects: An Approach for the Identification of Input Features. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 522-530. DOI: 10.5220/0010187905220530

@conference{icaart21,
author={Oliver Böhme. and Tobias Meisen.},
title={Predicting the Progress of Vehicle Development Projects: An Approach for the Identification of Input Features},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2021},
pages={522-530},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010187905220530},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Predicting the Progress of Vehicle Development Projects: An Approach for the Identification of Input Features
SN - 978-989-758-484-8
IS - 2184-433X
AU - Böhme, O.
AU - Meisen, T.
PY - 2021
SP - 522
EP - 530
DO - 10.5220/0010187905220530
PB - SciTePress

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