Performance Prediction through OEE-Model

Chintada, Anusha; Umasankar, V.

doi:10.24867/ijiem-2020-2-256

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…[29]. Anusha and Umasankar worked out a framework for major factors affecting the OEE and based on these data they developed a model for OEE prediction [30]. According to Backus et al knowledge of cycle time is essential for estimating OEE, hence they used a data mining approach to predict cycle time and WIP [31].…”

Section: Oee and Type Change At The Assembly Linesmentioning

confidence: 99%

Predicting the Impact of Product Type Changes on Overall Equipment Effectiveness Through Machine Learning

Dobra

Jósvai²

2023

Period. Polytech. Mech. Eng.

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Nowadays, Industry 4.0 and the Smart Manufacturing environment are increasingly taking advantage of Artificial Intelligence. There are more and more sensors, cameras, vision systems and barcodes in the production area, as a result of which the volume of data recorded during manufacturing and assembly operations is growing extremely fast. The interpretation and processing of such production-type data by humans is no longer possible effectively. In the Big Data domain, machine learning is playing an increasingly important role within data mining. This paper focuses on the product change processes of semi-automatic assembly line batch production and examines the impact of product type changes on the Overall Equipment Effectiveness (OEE) and attempts to determine future values through supervised machine learning. Using decision tree technology, the effect on the OEE value can be predicted with an accuracy of up to 1%. The presented data and conclusions come from a real industrial environment, so the obtained results are proven in practice.

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Section: Oee and Type Change At The Assembly Linesmentioning

confidence: 99%

Predicting the Impact of Product Type Changes on Overall Equipment Effectiveness Through Machine Learning

Dobra

Jósvai²

2023

Period. Polytech. Mech. Eng.

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“…Beberapa cara yang dilakukan untuk mengukur kinerja mesin adalah dengan mengukur Overall Equipment Effectiveness (OEE). Pengukuran OEE dilakukan antara lain oleh [2]- [5]. Penelitian tersebut bertujuan untuk mengidentifikasi den menganalisis OEE yang didapatkan dari mesin atau lantai produksi, dan selanjutnya mencoba meningkatkan efektifitasnya.…”

Section: Pendahuluanunclassified

Pengukuran Overall Equipment Effectiveness Pada Proses Pemotongan Industri Furniture

Yohanes Anton Nugroho

2023

Juritek

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The sawmill machine is one of the machines highly used in the cutting process n furniture production at PT X. The sawmill machine used at PT X experiences various problems, including damage and damage repair time in more than 15% of the total production time. The event of machine failures also causes several impacts, such as low target achievement and non-uniform quality. The Overall Equipment Effectiveness (OEE) gets an average OEE value for the period January 2021 - June 2021 of 46.79%, which is still far from the ideal standard value of Overall Equipment Effectiveness, which is 85%. Factors that greatly influence the low value of OEE based on. The sixth significant losses analysis is rework losses of 17.48%, followed by breakdown losses and reduced speed losses, which both have a percentage of 16.12%. Meanwhile, for Set up and Adjustment Loss, Idling Minor Stoppage, and Scrap or Yield Loss, the portions are relatively small, less than 5%.

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“…OEE is a comprehensive key performance indicator that measures machine performance and capability during optimal manufacturing implementation (Anusha & Umasankar, 2020;Thiede, 2023). FMEA is a risk assessment method used to control a process by providing a formal and detailed description of how severe failures could affect a system, the likelihood for it to happen, and the probability for it to be detected (Appollis et al, 2020;Mascia et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Quality Control Analysis of Porcelain Products Using Overall Equipment Effectiveness and Statistical Quality Control Methods

Nurprihatin,

Rembulan,

Andry

et al. 2023

Management and Production Engineering Review

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This study uses statistical quality control (SQC) and overall equipment effectiveness (OEE) to examine quality at a porcelain production firm. The study is motivated by the most frequently broken machines in 2019, is the Jigger 01 machine. This paper aims to evaluate the machine's effectiveness using the OEE method. The OEE determines the scope of the problem to be solved using the SQC method. The average OEE value in 2019 was 70%. Based on the SQC method, the product defect produced is still under control. However, the average defect is still above the company's tolerance limit of 10%. Consequently, this study offers enhancements utilizing the Failure Mode Effect Analysis (FMEA) technique. The results indicate that human resources and machines caused defective products. This paper contributes to providing several improvements that the company can apply to maximize its quality control analysis. After implementing the improvement, the OEE value increases to 74%.

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Performance Prediction through OEE-Model

Cited by 8 publications

References 18 publications

Predicting the Impact of Product Type Changes on Overall Equipment Effectiveness Through Machine Learning

Predicting the Impact of Product Type Changes on Overall Equipment Effectiveness Through Machine Learning

Pengukuran Overall Equipment Effectiveness Pada Proses Pemotongan Industri Furniture

Quality Control Analysis of Porcelain Products Using Overall Equipment Effectiveness and Statistical Quality Control Methods

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