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The development of motorcycle industry in Indonesia is quite rapid. The mode of transportation is a favorite the people of Indonesia, especially in industrial area. The average motorcycle user is a company employee because it facilitates access and avoids traffic. Motorcycle component production in Indonesia is spread across several companies, one of the companies that manufactures components made of plastic material has 16 injection machines. These machines have different performance, when analyzed using the OEE approach it is known that Machine 16 has the lowest performance compared to others at only 91.2%. Factors that affect the low efficiency of the machine due to the 7 biggest losses namely Dandori, Mold Repair, Machine Damage, re-setting, Material jams, robot damage and Cleaning Mold

Manufacturing enterprises give importance to determining machine performances in order to manage their operations and prioritize improvement activities. Overall Equipment Efficiency (OEE) is a tool that is frequently used by production managers to examine machine performances and is calculated by considering performance, quality and availability values. However, there are many different criteria that affect the performance of the machines. In this case, a multi-criteria decision-making (MCDM) approach is appropriate for handling machine performance values. The Preference Selection Index (PSI) is a MCDM method that is used for the evaluation of alternatives while weighting the criteria determined. This study was carried out to purpose of using the PSI method to generate the performance index of the machines. In addition to factors used in OEE calculation, MCDM problem with multiple criteria that supported by the literature was developed. The performance ranking of the machines was performed by PSI method with these criteria. The findings of this study suggest that when the more criteria are included in the studies for performance evaluation, the differences in the value of the results can be seen in the closer range and sensitivity. So, it would be useful to implement models that consider different criteria from OEE for machine performance evaluation.

The overall equipment effectiveness (OEE) is an indicator used in the management and continuous improvement of production systems, and is useful in identifying losses, thus reducing production costs. By analyzing the results of this indicator, the operation manager must make decisions to eliminate or reduce losses in the process. This study investigated the application of the OEE indicator in one production line in the pulp and paper industry. The implementation of OEE was performed in stages with a detailed analysis of the indicators that compose the OEE (quality, performance, and availability) to identify possible improvements. Thus, actions were implemented to improve the OEE quality index. This study provided important information that enabled the operation manager to diagnose and minimize the occurrence of failures and losses, which is often hidden and unknown to those involved in the production system.

Overall Equipment Effectiveness (OEE) is one of the tools to enhance the effectiveness of the utilization rate of machinery and equipment in Total Productive Maintenance (TPM). TPM is a principle of management to improve productivity and efficiency of the production company to use the machine effectively. This study measures the value of OEE on gas turbine engine Centaur 50 C3344 which held on operation and maintenance section in PT Perta Samtan Gas Kilang Ekstraksi NGL Prabumulih in the period, followed by analyzing the value of using Pareto analysis of the results obtained by the root causes of the OEE. The conclusion that the value of the analysis results obtained on average exceeded the standard value of OEE 85.63% from 85%, even though in one period the value of OEE less than standard (22.63%). Factors that may experience rapid drop predicted loss is breakdown losses 382 hours (46.53%) and idling minor stoppages 373 hours (45.43), reduce speed 48 hours or 5.85%, rework losses 13 hours or 1.58%, and set up and adjustment losses 5 hours or 0.61%. As a result of which may not have an impact in a short time, but will immediately attack the machine if there is no treatment of maintenance as continuously and directly.

Amaç: SMED yönteminin ayar sürelerine ve birim zaman maliyetine olan etkisini araştırmaktır.Yöntem: Ayar sürelerinin iyileştirilmesinde SMED metodolojisi kullanılmıştır. Elde edilen sonuçların, birim maliyete olan etkisi FTM sistem ile değerlendirilmiştir.Bulgular: Çalışma sonucunda iki ayar sürecinde 66% ve 62,5% iyileştirme sağlanmıştır. Ayar sürelerinde ve diğer duruşlarda sağlanan 1 dakikalık iyileştirme, birim maliyetlerde 0,0000514 TL azalma sağlamaktadır. Toplam ayar sürelerindeki iyileştirmeler, birim maliyetlerin 0,85 TL azaltmıştır. Sonuç: SMED uygulamalarında elde edilen zaman kazançlarının yanı sıra bu kazançların üretim birim maliyetine nasıl bir etkide bulunduğunun tespit edilmesi için, etkilerin maliyet sistemi üzerinden izlenmesi gereklidir.

