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Copyright of International Journal of Research in Industrial Engineering (2783-1337) is the property of Ayandegan Institute of Higher Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the last decade, metalworking firms around the world faced the challenge of embracing Industry 4.0. Amidst this, some machining companies battle with outdated, unconnected equipment on their shop floors. This work introduces a novel methodology linking machining events to electrical power behavior, enabling real-time monitoring of machine tool states. The prototype, OEE-Plus, tested on a legacy transfer line, extends Overall Equipment Effectiveness by incorporating an energy consumption indicator. The significant result is a practical solution empowering small to medium-sized enterprises to assess the efficiency and sustainability of their legacy machinery. [ABSTRACT FROM AUTHOR]

Overall equipment effectiveness (OEE) is a lean manufacturing tool that has been reported to improve the performance of a company significantly. The adoption of OEE has been mattering in the garment manufacturing sector in Pakistan. Numerous companies have implemented OEE and improved their production in the textile sector. The present research was conducted at ABC Textile Company in Karachi. Targets at the various departments of the company were not being achieved. In this regard, the production data for the previous 90 days was collected initially. Since there were the issues with availability of materials and accessories; this was the basic and main reason for delays and unachievable targets. After certain improvements, the overall equipment effectiveness (OEE) of each department was calculated. Data analysis was conducted in Microsoft Excel. Results indicated a significant increase in the value of OEE across all the departments after the process improvement. Since there is a separate tool to increase the availability (total productive maintenance), and quality (Six Sigma); they are implemented one by one. After their integrated implementation, the boost in various parameters of productivity will be noteworthy.

This paper will explore the objectives and advantages associated with the implementation of Total Productive Maintenance (TPM). It will specifically delve into the calculation of Overall Equipment Effectiveness (OEE) within a steel company based in Jordan. Furthermore, it will analyze the concept of the "big six losses" prevalent in various industries, encompassing factors such as quality, availability, and speed. A case study sourced from the electronics industry in Bangladesh will be presented, detailing observations collected over ten working days. During two months, multidisciplinary teams were assembled to assess the efficacy of collaboration between different departments in eliminating organizational silos and enhancing maintenance processes. Additionally, frontline workers on the production line were involved in adopting autonomous maintenance practices, contributing to daily maintenance operations. The outcome of this initiative revealed notable achievements, with the company attaining a 99% score in the quality factor of the OEE equation, 49% in availability, and 84% in performance. Recommendations for improvement were proposed, including the adoption of techniques such as Single Minute Exchange of Die (SMED), Computer Maintenance Management System (CMMS), and optimized production planning strategies. These suggestions aim to bolster maintenance procedures and enhance overall productivity within the industry.

Optimizing production in the plastic extrusion industry is a pivotal task for small scale industries. To enhance the efficiency in today’s competitive market being a small-scale manufacturer over their peers is challenging. With the limited resources, having constraints on manpower, capital, space, often facing fluctuations in demand and production, simultaneously maintaining high quality became very important for the success. Among the plethora of KPIS used in manufacturing, Overall Equipment Effectiveness (OEE) stands out as corner stone. In this study, we collected real-world data from a plastic extrusion company. i.e., an HDPE Pipe manufacturing company. It serves as the backdrop for our study, this is based on the plastic extrusion sector and set out a goal of enhancing OEE through a comparative investigation of various ML models. To forecast and estimate OEE values, we used various Machine Learning models and examine each algorithm’s performance using metrics like Mean Squared Error (MSE) and model comparisons using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), creating a comprehensive picture of each algorithm’s strength which enables the small businesses to make informed decisions and empowers them to stay agile and adapt to the changes in the manufacturing environment.

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

In the petrochemical industry, effective maintenance management is critical to operational efficiency and safety. Key indicators such as overall equipment effectiveness (OEE), reliability, safety incidents, and maintenance costs are commonly used by management to evaluate maintenance system performance. Increasing equipment availability and product quality can boost OEE and lead to fewer safety incidents and improved overall performance. Studies in Iran's petrochemical sector have shown that investing in maintenance and training can bring significant benefits. For example, data from 42 petrochemical complexes in Iran, covering 2015 to 2021, show an increase in equipment effectiveness and reliability over time. However, there was a slight slowdown in 2021 due to the impact of the Covid19 pandemic. Improvements in maintenance performance, the use of excellence models along with the necessary training have been associated with increased product realization and productivity growth. Research also shows that increased reliability can positively impact the reduction of safety incidents, environmental issues, and overall operating costs. By prioritizing maintenance excellence and adopting best practices, organizations can strengthen their competitiveness and sustainability in a dynamic business landscape. [ABSTRACT FROM AUTHOR]

