Overall Equipment Effectiveness Research Articles

Perbaikan Nilai Overall Equipment Effectiveness dengan Metode Total Productive Maintenance pada PT. Electric Vehicle Trimotorindo

This research was conducted to increase the Overall Equipment Effectiveness (OEE) value at PT Electric Vehicle Trimotorindo, which is an electric vehicle manufacturing company. The main problem faced by the company is the decline in OEE value every month due to lack of machine maintenance and deterioration in production quality. The method used in this study is Total Productive Maintenance (TPM), which involves identifying the factors that cause production losses through the analysis of Six Big Losses. The results show that the OEE value has increased from 78.61% to 80.36% after improvements through TPM, especially in increasing the quality rate from 88.48% to 92.23%. The conclusion of this study is that the application of TPM can improve the effectiveness of machine use and production quality, which ultimately contributes to the improvement of the company's productivity.

The Effect of Lean Manufacturing on Production / Operation for the Small and Medium Enterprise in Malaysia

At the start of 2022, the government announced its focus on supporting the development of small and medium enterprises (SMEs) to improve living standards following the COVID-19 pandemic. SMEs contribute significantly to the national economy, with the clothing industry playing a major role. This research applies lean manufacturing techniques to enhance the performance of SMEs in the clothing sector, specifically in T-shirt printing. Therefore the first objective was to collect, analyze processing times and recording of all issues encountered during production. The second objective involved using value stream mapping (VSM) to analyze the T-shirt printing process from raw material supply to finished product packaging. We also employed a why-why analysis to pinpoint the underlying causes of inefficiencies and devise specific solutions. The last objective focused on process improvement using VSM, along with the Kanban system, 5S and Make to Stock (MTS) to optimize block preparation and T-shirt printing. We selected Flexsilk Printshop as the case study. The findings showed a 66.59 % reduction in waiting time, a 30.77 % improvement in lead time, an increase in overall equipment effectiveness (OEE) from 93.3 to 97.45 % and a 50.41 % increase in efficiency.

Otomatisasi Perhitungan OEE pada Mesin Filling ILAPAK 50g Menggunakan PLC dan HMI

This study addressed the limitations in calculating Overall Equipment Effectiveness (OEE) manually, which frequently led to inaccuracies and inefficiencies in industrial processes. The purpose of this research was to design an automated OEE calculation system for the ILAPAK 50g filling machine to improve data accuracy and reduce dependency on manual input. A Design Science Research (DSR) methodology was employed, involving stages of system analysis, design, implementation, and evaluation. The system utilized key components, including an Omron CP2E PLC, proximity sensors, relays, and a Human-Machine Interface (HMI) for real-time data acquisition. The findings indicated that the automated system significantly improved data accuracy, achieving reliable, continuous OEE calculations across different machine shifts. The system successfully replaced manual logbook methods, reducing human estimation errors, and displayed OEE data locally on the HMI, making it accessible for operational decisions. Furthermore, the implemented automation reduced overall downtime, enhanced machine availability, and minimized errors associated with manual record-keeping. This research has practical implications for industries aiming to optimize production efficiency. The automated OEE system demonstrated potential for scalability to other machines in manufacturing settings, highlighting its adaptability and utility in modern industrial environments. Future research could explore integration with cloud-based databases to enhance data storage and accessibility, providing a foundation for advanced predictive maintenance and performance monitoring across multiple machines.

