Overall Equipment Effectiveness Research Articles

A REVIEW ON TOTAL PRODUCTIVE MAINTENANCE (TPM)

Total Productive Maintenance (TPM) is transforming manufacturing landscapes. This review investigates its impact, revealing how TPM fosters a culture of continuous improvement through employee involvement and proactive maintenance strategies. By analyzing 30 research papers, it unveils the potent link between TPM and increased productivity alongside reduced waste. Highlighting Overall Equipment Effectiveness (OEE) as a crucial metric, the review emphasizes the importance of 5S as a foundational step for successful TPM implementation. With global adoption surging, TPM is solidifying its position as a world-class strategy for achieving peak manufacturing performance." Keywords: Total Productive Maintenance (TPM), OEE, Improve Productivity, 5S.

Automatic classification of smart sensor data for evaluating machine tool efficiency

AbstractThe assessment of production plant efficiency is crucial for optimizing the operational performance of manufacturing systems. In traditional facilities, automated data collection is limited and information primarily relies on operators declarations, which are prone to inaccuracy. There is therefore a need for readily accessible digital alternatives. This paper introduces a cost-effective method for classifying the status of machine tools using smart sensors to monitor their primary doors with minimal integration, and a streamlined algorithm for efficient data processing. The innovative algorithm was conceived using data collected in over 3 months in a manufacturing plant comprising 50 diverse machine tools engaged in batch production for the automotive industry, and is based on non-dimensional thresholds, making it suitable for generic applications requiring classification of repetitive patterns. Also, a realistic simulator was developed to provide reliable data for algorithm accuracy evaluation. The classification performance was fully tested using synthetic data, showing very good accuracy. In addition, the performance of the algorithm was compared to basic machine learning approaches further proving the validity of the proposed method. Ultimately, the classification algorithm was employed to assess the Overall Equipment Effectiveness (OEE) of the real plant machines, which were closely aligned with the estimates provided by the enterprise management.

Membangun Keunggulan Kompetitif: Penerapan OEE dan Six Big Losses untuk Meningkatkan Efisiensi Mesin I-12 di PT. BI

BI is a company that operates in the plastic packaging manufacturing industry. The main problem faced by PT. BI lies in the productivity and efficiency of the I-12 machine. The operational disruptions that occurred were a large number of breakdowns and less than optimal production levels so this machine did not reach the desired maximum capacity. The impact of these conditions can be detrimental to the company with the potential to reduce machine productivity and efficiency, as well as the possibility of incurring high costs. This research aims to evaluate the effectiveness of the I-12 engine by applying the Overall Equipment Effectiveness (OEE) method. According to the calculated data, the average Overall Equipment Effectiveness value from January to June 2022 is 81.32%, which means it does not meet the standards set by the Japan Institute of Plant Maintenance. Reduce speed losses on the I-12 engine have the largest percentage of the six big loss factors, namely 68.37%, which is the main cause of the decline in the effectiveness of the engine. Therefore, an in-depth analysis using a fishbone diagram is needed to find out the root cause of the problem. Keywords: Efficiency, Effectiveness, Overall Equipment Effectiveness, Six Big Losses

Analisis Mesin CNC Milling dengan Metode Overall Equipment Effectiveness dalam Mendeteksi Six Big Losses di PT. A

PT. A is a company engaged in the manufacturing industry with products produced in the form of (dies, jigs, and parts manufacturing). During the activity, the CNC milling machine Milltex Vex 580b experienced engine damage which resulted in the machine working unproductively and affecting the level of effectiveness of the CNC milling machine Milltex Vex 580b. Overall Equipment Effectiveness (OEE) is a method consisting of availability level, quality level, and performance level as a measurement of optimal machine performance tool productivity to maintain the effectiveness of a machine. To eliminate losses using Six Big Losses which are six big losses that can affect the level of machine effectiveness, the average OEE result is 69% where this result is still below world-class standards. This is due to losses caused by a reduction in loss speed of 42%. By analyzing the fishbone diagram (consequences) of this method, it can be concluded that the cause of the low effectiveness value in machine performance is due to the level of availability which is influenced by the reduction in loss speed.           Keywords: Effectiveness, Overall Equipment Effectiveness, Six Big Losses

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.