Maintenance is an activity that cannot be separated from the context of product manufacturing. It is carried out to maintain the components’ or machines’ function so that no failure can reduce the machine’s productivity. One type of maintenance that can mitigate total machine failure is predictive maintenance. Predictive maintenance, along with the times, no longer relies on visuals or other senses but can be combined into automated observations using machine learning methods. It can be applied to a toothpaste factory with a tube filling machine by combining the results of sensor observations with machine learning methods. This research aims to increase the Overall equipment effectiveness (OEE) to 10% by predicting the components that will be damaged. The machine learning methods tested in this study are random forest regression and linear regression. This study indicates that the prediction accuracy of machine learning with the random forest regression method for PHM predictive is 88%of the actual data, and linear regression has an accuracy of 59% of the actual data. After implementing the system on the machine for three months, the OEE value increased by 13.10%, and unplanned machine failure decreased by 62.38% in the observed part. Implementation of the system can significantly reduce the failure factor of unplanned machines.

The concept of quality industry is a journey initiated in the industry of Quality Circles (QC). A small group of members reviews their product shortcomings and endeavors to solve the issue. This as a whole, has a minimum impact on quality. Hence, the quality is moved from QC to the following category of Quality Assurance (QA) which is also a vision. Both QC and QA are such that, the efforts are made post quality defects in the process which implies that the quality factors are hindering in nature and not known to the operator. In this circumstance, the commodities are produced and good pieces are delivered to the customer despite the loss to the organization. This shortcoming are improved when the TPM practicing level are enhanced, further shifting the quality concept from QA level to Quality Maintenance (QM). This just reinsures that "a stitch in time, saves nine", that is precautions are taken before quality damage making this a 'cause oriented approach'. It is vital to maintain an optimum skill level to question the concept of "precautionary damage control". In this paper it is analyzed as the Quality hindering factors in TPM and importance of skill application in injection molding process. Input, in-process managements are with the prime TPM pillar which is quality although it is not practical to achieve zero quality defects. Hence, it is essential to understand that, besides the quality maintenance pillar, the autonomous maintenance, planned maintenance, Education and training and Focused Improvement pillar has to support the QM pillars. [ABSTRACT FROM AUTHOR]

Past studies suggest that people are often reluctant to use occupant evacuation elevators in case of fire. However, existing research is scarce and current knowledge is based on questionnaire studies and laboratory experiments. An unannounced evacuation experiment was therefore performed on the 16th floor of a 35-floor high-rise hotel building. Sixty-seven participants took part and eye-tracking glasses were used to collect data on exit choice and eye fixations. Three different scenarios were studied, including two different hotel room locations on the floor and a variation of guidance system for one of these locations, i.e., flashing green lights next to the evacuation sign at the elevators. Results suggest that people typically choose the elevator for evacuation, even if their hotel room was located closer to the evacuation stair. Flashing green lights next to an evacuation sign made people look more at this sign. However, in spite of looking more at the sign, the flashing light was not shown to significantly improve compliance with the sign. Also, the results suggest that a detector activated self-closing fire door without vision panels to the elevator lobby made it more difficult to find the evacuation elevators in an emergency. [ABSTRACT FROM AUTHOR]

The performance of a production work center can be measured in the five dimensions speed, quality, flexibility, dependability, and cost. Existing approaches such as the Overall Equipment Effectiveness (OEE) are either not covering all objectives or give only limited support for full utilization of the increasing amounts of available data in production. This article introduces a hierarchical status classification system that allows the integration of versatile and detailed data streams based on time series. Based on the classifications, a detailed calculation and visualization of work center performance indicators is introduced to support the prioritization of countermeasures for work center's performance increase. [ABSTRACT FROM AUTHOR]