Comparison of desirability function (DF) and overall equipment effectiveness (OEE) with mathematical model for optimization in fruit juice production process

Comparison of desirability function (DF) and overall equipment effectiveness (OEE) with mathematical model for optimization in fruit juice production process

Evaluation of OEE at Lifelong India Using Lean Practices: A Case Study

Today, the industry is facing issues related to nonvalue-added activities (NVA). By integrating lean practices, industries can systematically evaluate and enhance their overall equipment effectiveness (OEE), leading to more efficient and effective production processes. This alignment not only boosts productivity by eliminating nonvalue activities but also contributes to higher quality outputs and reduced operational costs as well. Manufacturers are required to use value-added approaches in their manufacturing management due to the intense rivalry in real-world markets and growing client needs. Through continuous improvement, lean manufacturing (LM) has been demonstrated to be a significant technique for getting rid of nonvalue-added operations in manufacturing organizations. OEE at Lifelong India is assessed using lean tools and techniques, including Pareto analysis, Why–Why analysis, Kaizen concept and evaluation of Kaizen ideas, as well as before-and-after comparison, in order to assess the operational performance of the organization. A questionnaire was designed with data collection in mind, and business professionals in the field were surveyed and contacted personally. This study indicates that widespread implementation of lean principles leads to an increase in OEE of 95.89%, a level of OEE that is unmatched in the industry and ensures flawless output.

Prediction of Overall Equipment Effectiveness in Assembly Processes Using Machine Learning

Abstract Nowadays, a lot of data is generated in production and also in the domain of assembly, from which different patterns can be extracted using machine learning methods with the support of data mining. With the support of various modern technical and Information Technology (IT) tools, the recording, storage and processing of large amounts of data is now a routine activity. Based on machine learning, efficiency metrics including Overall Equipment Effectiveness (OEE), can be partially predicted, but industrial companies need more accurate and reliable methods. The analyzed algorithms can be used in general for all production units or machines where production data is recorded by Manufacturing Execution System (MES) or other Enterprise Resource Planning (ERP) systems are available. This paper presents and determinates which most used machine learning methods should be combined with each other in order to achieve a better prediction result.

Implementation of a performance management system for environmental sustainability in an industrial organization

Abstract The paper explores the implementation of process performance management systems in the automotive industry, specifically focusing on improving the assembly line of an organization within this sector thanks to the introduction of cobots in the line of manufacturing electronic modules used in car seat control systems. With an emphasis on environmental sustainability and circular economy (CE) concepts, the study investigates the positive effects of optimizing mechanical and electromechanical systems within the assembly line. Leveraging concepts such as KAIZEN and Gemba Kaizen, along with the guiding principles of Just-In-Time (JIT), Continuous Improvement and Automation with a Human Touch (JIDOKA), the research aims to elucidate the potential benefits of such enhancements. Key performance indicators (KPIs) including Overall Equipment Effectiveness (OEE), First Pass Yield (FPY), and scrap rates were considered. Rejects, primarily stemming from human error in part handling, emerged as the major challenge facing this assembly line. To address this issue, collaborative robots were introduced to automate part handling, replacing manual processes with precise and reliable automation. Before the implementation, the cycle time stood at 13.6 units, which was reduced to 12.5 units post-implementation, resulting in a significant increase in productivity equating to 3000 more parts produced per week. OEE increasing from 80% to 87.74%, FPY rising from 96.42% to 98.48%, and the scrap rate decreasing from 0.06% to 0.02%. By addressing inefficiencies at the assembly line level, organizations can achieve significant improvements in performance metrics while concurrently contributing to a greener and more sustainable future.

Collaborative Optimization of a Matrix Manufacturing System Based on Overall Equipment Effectiveness

When several traditional flow-shop lines operate in parallel, the operation mode with no communication between production lines will no longer be the optimal production paradigm. This paper describes matrix manufacturing systems (MMS) in a general manner from the perspective of related works, comparing different manufacturing organizational forms and their characteristics. Subsequently, MMS are extracted during the parallel production of multiple surface mount technology (SMT) lines. An overall equipment effectiveness (OEE) online calculation model and a collaborative optimization method are proposed based on the OEE of the MMS. The innovative idea of this study is to divide existing multiple parallel SMT lines into MMS. The efficiency of each matrix unit (MU) was calculated, and a collaborative optimization method was proposed based on an indicator (OEE). In this paper, an example of eight SMT lines is presented. The partitioning of MUs, OEE calculation of each MU, and the low OEE unit collaborative optimization method are described in detail. Through a case study, the architecture of the collaborative optimization model for the MMS was constructed and discussed. Finally, the improvement in the OEE proved the effectiveness and usability of the proposed architecture.