Integrating Machine Availability and Preventive Maintenance to Improve Productive Efficiency in a Manufacturing Industry

One of the major problems existing in manufacturing industries is low productive efficiency and high frequencies of machine breakdown or downtime. However, equipment maintenance is momentous for improving productive efficiency, methods of integrating preventive maintenance (PM) and Machine availability into improving productive efficiency in manufacturing industries has attracted considerable attention. This work showcased a strategic process improvement plan that can be used to improve production process with low productive efficiency. Lean Six Sigma (LSS) is used as the method for implementation of a successful process improvement. A case study was used to show how a successful implementation of the Lean Six Sigma Define, Measure, Analyze, Improve and Control (DMAIC) approach was implemented, how statistical analysis can be used to identify defects in products, and they were significantly improved. The Study is performed by conducting a qualitative and quantitative analysis using structured questionnaires, surveys, journals with the case a study that boasts of about 600 employees. Investigations showed that an average of 68% contributing to 79% productive efficiency apparently. However, the attempts of the decreasing downtime events and improving efficiency were based on scheduled maintenance checklist plan that is supported by the overall equipment effectiveness (OEE) benchmark as an indicator for affirming improvements. The linear regression model highlighted significant relationship between machine availability and productive efficiency. Proposed solutions using statistical analysis focused on sustainability, introducing a measurement model based on DMAIC criteria that demonstrated a significant improvement of 20% in machine availability, 44.3% in quality, 29.9% in productive efficiency and 57.4% OEE of the targeted case study. The research affirmed Lean Six Sigma (LSS) as a sustainable solution for reducing defects for quality issues, and maximizing efficiency in manufacturing industries, hence, emphasizing on resource optimization and continuous improvement in various study variables. Findings shows that Lean Six Sigma (LSS) as a methodology, is a catalyst for positive change, hence, improving machine performance, and customer satisfaction.

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.

Improving Equipment Effectiveness through Visual Stream Mapping: Some Exploratory Research Findings in the Ready-Made Garment (RMG) Sector

Improving equipment effectiveness is crucial for flexible manufacturing, ensuring that machines and tools perform their functions efficiently and consistently. Our study aimed to enhance the Overall Equipment Effectiveness (OEE) in Bangladesh’s Ready-Made Garments (RMG) manufacturing system. We used the DMADV methodology incorporating Visual Stream Mapping (VSM) and OEE. By utilising VSM, we identified issues, eliminated them in the design phase, and evaluated performance in the verification phase. We automated the material handling system to reduce handling time, and the result was a significantly improved OEE in the automated manufacturing system compared to the manual one. This study has numerous benefits in flexible manufacturing and operations management, from immediate efficiency improvements to long-lasting organisational cultural transformations. Thus, it’s a noteworthy topic for practical applications and research. Enhancing Equipment Effectiveness through Visual Stream Mapping 4.0 has broad-reaching implications, including improved productivity, reduced waste, increased efficiency, better resource utilisation, and a more agile and responsive manufacturing environment. Although OEE and VSM are frequently used separately in different manufacturing systems, this study’s novelty lies in their combined application within garment manufacturing.

