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

Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period AbstractBackground: 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Business Systems Research Journal de Gruyter

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

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Publisher
de Gruyter
Copyright
© 2020 Aljaž Ferencek et al., published by Sciendo
ISSN
1847-9375
eISSN
1847-9375
DOI
10.2478/bsrj-2020-0014
Publisher site
See Article on Publisher Site

Abstract

AbstractBackground: 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.

Journal

Business Systems Research Journal – de Gruyter

Published: Oct 1, 2020

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