Improvement Of Overall Equipment Effectiveness Research Articles

Improvement of Overall Equipment Effectiveness Through Planned Equipment Maintenance: A Case Study

Maintenance of equipment in a manufacturing facility is of great importance to ensure availability, performance, and production of goods at the right quality level. Overall Equipment Effectiveness (OEE) is a good key performance indicator for monitoring and controlling the reliability of equipment within a production system. Classifier is one of the major equipment forming an integral part of a production line within a coal fly ash processing plant. The classifier had a capacity to produce 45 tons per hour of product, but its productivity had reduced to 33 tons per hour. Through bottleneck identification, it was noted that some of the components of the classifier had worn and tear, thus needs to be replaced. Planned maintenance of the classifier was carried out for a period of five days on shutdown. Data was collected over 31 days before the maintenance and 31 days after maintenance for availability, performance and quality of goods produced to quantify OEE before and after the maintenance. Although the classifier was producing less than its capacity, the goods produced were within the acceptable quality level, thus 100%. Post the maintenance, the production rate increased by 30.30%. The OEE improved by 21.76%, which ultimately improved the availability of products to customers. The Turn-Around-Time of trucks on the despatch line improved by 29.76%. A maintenance programme was recommended to have a system in place to be followed for maintainability of the equipment, thus, to have a sustained OEE.

A rule-based machine learning methodology for the proactive improvement of OEE: a real case study

PurposeThe Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.Design/methodology/approachAssociation Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.FindingsThe method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.Originality/valueThe originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

Improvement of overall equipment effectiveness for manufacturing of ductile iron casting: a case study

Improvement of overall equipment effectiveness for manufacturing of ductile iron casting: a case study

Improvement of Overall Equipment Effectiveness for manufacturing of Ductile Iron Casting: a case study

Improvement of Overall Equipment Effectiveness for manufacturing of Ductile Iron Casting: a case study

OEE improvement of mining shovels by survival analysis and linear optimisation as per sustainable development goals

ABSTRACT Sustainability is acknowledged as an emerging megatrend in business that significantly affects companies’ survival and competitiveness in the market-place. Environmental, global workforce and complex supply chain networks have created pressures to have a clear vision and improve sustainability due to geopolitical dimensions. As per United Nations (UN), Sustainable Development Goals (SDGs), responsible production and consumption (SDG 12), and industry, innovation and infrastructure (SDG 9), and attain higher economic scales of productivity (SDG 8) oblige to drive productivity improvement. The mining maintenance costs constitute around 30% to 40% of the direct mining costs in mining due to diverse operating conditions. First, this article aims to develop the Cox regression Machine Learning (ML) model to derive shovels’ Remaining Useful Life (RUL). Second, formulate and model the maintenance schedule optimisation of mining equipment. Third, test and validate the cost optimisation model by deploying Decision Optimisation (DO) ILOG CPLEX to combine maintenance schedules of Preventive Maintenance (PM) and Predictive Maintenance (PdM). Finally, the data-driven actions demonstrate operating cost reduction through the metrics of Overall Equipment Effectiveness (OEE), Overall Throughput Effectiveness (OTE) and Impact Factor (IF) computation. Further, this article demonstrates the benefits of IF improvements through a case study. The combined optimised maintenance reduced shovels’ maintenance by 2.27 hours per combined schedule, which led to the potential ‘OEE’: improvement between 2.7% and 7.2% of different shovels and which is a measure of equipment productivity. The computed IF improvement for mining shovels is 49% and is aligned as per the SDG of responsible production.

Improvement of Overall Equipment Effectiveness for manufacturing of Ductile Iron Casting: a case study

Improvement of Overall Equipment Effectiveness for manufacturing of Ductile Iron Casting: a case study

Machine Tools’ Running State Monitoring in the Learning Factory—Based on Machine Learning Method and Vibration Data

Acquisition of machine tools’ running state data plays an important role in the process of workshop management and has great potential in the improvement of Overall Equipment Effectiveness (OEE). Therefore, it is necessary to identify and monitor the running state of machine tools. Machine learning, as a branch of Artificial Intelligence (AI) technology, can use readily available data to derive appropriate models and use them to make judgments about new situations, which means that AI technology is also becoming increasingly important in manufacturing. The Learning Factory provides a way for students and researchers to learn and research AI and condition monitoring by simulating an actual industrial environment. Advanced Manufacturing Technology Center (AMTC) Learning Factory at Tongji University (Shanghai, China) has designed an open and scalable training course on AI technology, including teaching and practice. A method based on machine learning technology is also put forward to monitor the running state of machine tools using vibration data. The Maximum Information Coefficient (MIC) algorithm is used to remove the relevant items from the original feature parameters and reduce the dimensionality to get the feature matrix; the classification model Random Forest is used to complete the classification mapping between the feature matrix and the target matrix to achieve the purpose of indirectly monitoring the machine tools’ running state. The Result could serve as a reference for other Learning Factory AI implementations. This article introduces the method of machine tools’ state monitoring and the design of the training course in detail.

