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"If you can not measure it, you can not improve it."(Lord Kelvin) It is a common opinion that productivity improvement is nowadays the biggest challenge for companies in order to remain competitive in a global market [1, 2]. A well-known way of measuring the effectiveness is the Overall Equipment Efficiency (OEE) index. It has been firstly develope...
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... you can not measure it, you can not improve it."(Lord Kelvin) It is a common opinion that productivity improvement is nowadays the biggest challenge for companies in order to remain competitive in a global market [ 1, 2]. A well-known way of measuring the effectiveness is the Overall Equipment Efficiency (OEE) index. It has been firstly developed by the Japan Institute for Plant Maintenance (JIPM) and it is widely used in many industries. Moreover it is the backbone of methodologies for quality improvement as TQM and Lean Production. The strength of the OEE index is in making losses more transparent and in highlighting areas of improvement. OEE is often seen as a catalyst for change and it is easy to understand as a lot of articles and discussion have been generated about this topic over the last years. The aim of this chapter is to answer to general questions as what to measure? how to measure? and how to use the measurements? in order to optimize the factory performance. The goal is to show as OEE is a good base for optimizing the factory performance. Moreover OEE’s evolu‐ tions are the perfect response even in advanced frameworks. This chapter begins with an explanation of the difference between efficiency, effectiveness and productivity as well as with a formal definition for the components of effectiveness. Mathe‐ matical formulas for calculating OEE are provided too. After the introduction to the fundamental of OEE, some interesting issues concerning the way to implement the index are investigated. Starting with the question that in calculat‐ ing OEE you have to take into consideration machines as operating in a linked and complex environment. So we analyze almost a model for the OEE calculation that lets a wider approach to the performance of the whole factory. The second issue concerns with monitoring the factory performance through OEE. It implies that information for decision- making have to be guaranteed real-time. It is possible only through automated systems for calculating OEE and through the capability to collect a large amount of data. So we propose an examination of the main automated OEE systems from the simplest to high-level systems integrated into ERP software. Even data collection strategies are screened for rigorous measurement of OEE. The last issue deals with how OEE has evolved into tools like TEEP, PEE, OFE, OPE and OAE in order to fit with different requirements. At the end of the chapter, industrial examples of OEE application are presented and the results are discussed. Overall equipment efficiency or effectiveness (OEE) is a hierarchy of metrics proposed by Seiichi Nakajima [3] to measure the performance of the equipment in a factory. OEE is a really powerful tool that can be used also to perform diagnostics as well as to compare production units in differing industries. The OEE has born as the backbone of Total Productive Mainte‐ nance (TPM) and then of other techniques employed in asset management programs, Lean manufacturing [4], Six Sigma [5], World Class Manufacturing [4]. By the end of the 1980’s, the concept of Total Production Maintenance became more widely known in the Western world [7] and along with it OEE implementation too. From then on an extensive literature [8, 9, 10, 11] made OEE accessible and feasible for many Western companies. Confusion exists as to whether OEE has indeed been an effectiveness or efficiency measure. The traditional vision of TMP referred to Overall Equipment Efficiency while now it is generally recognized as Overall Equipment Effectiveness. The difference between efficiency and effectiveness is that effectiveness is the actual output over the reference output and efficiency is the actual input over the reference input. The Equipment Efficiency refers thus to ability to perform well at the lowest overall cost. Equipment Efficiency is then unlinked from output and company goals. Hence the concept of Equipment Effectiveness relates to the ability of producing repeatedly what is intended producing, that is to say to produce value for the company (see Figure 1). Productivity is defined as the actual output over the actual input (e.g. number of final products per employee), and both the effectiveness and the efficiency can influence it. Regarding to OEE, in a modern, customer-driven “lean” environment it is more useful to cope with ...
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Citations
... Iannone and Nenni [13] analyzed different types of data collection strategies and underlined that in calculating OEE, it is important to consider machines operating in a linked and complex environment. Li et al. [14] proposed a revision of the OEE indicator for multi-product production system contexts; they proposed the multiproduct production system effectiveness (MSPE) indicator. ...
... The sheets of the data obtainable from the presses are reported in Table 1. Through the data collected via the injection molding machine's telemetry, for every machine (M i ), it is possible to calculate the OEE [13,15], considering its three fundamental elements. ...
... The real-time monitoring takes place through a special screen that allows the status of the single production unit to be viewed using different colors according to the status shown in Section 2.1. For performance analysis, however, the dashboard allows you to calculate the OEE indicator for each production unit in its sub-indicators-availability, performance, and quality [12,13]-as shown in Figure 7. For the sub-indicators, the dashboard also presents the trend over time, as shown in detail in Figure 8. Furthermore, the dashboard shows the Shutdown Time by Reason Type (STRT) and the Stop Time by Operator (STO), as shown in Figure 9. ...
