Online Overall Equipment Effectiveness (OEE) Improvement Using Data Analytics Techniques for CNC Machines

Miaolong, Yuan; Alghassi, Alireza; Zhao, Serina Fei; Wu, Sin Wah; Muhammad, Amirul; Cui, Jingfang; Myo, Kyaw Sett

doi:10.1007/978-3-030-67270-6_8

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Dalam konteks Total Productive Maintenance (TPM), salah satu metode yang digunakan adalah Overall Equipment Effectiveness (OEE). OEE biasanya diaplikasikan sebagai indikator kinerja mesin atau peralatan, yang dibagi menjadi tiga komponen utama: Ketersediaan (Availability), Kinerja (Performance), dan Kualitas (Quality) [19]- [22]. OEE adalah metrik yang mengukur efektivitas operasional suatu proses atau mesin.…”

Section: Pendahuluanunclassified

Evaluasi Efektivitas Mesin Ripple Mill Melalui Pendekatan Overall Equipment Effectiveness (OEE)

Gian Pramula,

Hamdy

2023

TMIT

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PT Perkebunan Nusantara V (PTPN V), sebuah perusahaan milik negara di sektor perkebunan kelapa sawit, menggunakan beragam mesin dan peralatan yang terhubung secara terintegrasi di Tanah Putih. Proses produksi berlangsung secara berurutan antar mesin, dan kerusakan pada satu stasiun bisa menghambat seluruh produksi. Untuk mengukur efektivitas peralatan, PTPN V Tanah Putih menerapkan metode Overall Equipment Effectiveness (OEE), yang merupakan bagian dari Total Productive Maintenance (TPM). OEE membagi penilaian kinerja peralatan menjadi tiga aspek: Rasio Ketersediaan, Rasio Kinerja, dan Rasio Kualitas. PTPN V Tanah Putih telah mencapai standar internasional dalam nilai OEE. Untuk mempertahankan dan meningkatkan efektivitas mesin, perusahaan melakukan pemeliharaan dan perawatan yang rutin.

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Section: Pendahuluanunclassified

Evaluasi Efektivitas Mesin Ripple Mill Melalui Pendekatan Overall Equipment Effectiveness (OEE)

Gian Pramula,

Hamdy

2023

TMIT

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“…The application layer (

) focuses on further data analysis to gather business intelligence and user interface design. At the application layer , the main KPIs are calculated for the machines based on the sensor data [ 41 ] and the changeovers are analyzed [ 42 ]. The ML-based techniques are mostly used for fault diagnosis [ 43 ] as real-time condition monitoring.…”

Section: Iot Layerwise Presentation Of Industry 40 and Operator 40 Re...mentioning

confidence: 99%

Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0

Ruppert

Darányi

Medvegy

et al. 2022

Sensors

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One of the main challenges of Industry 4.0 is how advanced sensors and sensing technologies can be applied through the Internet of Things layers of existing manufacturing. This is the so-called Brownfield Industry 4.0, where the different types and ages of machines and processes need to be digitalized. Smart retrofitting is the umbrella term for solutions to show how we can digitalize manufacturing machines. This problem is critical in the case of solutions to support human workers. The Operator 4.0 concept shows how we can efficiently support workers on the shop floor. The key indicator is the readiness level of a company, and the main bottleneck is the technical knowledge of the employees. This study proposes an education framework and a related Operator 4.0 laboratory that prepares students for the development and application of Industry 5.0 technologies. The concept of intelligent space is proposed as a basis of the educational framework, which can solve the problem of monitoring the stochastic nature of operators in production processes. The components of the intelligent space are detailed through the layers of the IoT in the form of a case study conducted at the laboratory. The applicability of indoor positioning systems is described with the integration of machine-, operator- and environment-based sensor data to obtain real-time information from the shop floor. The digital twin of the laboratory is developed in a discrete event simulator, which integrates the data from the shop floor and can control the production based on the simulation results. The presented framework can be utilized to design education for the generation of Industry 5.0.

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“…The relevance of these aspects is also emphasised by Braglia et al (2018): "one distinctive, and contemporarily appealing, feature of OEE with respect to other analogous KPIs is that it provides a breakdown structure for process losses that simplifies the task of evaluating the current performance and, at the same time, individuates both the source of losses and the corresponding remedy". In this sense, it is a crucial indicator used for short-term and long-term decision making (Wudhikarn, 2011): the use of this KPI can help management to unleash hidden capacity, eliminate waste, reduce overtime expenditures, decrease process variability, allow deferral of major capital investment (Muchiri and Pintelon, 2008;Yuan et al, 2021), and reduce the cost of ownership (Wudhikarn, 2011). Therefore, OEE continuously focuses the plant on the concept of zero-waste (Badiger and Gandhinathan, 2008) and is a partnership between maintenance and production functions in the organisations (Singh et al, 2021).…”

Section: Theoretical Background Oee Was Proposed Bymentioning

confidence: 99%

“…It represents a best practice lean metric (Braglia et al, 2018(Braglia et al, , 2019, and the gold standard for measuring manufacturing productivity (Nota et al, 2020). It is not only an operational measure, but also a driver of process and performance improvement activities within a manufacturing environment (Dal et al, 2000;Singh et al, 2018;Wudhikarn, 2010Wudhikarn, , 2012Yuan et al, 2021). Indeed, it permits strengthening and successfully controlling the utilisation of a machine or equipment (Elevli and Elevli, 2010), and providing the basis to set improvement priorities and perform root cause analyses (Muchiri and Pintelon, 2008).…”

Section: Theoretical Background Oee Was Proposed Bymentioning

confidence: 99%

Resource Overall Equipment Cost Loss indicator to assess equipment performance and product cost

Stefana

Cocca

Fantori

et al. 2022

IJPPM

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PurposeThis paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.Design/methodology/approachThe authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.FindingsThe paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.Originality/valueThe proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

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Online Overall Equipment Effectiveness (OEE) Improvement Using Data Analytics Techniques for CNC Machines

Cited by 5 publications

References 14 publications

Evaluasi Efektivitas Mesin Ripple Mill Melalui Pendekatan Overall Equipment Effectiveness (OEE)

Evaluasi Efektivitas Mesin Ripple Mill Melalui Pendekatan Overall Equipment Effectiveness (OEE)

Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0

Resource Overall Equipment Cost Loss indicator to assess equipment performance and product cost

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