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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. [ABSTRACT FROM AUTHOR]

Printing as a process itself generates many environmental concerns. The paper addresses ink management in terms of environmental issues in the label printing industry, focusing on its environmental implications. The goal is to demonstrate how a proper ink management system impacts overall printing process efficiency and environmental sustainability for printing companies. The paper introduces an empirical approach to managing components for label and packaging production, utilizing automatic ink dispensing systems. The results demonstrate that the proper management of ink dispensing to minimize waste in packaging printing is crucial for optimizing operating print costs, potentially reducing the amount of ink needed to prepare colors by 52% and achieving energy savings of 37%. This approach fulfills the goal of sustainability by addressing environmental, economic, and social concerns. By optimizing ink usage and energy consumption, companies can significantly reduce operating costs and enhance economic performance. Simultaneously, these practices improve product quality, meet consumer demands for sustainable packaging, and create better working conditions for employees. Future directions and practical implications for supporting operational excellence in production are also discussed. [ABSTRACT FROM AUTHOR]

Purpose: This research aims to investigate critical success factors, barriers and initiatives of total productive maintenance (TPM) implementation in selected manufacturing industries in Addis Ababa, Ethiopia. Design/methodology/approach: This study built and looked into a conceptual research framework. The potential barriers and success factors to TPM implementation have been highlighted. The primary study techniques used to collect relevant data were a closed-ended questionnaire and semi-structured interview questions. With the use of SPSS version 23 and SmartPLS 3.0 software, the data were examined using descriptive statistics and the inferential Partial Least Square Structural Equation Modeling (PLS-SEM) techniques. Findings: According to the results of descriptive statistics and multivariate analysis using PLS-SEM, the case manufacturing industries' TPM implementation initiative is in its infancy; break down maintenance is the most widely used maintenance policy; top managers are not dedicated to the implementation of TPM; and there are TPM pillars that have been weakly and strongly addressed by the case manufacturing companies. Research limitations/implications: The small sample size is a limitation to this study. It is therefore challenging to extrapolate the research findings to other industries. The only manufacturing KPI utilized in this study is overall equipment effectiveness (OEE). It is possible to add more parameters to the manufacturing performance measurement KPI. The relationships between TPM and other lean production methods may differ from those observed in this cross-sectional study. Longitudinal experimental studies and in-depth analyses of TPM implementations may shed further light on this. Practical implications: Defining crucial success factors and barriers to TPM adoption, as well as identifying the weak and strong TPM pillars, will help companies in allocating their scarce resources exclusively to the most important areas. TPM is not a quick solution. It necessitates a change in both the company's and employees' attitude and their values, which takes time to bring about. Hence, it entails a long-term planning. The commitment of top managers is very important in the initiatives of TPM implementation. Originality/value: This study is unique in that, it uses a new conceptual research model and the PLS-SEM technique to analyze relationships between TPM pillars and OEE in depth. [ABSTRACT FROM AUTHOR]

Enhancing machine efficiency is of fundamental concern in modern manufacturing. This study compares the desirability function (DF) and overall equipment effectiveness (OEE) using a mathematical model for optimizing the fruit juice production process. This study integrated these methodologies to enhance fruit juice production efficiency, productivity, and quality by utilizing advanced optimization techniques and analytical methods. The desirability function method can be optimized for the process conditions of fruit juice: temperature of 60 °C, filling setup of 190 ml, and pressure of 0.4 MPa. It achieved a defect of 203 bottles monthly, a rework of 681 bottles, a downtime of 250 min, and an OEE of 0.7965 or 79.65%, which can reduce the total cost of the present method and the OEE with a mathematical model of 28.01% and 11.87%, respectively. The energy consumption of the fruit was determined to be 9,681.33 MJ. The overall energy consumption can be classified into three categories: electric, thermal, and manual energy, representing 18.36%, 81.29%, and 0.35% of the total energy consumption, respectively. Overall, the results highlight techniques and insights for improving the processes to meet the business needs and operations of the beverage industry. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the buffer allocation problem on a serial production line with five workstations and four buffer locations with unreliable operating conditions. Originally, this work was used as an optimization criterion to maximize the Overall Equipment Effectiveness indicator; said indicator is used to evaluate the performance of the processes in Lean Manufacturing; a comparison of the generated solution configurations is made with respect to other optimization criteria such as the minimization of the average work-in-process inventory and the maximization of Throughput. Three case studies involving the production line operating in a balanced and unbalanced manner are examined. The evaluation method used in this document is simulation. On the other hand, an exhaustive enumeration of the analyzed solution space is made. The results report the optimal allocation of buffers in the case studies and the differences that exist in the distribution of these in the optimization criteria investigated. [ABSTRACT FROM AUTHOR]

To become more competitive, less dependent on financial support and more attractive for investors, wind energy needs to reduce its final cost of energy. According to Levelized Cost of Energy, there are two ways to achieve this goal, by reducing costs or increasing production. Overall Equipment Effectiveness (OEE) is a widely used metric in manufacturing systems, supporting operators to enhance productivity by reducing operational losses. Therefore, this study aims to perform a qualitative literature review of the main operational losses following the OEE metric, namely availability, performance and quality, adjusting it to wind energy systems. Introduction of this metric can be a valuable tool towards an integrated indicator linking production and losses, allowing comparison between assets deployed in different settings. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study uses statistical quality control (SQC) and overall equipment effectiveness (OEE) to examine quality at a porcelain production firm. The study is motivated by the most frequently broken machines in 2019, is the Jigger 01 machine. This paper aims to evaluate the machine's effectiveness using the OEE method. The OEE determines the scope of the problem to be solved using the SQC method. The average OEE value in 2019 was 70%. Based on the SQC method, the product defect produced is still under control. However, the average defect is still above the company's tolerance limit of 10%. Consequently, this study offers enhancements utilizing the Failure Mode Effect Analysis (FMEA) technique. The results indicate that human resources and machines caused defective products. This paper contributes to providing several improvements that the company can apply to maximize its quality control analysis. After implementing the improvement, the OEE value increases to 74%. [ABSTRACT FROM AUTHOR]