Improvement of overall equipment effectiveness for manufacturing of ductile iron casting: a case study

Improvement of overall equipment effectiveness for manufacturing of ductile iron casting: a case study

Real-Time OEE for Industry 4.0 Learning and Practice Training

Overall Equipment Effectiveness (OEE) is a well-established KPI metrics to measure the equipment effectiveness. OEE has been widely used for many industries as a quantitative tool for measuring of productivity. Singapore institute of manufacturing technology (SIMTech) designs the “Real-Time OEE for Industry 4.0 implementation and OEE improvement” training program using Learn-Practice-Implement (LPI) model. The learner could practice OEE implementation in Model Factory@SIMTech before actual implementation at customer site. This paper presents the training methodology and the benefit of the training program.

Performance augmentation of machining centers by improving the overall equipment effectiveness in subsystems

The demand for increase in performance of the manufacturing units is increasing day by day. This project work aims at improving the Overall Equipment Effectiveness (OEE) of a Hardinge 600 II series Vertical Machining Centre (VMC) machine by modification of its subsystems. The problems related to higher power consumption and decreases in the units produced per unit hour were taken for analysis. By analyzing the earlier conditions it was found that the productivity of the VMC machine was found to be limited because of low OEE. The installation of an accumulator in the hydraulic system of work holding device and a simplification of the chip removal mechanism was proposed on the VMC subsystem. Also these changes were only possible to be done effectively by removing the existing conveyer belt and introducing tray disposal system. After the suggested changes were made the machine was again investigated to check for any possible increase in the OEE. An increase of 40% in OEE was achieved after implementation of the suggested changes. The improvement in OEE could be followed in similar machines such as HMC machines, FMS setups and other machines through proper steps for better productivity of the manufacturing facility.

Improvement of overall equipment effectiveness of machining centre using tpm

Total Productive Maintenance (TPM) is a lean manufacturing technique that concentrates on improving the overall equipment effectiveness of a machine which is used for production. TPM ensures the availability of a machine and its implementation method involves continuous improvements which lead to reduced losses. This paper presents a case study on improving Overall Equipment Effectiveness (OEE) of equipment in a manufacturing company which manufactures steering housing and also to determine and address the facets which act as barriers in implementing TPM in the machine shop. The major issues faced by this company are the lack of stock in feed and tool changeover time which reduces the availability of the machine which in turn results in tremendous drop in OEE. The current level of OEE for that particular machine is found to be 54.09%. By implementing Lean concepts such as TPM and Single Minute Exchange of Dies (SMED) would improve OEE by 6.06%.

TOTAL PRODUCTIVE MAINTENANCE ON THE AIRBUS PART MANUFACTURING

An Indonesia manufacturing company producing Airbus wing parts experienced losses of more than $100,000 in 2017. The production of A 380 wing parts contributed to 73% of the total losses, and 37% came from parts production of A 350 and A 320. The study focused on the most significant contribution losses of A 380 wing parts that is Drive Rib 1. This paper aims at examining the root cause of the loss in A 380-Program, solving the root cause by proposing the alternative solution, and improving the performance in A 380 Program. By conducting the interview and archival data, the data were analyzed employing Root Cause Analysis (RCA) to discover the root cause of problems. Low reliability of the production machine (DGAL Cincinnati Milacron) was identified as a root cause indicated by Overall Equipment Effectiveness (OEE) at 50.3% used since the 1980s. According to Total Productive Maintenance (TPM), this study proposes three alternative business solutions (autonomous maintenance, quality maintenance, and machine replacement). By considering the results of OEE and Analytical Hierarchy Process (AHP) with three criteria (cost, benefit, and implementation), the problem-solution proposes two approaches, namely conducting the autonomous maintenance with an improvement of OEE about 7% for the short term period and organizing the machine replacement with a significant OEE improvement about 17.1%.

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.

A framework to improve training and development of workers’ technical skills: effects on operational performance during company relocation

A case study is presented in a food and beverage company, during the transfer of a production plant location. To address the problems of loss of industrial technicians and closing emerging gaps in knowledge of tasks’ execution to be performed by new technicians, the company has implemented mitigation actions: work characterization, the creation of standard procedures, and the implementation of operational control routines, among others. The process of technical skills development for the production center was systematized by a framework developed for this purpose. The use of this framework, combined with a specifically designed training program, allowed for improvements in technicians’ proficiency in performing operational tasks that have a direct influence on the process performance, as evidenced by the improvement of Overall Equipment Effectiveness. This framework could be adaptable to other manufacturing companies to improve Training and Development skills of their workers, aiming to create and transfer of intraorganizational knowledge.