Monitoring production systems is a key element for identifying waste and production efficiency, and for this purpose, the calculation of the Key Performance Indicator (KPI) Overall Equipment Effectiveness (OEE) is validly recognized in the scientific literature. The collection and analysis of the cause of the interruption of the plants is particularly useful in this sense. The use of Internet of Things (IoT) technology in order to automate data collection for the purpose of calculating the OEE and the causes of interruption is effective. Furthermore, the existing literature lacks research studies that aim to improve the data quality of important process data that cannot be collected automatically. This study proposes the use of IoT technologies to request targeted and intelligent information inputs from the operators directly involved in the process, improving the completeness and accuracy of the information through the real-time and smart combination of manual and automated data. The Business Process Model and Notation (BPMN) methodology was used to analyze and redesign the collection data process and define the architectural model with a deep knowledge of the specific process. The proposed architecture, designed for application to a plastic injection molding production line, comprises several elements: the telemetry of the injection molding machine, an intervention request system, an intervention tracking system, and a human–system interface. Furthermore, a dashboard was developed using the Power BI software, 2.122.746.0 version, to analyze the information collected. Reducing the randomness of manual data makes it possible to direct production efficiency efforts more effectively, helping to reduce waste and production costs. Reducing production costs appears to be strongly linked to reducing environmental impacts, and future studies will be able to quantify the benefits obtained from the solution in terms of environmental impact.
... To ensure the overall manufacturing process is optimized correctly and has a reliable flow, evaluation process must be performed in a good manner using specific tools and methods. The most popular performance tool in the manufacturing process efficiency is Overall Equipment Efficiency (OEE) (Rasib & Ebrahim, 2017;Iannone & Nenni, 2015). OEE is a good tool to diagnose the manufacturing process, reveal the area for improvement and losses area more transparently. ...
... To perform an accurate OEE measurement, an automated data collecting system (data mining) from all related equipment must be implemented. It should be able to collect a large amount of real time data and perform a real time calculations for decision making (Iannone & Nenni, 2015). Data quality and accuracy for OEE measurement were very crucial, it should have a low human dependency and intervention. ...
... The result could use as a performance indicator for machine or production line. OEE could be calculated Equation (1) parameters has been explained by Japan Institute of Plant Maintenance (Nakajima, 1988;Iannone & Nenni, 2015). Availability depicts the condition of a machine being ready to produce a product within a specific timeframe. ...
The constant demand for food and beverages to sustain human life drives fierce competition among manufacturers, focusing on product excellence in terms of timeliness, quality, and pricing. The key to competitiveness depends in optimizing manufacturing processes by efficiently utilizing company resources. To ensure the overall optimization and reliable flow of manufacturing processes, a systematic evaluation process must be used, Overall Equipment Efficiency (OEE) stands out as a prominent performance measurement metric in manufacturing process efficiency. OEE serves as a valuable diagnostic tool, exposing areas for improvement and losses transparently. Accurate OEE measurement necessitates the implementation of an automated data collection system with minimum human dependencies, human intervention, and conducting on-the-fly calculations to informed the stakeholder/user. Data quality and accuracy in OEE measurement is very critical. Low quality and accuracy data could lead to false decision. OEE categorizes losses into six groups loss to pinpoint significant factors for potential improvement. Once OEE could be maintain at high level with high data accuracy and right improvement point, an optimum manufacturing process, and cost effective in manufacturing expenses will be achieve. Base on the result comparison for OEE result before and after the system implementation, positive improvement in OEE could reach 8.06%. This scenario be adopted by other company, and could become a model for 1st phase journey in company digital transformation.
... where a is the availability (%), p is the performance (%), q is the quality (%) [4]. Numerous systems, among others Manufacturing Execution System (MES), Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) provide assistance in the automatic recording, processing and storage of OEE values in the assembly lines [5][6][7]. ...
In the field of industry, especially in the production areas, it is particularly important that the monitoring of assembly efficiency takes place in real-time mode, and that the related data-based estimation also works quickly and reliably. The Manufacturing Execution System (MES), Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems used by companies provide excellent support in data recording, processes, and storing. For Overall Equipment Effectiveness (OEE) data showing the efficiency of assembly lines, there is a regular need to determine expected values. This paper focuses on OEE values prediction with Multiple Linear Regression (MLR) as supervised machine learning. Many factors affecting OEE (e.g., downtimes, cycle time) are examined and analyzed in order to make a more accurate estimation. Based on real industrial data, we used four different methods to perform prediction with various machine learning algorithms, these were the cumulative, fix rolling horizon, optimal rolling horizon and combined techniques. Each method is evaluated based on similar mathematical formulas.
