| Title: | Deep learning predictive models for terminal call rate prediction during the warranty period |
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| Authors: | ID Ferencek, Aljaž (Author)
ID Kofjač, Davorin (Author)
ID Škraba, Andrej (Author)
ID Sašek, Blaž (Author)
ID Kljajić Borštnar, Mirjana (Author) |
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| Files: |  Ferencek-2020-Deep_Learning_Predictive_Models.pdf (834,28 KB)
MD5: EAFD6A23EDB839280584E0A7B75DD7D9
 https://doi.org/10.2478/bsrj-2020-0014
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FOV - Faculty of Organizational Sciences in Kranj
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| 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. |
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| Keywords: | manufacturing, product lifecycle, management product failure, machine learning, prediction |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 23.04.2020 |
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| Article acceptance date: | 06.07.2020 |
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| Publication date: | 29.10.2020 |
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| Publisher: | Bussiness information technology |
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| Year of publishing: | 2020 |
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| Number of pages: | Str. 36-50 |
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| Numbering: | Letn. 11, št. 2 |
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| PID: | 20.500.12556/DKUM-91620  |
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| UDC: | 658.5 |
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| ISSN on article: | 1847-9375 |
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| COBISS.SI-ID: | 32488195 |
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| DOI: | 10.2478/bsrj-2020-0014 |
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| Publication date in DKUM: | 21.01.2025 |
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| Views: | 157 |
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| Downloads: | 10 |
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| Metadata: |    |
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| Categories: | Misc.
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