• A new mathematical model for the performance loss of complex machines is derived. • This loss is triggered by run-time variations of environmental parameters. • The ability of the machine to adapt to such changes determines the overall loss. • Increasing the maximum performance alone will decrease the mean performance. • Fast and intelligent re-configurations of machines are necessary. It is a phenomenon well-known to professional and private users of complex machines: in most situations these machines do not deliver their full performance. This difference between the actual achieved performance and some maximal performance obtained in benchmarks varies between different use cases but poses crucial questions concerning the resulting gap in any case. Is it possible to reduce this manifest inefficiency by changing the machine design or the process characteristics in order to increase end user satisfaction and confirm the rationale of the machine investment? In this article we present a novel systematic derivation and compilation of the causes of this performance loss based on geometric and stochastic models that may help in analysing and optimising the average performance of complex machines in real-world scenarios. [ABSTRACT FROM AUTHOR]

Planned and unplanned downtime emanating from maintenance, production, and operational function adversely affects the performance of manufacturing plants. This paper develops joint maintenance, production and process/operations control policies integrating Opportunistic Maintenance (OM), which takes advantage of external (operational-related downtimes) and internal maintenance opportunities. The study quantifies the effects of maintenance strategies on the plant's performance, here, the overall equipment effectiveness (OEE), which importantly presents insights on maintenance decision support for production facilities facing significant operational-related stoppages. The study integrates both economic and structural dependence and models the influence of alternative maintenance actions of different maintenance policies, on product quality. The developed model is validated by applying to a multi-unit repairable, imperfectly maintained raw meal grinding system of a cement plant. From the simulation modelling results, integration of the opportunistic maintenance approach, complementary to corrective, preventive and condition-based maintenance strategies, show more enhanced equipment performance, as measured through the OEE. [ABSTRACT FROM AUTHOR]

As a service company, service quality and speed of service are the main focuses of the company. This company is engaged in terminal handling, especially containers. In carrying out operational activities to transport containers from the dock to the container yard, it uses fifty terminal truck. Operational activities for transporting containers are carried out 24 hours a day so that maintenance is needed. Therefore, the operational terminal trucks do not experience sudden damage and disrupt operations. This will have an impact on the time used to be less productive. This study aims to determine the effectiveness of the Terminal Truck by using Overall Equipment Effectiveness (OEE). The results of this study found that the TT was still below the standard set by JIPM, that is 85%. Meanwhile losses, the significant that affect the effectiveness of TT are idling and minor stoppage losses. [ABSTRACT FROM AUTHOR]

In recent decades, competition has increased dramatically, and customers have become increasingly demanding for product quality, product delivery time, and product cost. As a result, in order to gain the trust of customers and secure new projects, companies should be certified in accordance with international standards. At the same time, these companies should implement new strategies in order to improve the performance of the manufacturing process. Total productive maintenance, required by the International Automotive Task Force standard 16949, is the most effective maintenance strategy for increasing the availability of existing equipment, improving product quality, and reducing waste. This paper presents an analysis of the maintenance management requirements in international standards, a comparison of several standards, and the implementation of total productive maintenance in an activity that is not intended for the automotive industry-industrial cables. The implementation of the total productive maintenance has been carried out in a cable manufacturing company. The overall equipment effectiveness of the machines in industrial cable workshop has been examined, and it has been concluded that the major losses are due to breakdowns. [ABSTRACT FROM AUTHOR]

Objective: The aim of this paper is to understand a production system of a forging and presses structure of a centenary factory in terms of a cultural change on the management approach. Methodology: The study was focused on many aspects such as the pieces production lead time, a time study of the setups, the Overall Equipment Effectiveness determination for the Computer Numeric Control machinery and an analysis of the production cells operational flow. Findings: For the aspects that were analysed during the study, the problems are identified, and the root causes determined. At the end of the assessment stage Lean tools and concepts for fixing the problems were proposed, like new rules for production planning, setups procedures, 5S toll usage and a Lean implementation plan that is adapted to the company. Value Added: The high industrial competitivity has dictated the development for this sector that – allied with the inconstant and unstable economic environment – makes the companies very vulnerable and highly dependent of the global market. This is a concern of special relevance for the Small and Medium Size companies that are increasingly adopting Lean solutions to continuously improve their operational value chain and the management approach, for a more systemic way of thinking. Recommendations: For some of the solutions an impact study with their implementation was made. An analysis of the success factors proposed in the literature that were verified during the assessment stage of the case study was performed as well. [ABSTRACT FROM AUTHOR]

