[Zurück]

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, S. 13 - 23.

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.

Predictive Asset Management, Obsolescence Management, Partial Least Squares Regression, Data Analytics

http://dx.doi.org/10.5220/0006859100130023

https://publik.tuwien.ac.at/files/publik_271107.pdf