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]

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

Technology enabling drug serialisation technology was introduced by regulators to enhance security in pharmaceutical supply chain and protect drugs from infiltration by falsified and substandard medicines. The introduction of systems for serialisation required huge financial outlays manufacturers of pharmaceuticals. This study investigated the impact of serialisation on the operational efficiency and productivity in Irish pharmaceutical sites. A qualitative study was conducted with 11 manufacturing sites in Ireland. The participating companies operated a total of 114 pack-lines, representing approximately 65% of the automated packing lines in the country. The study found that serialisation had a negative effect on packaging production line OEE and line availability and on the individuals cost per unit of packaged pharmaceuticals. The research results estimated that the capital costs of serialisation were four times greater than those estimated by the regulators. There was a 4.1 cents average cost per pack for serialisation with high volume sites reporting an annual cost of serialisation of up to €4.5 m per annum and a 2.7% increase in the average cost of goods sold. A pattern whereby where many pharmaceutical manufacturers are transitioning from smaller batch production and moving toward larger batch production sizes in order to increases efficiencies was identified. The research also proposed the use of a serialisation depreciation factor as a method to determine the impact of serialisation on the cost of goods sold. This is the first study of its kind into the cost of serialisation from a manufacturer's viewpoint and studying the effects of serialisation on productivity, line availability and operational efficiency. [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 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.

Overall equipment effectiveness (OEE) is a key indicator to measure the effectiveness of production systems. This paper aims to evaluate and improve a plastic production line based on OEE evaluation. An integrated framework is proposed to enhance the production system efficiency. This paper presents the data for a Plastic production line in Jordan under real working conditions. The data covers three months. A framework process to improve the OEE of the Plastic production system was proposed. Six major stoppage losses were inspected with the help of Pareto analysis. Furthermore, the actual availability, efficiency, and quality rate measures, together with the whole OEE for each working day, week, and month of the production line were shown. The methodology is based on determining the OEE of a Plastic production line after determining the causes of failures. The fishbone diagram tool is used to determine the root causes of failures. To improve the OEE measure, several losses are identified. The results reveal that the company should improve its policy to improve the production line’s performance and reduce losses. Top management should also pay attention to reducing the speed losses, which consist of 58.1%, and eliminate the planned and unscheduled disruptions covering 12.73% of all losses. This can be achieved by establishing a proper operation management procedure and strategy. This, in turn, optimized the equipment’s effectiveness. The quality procedure should include the changeover program that may be executed every day. Similarly, all preventive maintenance procedures for the six machines should be properly executed in predetermined intervals. There are several limitations in the research. Firstly, the research case study is only the plastic production system. Secondly, the research is related to the downtime or stoppage by analyzing it using fishbone diagram. Further, supported by other techniques such as the Pareto chart, six big losses analyses and CED. This research conducted on a Plastic industry. However, similar studies can be carried out in future in other manufacturing industries like electronic, pharmaceutical, textile industries, etc., and service industry. However, as future research work the contributions of this paper with other lean manufacturing concept like six sigma, quality function deployment, TQM, and just-in-time manu-facturing, can also be conducting to assess the overall production line efficiency. On the other hand, several statistical tests can be implemented based on data collected of TPM performance indicators. The proposed method supports policymakers in their decision-making process on the operations management line. Further-more, it improves the production systems’ productivity quality, and performance, reducing unplanned stop-pages and breakdowns, and reducing maintenance costs.

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]

Manufacturing management and operations place heavy emphasis on monitoring and improving production performance. This supervision is accomplished through strategies of manufacturing performance management, a set of measurements and methods used to monitor production conditions. Over the last 30 years, the most prevalent measurement of traditional performance management has been overall equipment effectiveness, a percentile summary metric of a machine's utilization. The technologies encapsulated by Industry 4.0 have expanded the ability to gather, process, and store vast quantities of data, creating the opportunity to innovate on how performance is measured. A new method of managing manufacturing performance utilizing Industry 4.0 technologies has been proposed by McKinsey & Company (New York City, NY, USA), and software tools have been developed by PTC Inc. (Boston, MA, USA) to aid in performing what they both call digital performance management. To evaluate this new approach, the digital performance management tool was deployed on a Festo (Esslingen, Germany) Cyber-Physical Lab (FCPL), an educational mock production environment, and compared to a digitally enabled traditional performance management solution. Results from a multi-day production period displayed an increased level of detail in both the data presented to the user and the insights gained from the digital performance management solution as compared to the traditional approach. The time unit measurements presented by digital performance management paint a clear picture of what and where losses are occurring during production and the impact of those losses. This is contrasted by the single summary metric of a traditional performance management approach, which easily obfuscates the constituent data and requires further investigation to determine what and where production losses are occurring. [ABSTRACT FROM AUTHOR]

