Overall Equipment Effectiveness Value Research Articles

Optimizing Auto Manufacturing: A Holistic Approach Integrating Overall Equipment Effectiveness for Enhanced Efficiency and Sustainability

In the face of pandemic-induced emergencies and unpredictable natural disasters, industries are compelled to implement rescue plans to mitigate unexpected risks. In this context, Overall Equipment Effectiveness (OEE) is considered as a key metric, followed by sustainability efforts to manage unforeseen risks, encompassing social, environmental, and economic aspects. OEE is considered as a lean tool to determine the efficiency of equipment or processes on par with the world class OEE standard, i.e., 85%. Performance, Availability and Quality as three main drivers of OEE. This research study explores the implementation of OEE in conjunction with sustainability principles in an auto sector manufacturing firm, aiming to enhance operational efficiency and sustainability practices. The research involves a 12-week initial session from April to June 2022, followed by an analysis of July to September 2022, resulting in an impressive OEE value of 48%. Notable improvements in Availability (89.75%), Performance (72.68%), and Quality (73.82%) contribute significantly. The analysis reveals enhancements in scrap rework (17%), training (16%), maintenance (13%), material availability (12%), and production capability (11%). Achievements include improvements in green profile (25%), health and safety (20%), and energy efficiency (25%), along with reductions in carbon dioxide emissions (21%), waste management (17%), and scrap (15%). This research underscores the commitment of the case study industry to sustainable development and economic growth, showcasing significant enhancements in product quality and efficiency. The integration of sustainability principles into OEE initiatives is pivotal for modern industrial optimization. The study results highlight the profound significance of this synergistic relationship, particularly within the blending section, driving substantial positive outcomes in manufacturing processes and operational excellence. The implementation of sustainability efforts not only mitigates risks and fosters growth for automotive manufacturers but also yields environmental benefits. Based on findings of this study, a roadmap for automotive manufacturers is devised to achieve robust OEE while concurrently reaping economic and environmental rewards by employing sustainability principles.

Evaluation of overall equipment effectiveness (OEE) for mining equipment (shovel-truck): A case study Manisa-Soma (Turkey) open pit mine

Mining is a costly activity that requires huge investments and large equipment. Therefore, it is crucial to use mining equipment efficiently to maximize productivity. This requires the continuous analysis of equipment efficiency and taking action to reduce negative impacts. Accurately estimating equipment efficiency is essential to increase its effectiveness. One globally recognized and accepted measurement is Overall Equipment Effectiveness (OEE). In this study was conducted to calculate the OEE values of shovels and trucks, which are the primary equipment used in open pit mining. The study presented different loss times and quality values while calculating the OEE values of mining equipment. The OEE values of electric trucks and shovels were found to be consistent with actual work quantities in the Manisa-Soma Lignite open pit operation. This study is the first applied study of OEE analysis in Turkey.

Maximizing Machine Efficiency in Indonesia's Noodle Industry

This study utilizes the Total Productive Maintenance (TPM) method, focusing on the Overall Equipment Effectiveness (OEE) to evaluate machine efficiency within a notable Indonesian noodle manufacturer. In the rapidly growing food manufacturing industry, particularly dry and instant noodles, machine performance is critical for meeting increasing domestic and international market demands. Results reveal that the OEE values are below industry standards, primarily due to speed reductions, idle and minor stoppages, process defects, and breakdown losses. These findings emphasize the urgent need for preventive maintenance strategies to mitigate downtime and enhance productivity, providing valuable insights for global industries facing similar challenges. Highlights: Rapid growth in the food manufacturing industry demands increased productivity. Machine performance is crucial for maintaining optimal production processes. Total Productive Maintenance (TPM) and Overall Equipment Effectiveness (OEE) methods help measure and improve machine efficiency. Keywords: Total Productive Maintenance, Overall Equipment Effectiveness, Six Big Losses.

