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A Comprehensive Prediction Approach for Hardware Asset Management

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Data Management Technologies and Applications (DATA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 862))

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Abstract

One of the main tasks in hardware asset management is to predict types and amounts of hardware assets needed, firstly, for component renewals in installed systems due to failures and, secondly, for new components needed for future systems. For systems with a long lifetime, like railway stations or power plants, prediction periods range up to ten years and wrong asset estimations may cause serious cost issues. In this paper, we present a prediction approach combining two complementary methods: The first method is based on learning a well-fitted statistical model from installed systems to predict assets needed for planned systems. Because the resulting regression models need to be robust w.r.t. anomalous data, we analyzed the performance of two different regression algorithms – Partial Least Square Regression and Sparse Partial Robust M-Regression – in terms of interpretability and prediction accuracy. The second method combines these regression models with a stochastic model to estimate the number of asset replacements needed for existing and planned systems in the future. Both methods were validated by experiments in the domain of rail automation.

This work is funded by the Austrian Research Promotion Agency (FFG) under grant 852658 (CODA).

Alexandra Mazak is affiliated with the Christian Doppler Laboratory on Model-Integrated Smart Production at TU Wien.

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Notes

  1. 1.

    Please note, that in this article we use the term “asset” in the sense of physical components such as hardware modules or computers but not in the sense of financial instruments.

  2. 2.

    www.r-project.org.

  3. 3.

    http://mevik.net/work/software/pls.html.

  4. 4.

    Implemented with the sprm package in R - https://cran.r-project.org/web/packages/sprm/.

  5. 5.

    A detailed differentiation of MTBF and MTTF (Mean Time To Failure) is beyond the scope of this paper.

  6. 6.

    We use a simple representation of time: years started from some absolute zero time point. This simplifies arithmetic operations on time variables.

  7. 7.

    A probability mass function (PMF) is the discrete counterpart of a probability distribution function (PDF). A PMF f(n) corresponding to a random variable X is \(\text {Pr}(X=n), n \in \mathbb {N}\).

  8. 8.

    Although the example is based on real-world data, we had to change some numbers, MTBF, and order probabilities in order to not disclose business information. The data are realistic but do not reflect actual business data.

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Authors and Affiliations

  1. Siemens AG Österreich, Corporate Technology, Vienna, Austria

    Alexander Wurl, Andreas Falkner, Alois Haselböck & Simon Sperl

  2. TU Wien, Business Informatics Group, Vienna, Austria

    Alexandra Mazak

  3. Institute of Statistics and Mathematical Methods in Economics, TU Wien, Vienna, Austria

    Peter Filzmoser

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  1. Alexander Wurl
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  2. Andreas Falkner
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Correspondence to Alexander Wurl .

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Editors and Affiliations

  1. RWTH Aachen University, Aachen, Nordrhein-Westfalen, Germany

    Christoph Quix

  2. University of Coimbra, Coimbra, Portugal

    Jorge Bernardino

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Wurl, A., Falkner, A., Filzmoser, P., Haselböck, A., Mazak, A., Sperl, S. (2019). A Comprehensive Prediction Approach for Hardware Asset Management. In: Quix, C., Bernardino, J. (eds) Data Management Technologies and Applications. DATA 2018. Communications in Computer and Information Science, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-26636-3_2

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  • DOI: https://doi.org/10.1007/978-3-030-26636-3_2

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-26636-3

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