The semiconductor manufacturing sector has contributed to the advancement of technical development in the sphere of industrial applications, but one crucial factor that cannot be overlooked is the evaluation of a machine's state. Despite the presence of advanced equipment, data on their performances are not properly reviewed, resulting in a variety of concerns such as high rejection rates, lower production output, manufacturing overhead cost issues, and customer complaints. This study's goal is to evaluate the performance of die attach machines made by a prominent subcontractor semiconductor manufacturing business in the Philippines; our findings will provide other organizations with important insights into the appropriate diagnosis of productivity difficulties via productivity metrics analyses. The study focuses on a specific type of die attach machine, with machine 10 showing to be the most troublesome, with an overall equipment effectiveness (OEE) rating of 43.57%. The Failure Mode and Effects Analysis (FMEA) identified that the primary reasons for the issue were idling, small stoppages, and breakdown loss resulting from loosened screws in the work holder. The risk priority number (RPN) was calculated to be 392, with a severity level of 7, an occurrence level of 7, and a detection level of 8. The findings provide new insight into the methods that should be included in the production process to boost efficiency and better suit the expectations of customers in a highly competitive market. [ABSTRACT FROM AUTHOR]

Amaç: Üretim sektöründeki bir firmanın 2018-2019 yılı orjinal verilerinden türetilmiş sıralı ölçekteki Toplam Ekipman Etkinliği (TEE) puanı üzerinde etkili olan değişkenlerin makine öğrenim algoritmaları ile modellenmesi, yorumlanması ve model performanslarının karşılaştırılması çalışmanın temel amacıdır. Yöntem: TEE puanının modellemesinde karar ağaçları (CART, CHAID), lojistik regresyon (LogR) ve yapay sinir ağları (YSA) kullanılmıştır. Kurulan modellerin performans değerleri “duyarlılık”, “seçicilik”, “kesinlik” ve “doğruluk” kriterlerine göre hesaplanmıştır. Modelleri yorumlarken karar ağaçları ve YSA sonuçları için yüzdelerden, LogR için odds oranından yararlanılmıştır. Bulgular: Modellerde TEE puanı üzerinde “saat”, “üretim”, “tecrübe” ve “kayıp metre” değişkenleri incelenmiştir. Performans karşılaştırmasında en iyi sonuç veren algoritmanın sıralı LogR olduğu ve bu modele göre üretimin düşük ve çalışanlarının daha az tecrübeli olduğu firmalarda daha “düşük” TEE puanı elde edilirken, kayıp metresi daha az olan firmalarda daha “yüksek” TEE” puanı alma şanslarının olduğu saptanmıştır. Özgünlük: Literatürde sürekli olarak modellenen TEE puanının kategorik hale getirilerek sınıflar arasındaki farklılığın belirlenmesiyle firmaların kendi konumlarını belirlemesi sağlanmıştır. Böylece firmalar kategorisini belirleyip seçilen modeldeki önemlilik sırasındaki faktörlerini değiştirerek bir üst kategoriye daha hızlı çıkabilecektir. Literatürde kategorik olanTEE puanını makine öğrenim algoritmaları ile çözümleyen modellerin olmaması bu çalışmanın özgünlüğü olarak belirlenmiştir.

Copyright of Verimlilik Dergisi is the property of Verimlilik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

Nowadays, industrial businesses are more aware of the value of machine maintenance, and more especially, the adoption of an effective maintenance strategy. Total Productive Maintenance (TPM), which involves everyday chores involving the entire workforce, increases equipment efficiency, prevents breakdowns, and promotes autonomous operator maintenance. TPM is a fantastic technique for maintaining buildings and machines. This article provides research and a review of TPM implementation in an RMG Industry to help enhance Overall Equipment Effectiveness (OEE). Data from the past have been studied, and the findings obtained in terms of motivated employees, improved OEE, and a decrease in the number of rejects/accidents on the production line are fairly positive. The methodology calls for gradually applying lean principles, Autonomous Maintenance (AM), 5S, and planned maintenance. After TPM deployment on the critical machine, improvements in availability, performance, and quality are seen boosting the overall efficacy of the equipment. A comparison of OEE before and after implementation demonstrates the effectiveness of TPM deployment throughout the industry.