WAYS TO IMPROVE THE EFFICIENCY OF TECHNOLOGICAL EQUIPMENT

Increasing the productivity of production systems and equipment in the meat industry is inextricably linked with maintenance activities. The article analyzes the influence of manufacturability, hygiene, reliability, simplicity of equipment design on the main indicators of the quality of meat products. It is shown that the Total Productive Maintenance (TRM) and Lean production strategy is an important tool for evaluating the Overall Equipment Effectiveness (OEE) indicator, which is used to evaluate the main types of losses that reduce the efficiency of equipment. The relationship of structural and mechanical characteristics of food masses, humidity of raw materials, temperature and viscosity, processing pressure, density on the optimal operating modes of the equipment is analyzed. Using the OEE concept, the time losses associated with the functioning of the equipment are determined. The types of losses are classified: downtime and changeover (availability or readiness) of equipment, short-term shutdown of equipment and reduction of processing speed (equipment performance), defective products and product losses at startup (quality) of equipment. The application of this method of studying the overall efficiency of the equipment can be applied to any technological equipment used in the meat industry and will eliminate many problems that arise during the operation of technological equipment, improve its technical and economic indicators, develop a system of measures to improve its maintenance and repair. Using this concept, it is possible to systematize the factors that reduce the efficiency of the equipment. At the same time, it is proved that information about the factors affecting the loss of equipment operation time allows you to create a system for maintenance and repair of equipment.

Optimizing Quality Control on Electric Vehicle Production Lines with AI and Machine Learning

The study explores the use of AI and ML technologies to optimize quality control in EV production lines. By applying neural networks and machine learning algorithms, the research achieved significant improvements: a 74.4% reduction in torque deviation, a 75.9% enhancement in speed consistency, and a 70.8% decrease in defect rates. These gains also resulted in a 16.0% reduction in production cycle time and a 50.0% decrease in downtime, leading to an 8.4% increase in Overall Equipment Effectiveness (OEE). The methods employed included AI-driven predictive maintenance, real-time monitoring, and statistical process control (SPC). Despite the clear benefits, challenges such as integrating these technologies with existing systems and ensuring robust data infrastructure remain. Future research should focus on refining these approaches and extending their application across the automotive industry.

Analisis Produktivitas pada Mesin BM 206 D-2 Menggunakan Metode Total Productive Maintenanace (TPM) dengan Pendekatan Overall Equipment Efectiveness (OEE) di PT. Berlina Tbk

Productivity is key to success in the manufacturing industry, particularly at PT.BERLINA Tbk, which specializes in plastic packaging manufacturing. This study focuses on enhancing the operational effectiveness of the BM 206 D-2 machine trough the implementation of Total Productive Maintenance (TPM) and measurement of Overall Equipment Effectiveness (OEE) as well as the identification and mitigation of Six Big Losses. The research involves two sampling periods, each spanning two quarters. During the first period, the average OEE value of 66% indicated that the greatest losses were due to machine breakdown or Breakwon Losses, related to deficiencies in operator and technician skills, inadequate planes maintenance, and machine cleanliness. Following the implementation of improvements based on these findings, the average OEE incrased to 71% in the second period. These results underscore the importance of enhancing technical skills, effective maintenance, and strict cleanliness to improve productivity and reduce production losses.