Overall equipment effectiveness (OEE) describes production efficiency by combining availability, performance, and quality and is used to evaluate production equipment's performance. This research's aim is to investigate the potential of the feature selection techniques and the multiple linear regression method, which is one of the machine learning techniques, in successfully predicting the OEE of the corrugated department of a box factory. In the study, six different planned downtimes and information on seventeen different previously known concepts related to activities to be performed are used as input features. Moreover, backward elimination, forward selection, stepwise selection, correlation-based feature selection (CFS), genetic algorithm, random forest, extra trees, ridge regression, lasso regression, and elastic net feature selection methods are proposed to find the most distinctive feature subset in the dataset. As a result of the analyses performed on the data set consisting of 23 features, 1 output and 1204 working days of information, the elastic net - multiple linear regression model, which selects 19 attributes, gave the best average R² value compared to other models developed. Occam's razor principle is taken into account since there is not a great difference between the average R² values obtained. Among the models developed according to the principle, the stepwise selection - multiple linear regression model yielded the best R² value among those that selected the fewest features. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to identify the relationship between different maintenance practices and productive efficiency in continuous process productive plants as well as the moderating effect of good training practices. Design/methodology/approach: The empirical data were drawn from a database containing 609 observations of 29 productive units. Scales were validated using the Q-sort method. The panel data technique was used as the analysis methodology, with the inclusion of fixed effects for each productive plant. Findings: Maintenance practices can effectively contribute to increasing the overall equipment effectiveness (OEE) of firms. Application of predictive maintenance practices should be considered as the primary training tool. Research limitations/implications: This study used a secondary database, limiting the research design and data manipulation. Practical implications: The article provides practitioners with an analysis of maintenance practices by category (predictive, preventive and corrective), and the impact of each practice on the OEE of continuous process productive plants. Moreover, it explores the importance of training for extracting more results from maintenance practices. Social implications: Companies are investing in new technologies, but it is also essential to invest in training people. There is a demand for Industry 4.0 through the introduction of upskilling and reskilling programs. Originality/value: This study used practice-based view (PBV) theory to explain how maintenance practices help firms achieve greater OEE. Furthermore, it introduced training practice as a moderating variable in the relationship between maintenance practices and OEE. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to predict one of the key performance indicators used to improve continually production systems using machine learning techniques known by the ability to teach the machine to perform complex things as opposed to simple statistical methods by giving this machine the historical dataset, according to the kind of machine learning the authors will use, the machine will be able to predict a new output data from the input data given by the user. Design/methodology/approach: This work is divided into six sections: In the first section, the state of art for OEE, machine learning, and regression models. In the second section, the methodology, followed by an experimental study conducted in an automotive company specialised in the manufacturing of manual transmissions. Findings: The three models show a very high accuracy (higher than 99%), a comparison between these three models was done using three indicators, namely mean absolute error (MAE) mean square error (mean squared error and mean absolute percentage error which shows that the best model is the least angle followed by Bayesian Ridge and automatic relevance determination regression. Originality/value: As the authors can see many works were done in the different production systems for prediction, the most relevant works were done to predict a parameter in the production system such as The prediction of part thickness in aluminium hot stamping process with partition temperature control the prediction of CO2 trapping performance the prediction of crop yield the prediction of lean manufacturing in automotive parts industry the contribution of the work will be to use the machine learning techniques to predict the key performance indicator "used to measure manufacturing efficiency" which is the overall equipment effectiveness used in the authors' case to measure the improvement of the production system. [ABSTRACT FROM AUTHOR]

Since its discovery, the COVID-19 virus spread all over the world and caused millions of deaths, this paper focuses on studying the impact of the pandemic on the connected and non-connected automotive production lines. This study is developed on two production lines in an automotive manufacturing factory that assembles 700 cars per day and the study is elaborated following two main steps: firstly, studying the impact of the virus spreading on the OEE "Overall Equipment Effectiveness" of the production lines, which is a quantitative metric used for the evaluation of the line effectiveness based on availability, performance and quality, and secondly analyzing the relationship between these factors and the OEE using the Design of Experiments method. [ABSTRACT FROM AUTHOR]

