Contributions to Proceedings:
A. Wurl, A. Falkner, A. Haselböck, A. Mazak, S. Sperl:
"Combining Prediction Methods for Hardware Asset Management";
in: "Proceedings of the 7th International Conference on Data Science, Technology and Applications",
SciTePress,
2018,
ISBN: 978-989-758-318-6,
13
- 23.
English 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.
Keywords:
Predictive Asset Management, Obsolescence Management, Partial Least Squares Regression, Data Analytics
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.5220/0006859100130023
Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_271107.pdf
Created from the Publication Database of the Vienna University of Technology.