Effect of Data Integration Techniques on Operational Efficiency in Manufacturing Industries in Iran

Purpose: The aim of the study was to assess the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that by seamlessly amalgamating data from disparate sources across production lines, supply chains, and customer feedback systems, manufacturers have been able to streamline processes, optimize resource allocation, and achieve significant cost savings. For instance, real-time data integration facilitates timely decision-making, allowing for adaptive production planning and inventory management. Furthermore, the integration of advanced analytics and machine learning algorithms has enabled predictive maintenance strategies, reducing downtime and enhancing overall equipment effectiveness (OEE). These technological advancements not only bolster operational efficiency but also foster innovation, as companies leverage integrated data insights to drive continuous improvement initiatives and meet evolving consumer demands. Implications to Theory, Practice and Policy: Resource-based view (RBV), technology acceptance model and dynamic capabilities theory may be used to anchor future studies on assessing the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Manufacturing firms should prioritize strategic implementation plans for data integration technologies to ensure that investments in IoT, AI, ETL, data warehousing, data virtualization, and APIs are aligned with their overall business objectives. Policymakers should provide incentives and support for the adoption of advanced data integration technologies in the manufacturing sector.

A Novel Out-of-Control Action Plan (OCAP) for Optimizing Efficiency and Quality in the Wafer Probing Process for Semiconductor Manufacturing.

The out-of-control action plan (OCAP) is crucial in the wafer probing process of semiconductor manufacturing as it systematically addresses and corrects deviations, ensuring the high quality and reliability of semiconductor devices. However, the traditional OCAP involves many redundant and complicated processes after failures occur on production lines, which can delay production and escalate costs. To overcome the traditional OCAP's limitations, this paper proposes a novel OCAP aimed at enhancing the wafer probing process in semiconductor manufacturing. The proposed OCAP integrates proactive measures such as preventive maintenance and advanced monitoring technologies, which are tested and verified through a comprehensive experimental setup. Implementing the novel OCAP in a case company's production line reduced machine downtime by over 24 h per week and increased wafer production by about 23 wafers per week. Additionally, probe test yield improved by an average of 1.1%, demonstrating the effectiveness of the proposed method. This paper not only explores the implementation of the novel OCAP but also compares it with the traditional OCAP, highlighting significant improvements in efficiency and production output. The results underscore the potential of advanced OCAP to enhance manufacturing processes by reducing dependency on human judgment, thus lowering the likelihood of errors and improving overall equipment effectiveness (OEE).

Analisis Efektivitas Mesin Bordir Komputer Menggunakan Metode Overall Equipment Effetivenes (Oee) dan Six Big Loses pada UD. Jadi Gumilang Sukorejo

The garment company UD. Jadi Gumilang is a home-based industry located in Mojotengah Village, Sukorejo District, Pasuruan Regency, East Java. UD. Jadi Gumilang engages in the production of bags, clothes, uniforms, and embroidery, which are produced only when there are customer orders. Frequent damage to the computerized embroidery machine occurs because operators do not perform routine maintenance as per the garment's regulations. To address this issue, the Overall Equipment Effectiveness (OEE) value was calculated to analyze the major factors causing downtime. OEE itself is the result of multiplying the variables of Availability (90%), Performance (95%), and Quality (99%). After calculating the OEE value, the six big losses values were calculated to identify the highest losses that cause low OEE values. Then, from the highest loss values, a fishbone diagram (cause-and-effect diagram) analysis was conducted to identify the factors causing damage to minimize the recurrence of similar issues in the future. The results of the study show that out of the 2 computerized embroidery machines, one machine has an OEE value below 85%, which is the computerized embroidery machine 1 (84.64%). From this value, the six big losses calculations reveal that the largest loss is idling and minor stoppages losses, amounting to 20.02%. After conducting a fishbone diagram analysis, the factors causing idling and minor stoppages losses were identified as Human, Machine, Method, and Raw Material. Suggestions to prevent idling and minor stoppages losses include frequently cleaning the production area after production, regularly lubricating the machine to prevent wear, always checking the machine's condition before use, and others.

