Combining Prediction Methods for Hardware Asset Management

Alexander Wurl, Andreas Falkner, Alois Haselböck, Alexandra Mazak, Simon Sperl

2018

Abstract

As wrong estimations in hardware asset management may cause serious cost issues for industrial systems, a precise and efficient method for asset prediction is required. We present two complementary methods for forecasting the number of assets needed for systems with long lifetimes: (i) iteratively learning a well-fitted statistical model from installed systems to predict assets for planned systems, and - using this regression model - (ii) providing a stochastic model to estimate the number of asset replacements needed in the next years for existing and planned systems. Both methods were validated by experiments in the domain of rail automation.

Paper Citation

in Harvard Style

Wurl A., Falkner A., Haselböck A., Mazak A. and Sperl S. (2018). Combining Prediction Methods for Hardware Asset Management.In Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-318-6, pages 13-23. DOI: 10.5220/0006859100130023

in Bibtex Style

@conference{data18,
author={Alexander Wurl and Andreas Falkner and Alois Haselböck and Alexandra Mazak and Simon Sperl},
title={Combining Prediction Methods for Hardware Asset Management},
booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2018},
pages={13-23},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006859100130023},
isbn={978-989-758-318-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Combining Prediction Methods for Hardware Asset Management
SN - 978-989-758-318-6
AU - Wurl A.
AU - Falkner A.
AU - Haselböck A.
AU - Mazak A.
AU - Sperl S.
PY - 2018
SP - 13
EP - 23
DO - 10.5220/0006859100130023