This research aims to improve J-type Anchor bolt installation in the column structure process. Determining the impact of the joining process on those Anchor bolts and column bases, the work was concerned with the Overall Equipment Effectiveness (OEE) data. Three major process indicators corresponded to the scale of OEE; Availability, Performance, and Quality rate. The use of jig and fixture has been a well-established approach to control the column base within the standard position. All of the control points have been measured on the 3D axis, as 2D in the support plane and the vertical level of the column base. Consequently, the problem of downtime loss due to the adjustment of column base position was decreased. Complete one job installation of a column with 36 CM2 cross-section and 20m of the height, took an averaged-cycle time of 22 minutes. Also, the before and after improvement of the process were compared resulting to an increase in the rate of the first right job by 45 percent. Availability increased by 4%, Performance increased by 5%, Quality rate increased by 4%. resulting in an overall equipment effectiveness increase of 11%, and the Process Capability Index (CP) was increased from 1.14 to 2.22 leading to suitable operating costs. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to demonstrate industrial Internet of Things (IIoT) solution to improve the equipment condition monitoring with equipment status data and process condition monitoring with plasma optical emission spectroscopy data, simultaneously. The suggested research contributes e-maintenance capability by remote monitoring in real time. Design/methodology/approach: Semiconductor processing equipment consists of more than a thousand of components, and unreliable condition of equipment parts leads to the failure of wafer production. This study presents a web-based remote monitoring system for physical vapor deposition (PVD) systems using programmable logic controller (PLC) and Modbus protocol. A method of obtaining electron temperature and electron density in plasma through optical emission spectroscopy (OES) is proposed to monitor the plasma process. Through this system, parts that affect equipment and processes can be controlled and properly managed. It is certainly beneficial to improve the manufacturing yield by reducing errors from equipment parts. Findings: A web-based remote monitoring system provides much of benefits to equipment engineers to provide equipment data for the equipment maintenance even though they are physically away from the equipment side. The usefulness of IIoT for the e-maintenance in semiconductor manufacturing domain with the in situ monitoring of plasma parameters is convinced. The authors found the average electron temperature gradually with the increase of Ar carrier gas flow due to the increased atomic collisions in PVD process. The large amount of carrier gas flow, in this experimental case, was 90 sccm, dramatically decreasing the electron temperature, which represents kinetic energy of electrons. Research limitations/implications: Semiconductor industries require high level of data security for the protection of their intellectual properties, and it also falls into equipment operational condition; however, data security through the Internet communication is not considered in this research, but it is already existing technology to be easily adopted by add-on feature. Practical implications: The findings indicate that crucial equipment parameters are the amount of carrier gas flow rate and chamber pressure among the many equipment parameters, and they also affect plasma parameters of electron temperature and electron density, which directly affect the quality of metal deposition process result on wafer. Increasing the gas flow rate beyond a certain limit can yield the electron temperature loss to have undesired process result. Originality/value: Several research studies on data mining with semiconductor equipment data have been suggested in semiconductor data mining domain, but the actual demonstration of the data acquisition system with real-time plasma monitoring data has not been reported. The suggested research is also valuable in terms of high cost and complicated equipment manufacturing. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to prioritize the factors for the successful implementation of total productive maintenance (TPM). Design/methodology/approach: The technique used for prioritization is the analytical hierarchy process (AHP). Findings: The commitment and involvement of the top management, i.e. the leadership team, is the most critical success factor in the successful implementation of TPM. Employee training is another vital factor. Top management should also encourage a culture favorable for information flow, equipment ownership, the involvement of people and quality management throughout the organization. Research limitations/implications: Manufacturing organizations interested in improving productivity through the implementation of TPM should first involve the leadership team and seek their full support and train all the employees in this philosophy. However, the findings cannot be generalized for global application due to the inputs taken from experts in AHP from limited geography. Practical implications: Reducing production costs is a universal expectation of business leaders. TPM can be used as a long-term strategy to improve productivity by the organization. Social implications: All employees have to be trained in this philosophy, and as part of the training and the implementation of TPM, they feel empowered and committed to the organization. Originality/value: This study has illustrated the use of AHP for the prioritization of success factors. Prioritization of success factors will help in strategy formulation by management for effective maintenance. It will help in improving the productivity and performance of the organization. [ABSTRACT FROM AUTHOR]

Due to intense global competition, manufacturing companies need to improve and optimize productivity, increase production and flexibility, and gain competitive advantages. Companies have begun to look for new approaches and strategies in the field of production management. This includes tools like Single Minute Exchange of Die (SMED) and Total Productive Maintenance (TPM), which aim to manage machines and equipment, reduce waste and lead time, and ensure and enhance competitiveness. The main objective of this study is to explore the connection and the context of the use of industrial engineering tools, namely SMED and TPM, and to monitor and evaluate Overall Equipment Effectiveness (OEE) in industrial companies in the Czech Republic. A comprehensive questionnaire survey of 200 companies and a detailed evaluation of the operational performance of 92 companies were carried out from the third quarter of 2019 to the third quarter of 2020. It was found out that 20.5% of companies implemented SMED, 26.5% of companies implemented TPM, and 54.0% of companies did not monitor and evaluate OEE. The research confirms the theoretical link between SMED and OEE and between OEE and TPM. The statistical significance of the use of SMED and TPM separately was not demonstrated. However, the mutual interaction of using TPM and SMED on the OEE level was proved. [ABSTRACT FROM AUTHOR]

Maintaining machinery efficiency is a challenge in modern manufacturing, with companies adopting Total Productive Maintenance (TPM) strategies and using Overall Equipment Effectiveness (OEE) as a key metric for operational improvements. Industry 4.0 features have provided advanced data processing tools to assess machine performance, but effectively leveraging this data to enhance OEE remains challenging due to their multisource nature. To fully leverage the vast amount of data, new analytical methodologies are essential, not only to identify production events negatively impacting OEE but also to enhance positive events, thereby supporting strategic decision-making for TPM implementation. This study addresses this need by introducing a new data-driven methodology that integrates lean TPM principles with I4.0 analytical tools. Specifically, the proposed framework provides company managers with a structured approach, employing a dual application of Association Rule Mining (ARM) and Network Analysis (NA) to discover hidden relationships within OEE factors—availability, performance, and quality—and between key factors and operational variables. By assessing the impacts on equipment effectiveness, this methodology aims to prevent negative domino effects from adverse events while promoting positive events. Finally, a data-driven decision support system provides a long-term roadmap for determining which TPM pillars and associated actions should be prioritized for improvement. A case study demonstrates the framework's effectiveness, leading to key outcomes: implementation of 24 kaizen actions, achievement of 7 out of the 8 TPM pillars, and a 14% improvement in OEE. [ABSTRACT FROM AUTHOR]

The maintenance of hospital equipment is of fundamental importance for an adequate medical diagnosis. The conduct of this maintenance can be managed in different strategies. The purpose of this study is to systematically review the main types of maintenance used by healthcare organizations to manage hospital equipment between the period 2010 and 2020. For this, a literature review was carried out in the electronic databases PubMed and Capes Journals Portal/MEC. The predominance of the application of preventive maintenance in the hospital sector was highlighted, however other methodologies such as predictive maintenance, evidence-based and reliability centered maintenance (RCM) were proposed as a way to obtain efficient results and mitigate the risk of failures. It has been proposed that the use of mathematical approaches should be further explored in this area. Therefore, increases in the overall effectiveness of hospital equipment are expected. [ABSTRACT FROM AUTHOR]

