magistrsko delo Organizacija in management informacijskih sistemov
Amira Mujanović (Author), Davorin Kofjač (Mentor), Andrej Škraba (Mentor)

Abstract

Magistrska naloga obravnava razvoj modela za napovedovanje odpovedi izdelkov v garancijski dobi. Z odpovedovanjem izdelkov in problematiko zagotavljanja popravil v garancijski dobi se soočajo vsa proizvodna podjetja. Zagotavljanje popravil v garancijskem roku podjetjem predstavlja strošek, ki ga poskušajo minimizirati s pomočjo predvidevanja deležev odpovedi. Najpogosteje se napovedi izvedejo z empiričnimi modeli, ki so zgrajeni na preteklih podatkih o podobnih izdelkih in prilagojeni glede na izkušnje. V sklopu magistrske naloge smo s pomočjo različnih metod strojnega učenja in realnih podatkov razvili napovedni model in ocenili uspešnost napovedovanja. Najboljše rezultate napovedovanja smo dobili pri ansamblih regresijskih dreves, pri katerih smo podatke prilagodili eksponentnem modelu. Za zaključek smo pripravili priporočila kateri model uporabiti ob omejenem poznavanju podatkov o odpovedih.

Keywords

napovedni model;odpoved izdelka;garancijski rok;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UM FOV - Faculty of Organizational Sciences
Publisher: [A. Mujanović]
UDC: 004
COBISS: 7554067 Link will open in a new window
Views: 1153
Downloads: 148
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Other data

Secondary language: English
Secondary title: PRODUCT FAILURE PREDICTION WITH MACHINE LEARNING METHODS
Secondary abstract: Master's thesis deals with the development of a model for predicting the failure of products during the warranty period. All of the manufacturing companies are facing the product failure problem and problem with offering the possibility of repairing those products. Providing guarantees represents costs, which companies are trying to minimize by predicting the failure rates. Most often, this is done with empirical models, which are built on historical data for similar products and customized based on experiences. As part of the master's thesis, we developed different models using various methods of machine learning and real data. After development, we assessed the quality of prediction for each model. Ensembles of regression trees obtained the best results; in that case, the data was fitted to the exponential model. To wind up, we prepared recommendations which model to use in different scenarios.
Secondary keywords: Prediction model;product failure;warranty period;quality;machine learning;neural networks;regression trees.;
URN: URN:SI:UM:
Type (COBISS): Master's thesis/paper
Thesis comment: Univ. v Mariboru, Fak. za organizacijske vede
Pages: 52 f.
ID: 9128242
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