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

A New Indicator for Measuring Efficiency in Urban Freight Transportation: Defining and Implementing the OEEM (Overall Equipment Effectiveness for Mobility)

Urban freight transportation is the activity that has the greatest impact on urban areas in terms of sustainability and livability, and it is, therefore, necessary to reduce its impact. Currently, there is a lack of methodologies to validate the methods proposed by companies to reduce their impacts. The proposed methodology presents the implementation of a KPI (Key Performance Indicator) based on the triple bottom line approach: economic, social and environmental, since a company with good results on the “triple bottom line” will experience an increase in its economic profitability and its environmental commitment while reducing the impacts that generate negative perceptions of it. This KPI is the OEEM (Overall Equipment Effectiveness for Mobility), a redesign of the well-known OEE (Overall Equipment Effectiveness), but adapted to the needs of urban freight transportation since this indicator provides a quick overview of the efficiency or performance of the activity according to five components: quality of deliveries, vehicle utilization, availability of the vehicle–driver tandem and efficiency (result of traffic and efficiency of delivery stops). The methodology developed will be implemented in a case study where the KPI will be calculated on the basis of real-time data and visualized on a control panel; thanks to this KPI, the company will be able to validate whether the measures taken have a positive or negative impact.

Efektivitas Mesin Simpac Small Stamping 300 T dengan Metode Overall Equipment Effectiveness di PT. XYZ

The machine is the main component in carrying out the production process. Maintenance of production process facilities is an activity to support smooth production. The production process facility in the form of a machine is maintained in the same condition as when it was new or reasonable conditions to carry out operations, when the machine is damaged the production process will be affected and the most fatal process production process stopped Problems at PT. XYZ is a machine that often experiences downtime, after direct observation of problems at PT. XYZ must use the Overall Equipment Effectiveness method to determine the overall effectiveness of equipment with this method the problems of downtime, loss time, cycle time, defects, operation time, and loading time will be known how long the ineffectiveness is, Based on the January 2023 values of 90%, February 90%, and March 84%, and performance calculations in January 99%, February 64%, and March 100%, but in these three months it has not met the international standard in OEE of 85%. Keywords: Down Time, Efektivitas, Loss Time, Overall Equipment Effectiveness

Model-Driven Bayesian Network Learning for Factory-Level Fault Diagnostics and Resilience

We propose to use engineering models for Bayesian Network (BN) learning for fault diagnostics at the factory-level using key performance indicators (KPIs) such as overall equipment effectiveness (OEE). OEE is widely used in industry and it measures sustainability by capturing product quality (e.g., less scrap) and measures resilience by capturing availability. A major advantage of the proposed approach is that the engineering models are likely to be available long before the corresponding digitalized smart factory becomes fully operational. Specifically, for BN structure learning, we propose to use analytical queueing theory models of the factory to elicit the structure, and to carry out intervention we propose to use designed experiments based on discrete-event simulation models of the factory. For parameter learning, we apply a qualitative maximum a posteriori (QMAP) method and propose additional expert constraints based on the law of propagation of uncertainty from queueing theory. Furthermore, the proposed approach overcomes the challenge of obtaining balanced-class data in BN learning for fault diagnostics. We apply the proposed BN learning approach to (i) a 4-robot cell in our laboratory and (ii) a robotic machining cell in a commercial vehicle factory. In both cases, the proposed method is found to be efficacious in accurately learning the BN structure and parameter, as measured using structural-hamming distance and Kullback–Leibler divergence score, respectively. The proposed approach can pave the way for a new class of resilient and sustainable smart manufacturing systems.

Towards a generic framework of OEE monitoring for driving effectiveness in digitalization era

In today's dynamic business environment, achieving and sustaining operational efficiency is paramount for organizations. To remain competitive and meet evolving customer demands, companies strive to optimize their operations and enhance efficiency. Over time, various indicators have been developed to assess efficiency across multiple operational dimensions, aligning with technological advancements and industrial practices. One such indicator is Overall Equipment Effectiveness (OEE), which evaluates equipment efficiency based on availability, performance, and quality. However, with the advent of the digital industry, the management of data for efficiency calculations has become increasingly complex.In this context, this paper proposes a digitized, real-time OEE calculation system that leverages advanced technologies to continuously monitor OEE, enable real-time decision-making, and drive performance improvements. Harnessing the potential of digitalization, the system facilitates seamless data collection, analysis, and reporting while automating manual processes. By focusing on OEE as a comprehensive efficiency indicator, the proposed system aims to enhance organizational performance and establish a foundation for calculating other efficiency measures.

