Overall Equipment Effectiveness Performance Research Articles

Optimizing Overall Equipment Effectiveness Using Kepner-Tregoe Decision Analysis: A Case Study of Semi-lean Pumps in Ammonia Plant 2 of PT Pupuk Kalimantan Timur

The increasing demand for Ammonia production requires innovative approaches to ensure equipment reliability and operational efficiency in its production facility. This study employs a multi-faceted approach to address the challenges associated with the reliability of Semi-lean Pump 107-JB in Ammonia Plant 2 of PT Pupuk Kalimantan Timur, a critical equipment in its production facility. Utilizing the Kepner-Tregoe Decision Analysis Method, the study evaluates various alternative solutions to enhance the pump’s reliability and overall equipment effectiveness (OEE). The research identifies the root causes of the pump’s low reliability, indicated by its declining OEE from 2021-2022. It is done through a series of OEE calculations, 6 Big Losses analysis, and Current Reality Tree analysis. Four major phenomena contributed to the issue: inadequate maintenance procedures and equipment, pump operation beyond its design capacity, insufficient training and competency of manpower, and improper material selection and compatibility testing. The study employs a combination of SCAMPER techniques and the Kepner-Tregoe Decision Analysis framework to develop feasible alternative solutions, which categorizes evaluation criteria into ‘Musts’ and ‘Wants.’ Factors such as cost, feasibility, and potential impact on OEE are assessed. Among the alternatives evaluated, implementing IoT Probe Sensors for real-time monitoring emerged as the most effective solution, addressing the research objective. The thesis culminates in the development and execution of a Maintenance Implementation Plan, which is subsequently assessed through OEE performance metrics. Post-implementation, the Semi-lean Pump 107-JB is estimated to experience an increase in OEE from 63.63% in 2022 to 96.74% in 2024, significantly reducing downtime and contributing an estimated savings of $12.6 million to the company. The research provides actionable insights for improving Semi-lean Pump 107-JB’s reliability. It offers a structured methodology for evaluating alternative solutions for similar applications in industrial settings, particularly rotating equipment in the petrochemical industry.

Analisis Efektifitas Kinerja Mesin Cutting Manual Dan Otomatis Menggunakan Metode OEE (Overall Equipment Effectiveness ) Di PT. XYZ

Competition in the manufacturing industry is currently getting tougher due to rapid developments from time to time. The use of tools or machines as a supporting factor for the operation is something that cannot be separated in the industrial world. Therefore, engine performance is a factor that must be considered. Over time, machine performance will decline due to age, maintenance and minimal experience of human resources. In improving performance (effectiveness) to obtain productivity, researchers can calculate the level of performance effectiveness of manual and automatic (CNC) cutting machines. By measuring using the OEE (Overall Equipment Effectiveness) method. The steps in measuring the OEE method are carried out by taking into account Availability (A), Performance Efficiency (P) and Product Quality (R). Based on the advantages of OEE, further analysis of the calculation of Six big losses is carried out. Where the Overall Equipment Effectiveness (OEE) value of the Cutting Automatice (CNC) machine is 73.24% while the manual cutting machine is 55.32%. Both machines have not met the ideal OEE performance value of 85%. While the results of the calculation of Six big losses obtained a large value and affect the effectiveness of the two machines to be minimized is the Breakdown loss factor, Setup and Adjustment Loss and Defect Loss Process. So that continuous and periodic improvements are needed to maintain the effectiveness of both machines and increase productivity. Keywords : Machine performance, Overall Equipment Effectiveness (OEE), Six Big Losses, productivity and automatic (CNC) and manual cutting machines.