Evaluation of The Effectiveness of Die Casting Machines Using Overall Equipment Effectiveness (OEE)

PT YKK Zipco Indonesia is a manufacturing company that produces zipper components, namely chains and sliders. The manufacture of slider products goes through several departments, namely die casting, assembling, painting and plating. The process of making slider products begins with the die casting department which carries out the production process using 17 machines. The problem that exists in the die casting department is that the level of productivity is quite low, so an analysis of the effectiveness of the die casting machine is needed. The aim of this research is to evaluate the performance of the production process in order to increase the efficiency and effectiveness of the company as a whole. The method used in this research is overall equipment effectiveness (OEE) and six Big Losses. The Overall Equipment Effectiveness (OEE) method is used to measure the effectiveness of production process machine performance in manufacturing companies using the parameters availability, performance and quality. In OEE there are six big losses which are categorized into 3 main categories, namely quality loss, downtime loss and speed loss. Then an analysis was carried out using the Pareto diagram and fishbone diagram to find the root causes of the problems that occurred. The results of this research, namely the results of OEE calculations on the C – 07 Die Casting machine, obtained an average OEE value of 78.6%. Based on the six big losses calculation, breakdown losses occupy the highest value with a percentage of 23.1%, making it the losses that most influence the value of machine effectiveness.

Optimizing Maintenance Efficiency with OEE Method

This study investigates the effectiveness of roving machines in a production environment by employing the Overall Equipment Effectiveness (OEE) methodology. High downtime of the roving machine significantly hampers production processes, leading to reduced machine performance and speed, resulting in suboptimal OEE values. Through OEE calculations, a value of 66.92% was obtained, highlighting considerable room for improvement towards achieving world-class standards. The findings underscore the necessity for targeted enhancements to mitigate machine downtime, thereby enhancing overall production efficiency and competitiveness in the manufacturing sector. Highlights : Efficient utilization of roving machines through OEE optimization is crucial for enhancing production performance. High downtime of roving machines significantly impedes production processes, leading to decreased efficiency and competitiveness. Addressing downtime issues can lead to substantial improvements in overall production efficiency, aligning operations with global standards. Keywords: Roving machine, Overall Equipment Effectiveness (OEE), Production efficiency, Downtime reduction, Manufacturing competitiveness

TINJAUAN PRODUCTIVE MAINTENANCE MESIN LAMINASI PACKAGING PADA PT. ULTRA PRIMA PLAST

This research was conducted to measure the effectiveness and application of the Total Productive Maintenance (TPM) method, especially on the Focused Improvement pillar on the Printing Packaging machine at PT. Ultra Prima Plast. The research was conducted using quantitative data and an Overall Equipment Effectiveness (OEE) analysis approach. The OEE value was analyzed using Six Big Losses (SBL) to find the dominant factor causing the loss. The loss factor is then classified with the 80/20% formula as a reference for making Fishbone Diagrams. Fishbone Diagram analysis is carried out to find the cause and effect or root cause of each loss. The root of the problem obtained is then given a suggestion for improvement and analyzed using the 5W+1H method. The results of the study obtained an average value of Overall Equipment Effectiveness (OEE) of 28%. This value is obtained from several calculations including the Availability Rate 97%, Performance Rate 20% and Quality Rate 98%. This value is still below the world category that has been seet by JIPM. which is 85%. The losses on the printing packaging machine are yield losses wich is 58% and reduce speed losses which is 23%. The roots on the problem found were the absence of planed maintenance and autonomus maintenance on Laminasi packaging machine and unmaintained environment inadequate knowlage of human resources and negligance of work procedures in the field.

Integration of Overall Equipment Effectiveness and Six Sigma Approach to Minimize Product Defect and Machine Downtime

This study was conducted in a company that produces palm oil-based products such as cooking oil and margarine. The study aimed to encounter defects in packaging pouches. This study integrated the overall equipment effectiveness (OEE) with the six sigma DMAIC method. The OEE was performed to measure the efficiency of the machine. Three factors were measured in OEE: availability, performance, and quality. These factors were calculated and compared to the OEE world-class value. Then, the Multiple Linear Regression was performed using SPSS to determine the correlation between measurement variables toward the OEE value. Lastly, the six sigma method was implemented through the DMAIC approach to find the solution and improve the packaging quality. Supposing the recommendations are implemented, the OEE is expected to increase from 82% to 85%, with availability ratio, performance ratio, and quality ratio at, 99%, 86%, and 99.8%, respectively.