Nowadays, in the domain of production logistics, one of the most complex planning processes is the accurate forecasting of production and assembly efficiency. In industrial companies, Overall Equipment Effectiveness (OEE) is one of the most common used efficiency measures at semi-automatic assembly lines. Proper estimation supports the right use of resources and more accurate and cost-effective delivery to the customers. This paper presents the prediction of OEE by comparing human prediction with one of the techniques of supervised machine learning through a real-life example. In addition to descriptive statistics, takt time-based decision trees are applied and the target-oriented OEE prediction model is presented. This concept takes into account recent data and assembly line targets with different weights. Using the model, the value of OEE can be predicted with an accuracy of within 1% on a weekly basis, four weeks in advance.

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

European economy faces two main problems in manufacturing sector: inefficient production with high costs and long time to supply new products to market. With these obstacles, economies cannot compete with other developing countries like China. In order to overcome these problems, leading firms works on new paradigms like lean production, digitilization and industry 4.0. Firms could simplify production lines by dealing with all the wastes, delays, faults etc. through the workplace. So they can shorten the production cycle times, data entry time, waiting semi-finished products, faults and scraps in the production lines. Industry 4.0 applications helps lean manufacturing lines to realize more efficient and quality production sytems. Because, these applications will make the European economy grow increasing productivity and added value of the products. In today’s industry digitization is vital for manufacturing processes. Especially in automotive supplier industry getting data from the machines and production lines by digital panels and monitors is vital for efficient production. Rim industry is one of the suppliers of automotive manufacturers. In order to provide quality and high performance products in time, rim producers try to apply lean manufacturing systems and industry 4.0 applications to their production lines. Jantsa is one of the biggest rim producer of Turkey with 200 diffrent sizes and over 3,000 different designs and models. The factory is located on 95,000 square meters area with the production capacity of nearly1,900,000 wheels annually. They expert 75 % of these wheels to over 80 countries worldwide. The digitilization project realized in Jantsa aims to get information about the rim production lines and make necessary improvements in order to increase efficiency. The name of the project is Production Data Collection and Barcoded Product Tracking Automation. OEE (Overall Equipment Effectiveness) calculation method is used in order to digitilize production lines in lean systems for improving processes. According to test data we have three factors effecting OEE calculation: availability, performance and quality. When we make the calculation the test results of OEE is 93.1%. It is fairly high according to international production standards. The focus should be on increasing the performance of machines and equipment used in the operation.

The performance indicator, Overall Equipment Effectiveness (OEE), is one of the most important ones for production control, as it merges information of equipment usage, process yield, and product quality. The determination of the OEE is oftentimes not transparent in companies, due to the heterogeneous data sources and manual interference. Furthermore, there is a difference in present guidelines to calculate the OEE. Due to a big amount of sensor data in Cyber Physical Production Systems, Machine Learning methods can be used in order to detect several elements of the OEE by a trained model. Changeover time is one crucial aspect influencing the OEE, as it adds no value to the product. Furthermore, changeover processes are fulfilled manually and vary from worker to worker. They always have their own procedure to conduct a changeover of a machine for a new product or production lot. Hence, the changeover time as well as the process itself vary. Thus, a new Machine Learning based concept for identification and characterization of machine set-up actions is presented. Here, the issue to be dealt with is the necessity of human and machine interaction to fulfill the entire machine set-up process. Because of this, the paper shows the use case in a real production scenario of a small to medium size company (SME), the derived data set, promising Machine Learning algorithms, as well as the results of the implemented Machine Learning model to classify machine set-up actions. [ABSTRACT FROM AUTHOR]

Overall equipment effectiveness (OEE) is a key performance indicator used to measure equipment productivity. The purpose of this study is to review and analyze the evolution of OEE, present modifications made over the original model and identify future development areas. This paper presents a systematic literature review; a structured and transparent study is performed by establishing procedures and criteria that must be followed for selecting relevant evidences and addressing research questions effectively. In a general search, 862 articles were obtained; after eliminating duplicates and applying certain inclusion and exclusion criteria, 186 articles were used for this review. This research presents three principal results: (1) The academic interest in this topic has increased over the last five years and the keywords have evolved from being related to maintenance and production, to being related to lean manufacturing and optimization; (2) A list of authors who have developed models based on OEE has been created; and (3) OEE is an emerging topic in areas such as logistics and services. To the best of our knowledge, no comparable review has been published recently. This research serves as a basis for future relevant studies. [ABSTRACT FROM AUTHOR]

