Toplam Ekipman Etkinliğine Etki Eden Faktörlerin Makine Öğrenim Yöntemleri ile Analizi

Year 2024, , 171 - 184, 30.04.2024

Özgül Vupa Çilengiroğlu , İlke Genç

https://doi.org/10.51551/verimlilik.1266852

Abstract

Amaç: Üretim sektöründeki bir firmanın 2018-2019 yılı orjinal verilerinden türetilmiş sıralı ölçekteki Toplam Ekipman Etkinliği (TEE) puanı üzerinde etkili olan değişkenlerin makine öğrenim algoritmaları ile modellenmesi, yorumlanması ve model performanslarının karşılaştırılması çalışmanın temel amacıdır.
Yöntem: TEE puanının modellemesinde karar ağaçları (CART, CHAID), lojistik regresyon (LogR) ve yapay sinir ağları (YSA) kullanılmıştır. Kurulan modellerin performans değerleri “duyarlılık”, “seçicilik”, “kesinlik” ve “doğruluk” kriterlerine göre hesaplanmıştır. Modelleri yorumlarken karar ağaçları ve YSA sonuçları için yüzdelerden, LogR için odds oranından yararlanılmıştır.
Bulgular: Modellerde TEE puanı üzerinde “saat”, “üretim”, “tecrübe” ve “kayıp metre” değişkenleri incelenmiştir. Performans karşılaştırmasında en iyi sonuç veren algoritmanın sıralı LogR olduğu ve bu modele göre üretimin düşük ve çalışanlarının daha az tecrübeli olduğu firmalarda daha “düşük” TEE puanı elde edilirken, kayıp metresi daha az olan firmalarda daha “yüksek” TEE” puanı alma şanslarının olduğu saptanmıştır.
Özgünlük: Literatürde sürekli olarak modellenen TEE puanının kategorik hale getirilerek sınıflar arasındaki farklılığın belirlenmesiyle firmaların kendi konumlarını belirlemesi sağlanmıştır. Böylece firmalar kategorisini belirleyip seçilen modeldeki önemlilik sırasındaki faktörlerini değiştirerek bir üst kategoriye daha hızlı çıkabilecektir. Literatürde kategorik olanTEE puanını makine öğrenim algoritmaları ile çözümleyen modellerin olmaması bu çalışmanın özgünlüğü olarak belirlenmiştir.

Keywords

Toplam Ekipman Etkinliği, Karar Ağaçları, Sıralı Lojistik Regresyon, Yapay Sinir Ağları

Supporting Institution

yoktur

Project Number

yoktur

Analysis of the Factors Affecting the Total Equipment Efficiency with Machine Learning Methods

Abstract

Purpose: The main purpose of the study was determined as modeling, interpreting and comparing the model performances of the variables with machine learning algorithms. Based on the original data in 2018-2019 from a company operating in the manufacturing sector, variables that are effective on the Overall Equipment Effectiveness (OEE) score in the ordinal scale obtained by the simulation study are used.
Methodology: Decision trees (CART, CHAID), logistic regression (LogR) and artificial neural networks (ANN) were used in modeling the OEE score. The performance values of the established models were calculated according to the criteria of “sensitivity”, “specificity”, “precision” and “accuracy”. While interpreting the models, percentages were used for decision trees and ANN results, and odds ratio was used for LogR.
Findings: In the models, "hour", "production", "experience" and "lost meter" variables were examined on OEE score. By comparing the performance criteria, it was determined that the algorithm that gave the best results was ordinal LogR. It has been determined that those with low production and less experience have a “lower" OEE score, and those with less lost meters have a higher chance of getting a “higher" OEE score.
Originality: OEE, which is modeled as a continuous in the literature, was made categorical and the companies were able to determine their own positions by determining the difference between the classes. Thus, companies will be able to move up to the next category faster by determining their category and changing the variables in order of importance in the selected model. The lack of models in the literature that analyze categorical OEE with machine learning has been determined as the originality of this study.

Keywords

Overall Equipment Effectiveness, Decision Trees, Ordinal Logistic Regression, Artificial Neural Networks

Project Number

yoktur

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