Optimizing Digging Equipment Productivity Using Overall Equipment Effectiveness (OEE) Method in Coal Overburden Mining Activities

Coal companies have an overburden stripping production target of 95,000 bcm in January 2022, while the realization of production with the Sumitomo SH 350 LHD excavator and Hitachi Zaxis 350 H excavator is only 77,000 bcm or 82% of the production target. The purpose of this study was to obtain an analysis of the productivity of the Sumitomo SH 350 LHD Excavator (40) and Hitachi Zaxis 350 H Excavator (31) in the overburden stripping activity in January 2022, analyze the obstacle factors that caused the available working hours to be reduced by using the Fishbone diagram method, get an analysis of the Overall Equipment Effectiveness (OEE) value of the Sumitomo SH 350 LHD Excavator (40) and Hitachi Zaxis 350 H (31) Excavator before being optimized, and get the analysis and productivity of the Sumitomo SH 350 LHD Excavator (40) and the Hitachi Zaxis 350 H Excavator (31) which has been optimized with the implementation of Overall Equipment Effectiveness (OEE) to achieve the overburden stripping production target. After analysis and improvement efforts, the total overburden stripping production was 149,000 bcm, which means that it has reached the target and even exceeded the production target of 95,000 bcm/month with the OEE value of the digging equipment of 41% and 43%, respectively. However, the OEE value is still very low compared to the world-class standard OEE value, which is 85% and there is still room for improvement.

UNJUK KERJA MESIN VERTICAL RAW MILL PLANT 10

PT X. is a cement manufacturing company. which experiences production losses due to the high value of downtime. Based on downtime data, the largest machine downtime value is found in the Raw Mill, so the Raw Mill machine becomes the object of research. This study aims to measure the effectiveness of the Raw Mill machine in the application of Total Productive Maintenance (TPM). The method used is Overall Equipment Effectiveness (OEE) to measure machine effectiveness and loss factor analysis with the Six Big Losses method. The results found that the average OEE value in 2022 was below the international OEE standard of 85%, namely 78% for Raw Mill Plant 10 in the 2019 period and 74% in the 2022 period. The low performance of the Raw Mill Plant 10 machine is caused by the breakdown loss factor and the reduced speed loss factor. The root causes found are lack of mechanical training and lack of control for spare parts. The recommendations given are the implementation of predictive maintenance, improving training facilities, and creating a spare parts inventory management system.

Measurement Of Total Productive Maintenance (TPM) Using The Overall Equipment Effectiveness (OEE) Method On Fermentation Machinery At Pt. Indo Acidatama Tbk

Ethanol, or ethyl alcohol, has many applications in a variety of industries, including as a solvent, fuel, and in pharmaceuticals. Demand for ethanol in Indonesia is high, but domestic production is not sufficient, which prompted the establishment of PT. Indo Acidatama Tbk in 1986. This company produces ethanol from sugar cane molasses through a fermentation process using the yeast saccharomyces cerevisae strain Kyowa. The fermentation process requires good care to ensure optimal results and prevent machine failure. This research aims to measure the Overall Equipment Effectiveness (OEE) value of the fermentation machine at PT Indo Acidatama, identify the factors that influence the production performance of the fermentation machine, and determine the main causes of losses that affect the effectiveness of the fermentation machine. The research results show that the OEE value of the fermentation machine at PT Indo Acidatama in January 2023 is 85%, which is in accordance with JIP standards. The main causes of loss of machine effectiveness are breakdown losses (8.7%), reduced speed losses (5.9%), and rework losses (0.2%). To increase the effectiveness of fermentation machines, it is recommended to adjust process parameters, provide training to staff, and carry out regular data monitoring.

Increased Productivity of Packing Machines Through Implementation of Total Productive Maintenance Using the Overall Equipment Effectiveness Method Case Study of PT. GFPJ

In the world's manufacturing industry, increasing productivity is very important in fulfilling domestic and international markets as well as maintaining customer confidence. The company is trying to increase productivity in production. The fact that the packing machine is in an inefficient condition and the management is trying to implement it. in Total Productive Maintenance (TPM) to maximize the effectiveness of machine facilities in the industry. Then corrective actions are tested using the Overall Equipment Effectiveness (OEE) method, which is the calculation method used in this OEE value is determined by availability, performance and quality. The low OEE value of the packing machine at PT. GFPJ results from the availability value of = 60% for the results of the performance value of = 100% and the value of quality = 93%, it can be concluded that the OEE value obtained on the machine packing is 59.86%, while the international OEE standard is 87%, so it needs improvement. The factor that becomes a priority for improvement is the performance or performance of the machine in producing concrete, because the performance value is the lowest value of the three factors that affect the OEE value.