Nowadays, the survival and success of companies depend on improving on-time delivery and cost reduction. To reach this target, it is necessary to get improvements, standardization, and controlling of the manufacturing systems. Therefore, the objective of this scientific work is to perform a case study, based on the implementation of new single-minute exchange of die (SMED) framework in an oil and gas company. This novel framework was developed based on a practical application of strategies such as improvements by ECRS (eliminate, combine, reduce, and simplify), standardized work (SW), and OEE (overall equipment effectiveness) control applied to the production processes. The findings achieved in this work showed setup time was improved by 91.6%, it moved from 1 h 44 min 56 s (6,296 s) to 8 min 52 s (532 s), and OEE increase 44.6%. And, the setup activities were standardized. Besides, on every workpiece manufactured, its setup time was measured and compared against to the target achieved. So, the novel framework developed has potential to provide competitiveness' strategies to different branches of the industry. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the study is to test if it, by the use of a survey methodology, is possible to measure managers' awareness on, and specifically if there exist preconceived beliefs on, overall equipment effectiveness (OEE) results. The paper presents the design of the survey methodology as well as a test of the survey in one case company. Design/methodology/approach: Actual OEE logs from a case company are collected and a survey on the data is designed and managers at the same case company are asked to answer the survey. The survey results are followed-up by an interview study in order to get deeper insights to both the results of the survey as well as the OEE strategy at the case company. Findings: The findings show that the managers at this particular case company, on a general level, does not suffer too much from preconceived beliefs. However, it is clear that the managers have a preconceived belief that lack of material is logged as a loss much more often than what it actually is. Research limitations/implications: The test has only been performed with data from one case company within the automotive manufacturing industry and only the managers at that case company has been active in the test. Practical implications: The survey methodology can be replicated and used by other companies to find out how aware their employees are on their OEE results and if possible preconceived beliefs exists. Originality/value: To the authors' knowledge, this is the first attempt at measuring if preconceived beliefs on OEE results exist. [ABSTRACT FROM AUTHOR]

In manufacturing industries, well-maintained machines ensure their maximum utilization with good product quality, efficient time, and minimum cost. The objective of this study is to investigate a socks manufacturing line to propose an advanced maintenance plan based on Overall Equipment Effectiveness (OEE) to improve the overall maintenance along with better machine performance and product quality. Firstly, a socks manufacturing unit was selected and its maintenance-related problems were identified. It was observed that, among the six big losses, three losses have the major contribution in reducing the world-class value of OEE. Then a framework was designed based on the OEE model with the amalgamation of problem-solving tools to overcome the identified problems and reduce the contribution of three big losses. After the successful implementation of the proposed model, a significant reduction in the value of three big losses was observed that ultimately improve the value of OEE factors for the socks knitting machines by 2.18%. The statistical analysis is also conducted using three types of statistical tests, the Normality test, Wilcoxon signed-rank test, and Paired samples t -test. Kolmogorov–Smirnov (KS) test by Statistical Analysis Software (SAS). This paper points out a new proposed model of advanced OEE, which can improve machine maintenance, productivity, and quality in a better way as compared to the conventional OEE model. [ABSTRACT FROM AUTHOR]

The purpose of this investigation is to compute overall equipment effectiveness (OEE) in the small-scale industry. The novel approach is introduced to detect bottlenecks by which OEE can be improved. This study attempts to help small-medium enterprises in analyzing performance in a better way. The automotive industry was chosen for conducting the research. The present study is comprised of three phases. In the first phase, OEE was computed and compared with world-class manufacturing. The second phase included three-level of Pareto analysis followed by making fishbone diagram to mitigate the losses. The third phase conducted improved OEE in the industry. There are seven major losses present in the industry that adversely affect the effectiveness of machine in any industry. This approach can reduce these losses and improve the quality, asset utilization (AU), OEE, total effective equipment performance (TEEP) and productivity of the machine. The study exposes that Pareto analysis uncovers all the losses and works on the principle of 80/20 rule. The major losses were thoroughly explored with the help of the fishbone diagram and solutions were implemented at the shop floor. As a result, availability, performance, quality, OEE, AU, and TEPP show improvements by 4.6%, 8.06%, 6.66%, 16.23%, 4.16%, and 14.58%, respectively. The approach offers a good opportunity for both researchers and small-medium enterprises around the world to analyze the indicators of production losses, performance, and productivity in the manufacturing industry. [ABSTRACT FROM AUTHOR]

Key performance indicators are tools for management, decision support and forecasting; they reflect the strategy and vision of the company in terms of objectives and allow to always staying in step with the expectations of the stakeholders. Accurate forecasting of the indicators allows decisions to be reoriented to ensure performance optimization while reducing both cost and effort. This paper aims to apply different machine learning methods, namely support vector regression, optimized support vector regression (using genetic algorithm), random forest, extreme gradient boosting and deep learning to predict the overall equipment effectiveness as a case study. We will make use of several configurations of the listed models in order to provide a wide field of comparison. The data used to train our models were provided by an automotive cable production industry. The result shows that the configuration in which we used cross-validation technique, and we performed a duly splitting of data, provides predictor models with the better performances. [ABSTRACT FROM AUTHOR]

