20.500.12556/DKUM-91620 Deep learning predictive models for terminal call rate prediction during the warranty period 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. manufacturing product lifecycle management product failure machine learning prediction proizvodnja življenjski cikel izdelka strojno učenje napoved true false true Bussiness information technology Angleški jezik Slovenski jezik Znanstveno delo 2025-01-21 08:09:40 2025-01-21 08:36:23 2025-01-22 03:11:11 0000-00-00 00:00:00 2020 0 0 Str. 36-50 št. 2 Letn. 11 2020 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2020-04-23 2020-07-06 2020-10-29 658.5 32488195 10.2478/bsrj-2020-0014 1847-9375 Ferencek-2020-Deep_Learning_Predictive_Models.pdf Ferencek-2020-Deep_Learning_Predictive_Models.pdf 1 EAFD6A23EDB839280584E0A7B75DD7D9 3f225e12f73f4711b7eb21debee5d950aa795fcbd611383a3f1bb62ce162cae7 4fe929af-d7ca-11ef-b93e-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=188003 https://doi.org/10.2478/bsrj-2020-0014 0 affbfe25-d7c6-11ef-b93e-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=188002 Fakulteta za organizacijske vede 0 0 0