PT X is one of the companies that produces cement in Indonesia. Engine breakdowns do happen occasionally in raw mill machines in PT X. This study analyzed the cause of occasional breakdowns. The CC-2 raw mill machine had an average operational time of 4.8 days per week and a total breakdown time of 137.76 h. Overall equipment effectiveness (OEE) analysis revealed an average OEE of 57%, with the performance rate being the most significant influence. The OEE of the CC-2 raw mill machine is below 65%, which is unacceptable, because it causes significant economic losses and very low company competitiveness. Failure mode and effects analysis (FMEA) identified various causes for breakdowns, such as gas supply failure, wear/fatigue, electrical issues, and raw material quality. Suggestions for solutions are provided, including repairs, replacements, and preventive maintenance. [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]

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 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]

Both the Lean philosophy and occupational safety and health have been widely studied, although this has usually been carried out independently. However, the correlation between Lean and occupational safety and health in the industrial context is still underexplored. Indeed, Lean tools can be applied to ensure the best safety environment for workers in each kind of manufacturing process, and this deserves to be studied. The study described here aims to understand the influence of each of a set of four Lean tools used in an industrial context with a strong manual labor component, seeking to determine the influence of each of these Lean tools on the increase in safety obtained through their application. For this purpose, four Lean tools that are quite commonly applied are selected, taking into account previously presented work that pointed to the positive influence of the application of each of these tools on worker safety: total productive maintenance system, Gemba walk, visual management and Yokoten. This study aims to apply these Lean tools and to analyze their impact on productivity, and then, on the safety of a company selected as a target in order to validate the concept. For this purpose, a new tool is created. In the first instance, the tool analyzes the current state of the productive process and the safety level through the study of the risk levels detected in the plant. In terms of productivity results, a reduction between 7% and 12% in cycle time is achieved in four areas of the plant. The feedback from employees showed increased satisfaction with the processes' simplification. To conclude, a 50% reduction in the number of work accidents per month is observed as a result of the implementation of Lean tools. The influence of the selected Lean tools on increasing both productivity and safety is clear, and our results prove the selection of tools to be largely adequate. [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]

Overall Equipment Effectiveness (OEE) is the foundation for the growth of the process industries as it ensures maintenance, performance and productivity updation. Through its usefulness to integrate with miscellaneous performance-enhancing strategies such as Total Productivity Maintenance (TPM), 5S, lean manufacturing (LM), total quality management (TQM) six sigma etcetera, OEE has now become a vital tool for necessary implementation. This study has analyzed the factors and sub-factors associated with OEE in the sugar industries of India. The study reveals the implication of general input/output, direct/indirect and associated indicating factors through a structured questionnaire. These factors accumulate information such as utilization of raw materials and the production environment, processing systems, labor effectiveness and circumstances under which these organizations work. The structured questionnaire was formulated after the discussions and suggestions of consultants, officials of the sugar industry, senior executives, whereas some questions were developed directly from the available literature. This study utilizes a survey-based research approach with a sample size of 104 process industries.The study supports the empirical research to get the benefits of this performance-enhancing approach. The response was recorded by the prominent 104 sugar mill industries of the country. 36 sugar industries were covered by multiple visits in person and 68 industries through official emails and prepaid envelopes. [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.

Manufacturing management and operations place heavy emphasis on monitoring and improving production performance. This supervision is accomplished through strategies of manufacturing performance management, a set of measurements and methods used to monitor production conditions. Over the last 30 years, the most prevalent measurement of traditional performance management has been overall equipment effectiveness, a percentile summary metric of a machine’s utilization. The technologies encapsulated by Industry 4.0 have expanded the ability to gather, process, and store vast quantities of data, creating the opportunity to innovate on how performance is measured. A new method of managing manufacturing performance utilizing Industry 4.0 technologies has been proposed by McKinsey & Company (New York City, NY, USA), and software tools have been developed by PTC Inc. (Boston, MA, USA) to aid in performing what they both call digital performance management. To evaluate this new approach, the digital performance management tool was deployed on a Festo (Esslingen, Germany) Cyber-Physical Lab (FCPL), an educational mock production environment, and compared to a digitally enabled traditional performance management solution. Results from a multi-day production period displayed an increased level of detail in both the data presented to the user and the insights gained from the digital performance management solution as compared to the traditional approach. The time unit measurements presented by digital performance management paint a clear picture of what and where losses are occurring during production and the impact of those losses. This is contrasted by the single summary metric of a traditional performance management approach, which easily obfuscates the constituent data and requires further investigation to determine what and where production losses are occurring.