Monitoring of overall equipment effectiveness by multivariate statistical process control

PurposeThis study aims to monitor the overall equipment effectiveness (OEE) indicator that is one of the best indicators used to monitor the performance of the company by the multivariate control chart.Design/methodology/approachTo improve continually the performance of a company, many research studies tend to apply Lean six sigma approach. It is one of the best ways used to reduce the variability in the process by using the univariate control chart to know the trend of the variable and make the action before process deviation. Nevertheless, and when the need is to monitor two or more correlated characteristics simultaneously, the univariate control chart will be unable to do it, and the multivariate control chart will be the best way to successfully monitor the correlated characteristics.FindingsFor this study, the authors have applied the multivariate control chart to control the OEE performance rate which is composed by the quality rate, performance rate and availability rate, and the relative work from which the authors have adopted the same methodology (Hadian and Rahimifard, 2019) was done for project monitoring, which is done by following different indicators such as cost, and time; the results of this work shows that by applying this tool, all project staff can meet the project timing with the cost already defined at the beginning of the project. The idea of monitoring the OEE rate comes because the OEE contains the three correlated indicators, we can’t do the monitoring of the OEE just by following one of the three because data change and if today we have the performance and quality rate are stable, and the availability is not, tomorrow we can another indicator impacted and, in this case, the univariate control chart can’t response to our demand. That’s why we have choose the multivariate control chart to prevent the trend of OEE performance rate. Otherwise, and according to production planning work, they try to prevent the downtime by switching to other references, but after applying the OEE monitoring using the multivariate control chart, the company can do the monitoring of his ability to deliver the good product at time to meet customer demand.Research limitations/implicationsThe application was done per day, it will be good to apply it per shift in order to have the ability to take the fast reaction in case of process deviation. The other perspective point we can have is to supervise the process according to the control limits found and see if the process still under control after the implementation of the Multivariate control chart at the OEE Rate and if we still be able to meet customer demand in terms of Quantity and Quality of the product by preventing the process deviation using multivariate control chart.Practical implicationsThe implication of this work is to provide to the managers the trend of the performance of the workshop by measuring the OEE rate and by following if the process still under control limits, if not the reaction plan shall be established before the process become out of control.Originality/valueThe OEE indicator is one of the effective indicators used to monitor the ability of the company to produce good final product, and the monitoring of this indicator will give the company a visibility of the trend of performance. For this reason, the authors have applied the multivariate control chart to supervise the company performance. This indicator is composed by three different rates: quality, performance and availability rate, and because this tree rates are correlated, the authors have tried to search the best tool which will give them the possibility to monitor the OEE rate. After literature review, the authors found that many works have used the multivariate control chart, especially in the field of project: to monitor the time and cost simultaneously. After that, the authors have applied the same approach to monitor the OEE rate which has the same objective : to monitor the quality, performance and availability rate in the same time.

An integrated OEE framework for structured productivity improvement in a semiconductor manufacturing facility

PurposeThe purpose of this paper is to introduce a practical integrated overall equipment effectiveness (OEE) framework that encompasses the core characteristics of OEE.Design/methodology/approachThe paper reviewed the backgrounds of OEE and improvement frameworks and explored the limitations. An integrated OEE framework was developed by synergizing the strengths of OEE and improvement frameworks to complement the shortcomings. This new framework underlies the OEE concepts and provides structural improvement steps. It was applied to systematically assist and guide OEE practitioners in a case study.FindingsThe review of OEE literature found that there is a lack of improvement frameworks with systematic steps specifically developed for OEE implementation. Conversely, a review on improvement frameworks of different methodologies revealed that they do not fully capitalize on the use of performance measures as benchmarks and improvement drivers. An integrated framework that incorporated the advantages of both OEE and conventional improvement frameworks was developed and validated through a case study over a period of 38 weeks. The OEE performance before the improvements was low (73.4 percent) due to high availability losses (76.5 percent). Both OEE and availability achieved the target of 76.5 percent and 80 percent, respectively, after using the framework for improvements.Research limitations/implicationsThe reviewed papers represent a sample of papers present in the literature and were selected based on relevancy. A greater number of papers incorporated into the literature review would certainly bring out a more comprehensive study.Practical implicationsThe proposed integrated OEE framework provides OEE practitioners with systematic directions and steps combined with benchmarking and loss prioritization features for effective improvement efforts. In addition, it provides overview for the practitioners to make better decisions in project management. This helps address the common issues of practitioners not sure of what the next improvement step is. A case study using the proposed framework at a semiconductor company had successfully achieved the OEE benchmarks and set target for conversion time.Originality/valueThis paper provides a new integrated OEE framework offering a systematic approach toward implementing OEE improvements.

