| Naslov: | Deep learning predictive models for terminal call rate prediction during the warranty period |
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| Avtorji: | ID Ferencek, Aljaž (Avtor)
ID Kofjač, Davorin (Avtor)
ID Škraba, Andrej (Avtor)
ID Sašek, Blaž (Avtor)
ID Kljajić Borštnar, Mirjana (Avtor) |
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| Datoteke: |  Ferencek-2020-Deep_Learning_Predictive_Models.pdf (834,28 KB)
MD5: EAFD6A23EDB839280584E0A7B75DD7D9
 https://doi.org/10.2478/bsrj-2020-0014
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Znanstveno delo |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: | FOV - Fakulteta za organizacijske vede
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| Opis: | 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. |
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| Ključne besede: | manufacturing, product lifecycle, management product failure, machine learning, prediction |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Objavljena publikacija |
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| Poslano v recenzijo: | 23.04.2020 |
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| Datum sprejetja članka: | 06.07.2020 |
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| Datum objave: | 29.10.2020 |
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| Založnik: | Bussiness information technology |
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| Leto izida: | 2020 |
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| Št. strani: | Str. 36-50 |
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| Številčenje: | Letn. 11, št. 2 |
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| PID: | 20.500.12556/DKUM-91620  |
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| UDK: | 658.5 |
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| COBISS.SI-ID: | 32488195 |
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| DOI: | 10.2478/bsrj-2020-0014 |
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| ISSN pri članku: | 1847-9375 |
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| Datum objave v DKUM: | 21.01.2025 |
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| Število ogledov: | 160 |
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| Število prenosov: | 10 |
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| Metapodatki: |    |
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| Področja: | Ostalo
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