Improving the productivity of a manufacturing firm is very imperative in order to obtain high profits in such a competitive market. Equipment is an important part of a manufacturing system and its efficiency directly affects the quality and cost of the product contributing to overall productivity of a manufacturing plant. This paper focuses on calculating the overall equipment effectiveness (OEE) at a local pharmaceutical industry and investigates the key factors which influence the overall equipment effectiveness. OEE is an important tool which identifies areas that may have bottleneck in the production line, covering three major aspects such as the availability, performance, and quality rate of the output of equipment. On the basis of OEE, six significant losses were identified. The preliminary results of the analysis showed that OEE for the dry suspension (D/S) section is only 23% which is substantially below the benchmark of OEE level i.e. 85%. The results also presented that prime cause involves a high difference between the performance level of world class and dry suspension (D/S) process which is due to unavailability of machines for a longer period of time and malfunctioned machines producing products in large quantities that need to be reworked or scraped. In order to achieve the world class level of OEE, the performance level of dry suspension (D/S) process needs to be enhanced to 95%. A preventive maintenance strategy could be implemented in the industry in order to avoid inefficient practices and enhance the performance of the whole plant. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to develop a novel training material for the performance indicator overall equipment effectiveness (OEE) in the form of a game-based learning (GBL) approach to be used in Industry and University context. The paper will present the development of the game as well as results from tests of the game with Industry employees and University students. Design/methodology/approach: The data that are used in the game have been acquired from real OEE data logs of a reference company. The game has been refined iteratively using feedback from the participants of the tests. Findings: The paper presents the game with its components and learning objectives. A comparison of various theoretical factors on GBL and the novel training material is performed and future improvements are suggested. Research limitations/implications: The game has been developed based on OEE data logs from only one reference company. Practical implications: The training material and specifically the game can be used to train Industrial workers and University students to better envision OEE as a performance indicator. Originality/value: Serious games on lean manufacturing have been developed and played for a long time. While some of these games include OEE as an important result parameter, none really demonstrates how it is measured and analyzed. [ABSTRACT FROM AUTHOR]

The Overall Equipment Effectiveness (OEE) significantly improves with the integrated optimization of preventive maintenance and quality control. This paper presents a new mixed-integer non-linear programming model. This model determines the optimal preventive maintenance interval as well as the optimal parameters of the X control chart taking into account the OEE. The optimization performs by considering the production system as a continuous-time homogeneous Markov chain, to minimize the costs per time unit and maximize OEE. A numerical example is used to evaluate this new model. Sensitivity analysis performs to determine the effect of the model parameters on optimal decisions. This analysis further illustrates the relationship between preventive maintenance and statistical quality control and also their effect on OEE. [ABSTRACT FROM AUTHOR]

Loss of production speed is an unavoidable reality for process manufacturers. Reduced production speeds are shown to consume 9–15% of available production capacity in various production contexts and create substantial costs for capital-intensive process industries. Amongst the least examined of the six big efficiency losses measured within total productive maintenance, speed loss presents significant opportunities for potential efficiency improvements in manufacturing companies. Based on the literature, this paper presents a framework of the factors related to speed loss, including three overall dimensions: technology factors, human factors and product factors. Next, a case study of two production lines to investigate this framework and quantify the scale of speed loss for the factors identified in the case study. For quantification, generalised least squares regression is performed to study the relationship between each factor and speed loss. The analysis of the production data reveals that technology and human factors have the strongest correlations with speed losses in this industry and account for the most speed loss. This research can directly support operational improvement initiatives in practice by identifying the factors with the strongest relationships to speed loss, aiding practitioners to select the most relevant means to improve speed and identify appropriate overall equipment effectiveness targets. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to improve production effectiveness of equipment by facilitating the implementation of the key principles of total productive maintenance (TPM).Design/methodology/approach A sequential TPM-based scheme consisting of 13 procedural steps is proposed. The steps cover the basic aspects of a generic improvement system, i.e. planning, implementation, checking, corrective action and control.Findings The proposed scheme was effective in increasing the overall equipment effectiveness by 62.6 percent over a nine-month period only. This was a direct reflection of improvements in equipment availability, efficiency and product quality. A positive feedback regarding the smooth implementation of the scheme was also received from the responsible maintenance staff.Research limitations/implications The proposed scheme is intended mainly to the manufacturing industrial sector, which utilizes failure-prone equipment in running operations.Originality/value This study presents an original scheme that tries to avoid the many barriers of success frequently encountered during the implementation of TPM schemes, as reported in the literature. This scheme is unique in integrating between 5S and safety, health and environment initiatives, by capitalizing on the close relation between the two initiatives, and simplifying procedures for measuring how well the two initiatives are implemented in an organization in one score card. Different from previous studies, the scheme treats both "education and training" and 6S as a foundation to the core TPM principles. [ABSTRACT FROM AUTHOR]

The rapid proliferation of the Internet of Things (IoT) increases the network complexity and the data traffic volume significantly, and it may lead to the failure of IoT systems. Smart manufacturing is enabled by IoT connected devices; therefore the demand for clear acquisition of IoT data as an important source of performance measurement presents manufacturing companies with a major challenge. To cope with IoT fault problems, we developed an IoT data anomaly response model. Development procedure comprises three steps: configuration of the IoT-based performance measurement process with a key performance indicator, overall equipment effectiveness, and IoT anomaly response model architecture; implementation of algorithms for an IoT fault case classification, data anomaly detection and mitigation; and validation of the proposed algorithms through experimental simulation. The simulation results show that the proposed algorithms have a positive impact on IoT data anomaly detection and mitigation, thus enabling a response to the IoT fault problem in smart manufacturing. [ABSTRACT FROM AUTHOR]

Evaluation of manufacturing equipment performance has been very important in production-related functions such as planning, scheduling, and maintenance. Nevertheless, low accuracy of performance measurements can mislead decision makers. In this study, overall equipment effectiveness (OEE) is considered as a performance indicator of manufacturing equipment. In practice, components of OEE may consist of uncertainty due to manual or semiautomatic measurement systems. As a consequence, true performance of equipment may be masked by the uncertainty of measurements. In this study, two types of uncertainty are considered in production speed and stoppage duration measurements, which are used in calculating OEE components. When the measurements have uncertainty due to use of linguistic terms or some minor stoppages, idling, or speed losses being ignored, fuzzy arithmetic is used as a method to handle uncertainty. In some low accuracy cases, best guess interval estimates of operators may better reflect the state than just providing a point estimate. For such cases, interval arithmetic is used as a method to handle uncertainty. Implementation of the methods are illustrated using two real-world examples and a software is provided for practitioners. Proposed methods help making better informed decisions using OEE under measurement uncertainty of production speed and stoppage durations. [ABSTRACT FROM AUTHOR]

