Predicting the Progress of Vehicle Development Projects: An Approach for the Identification of Input Features

Oliver Böhme, Tobias Meisen

2021

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.

Paper Citation

in Harvard Style

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, pages 522-530. DOI: 10.5220/0010187905220530

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Böhme O.
AU - Meisen T.
PY - 2021
SP - 522
EP - 530
DO - 10.5220/0010187905220530