... As a result, manufacturers are facing the challenge of managing the performance of their manufacturing systems to ensure that they operate efficiently and effectively, which can be achieved by improving Overall Equipment Effectiveness (OEE) [1]. A key approach to improve OEE is to enable runtime control, managing the performance of the manufacturing system in real-time, including continuous monitoring and decision making [2]. ...
... Problems at counter check operation: Under this operation, two types of problems exist, and both affect the output of the contour check. The first problem is type 1 and type 2 errors (Iannone and Nenni, 2013). This error occurs when the human operator inspects the FGPs after the contour check alerts for a defect and decides to accept an FGP of poor quality or reject an FGP of acceptable quality. ...
... Applying the DMADV methodology has aided in achieving the objectives of this project by improving Company ER's new APS OEE to 89.8%, which exceeds the initial target of 85%. According to (Iannone and Nenni, 2013;Rahi, 2017), the world-class OEE percentage is from 85% to 92%. This was achieved by first measuring the current OEE and analyzing it through VSM to capture improvements and reduce unnecessary losses in the process. ...
Purpose
Saudi Arabia's 2030 vision targets an increase of 34% in non-oil revenue participation in the GDP, thus the need for automation and digital transformation. The Company ER is a market leader producing high-quality dairy products in the Kingdom and is a pioneer in the production industry. The company has recently increased the capacity of its milk factory to meet its vision. An investment was made to automate the pallet handling procedures at the milk factory to provide increased production for daily consumption. The new automation transition in Company ER's milk factory provides a unique opportunity to utilize lean management tools to improve the current automated processes before commercialization.
Design/methodology/approach
OEE (overall equipment effectiveness) will monitor losses for different operational losses in the new automated system and indicate system improvements, with 85% as the target. Based on DMADV (design, measure, analyze, design and validate) methodology, this study analyzes the entire automated pallet handling system. It uses lean tools to identify areas for improvement, identify waste elements and propose solutions to achieve Company ER's OEE targets.
Findings
In this paper, the outcomes will be presented as documented solutions that address the losses encountered in the production system, showing a 12.8% increase in the system's OEE.
Research limitations/implications
Owing the time and resource constraint, this study only involved automated pallet handling procedures in a milk production facility. Hence, the generalization of the result is slightly limited. More studies in several different processes and sectors are required.
Practical implications
This study provided a valuable tool for researchers for gaining deeper understanding regarding the lean manufacturing and its implementation. For practitioners, it is useful to evaluate the degree of lean manufacturing tools in their material handling systems.
Originality/value
This study is the first attempt to develop lean manufacturing constructs for evaluating the automated pallet handling procedures in a milk production facility.
... Flexible and reconfigurable assembly systems operate at high level of customization [19,20]. Various systems, among others Manufacturing Execution System (MES) and Enterprise Resource Planning (ERP), provide assistance in the automatic collection and storage of OEE values in the assembly lines of industrial companies [21][22][23]. ...
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.
... The OEE calculation consists of the determination of the three indices, and consequent multiplication between them: availability index, performance index and quality index (Iannone & Maria, 2015). As the following equation describes: ...
... The first index of the indicator, availability index, shows the percentage of effective working time in relation to the time scheduled for production (Iannone & Maria, 2015): ...
... TEU (Total Equipment Utilization) or also known as OOE (Overall Operations Effectivity) and TEEP (Total Equipment Effective Performance). For the calculation of these two indicators in comparison with the OEE, what changes is the way in which availability is measured (Iannone & Maria, 2015) While in the OEE, only interruptions that are not foreseen during the process are considered, such as breakdowns or setups. Although among these three indicators, OOE is the least common, it allows visibility under both scheduled and unscheduled stops such as training or meals (Iannone & Maria, 2015). ...
This article is the result of a project in a reference company in the aviation sector in Portugal in its department of strategy and continuous improvement. The aim of the project was the implementation of the Total Productive Maintenance (TPM) philosophy at a corporate level. The pilot equipment for the implementation is in an aircraft structures production unit and preforms the initial machining of simple metal parts. For the implementation of the philosophy, indicators for monitoring the production process were integrated into the equipment, as well as the management of all the deliverables necessary for the operation in the philosophy of autonomous maintenance. Finally, the course of the entire process is analysed, improvements are suggested for the following equipment and the gains in production are identified.
... The OEE on the other hand divides the equipment performance into three areas: availability, performance, and quality. 11 The OEE in the battery cell production can be improved among others through process optimization, higher utilization degree, autonomous and scheduled/planned maintenance activities and trainings. Due to the greater experiences on the Asian factory side, cycle time and OEE are evaluated higher compared to Europe and the US. ...
