Assembly Line Overall Equipment Effectiveness (OEE) Prediction from Human Estimation to Supervised Machine Learning

Nowadays, in the domain of production logistics, one of the most complex planning processes is the accurate forecasting of production and assembly efficiency. In industrial companies, Overall Equipment Effectiveness (OEE) is one of the most common used efficiency measures at semi-automatic assembly...
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Autor*in:	Péter Dobra [verfasserIn] János Jósvai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	OEE assembly line machine learning prediction decision tree

Übergeordnetes Werk:	In: Journal of Manufacturing and Materials Processing - MDPI AG, 2018, 6(2022), 3, p 59
Übergeordnetes Werk:	volume:6 ; year:2022 ; number:3, p 59

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/jmmp6030059

Katalog-ID:	DOAJ042665795

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source	In Journal of Manufacturing and Materials Processing 6(2022), 3, p 59 volume:6 year:2022 number:3, p 59
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callnumber-first	T - Technology
author	Péter Dobra
spellingShingle	Péter Dobra misc T58.7-58.8 misc OEE misc assembly line misc machine learning misc prediction misc decision tree misc Production capacity. Manufacturing capacity Assembly Line Overall Equipment Effectiveness (OEE) Prediction from Human Estimation to Supervised Machine Learning
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topic_title	T58.7-58.8 Assembly Line Overall Equipment Effectiveness (OEE) Prediction from Human Estimation to Supervised Machine Learning OEE assembly line machine learning prediction decision tree
topic	misc T58.7-58.8 misc OEE misc assembly line misc machine learning misc prediction misc decision tree misc Production capacity. Manufacturing capacity
topic_unstemmed	misc T58.7-58.8 misc OEE misc assembly line misc machine learning misc prediction misc decision tree misc Production capacity. Manufacturing capacity
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title	Assembly Line Overall Equipment Effectiveness (OEE) Prediction from Human Estimation to Supervised Machine Learning
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title_sort	assembly line overall equipment effectiveness (oee) prediction from human estimation to supervised machine learning
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title_auth	Assembly Line Overall Equipment Effectiveness (OEE) Prediction from Human Estimation to Supervised Machine Learning
abstract	Nowadays, in the domain of production logistics, one of the most complex planning processes is the accurate forecasting of production and assembly efficiency. In industrial companies, Overall Equipment Effectiveness (OEE) is one of the most common used efficiency measures at semi-automatic assembly lines. Proper estimation supports the right use of resources and more accurate and cost-effective delivery to the customers. This paper presents the prediction of OEE by comparing human prediction with one of the techniques of supervised machine learning through a real-life example. In addition to descriptive statistics, takt time-based decision trees are applied and the target-oriented OEE prediction model is presented. This concept takes into account recent data and assembly line targets with different weights. Using the model, the value of OEE can be predicted with an accuracy of within 1% on a weekly basis, four weeks in advance.
abstractGer	Nowadays, in the domain of production logistics, one of the most complex planning processes is the accurate forecasting of production and assembly efficiency. In industrial companies, Overall Equipment Effectiveness (OEE) is one of the most common used efficiency measures at semi-automatic assembly lines. Proper estimation supports the right use of resources and more accurate and cost-effective delivery to the customers. This paper presents the prediction of OEE by comparing human prediction with one of the techniques of supervised machine learning through a real-life example. In addition to descriptive statistics, takt time-based decision trees are applied and the target-oriented OEE prediction model is presented. This concept takes into account recent data and assembly line targets with different weights. Using the model, the value of OEE can be predicted with an accuracy of within 1% on a weekly basis, four weeks in advance.
abstract_unstemmed	Nowadays, in the domain of production logistics, one of the most complex planning processes is the accurate forecasting of production and assembly efficiency. In industrial companies, Overall Equipment Effectiveness (OEE) is one of the most common used efficiency measures at semi-automatic assembly lines. Proper estimation supports the right use of resources and more accurate and cost-effective delivery to the customers. This paper presents the prediction of OEE by comparing human prediction with one of the techniques of supervised machine learning through a real-life example. In addition to descriptive statistics, takt time-based decision trees are applied and the target-oriented OEE prediction model is presented. This concept takes into account recent data and assembly line targets with different weights. Using the model, the value of OEE can be predicted with an accuracy of within 1% on a weekly basis, four weeks in advance.
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title_short	Assembly Line Overall Equipment Effectiveness (OEE) Prediction from Human Estimation to Supervised Machine Learning
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