Implementation of Total Productive Maintenance in Centrifugal Pump Maintenance At Pt. Fajar Surya Wisesa

PT. Fajar Surya Wisesa specializes in manufacturing packaging paper, with its factory located in the WWTP Department. The company is dedicated to enhancing the productivity and operational efficiency of its Centrifugal Pump machines through effective maintenance strategies. This study employs a quantitative approach to assess the Overall Equipment Effectiveness (OEE), encompassing availability, performance efficiency, and quality rate, alongside a Six Big Losses analysis. The findings revealed that the average machine availability reached 98%, with a notable decrease observed in August due to downtime incidents. Performance efficiency consistently exceeded 90%, although higher production outputs occasionally led to efficiency declines. Quality rates remained stable and high, signaling advancements in production processes and quality control measures. Overall, the average OEE value attained 88%, surpassing the global standard of 85%. To further optimize Centrifugal Pump machine effectiveness, proposed enhancements include operator training, regular performance assessments, improved operator welfare considerations, selection of premium raw materials, updates to standard operating procedures (SOPs), scheduled preventive maintenance, enhanced workplace safety measures, and investments in energy backup systems. In conclusion, these initiatives have effectively elevated the performance and operational quality of the Centrifugal Pump machines at PT. Fajar Surya Wisesa.

Analysis of Overall Equipment Effectiveness (OEE) as an Effort to Increase The Productivity Filling Line of Lithos Packaging Lubricant

This research was conducted at a company engaged in the lubricants business. This study examines the filling station lithos packaging. The lithos packaging filling station consists of 6 filling lines (FL), namely FL-1, FL-2, FL-3, FL-4, FL-5, and FL-6. From each filling, the line will be observed and analyzed in the format of the automation machine program. Based on the observations and analysis, it was found that FL-4 has an availability rate, performance rate, and quality rate below standard, so it is necessary to identify the cause of the low Overall Equipment Effectiveness (OEE) value of FL-4. This study analyzes the factors causing the low Overall Equipment Effectiveness (OEE) score on FL-4 using Six Big Losses and Fishbone diagrams. From the research results, it was found that several factors caused the less productive FL-4, namely breakdown losses with a value of 48%, setup and adjustment losses with a value of 3%, idling and minor stoppages with a value of 7%, reduced speed losses with a value of 41%, and defect losses with a value of 1%. An analysis was carried out using a fishbone diagram to determine the root cause of the high value of breakdown losses, reduced speed losses followed by idle and minor stoppage losses, and setup and adjustment losses. Based on the fishbone diagram, it was found that the factors causing the FL-4's unproductivity in terms of machine factors, method factors, human factors, material factors, and environmental factors. The causes of unproductive FL-4 are damage to the machine, downtime, the operator's less responsiveness to machine problems, operator expertise, decreased work ethic, defective materials, late material stocks, and hot room temperatures. Based on this, the proposed improvements given are to schedule machine maintenance at a specified time (preventive maintenance), not by the factor of damage (breakdown maintenance), make data guide for product change for each machine and attach it to each machine in full so that This downtime can be reduced, placing technical operators who focus on each filling line at least one technical operator, to speed up downtime and not disrupt the production process, add fans to speed up air circulation to reduce heat in the area and change material suppliers to get better quality.

Advanced Analysis and Problem-Solving in the Industry: A Strategic Approach to Assisting PT. XYZ in Overcoming Operational Challenges

This research aims to solve operational challenges at PT. XYZ using advanced methodologies and strategic approaches, with a specific focus on enhancing Overall Equipment Effectiveness (OEE) and Total Productive Maintenance (TPM). OEE serves as a critical performance metric encompassing availability, performance, and quality to provide a comprehensive view of equipment efficiency. The integration of TPM, emphasizing proactive and preventive maintenance, further supports these goals by engaging all employees in maintaining and improving equipment performance. The research prioritizes first-tier machine improvements such as optimizing the gripper area, pneumatic systems, strap holding clamps area, nose wire, transfer conveyor, and cutting area. Additionally, second-tier enhancements in production systems involve refining scheduling processes to prevent operator shortages, providing ongoing training to maintain operator proficiency, and ensuring readiness across all production needs. These measures aim to systematically address the root causes of inefficiencies, ultimately enhancing overall operational effectiveness in alignment with organizational objectives and standards, thereby fostering a more streamlined and productive manufacturing environment.

