Deep learning predictive models for terminal call rate prediction during the warranty period

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

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

proizvodnja;življenjski cikel izdelka;strojno učenje;manufacturing;product lifecycle;machine learning;

Data

Language:	English
Year of publishing:	2020
Typology:	1.01 - Original Scientific Article
Organization:	UM FOV - Faculty of Organizational Sciences
Publisher:	Bussiness information technology
UDC:	658.5
COBISS:	32488195
ISSN:	1847-9375
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Downloads:	0
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Other data

Secondary language:	Slovenian
Secondary keywords:	proizvodnja;življenjski cikel izdelka;strojno učenje;napoved;
Type (COBISS):	Scientific work
Pages:	str. 36-50
Volume:	ǂVol. ǂ11
Issue:	ǂno. ǂ2
Chronology:	2020
DOI:	10.2478/bsrj-2020-0014
ID:	25746570