The mass production companies need to seek high efficiency in the use of equipment and human resources, as well as in the consumption of their inputs. One of the key methods to address these challenges is the adoption of Overall Equipment Effectiveness, derived from Total Productive Maintenance. This work aims to propose a new efficiency indicator, called Overall Machinery Effectiveness, to be applied in an automotive company in Brazil that adopted Overall Equipment Effectiveness indicator. The studied company made available production data from ten months, associated to two Press machines, generating twenty Decision Making Units for Data Envelopment Analysis and Bi-Objective Multiple Criteria Data Envelopment Analysis models application. As results, Press #2 was identified as being the most critical because, among the first ten DMUs in the efficiency ranking, seven are associated to Press #1. The targets values recommended by the new indicator were considered feasible to be implemented by the company, thus validating in practice the new proposed procedure for the management of machines effectiveness. Moreover, the identification of the relevant variables (input and output) for the Press #1, and Press #2, allowed the decision maker to act in the best way to increase their efficiency. [ABSTRACT FROM AUTHOR]

In the energy sector, significant investments were made to improve the characteristics of the existing integrated electrical system favoring the transition to a more sustainable, efficient, flexible, reliable and high quality system. Therefore, it is required to have reliable and robust metrics that try to include these concepts in the form of indicators for the KPI and its subsequent analysis. This paper addresses that problem and in order to tackle it introduces a novel methodology to measure the effectiveness of an integrated electrical system. Such methodology is based on the extension of the OEE concept to the energetic domain considering the multiple uncertain factors affecting OEE, and establishing a relation among them through the Planning Factor (PF), which relate strategic and operational concepts. In the experimental part of the paper, we have used this novel methodology to analyze and measure the effectiveness of the Spanish integrated electrical system without losing generality, and using a confidence interval of 95% an OOEE of 0.96 and SOEE of 0.652 have been obtained, which classifies the Spanish system as an advanced one. In this way, different settings or electrical systems can be compared an assessed. [ABSTRACT FROM AUTHOR]

A vital component of continuous improvement in production environments is measuring performance. For instance, in the total productive maintenance philosophy, overall equipment effectiveness is used as a key performance indicator. However, the validity of this and other performance indicators is a question when the measurements cannot be obtained accurately. In this study, measurements on production line speeds and unplanned stoppage durations are assumed to have measurement errors. A balance equation is developed for production loss amounts by formulating the relationship between accurate measurements and measurements with error. To obtain corrected values of the line speed and unplanned stoppage durations by using the measurements given by an operator, a nonlinear mathematical model is developed. It is shown that the solution to the mathematical formulation is unique and global optimum. The problem and its proposed solution is presented by a real world example from a PVC profile manufacturing, which motivated this research. A particular advantage of the proposed approach would be in obtaining better measures for process performance. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to formulate a benchmark to increase the tyre curing press production rate while minimizing tyre curing press downtime and maintenance cost with the help of a maintenance management technique based on overall equipment effectiveness (OEE).Design/methodology/approach The methodology is based on determining the OEE of tyre curing press before and after rectifying the causes of failures. The failure mode and effect analysis (FMEA) technique is used to find out the root causes of repetitive failures in tyre curing press by using the risk priority number.Findings A significant change in the value of OEE is observed after rectifying the repetitive failures, which were determined using the FMEA technique. Thus, it is concluded that the OEE and FMEA assist in improving the industrial performance and competitiveness of the production equipment studied.Research limitations/implications This study is limited to determining the OEE of single equipment only, not the whole production system. Manufacturing facilities are dependent on the operating environment; therefore a comparison of two different manufacturing plants based on the OEE value would not be justified.Practical implications This study can be applied in any tyre manufacturing industry in order to take competitive benefits, such as reduction in equipment downtime, increased production and reduction in maintenance cost.Originality/value The angle from which the paper approaches the bottleneck problem in a tyre production line is original for the studied company and shows positives results. It allows the company to apply the same approach in its other production equipment, lines and factories to achieve improvement in industrial performance and competitiveness. [ABSTRACT FROM AUTHOR]

The Overall Equipment Effectiveness (OEE) calculation is a metric that gives us daily information about how effectively the machine is running and which of the six big losses we need to improve. Overall equipment effectiveness is not the only indicator to assess a production system, but it is certainly very important if our goal is improvement. OEE is sometimes referred to as Utilization and is the percentage of your total productive capacity that you are actually using. The aim of this paper is to make a review on the method of calculus of the equipment performance indicator, Overall Equipment Effectiveness. [ABSTRACT FROM AUTHOR]

Total Productive Maintenance is one of the application techniques, which is a widely accepted form of lean management system, at business and production management. OEE (Overall Equipment Effectiveness) technique is used in Total Productive Maintenance for assessing equipments' being used efficiently or not in any organizations and applications. In this study, testing the applicability of two different OEE approaches and comparing the obtained results in the daily newspaper printing having a unique working conditions are aimed. In the application part of the study, traditional OEE calculation method and OEE calculations with a new approach was made in a printing press which performs the publication of the largest circulation newspapers in Turkey. Datas were collected during six months and calculations were made with two mentioned methods for each day in which the datas were collected. The evaluation about the applicability of two OEE approaches in the printing sector was made according to the obtained results after the calculations being made. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to describe the overall equipment cost loss (OECL) methodology and an implementation of this methodology, to compare the outcomes of OECL with those of overall equipment effectiveness (OEE), and finally to identify the benefits offered by this new methodology. Design/methodology/approach – The proposed methodology, OECL, combines six large loss models and a financial model in the performance evaluation of equipment. The six large losses are converted into monetary units. OECL is a new way of evaluating equipment performance that differs from the original OEE methodology and overcomes some of the limitations of OEE. This new methodology can be used to rank problematic machines by accounting for production elements together with finance elements. Findings – The OECL and OEE methodologies rank problematic machines differently. Research limitations/implications – Efforts were made in this research to identify factors affecting OECL outcomes, but it was found that it was not possible to apply OECL to all scenarios. Practical implications – The OECL model can be implemented in a real manufacturing company to help decision-makers better determine the magnitudes of equipment problems and rank problematic pieces of equipment appropriately. Originality/value – This OECL method is able to overcome some of OEE’s weaknesses. It can properly prioritise problematic machines by considering both cost and losses. [ABSTRACT FROM AUTHOR]