Pengukuran Kinerja Mesin Sewage Treatment Plant (STP) menggunakan Metode Overall Equipment Efeectiveness

One of the important things in supporting the operational management of the building which is part of the building's mechanical and electrical aspects is the building's domestic liquid waste management system. The availability of liquid waste production cannot be separated from the performance of the Sewage Treatment Plan (STP) machine as a work tool that produces domestic liquid waste production in HDI Hive Menteng Building. STP machine maintenance is one of the important factors in ensuring the quality of liquid waste production in accordance with the Environmental Service (DLH) quality standards. The machine maintenance system can be used as a benchmark for measuring the effectiveness of STP machines. Machine performance with a high rhythm without stopping 24 hours which continues to increase and is comparable to the tenant accoupancy level, of course requires anticipatory action for machine breakdown problems. Overall Equipment Effectiveness (OEE) is a method used to measure and maximize machine effectiveness based on three categories, namely Availability rate, Performance rate, Quality rate. From the results of calculating the OEE value for the STP machine at PT. Gaia Care International) of 82.96%, in general the OEE value is still below the JIPM standard value, namely greater than 85%. For this reason, in this research, an analysis was carried out using a fishbone diagram, which is used to determine the causes and effects of not achieving the effectiveness of the STP machine. Based on the results of the Fish Bone Diagram analysis with several factors including; Machines, methods, people and materials can be concluded as factors that cause a decrease in the OEE value of STP machines, namely rework losses which are quite high so that they affect the Quality OEE value in STP machine measurements. The proposal given to increase the effectiveness of the STP Machine for controlling Rework Losses is to improve the general cleaning routine and increase the Backwash Pump Capacity in the aeration tank and Inlet.

Analysis of Cut Size Paper Cutting Machine Performance Using Total Productive Maintenance Concept (Case Study: PT. RST)

PT. RST, a leading paper factory in Indonesia, prioritizes timely production by optimizing productivity, efficiency, and paper quality. This study assesses the reliability of the Cut Size (CS) paper cutting machine, which is enhanced through the implementation of Total Productive Maintenance (TPM) as a management philosophy to ensure high-efficiency operations. To evaluate machine performance, Overall Equipment Effectiveness (OEE) measurement is used. From data collected from January to September 2023, an analysis is conducted to improve the performance of the CS machine, which has not met its set target. The average OEE value of 70.9% falls short of the target of 72%. This research aims to improve the performance of the CS machine through the TPM approach, focusing on the Focused Maintenance pillar. Using tools such as Pareto diagram, why-why analysis, fishbone diagram, and the 5W1H method, several sources of problems causing the decrease in OEE value will be identified and addressed.

Analisis Kinerja Mesin Welding dengan Menggunakan Metode Overall Equipment Effectiveness (OEE) Pada Divisi Alat Berat di PT. XY

XY is a manufacturing industry engaged in the production of defense equipment. In the production process, machines are one of the important things. As one of the most important aspects in the manufacturing process, machine productivity must always be improved. Therefore, methods are needed to increase productivity in line with fluctuating business needs. Overall Equipment Effectiveness (OEE) is a method that can be used to determine the productivity of a machine. OEE is related to the availability ratio, performance ratio, and quality ratio of the production process. The multiplication of these three factors produces the company's OEE value which will later be compared with the Japan Institute of Plant Maintenance (JIPM) standard OEE value. Research was carried out on welding machines at PT. XY. The machine is used to make bumpers. Based on calculations, the welding machine obtained an availability ratio (83%), quality ratio (100%) and performance efficiency (87%). So, if the three are multiplied, we get an OEE value of 72%, it can be seen that the OEE value is still below the global standard of 85%. Keywords: Availability Ratio, Overall Equipment Effectiveness (OEE), Performance Ratio, Quality Ratio, Welding Machine