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

Ferencek Aljaž (), Kofjač Davorin (), Škraba Andrej (), Sašek Blaž () and Borštnar Mirjana Kljajić ()
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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

Business Systems Research, 2020, vol. 11, issue 2, 36-50

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.

Keywords: manufacturing; product lifecycle; management product failure; machine learning; prediction (search for similar items in EconPapers)
JEL-codes: C45 C53 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:bit:bsrysr:v:11:y:2020:i:2:p:36-50:n:4

DOI: 10.2478/bsrj-2020-0014

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