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Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period

Author

Listed:
  • Ferencek Aljaž

    (Faculty of Organizational Sciences, University of Maribor, Slovenia)

  • Kofjač Davorin

    (Faculty of Organizational Sciences,University of Maribor, Slovenia)

  • Škraba Andrej

    (Faculty of Organizational Sciences, University of Maribor, Slovenia)

  • Sašek Blaž

    (Faculty of Organizational Sciences,University of Maribor, Slovenia)

  • Borštnar Mirjana Kljajić

    (Faculty of Organizational Sciences, University of Maribor, Slovenia)

Abstract

Background: This paper addresses the problem of products’ terminal call rate (TCR) prediction during the warranty period. TCR refers to the information on the amount of funds to be reserved for product repairs during the warranty period. So far, various methods have been used to address this problem, from discrete event simulation and time series, to machine learning predictive models.

Suggested Citation

  • Ferencek Aljaž & Kofjač Davorin & Škraba Andrej & Sašek Blaž & Borštnar Mirjana Kljajić, 2020. "Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period," Business Systems Research, Sciendo, vol. 11(2), pages 36-50, October.
    • Handle: RePEc:bit:bsrysr:v:11:y:2020:i:2:p:36-50:n:4
    DOI: 10.2478/bsrj-2020-0014
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    References listed on IDEAS

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    1. Shi, Zhongtuo & Yao, Wei & Zeng, Lingkang & Wen, Jianfeng & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu, 2020. "Convolutional neural network-based power system transient stability assessment and instability mode prediction," Applied Energy, Elsevier, vol. 263(C).
    2. J.F. Lawless, 1998. "Statistical Analysis of Product Warranty Data," International Statistical Review, International Statistical Institute, vol. 66(1), pages 41-60, April.
    3. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    4. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    5. Wu, Shaomin & Akbarov, Artur, 2011. "Support vector regression for warranty claim forecasting," European Journal of Operational Research, Elsevier, vol. 213(1), pages 196-204, August.
    6. Arnerić Josip & Poklepović Tea & Teai Juin Wen, 2018. "Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data," Business Systems Research, Sciendo, vol. 9(2), pages 18-34, July.
    7. Murthy, D. N. P. & Djamaludin, I., 2002. "New product warranty: A literature review," International Journal of Production Economics, Elsevier, vol. 79(3), pages 231-260, October.
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    More about this item

    Keywords

    manufacturing; product lifecycle; management product failure; machine learning; prediction;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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