The market globalization and worldwide competition has raised the level of demand in the industrial sector. Due to this aggressive competitiveness, some management strategies and tools needed to start to be applied in the industry. For example, maintenance has become increasingly important in the production planning and strategy of some companies. This work focuses on the implementation of a strategic maintenance plan, applied in an industrial context, namely in a company of Clutches and Hydraulic Controls. The Total Productive Maintenance (TPM) methodology application was worked on and implemented at two sections, CNC Lathes and CNC Machining Centers, to focus on eliminating losses due to inefficiencies. In this way, the main problems detected in relation to the cells were eliminated. New autonomous maintenance procedures (AM) and preventive maintenance plans with well-defined schedule were also developed. The results obtained were very positive: there was a decrease of 23% in breakdowns in CNC lathes sector and 38% in CNC machining centers sector. In global terms, there was an increase in the availability of the machines and the Overall Equipment Effectiveness (OEE) of approximately 5%. The implementation procedure and the compromise adopted by the whole company staff and management was the secret to success. [ABSTRACT FROM AUTHOR]

Equipment performance is very important in the production process. Equipment performance can be determined by overall equipment effectiveness performance (OEE) and maintenance strategies. This study encourages the use of OEE, in addition to the estimated total maintenance costs, of rental equipment as a consideration in determining optimal maintenance strategies. Meanwhile, the proposed maintenance strategies are corrective maintenance (CM) and a combination of CM with preventive maintenance (PM). The aim of this study was to obtain a maintenance strategy that would minimize the estimated total maintenance costs and increase OEE. Mathematical models of estimated total maintenance costs are developed based on maintenance strategies generated by each maintenance combination. The results of this study showed that when a rental period increases by two years, a combination of CM and PM strategies will cause maintenance costs to increase by 37.54%. Meanwhile, if the lessor only does CM, the increase will be greater (i.e., 55.12%). Comparison of the two strategies revealed that the combination of PM with CM is more efficient than CM alone. Further, OEE experienced an average decline of 3.7% despite the maintenance strategy. [ABSTRACT FROM AUTHOR]

Overall equipment effectiveness (OEE) is a framework to measure the efficiency and effectiveness of the manufacturing process. It is a measurement tool used to indicate how effectively machines are running. It measures if machines are utilized, how much of the time machines are operating the speed at which output is manufactured, and the percentage of the output that is of the right quality. Looking at it another way, OEE examines productivity loss in terms of the impact on machine or equipment utilization, equipment availability, equipment performance, and quality of output. This paper presents a comprehensive approach to measuring OEE for blast furnaces and explains how the individual metrics are calculated. The metrics that are used to calculate OEE highlight which process losses show the highest improvement opportunities so that business can put the effort in the right issues to improve equipment effectiveness. Daily monitoring using emerging digital technologies and further root cause analysis using data analytics can help to take corrective actions in the shop floor and can go a long way in improving plant OEE. [ABSTRACT FROM AUTHOR]

For years, management of companies has been turning to proven techniques and tools to achieve even higher productivity. They hope for, if possible, all-inclusive, easy and efficient procedures to manage their production systems. In principle, this applies to all organizations, regardless of the sector or the company focus. Especially in industrial enterprises with different machines and equipment for production processes, the quantitative indicator „Overall Equipment Effectiveness" (OEE) has been used in recent years as a key indicator to monitor their efficiency and process performance. This indicator provides relatively simple information about the possibilities and utilization of machines and equipment as well as the quality achieved in the production processes. If the OEE indicator is determined correctly, based on the available and correct data in a system, processes can be better controlled, managed and improved. However, it is important to properly monitor (decode) the data and to understand them. The paper presents the potential but also the limits (obstacles) of Overall Equipment Effectiveness (OEE) on case studies in selected domestic enterprises in the Moravian-Silesian region. [ABSTRACT FROM AUTHOR]