Overall equipment effectiveness (OEE): a review and development of an integrated improvement framework

The paper reviews the background of OEE and various improvement frameworks and explores their limitations. The review found that there is a lack of improvement frameworks with systematic steps specifically developed for OEE implementation. In addition, it was found that improvement frameworks of different methodologies do not fully capitalise on the use of performance measures as benchmarks and improvement drivers although they provide structured improvement steps/guidelines. These findings show there is a need for a new integrated OEE improvement framework that incorporates the advantages of both OEE and improvement frameworks. By encompassing the core characteristics of OEE and synergising the strengths of improvement frameworks, the new framework will provide OEE practitioners a systematic guidance in carrying out improvement implementation. It will also provide overall directions on how to carry out OEE improvements and allow the practitioners to make better decisions regarding project management.

Plant Effectiveness Improvement of Overall Equipment Effectiveness Using Autonomous Maintenance Training, - A Case Study

Plant Effectiveness Improvement of Overall Equipment Effectiveness Using Autonomous Maintenance Training, - A Case Study

Model for improvement of overall equipment effectiveness of beerfilling lines

In this article, the efficiency loss of a beer filling line is investigated and modeled to improve its system performance in terms of overall equipment effectiveness and productivity. First, research is aimed at problems related to beer filling line resulting from poor production management where utilization and productivity of filling machines are low. This article derives the overall equipment effectiveness of filling machines and overall throughput effectiveness of the filling line, with the overall throughput effectiveness of general system (OTE(S)) obtained based on these measurements. Based on these metrics, the efficiency evaluation system is established. Next, according to the efficiency evaluation indexes, overall equipment effectiveness, and OTE(S), the overall efficiency of filling machines and filling line are calculated, respectively. From the results, the stenciling machine has the lowest efficiency. However, considering overall throughput effectiveness indexes and production capacity, unit 3 is the bottleneck and limits the entire productivity. Finally, efficiency loss is analyzed based on the overall equipment effectiveness and OTE(S) calculation results. Using a fishbone diagram for analysis, the cause of equipment loss is summarized and countermeasures are offered. Theory of constraint is used to analyze the efficiency loss of the filling line and give suggestions for improvement. This research can help beer enterprises achieve continuous improvement of equipment efficiency and production capacity.

Application of Lean Manufacturing in a Sewing Line for Improving Overall Equipment Effectiveness (OEE)

The purpose of this paper is to implement lean in a sewing line, analyzing the layout, process flow and batch size, in order to improve Overall Equipment Effectiveness (OEE). To understand the overall performance and scope of improvement the existing layout and process flow are analyzed exquisitely. After that, the authors proposed a new layout reorganizing the process flow that eliminated backflows and reduced transportation time. The authors found that batch size has significant effect on waiting time and transportation time. Smaller batch size increases transportation time and decreases waiting time, and vice versa. For batch size optimization, summation of waiting time and transportation time for different batch size is calculated and the smallest one is selected as optimum. Through the application of reorganized process flow in proposed layout and optimum batch size, reduction in transportation time by 30.95% and increment in OEE by 3.75% have been achieved. Following the instruction of this paper, any organization can measure OEE and improve it by optimizing batch size, reorganizing process flows, redesigning layout and eliminating back flows. In this research the authors only redesigned layout, reorganized process flows and optimized batch size that lead to an improvement in OEE but it is still far behind as compared to world class OEE. Lean is enormous with its numerous tools and philosophies and it says that there is no ultimate destination on the improvement journey. There are many other tools and philosophies of lean which can be applied for further improvement.

TPM and RCM Implementation in Textile Company for Improvement of Overall Equipment Effectiveness

The main of this study was to improve overall equipment effectiveness (OEE) at a Bahirdar textile share company through the implementation of the integrated system of TPM with the RCM. The study aims initially interviews, reviewing documentation and historical records, direct and participatory observation were used as data collection methods during the research to identifying the major problems and problem areas of the company and unconditionally concluded that the high rate of unplanned downtime failure and the loss of the performance exist in the company. This can be recognized to the condition of equipment due to the different factor, ignorance of operator, low skill and spare part to mention. The neglected preventive maintenance system of the industry is also contributed to this effect. This research work then proposes maintenance system model based on the findings in the process. The study finally highlights how to combine the Reliability Centered Maintenance (RCM) with the implementation and execution of the different Total productive maintenance (TPM) to improve the overall equipment attentiveness.

Evaluating the Performance of a Production Line by the Overall Equipment Effectiveness: An Approach Based on Best Maintenance Practices

The work presented in this paper constitutes a contribution to improve and evaluate the performance of a production line, by using the Overall Equipment Effectiveness (OEE) indicator. In this paper, we propose and implement an OEE improvement approach based on Best Maintenance Practices (BMPs) and apply them to a case study of an automotive wiring mechanical machine. To do this, we conducted an internal benchmarking study to select BMPs and we applied them to improve the availability of the machine and the total performance of the OEE. By applying these Best Maintenance Practices (BMPs) to this machine, we were able to improve its availability from 50 % as average in 2012 to 80 % in 2015. In addition, the OEE was improved from 39% as average in 2012 to 71% in 2015.