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.Objectives: In this paper, we address the above named problem by applying deep learning models to predict terminal call rate.Methods/Approach: We have developed a series of deep learning models on a data set obtained from a manufacturer of home appliances, and we have analysed their quality and performance.Results: Results showed that a deep neural network with 6 layers and a convolutional neural network gave the best results.Conclusions: This paper suggests that deep learning is an approach worth exploring further, however, with the disadvantage being that it requires large volumes of quality data.

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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