<abstract xmlns="http://www.w3.org/1999/xhtml">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.</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.

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

Business Systems Research Journal

This work is licensed under the Creative Commons Attribution 4.0 International License.

Background: This paper addresses the problem of products’ terminal call rate (TCR) prediction during the warranty period. TCR refers to the information on the...

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

Publié en ligne: 29 oct. 2020

Pages: 36 - 50

Reçu: 23 avr. 2020

Accepté: 06 juil. 2020

DOI: https://doi.org/10.2478/bsrj-2020-0014

Mots clés
manufacturing, product lifecycle, management product failure, machine learning, prediction

© 2020 Aljaž Ferencek et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Publié en ligne: 29 oct. 2020

Pages: 36 - 50

Reçu: 23 avr. 2020

Accepté: 06 juil. 2020

DOI: https://doi.org/10.2478/bsrj-2020-0014

Mots clésmanufacturing, product lifecycle, management product failure, machine learning, prediction

© 2020 Aljaž Ferencek et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
manufacturing, product lifecycle, management product failure, machine learning, prediction