Penerapan Metode Overall Equipment Effectiveness (OEE) dan Six Big Losses untuk Mengukur Efektivitas Mesin Packing pada PT. Surya Tsabat Mandiri

This research aims to investigate the risk of six big losses and the effectiveness of packing machines at PT Surya Tsabat Mandiri in South Lampung using monthly data on production of bread products from March 2021 to February 2022 used in the research. Research was carried out on packing machine objects using quantitative data or data that can be measured systematically. The Overall Equipment Effectiveness (OEE) method is applied to measure the total performance of equipment as a whole according to what must be done and to assess the dominant factors of the Six Big Losses which are dominantly used by the fishbone diagram approach. The research results show that the average OEE value of packing machines is 78.17% and is still below world standards (≥85%). Apart from that, evidence was found that the most dominant six big losses factors were idling and minor stoppage losses with a value of 16.85% and reduced speed losses with a value of 7.85%. Based on the fishbone diagram, the aspects that cause speed losses on packing machines are humans (lack of operator knowledge, lack of workers guarding bread baskets), machines (repair of damaged machines, unstable center and end seal temperatures), environment (unstable production room temperature) and method (non-periodic machine maintenance). The recommendation given to reduce these two losses is to use the TPM pillars, namely education and training, autonomous maintenance and planned maintenance.

Overall Equipment Effectiveness Prediction with Multiple Linear Regression for Semi-automatic Automotive Assembly Lines

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.

Application of Overall Equipment Effectiveness and Failure Mode Efffect and Criticality Analysis Methods to Improve Machine Performance Effectiveness

PT XYZ is a company that produces hot rolled coils (HRC) steel products. One of the machines used in the process is a rough mill machine in the milling process. The problem is downtime on the rough mill machine that disrupts the production process time. The purpose of this study is to determine the value of the effectiveness of the machine, find the largest losses that affect the value of the effectiveness of the machine, identify the cause of the biggest losses and provide recommendations for improvement steps. The methods used are overall equipment effectiveness (OEE), six big losses, and the failure mode effect and criticality analysis (FMECA). The results show that the average OEE value of PT XYZ is 80%, but this value was still not following the ideal standard. By identifying the six big losses, the most significant losses are reduced speed losses, with a total percentage value of 86% and an average time loss of 5,020 minutes. FMECA results obtain three components that cause the biggest losses: the engine motor, gearbox, and spindle. Recommendations for improvement include making and setting schedules, implementing predictive maintenance and preventive maintenance on rough mill machines, and regularly checking and replacing component spare parts.

Perhitungan Overall Equipment Effectiveness (OEE) untuk Mengetahui Efektivitas Mesin Produksi di PT. Z

PT. Z is a company that produces all packaging needs. The problems that exist in the production process are frequent occurrence of breakdowns on machines which result in decreased productivity. This study aims to calculate the Overall Equipment Effectiveness (OEE) value, analyze the six big losses factors, and analyze the root causes of problems from the Imprabox Matt Floor production process. From the results of the calculations that have been done, it is found that the OEE value of the Marking Process is 85.2%, Material Cutting is 68.2%, Material Printing is 75.8%, Printing is 47.9%, Welding Process is 36.6%, Assembly is 74.9% , Process Screw 81.3%. Only the marking process meets World Class OEE with an OEE value of 85.2%, and the smallest OEE value of all production processes is found in the welding process with an OEE value of 36.6%. The results of the Six Big Losses analysis and the root causes of the problem found that the lack of understanding of employees in operating machines is one of the main causes of the low OEE score. From these problems, proposed improvements in an effort to increase OEE are by conducting operator training, scheduling preventive maintenance, making machine checksheets, standardizing the welding process, and checking the machine periodically. Keywords: Cause and Effect Diagrams, OEE, Productivity, Six Big Losses

Pendekatan Overall Equipment Effectiveness (OEE) untuk Analisis Kerusakan Slitting Coil Machine (Studi Kasus)

As the industry grows, there is a greater demand for efficient production. This is especially true for producing steel pipes in high order. Producing steel pipes involves multiple components, including the coil stand, slitting coil machine, pinch roll and leveler, strip accumulator, edge milling, forming, high-frequency resistance welding (HFRW), and rotary cut-off. All of these components need to work correctly for optimal production. Maintenance is also essential to prevent problems and damage during production. One common problem is damage to the slitting coil machine, which cuts the coil or steel plate in the production process. This can cause delays and disruptions to production. The Overall Equipment Effectiveness (OEE) value is calculated and analyzed to evaluate the machine's performance. The slitting coil machine has an average OEE value of 62.3%, with an availability ratio of 94.6%, a performance ratio of 69.17%, and a quality ratio of 93.46%.