Purpose – Overall equipment effectiveness (OEE) provides a quantitative metric based on the elements availability, performance and quality for measuring the performance effectiveness of individual equipment or entire processes. Although these elements are important, other performance factors such as the efficient use of raw materials and the production environment (e.g. production system, logistics, labour, etc.) in which the equipment or process operates may also have a significant contribution to process performance. The purpose of this paper is to present an alternative measure derived from OEE, overall resource effectiveness (ORE), which considers these factors. Design/methodology/approach – The paper reviews the OEE’s background and explores its limitations. Then, it shows the conceptual and mathematical development of the ORE measure and the formulas used for its calculation. Empirical and simulation-based investigations and applications of ORE are carried out through two cases study for its validation. Findings – The results derived from both the empirical and simulation-based investigations demonstrate that OEE may not be an appropriate measure for some specific processes and that ORE may offer a more complete perspective on and information of key performance indicators. Practical implications – ORE can provide production managers with more complete information concerning the performance of their processes. This will allow them to take better decisions regarding the management and actions needed to improve their processes. Originality/value – This paper presents a novel and alterative approach to measure the performance of manufacturing equipment and processes. [ABSTRACT FROM AUTHOR]

Over the last few decades, surface mining industry has focused on utilization of large sized, high capacity automated equipment for getting greater production to meet the international market demands. In order to achieve high rate of production with low unit price, it is necessary to use equipment as affectively as possible. Overall Equipment Effectiveness (OEE) is a tool that is increasingly being used to measure the effective utilization of equipment in terms of their availability, performance and quality. This study was carried out for the measurement of Overall Equipment Effectiveness (OEE) of the mining equipment being utilized in DeraGazi Khan (DG) cement quarries. The OEE value of two shovels and six dumpers was calculated and were compared with the bench mark values. The values of OEE were found to be 75% and 50% for two shovels and for dumpers, it was found to vary from 49% to 56%. [ABSTRACT FROM AUTHOR]

Despite strong correlation between Lean and Green, companies have found it challenging to integrate and implement Lean and Green practices simultaneously especially when resources are limited. Although there are existing model, framework, and methodologies, some limitations and challenges still exist. This paper aims to overcome some of the limitations and challenges from the existing works. Specifically, one of the objectives is to propose a methodology that aims to integrate Lean and Green practices, and enables the implementation of the integrated Lean and Green practices in an easy and practical manner. To achieve this objective, the proposed methodology will adopt and streamline some of the approaches used in existing works. In this methodology, an easy-to-track metric called Carbon-Value Efficiency, which aims to integrate metrics derived from Lean and Green implementation, is introduced. The other key objective is to demonstrate the applicability of the proposed methodology using a case study of metal stamped parts production. The results show that Carbon-Value Efficiency can be improved by 36.3%, given an improvement in production lead time by 64.7% and a reduction in carbon footprint by 29.9%. This case study demonstrates that the proposed methodology helps to overcome the challenge in integrating and implementing Lean and Green practices, and achieve beneficial results. Future work could study other cases and explore other case specific supporting tools and techniques in order to enhance the proposed methodology. [ABSTRACT FROM AUTHOR]

In a highly dynamic world, due to technological, economic, political and social changes in the environment, it has been recognized that the production systems play a critical role in the competitiveness of an organization. In this research the authors propose the integration of lean thinking in managing a production system using the technique of discrete event simulation using Petri nets as the support methodology in the process of decision making. Decision making in a lean production system is a complex process because it has static (structural) and dynamic (operational) characteristics and a set of waste presented in it. This paper presents the design of a set of structures that represent different stages in a production process of tires and that is it possible to prove that the design of a model, using the methodology of Petri nets, helps to have an effective and efficient decision making for waste disposal, this thanks to the properties of such nets to model discrete events and analyse the dynamic behaviour of the system. Finally the simulation allows analysing a set of parameters through a systemic indicator OEE (Overall Equipment Efficiency) and demonstrate that the implementation of methods, techniques and tools proposed in the lean production theory generate synergy in the system. [ABSTRACT FROM AUTHOR]

The article reports on a study which compared workload management for infectious disease (ID) serological testing of two chemiluminescence immunochemistry systems, the Architect i2000SR by Abbott Diagnostics and the Liaison XL by Diasorin. Researchers loaded all secondary reagents and disposables and performed setup operations in order to put both systems in their best condition. They tested in parallel the panels that represent the most commonly requested patterns for screening and diagnosis.

Measuring availability and utilisation sets up the dynamics of a blame culture, siloed thinking and competition rather than collaboration. Availability and utilisation do not include rate losses, or quality losses. When overall equipment effectiveness (OEE) is measured these additional losses are included, that identify far more opportunities for improvement. More importantly OEE is an overarching asset management measure, focused at a high level. Everyone should be focused on OEE, not just what is going on in their department. This paper will outline a well proven theoretical management of change model, and show how to change an organisation intent on arguing minor details, into an organisation focused on the big picture. This paper describes the change process steps, from convincing the CEO, to having OEE embedded in the DNA of the organisation. Details included in this paper are: the key steps to embedding change, how to convince a CEO to adopt OEE, and leadership styles to engage the entire organisation. This paper describes the four year journey from Availability and Utilisation to having OEE embedded in the organisation. A significant number of change programs in large organisations fail (Kotter, 2007). This identifies an organisational change process that has a theoretical basis, and demonstrates how this model was used successfully to implement a major organisational change. [ABSTRACT FROM AUTHOR]