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.

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.

Analisis Peningkatan Efektifitas Mesin Cetak Produksi Tangki Air Dengan Metode Overall Equipment Effectiveness

The increase in customer demand for water tanks has resulted in companies having problems meeting all requests. This is mainly due to the high machine breakdown time, especially on Line 3 No 28 printing press. The company strives to fulfill all customer requests by maximizing the potential of its resources. This research aims to identify the causal factors of the high breakdown time of Line 3 No 28 printing press and to recommend an increase in the effectiveness of Line 3 No 28 Printing machines using the Overall Equipment Effectiveness (OEE) method. The stages carried out in this study were through field observations, collecting data from the company's ERP and Jasper software systems, analysis, and recommendations the improvement. Data collection through interviews was carried out by direct interviews with relevant expert respondents using a fishbone diagram and FMEA methods. The main cause of the long breakdown time is the delay in the material powder entering the production area from the weighing area and the thin printing of the tank. The advice given is that the company implements OEE calculations every day so that the effectiveness of each machine can be monitored, the company increases the awareness of autonomous maintenance for everyone who works for the company and controls the improvements that have been recommended.

Predictive Analytics for Production Line Downtime: A Comprehensive Study Using Advanced Machine Learning Models

This study delves into predictive analysis, employing advanced machine learning models to predict downtime in production lines. The research follows a detailed methodology, starting by exploring production lines and their conditions. The focal point is Overall Equipment Effectiveness (OEE), which includes availability, performance, and quality, with a special focus on downtime impacts. Through an OEE tracking system, downtime is categorized into planned and unplanned, offering insights into critical components affecting production line availability. The datasets cover three years, including runtime, unplanned downtime due to equipment failures, and idle times, emphasizing instances prone to frequent equipment failures. 
  
 Examining relationships through a heatmap reveals essential correlations crucial for accurate predictions. We outline the feature selection process and fundamental data preprocessing steps to maintain dataset integrity. Introducing machine learning models such as Xgboost, Prophet, Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM), each model is carefully configured with past window parameters and forecast horizons. To prevent overfitting, the study employs K-fold cross-validation and early stopping mechanisms, ensuring robust model performance. 
  
 Two datasets, comprising runtime, equipment-induced downtime, and idle time, are utilized for analysis. Data preprocessing involves a heatmap for characteristic-target correlation and basic steps like mean-based null value imputation. Results, presented in terms of Mean Absolute Error (MAE), underscore the superior performance of LSTM. The discussion interprets these findings, confirming the potential of LSTM for downtime prediction, supported by visualizations comparing actual and predicted downtimes. Future directions include comprehensive model comparisons, expanding metrics, and increasing datasets, building on the solid foundations established by this study.

ANALISA PERFORMANCE THRESER DI PABRIK KELAPA SAWIT (PKS) PTPN V SEI GARO DENGAN METODE OVERALL EQUIPMENT EFFECTIVINESS (OEE) DAN 5S

This study aims to analyze things that support the performance of thresher in palm oil mills, determine thresher performance with Overall Equipment Effectiveness (OEE) and 5S methods in palm oil mills and analyze and provide solutions to improve thresher performance with a Total Productive Maintenance approach using the Overall Equipment Effectiveness (OEE) method and 5S on thresher in palm oil mills.. This research was conducted with descriptive and qualitative methods in improving the production performance of threser machines to determine the problems of threser machines that cause a decrease in machine productivity by analysis and discussion. Based on the results of the analysis and description of OEE measurement results in the PTPN V Sei Garo Threser machine, Measurement of the level of machine effectiveness using the Overall Equipment Effectiveness (OEE) method at PTPN V Sei Garo obtained the largest percentage of OEE on the Thresher machine at 91.66% and the lowest at 878.79%. The factors that have the largest percentage of six big losses on thresher machines are Idling and Minor Stoppage Losses of 1% and Equipment Faillure Losses of 0.37%.