Improvement of OEE performance using a Lean Six Sigma approach: an Italian manufacturing case study

Lean Six Sigma is a well-consolidated approach for improving processes. However, for some companies the use and project management of Six Sigma tools and Lean Production is unclear. The aim of this paper is to illustrate the use and project management of Six Sigma tools and Lean Production by discussing a novel case study dedicated to the improvement of overall equipment effectiveness (OEE). The case study was conducted in a medium-sized company which produces injection moulded parts in plastic using several presses. One of these machines had a poor OEE with a large variability. This led to the company not satisfying a customer in terms of on-time delivery performance. The poor OEE led to high costs in terms of work-in-process and re-inspections of the products. Following a typical define-measure-analyse-improve-control (DMAIC) pattern, a dedicated team of the company defined the set-up times and the internal diameter of the product as critical to quality characteristics (CTQs). The root causes of the variability of the CTQs were identified through a cause-effect diagram and a Chi-square test. Afterwards, the team removed the root causes and improved the CTQs which have increased the OEE from 40% to 61%, reducing its variability. Tools such as 5S, Single Minute Exchange of Die and poka-yoke were implemented to remove the root causes.

Equipment performance evaluation in a production plant of traditional Italian cheese

Overall equipment effectiveness (OEE) is a metric that evaluates the effectiveness of a manufacturing operation. This study investigates the relationship between the factory management and the operation of a traditional Italian cheese production line. The analysis of failure and repair data of the line over a period of 26 months was carried out. Descriptive statistics at machine and at line level were computed. In addition, the factors availability (A), performance efficiency (P), and quality rate (Q) of the OEE were calculated. Results show that: (a) the OEE performance of the line is low (76.47%), considering the target of 85%. The main causes are speed and downtime losses, (b) the availability of the line is 88.41%, whereas the maintenance time ratio is 0.1309. Thus, although availability is acceptable, the maintenance time ratio of the line is high and should be optimised with the adequate maintenance policy. (c) Every 8.5 hours there is a failure in the production line that ranges between 0 and 1885 minutes. The zero minute indicates that after completing the repair process, a new failure immediately occurred on the line. (d) The mean time to repair (TTR) a failure is 67 minutes, which ranges between 5 and 300 minutes, and (e) the minimum mean time between failure (TBF) are observed at the coagulator machine with 1622 minutes, whereas the maximum mean TTR is noted at the forming machine with 75 minutes. Therefore, the maintenance strategy of these machines must be upgraded as soon as possible.

Overall Equipment Effectiveness Improvement through Total Productive Maintenance in Assembly Cell

The Overall Equipment Effectiveness (OEE) is an established method to monitor and improve the effectiveness of manufacturing processes. OEE is frequently used as a key metric in Total Productive Maintenance (TPM) and Lean Manufacturing environment. This paper focuses on the OEE measurement of the assembly line of steering gear in an automotive company (AGR Sdn Bhd) through the implementation of TPM. The average OEE measurement before implementation of TPM is 52.21% which is below the company’s target. The company used reactive maintenance, which is inherently “wasteful and ineffective. After the implementation of TPM, the OEE performance for the assembly department improved to 70.21%. The result shows that machine down time directly influence the OEE. The implementation of TPM enables the company to change their maintenance practices from reactive to proactive. The average availability percentage is 80.34% and performance efficiency average is 87.99%. This has directly contributed to a lower overall OEE performance compared to world standard. The rate of quality is maintained at an average of 99.54%.