The paper deals with the topic of facility management focusing on maintenance area and its importance for increasing company competitiveness. The importance of functional company facility management has risen dramatically in recent years. The reason for this is the increased pressure on cost reduction and additional value to the core business of the enterprise. The paper introduces a current theoretical literature-based framework for this topic in order to examine and analyse the supporting activities and processes connected with production facilities and maintenance particularly. The main aim of the paper is to show the importance of a maintenance management information system and the effects of its implementation on the overall equipment efficiency of the facility in the enterprise based on primary data gathered from a selected company. The data was processed in a case study using information from the maintenance department of the selected enterprise in the plastic-processing industry in selected years (2006, 2008, 2009 and 2010). As it shows, this particular system was intended to provide relevant information to workers responsible for adjusting and repairing machines, as well as to the management of the enterprise in order to help them to make the right decision. The results of the data analysis proved the importance of computerized maintenance management information system (CMMIS) implementation in improving Overall Equipment Efficiency (OEE) metrics and the efficient control of its production system. [ABSTRACT FROM AUTHOR]

This article presents a process improvement study applied at a local paper manufacturing company based on customized Lean Six Sigma methodologies. More specifically, the DMAIC (Define, Measure, Analyze, Improve, and Control) project management methodology and various lean tools are utilized to streamline processes and enhance productivity. Two performance measures namely production rate and Overall Equipment Effectiveness (OEE) are employed to evaluate the performance of the cutting and the printing machines before and after the DMAIC cycle. Obtained results indicate that the production rate increment for printing machines by 5% and for the cutting machines by 10%. Moreover, the OEE for the printing and cutting machines has increased by 21.6% and 48.45% respectively [ABSTRACT FROM AUTHOR]

Operation and maintenance (O&M) cost will be the key to the economic viability of large offshore wind farms planned worldwide. In order to support investment decisions a systematic mathematical approach to the O&M cost contributions is required prior to detailed engineering or even construction of the wind farm.Adopting the general terms of efficiency, productivity and effectiveness defined for production processes we introduce the Wind Farm Process and its Total Overall Equipment Effectiveness (TotalOEE) by considering wind farms performing a transformation of produced electrical energy to delivered (sold) electrical energy. This transformation process consists of an installation, i.e. properly selected wind energy converters and their arrangement to form a wind farm, and of a process comprising operation and maintenance. Both are the subject of optimization to maximize the annual energy output by minimizing the different kinds of losses.In a systematic approach to the causes and nature of losses in wind farms the terms theoretical production time, available production time and valuable production time are redefined in unit full load hours. Then, a calculation scheme is developed to quantify wind farm production losses in terms of planned or unplanned downtimes and speed losses and to relate the associated reduction of revenues ΔR to the theoretical maximum of annual wind park revenues Rtheo(park). It leads to the simple equation ΔR/Rtheo = TotalOEE – 1 < 0. [Copyright &y& Elsevier]

Total Value of Ownership (TVO) and Overall Equipment Effectiveness (OEE) analysis are novel tools capable of monitoring and analyzing industrial processes by assessing the efficiency of the entire instrumental equipment and calculating instrument capacity utilization. Such integrated analysis, measuring quality indicators of the testing process, could also provide new perspectives and methodologies for the workflow organization of clinical laboratories. In this study, TVO and OEE were employed for the evaluation of two different configurations of a therapeutic drug monitoring sector, comparing the results obtained for immunosuppressant (ISD) and anti-epileptic drugs (AED) analysis as well as checking their quantitative performance in terms of limit of quantification, accuracy and precision. TVO analysis was performed for ISDs, including the Total Direct Labor Time, Total Cycle Time and Turnaround Time as well as cost of testing. Instruments' performance and workload were assessed using OEE indicator, studying Availability, Performance and Quality factors. Total Cycle Time for a batch was 3.55 h, decreasing of 1.5 h in the new setting where personnel are engaged for 0.98 h, 25% of total testing time. The calculated cost per sample was 6.60 euro. Availability values were significantly higher for automated sample-handling system and ISDs analysis by LC-MS. Higher Performance values were obtained for LC-MS system for AED and other TDM. Quality values were >0.94 for all instruments. TVO and OEE proved to be applicable to clinical laboratory environment, quantifying benefits and costs of newly developed semi-automated therapeutic drug monitoring sector. This novel approach based on an integrated analysis may help activity planning and quality improvement and could be used in the future for benchmarking progress as a product/process comparison tool in other laboratory fields. [Display omitted] • Quality indicators for evaluating therapeutic drug monitoring sector organization. • Time and costs of analyses investigated by Total Value of Ownership. • Overall Equipment Effectiveness to assess Availability, Performance and Quality. • Automation coupled to LC-MS improves all novel indicators. • Integrated analysis as useful tool to help activity planning and improvements. [ABSTRACT FROM AUTHOR]

Measuring availability and utilisation sets up the dynamics of a blame culture, siloed thinking and competition rather than collaboration. Availability and utilisation do not include rate losses, or quality losses. When overall equipment effectiveness (OEE) is measured these additional losses are included, that identify far more opportunities for improvement. More importantly OEE is an overarching asset management measure, focused at a high level. Everyone should be focused on OEE, not just what is going on in their department. This paper will outline a well proven theoretical management of change model, and show how to change an organisation intent on arguing minor details, into an organisation focused on the big picture. This paper describes the change process steps, from convincing the CEO, to having OEE embedded in the DNA of the organisation. Details included in this paper are: the key steps to embedding change, how to convince a CEO to adopt OEE, and leadership styles to engage the entire organisation. This paper describes the four year journey from Availability and Utilisation to having OEE embedded in the organisation. A significant number of change programs in large organisations fail (Kotter, 2007). This identifies an organisational change process that has a theoretical basis, and demonstrates how this model was used successfully to implement a major organisational change. [ABSTRACT FROM AUTHOR]