... Bulent et al. (2000) investigated overall equipment effectiveness as a measure of operational improvement a practical analysis. Raffaele and Maria (2013) studied managing OEE to optimize factory performance while Huang et al. (2003) assessed manufacturing productivity improvement using effectiveness metrics and simulation analysis and Ylipaa et al. (2017) studied identification of maintenance improvement potential using OEE assessment. These studies, however, addressed the problem of improving management systems for oil and gas producing company using overall equipment effectiveness, but in a restricted manner. ...
Consideration of difficulties inherent in maintenance management system in Nigerian oil and gas industries, and to eliminate the tedium associated in the system and improve equipment performance. In this work an attempt was made to assess the equipment maintenance practice in Kwale gas recycling plant of Nigeria Agip Oil Company Limited. Overall equipment effectiveness (OEE) indicator was used to determine the performance of various types of pumps and Turbine power generators installed in the plant, Maintenance records of the pumps and gas Turbine generators provided data for the computation and analysis. The availability, performance and quality rate of the pumps and gas Turbine generators were used to assess the overall effectiveness of the equipment used in the system. Detailed analysis of the failure rate and downtime revealed that a Plan Do Check Act (PDCA) system which is a proactive (predictive) maintenance technique was not adopted in the plant. The highest OEE value of 8.9% obtained confirmed that the current maintenance strategy and policy of the company is very much below standard and unacceptable. Hence this work affirms that the use of PDCA will avert unexpected system failure through early detection of faults, improve safety operation, reliability and durability of equipment. Thereby far less (i) human effort is wasted and (ii) energy expended, both of which led to increase in availability and profitability.
... Visi TPM tradisional mengacu pada efisiensi keseluruhan mesin/peralatan yang sekarang secara umum dikenal sebagai OEE (Iannone & Elena, 2013). OEE diperkenalkan oleh Nakajima pada tahun 1988 sebagai bagian dari TPM dan fokus pada peralatan/mesin. ...
... OEE diperkenalkan oleh Nakajima pada tahun 1988 sebagai bagian dari TPM dan fokus pada peralatan/mesin. OEE adalah metode yang tepat untuk dapat digunakan sebagai sarana diagnostik serta untuk membandingkan unit produksi di industri yang berbeda (Iannone & Elena, 2013 Tsarouhas (2020). Dari penelitian yang telah dilakukan tersebut, secara umum OEE digunakan untuk meningkatkan produktivitas dengan menghilangkan kerugian-kerugian yang muncul. ...
Abstrak
Efisensi dan efektivitas dari sebuah mesin/peralatan memegang peranan penting dalam menentukan kinerja dari suatu organisasi. Penelitian ini dilakukan pada salah satu industri yang bergerak di bidang cetak kemasan dan berfokus pada salah satu mesin cetak. Metode overall effectiveness equipment (OEE) digunakan untuk mengetahui kinerja mesin. Setelah perhitungan OEE, maka dilakukan analisis six big losses yang terjadi pada mesin cetak untuk mengetahui jenis losses yang paling menyebabkan kerugian pada mesin. Hasil penelitian menunjukkan bahwa nilai OEE mesin cetak adalah 37,5%. Nilai OEE sebesar 37,5% sangat jauh dibawah standar dan dapat disimpulkan bahwa kinerja atau efektivitas dari mesin cetak sangat rendah. Berdasarkan analytical hierarchy process (AHP) didapatkan faktor-faktor penyebab kegagalan pada mesin cetak dengan urutan prioritas yaitu sumber daya manusia (SDM), mesin, metode, material, dan lingkungan. Faktor sumber daya manusia merupakan faktor yang paling mempengaruhi kegagalan dan diusulkan untuk lebih meningkatkan keterampilan serta kedisiplinan operator pada mesin cetak.
Abstract
The efficiency and effectiveness of a machine/equipment plays an important role in determining the performance of an organization. This research was conducted in one of the industries engaged in packaging printing and focused on one of the printing machines. Overall effectiveness equipment (OEE) method is used to see machine performance. After the OEE calculation, an analysis of the six big losses that occurred on the printing machine was carried out to determine the type of losses that caused the most losses to the machine. The results showed that the OEE value is 37.5%. The OEE value is below the standard and it can be concluded that the performance of the printing machine is very low. Based on the analytical hierarchy process (AHP), the factors causing the failure of the printing machine were found in order of priority, namely human resources (HR), machines, methods, materials, and the environment. The human resources factor is the factor that most influences failure and it is proposed to further improve the skills and discipline of workers on the printing machine.