Analysis of Centrifugal Pump Machine Performance Using the Overall Equipment Effectiveness (OEE) Method (Case Study: PT. XYZ)

Improving performance in a company is a key factor in increasing efficiency and productivity. However, often the improvement efforts made are ineffective because they do not address the root cause of the problem. The Total Productive Maintenance (TPM) concept with its components can clearly identify the root cause of the problem and its contributing factors, allowing improvements to be focused on the appropriate aspects. One method of measuring machine performance used is Overall Equipment Effectiveness (OEE), which calculates factors such as availability, performance efficiency, and quality level. In a case study at PT.XYZ, OEE was used to analyze the performance of distribution pump machines. During the period of August 2022, the OEE value for each pump machine reached 99%, which exceeded the world-class OEE of 85%. However, the analysis showed that Idling Minor Stop Losses was the main cause of losses that affected machine effectiveness, with a percentage of losses reaching 82%.

ANALISIS PRODUKTIVITAS MESIN SINGLE DASH MENGGUNAKAN METODE OVERALL EQUIPMENT EFFECTIVINESS (OEE) DAN FAILURE MODE AND EFFECT ANALYS (FMEA) (Studi kasus : PT. Kusuma Mulia Plasindo Infitex)

PT. Kusuma Plasindo Infitex is an industrial company engaged in the plastic sector. In this study, we analyzed the productivity of the Polypropylene (PP) plastic department of PT. Kusuma Mulia Plasindo Infitex. In the Polypropylene (PP) plastic department, there are four production machines. This study refers to one of the machines, namely the single dash machine using the Overall Equipment Effectiveness (OEE) method. The purpose of the OEE method is to find out the OEE value which shows the effectiveness of a single dash machine. From the research, it was found that the OEE value was 57%. Further research is carried out on the consequences of low OEE by using a fishbone diagram. In the fishbone diagram known process failures and their causes. This research was continued with the Failure Mode and Effect Analysis (FMEA) method. FMEA is an engineering technique used to determine, identify, and to eliminate known failures, failures, errors, and the like from a system, process and or service before it reaches the consumer. With FMEA aims to prevent the problem of process failures. From the results of research on the RPN value, it was found that 3 improvement plans that were prioritized immediately were 1. Making filter quality standards having a risk priority number (RPN) value of 560. 2. Carrying out machine inspections periodically or more frequently (for example, three times a week) with the system check list, has a risk priority number (RPN) value of 288. 3. Makes a standard when adding raw materials has a risk priority number (RPN) value of 216.

Defect Detection System on Stamping Machine Using the Image Processing Method

Quality products are very influential in creating profits for the company and are also closely related to the level of customer satisfaction. The higher the quality of the products produced by a company, the higher the satisfaction felt by consumers. The biggest challenge in the production process is achieving good quality with a product defect rate close to zero defect. Defects in the product are usually small. This is of course very difficult for workers to inspect each product for a long time. Thus, manual inspection is certainly ineffective and inefficient because humans have a saturation point and get tired if they work for a long time. Previous research on detecting defective objects using image processing has been carried out but has not been able to detect up to the shape and size, while in this study it can detect up to the shape and size. Therefore, to implement an automatic product defect detection system we will use image processing and RFID technology. Image processing is processing on the image using a computer so that the image quality becomes better and produces value information for each color. Image processing techniques consist of image conversion from RGB to grayscale, thresholding (binarization), and morphological operations (segmentation). While RFID is an identification method by using a means called an RFID label or transponder to store and retrieve data remotely This study aims to implement a control system on HMI and also a detection system on defect products using a visual inspection system with the aim of getting the machine effectiveness value. One method to get this value is the Overall Equipment Effectiveness (OEE) method. It is proven by implementing a visual inspection system that gets an accuracy rate of 95.97% to detect rejected products and optimize the OEE presentation value obtained. In this study, the implementation of the production monitoring system was successfully implemented with an average OEE value of 52.49%.

PENGUKURAN OVERALL EQUIPMENT EFFECTIVENESS PADA PROSES PEMOTONGAN INDUSTRI FURNITURE

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%.