Recently, Anstöter and co-workers [J. Am. Chem. Soc. 141, 6132 (2019)] have provided the first photoelectron spectroscopic determination of the anion-π bond strength (De) using iodide-hexafluorobenzene (I−·C6F6) as the archetypical system. In combination with an equation-of-motion coupled cluster theory, namely EOM-IP-CCSD(dT), using Dunning's aug-cc-pVDZ (aVDZ) basis set, De in I−·C6F6 was found to be −0.53 eV with an uncertainty less than 0.03 eV. The interaction was claimed to arise for a large part from correlation forces (41%) with only a 23% contribution from electrostatic forces. In the present work, we performed the coupled-cluster with single and double and perturbative triple excitations, CCSD(T), calculations. We found that CCSD(T)/aVDZ can have an uncertainty up to 0.113 eV due to the basis set incompleteness. Our calculations disclosed that the previous calculations on the electrostatic contribution are concealed by the contributions from the exchange and Pauli repulsion. The electrostatic contribution is actually determinant, being more than double of the correlation contribution in the I−·C6F6 complex at the equilibrium binding distance. [ABSTRACT FROM AUTHOR]

Investing in a Dental Chair Unit is an investment that requires a large amount of funds for the hospital, therefore the utilization of a Dental Chair Unit tool is a concern for hospitals. The importance of Dental Chair Unit utilization leads hospital to calculate the effectiveness of Dental Chair Unit devices using the Overall Effectiveness Equipment (OEE) method. OEE measures the effectiveness of the tool based on the availability ratio, performance ratio and quality ratio. The aimed of this study was to analyze the effectiveness of Dental Chair Unit in Mother and Child Hospital using the OEE method. This research was conducted at the dental clinic of Mother and Child Hospital on February 2019. This study was a descriptive study that was to calculate the OEE from a Dental Chair Unit then analyze the causes of the effectiveness of the tools obtained. The results of the calculation of OEE Dental Chair Unit device in a Mother and Child Hospital at Surabaya are 18,7%, far below the standard of 85%. The cause of the low OEE value is the low value of the Performance ratio that is far below the standard 85%. The low performance ratio of Dental Chair Unit tools in mother and child hospital is caused by the small number of patients who come to the dental clinic, so a little also uses the Dental Chair Unit. It is because of the competition with general hospital that surround this hospital. Another factor that cause of low OEE ratio is patient's didn't know the importance of dental health for mother and children. [ABSTRACT FROM AUTHOR]

Overall equipment effectiveness (OEE) is a well-accepted measure of performance in industry. This study investigates the relationship between the factory management and the operation of the limoncello production line. The analysis of failure and repair data of the line over a period of 8 months was carried out. Descriptive statistics at machine and at line level were computed. In addition, the components availability (A), performance efficiency (PE), and quality rate (QR) of the OEE were calculated. Failure and repair data analysis identify the critical points of the production process that need immediate action to improve the operation of the line. Results show that the components PE and QR should be improved immediately to optimise the productivity and the efficiency of the line. It can also be utilised in the beverage industry sector by the machinery manufacturers and the manufacturers of bottled products to improve the design and operation management of the bottling production lines. [ABSTRACT FROM AUTHOR]

The overall equipment effectiveness or efficiency (OEE) is a metric that has been accepted in the semiconductor industry. OEE is simple and clear, and standards and guidelines have been developed. Nonetheless, the literature indicates imperfections in applying OEE with regard to the time base and rate efficiency. As OEE lacks a proper framework, the equipment effectiveness (E) has been developed based on a systematic approach to the equipment. E considers the effectiveness of the equipment with respect to availability, speed and quality losses. Unlike OEE, E is a performance measure for stand-alone equipment, isolated from the environment. In addition, E uses the available effective time as a basis in contrast to OEE, which uses the total time as a basis for measurement. Finally, due to the fact that E is measured directly by the production and effective time, it does not depend on the utilization of the equipment, unlike OEE. Furthermore, it has been shown that OEE does not indicate the influence of downtime and rework, whereas E gives these influences correctly. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to build up and implement a framework of a lean performance indicator with collaborative participation. A new indicator derived from OEE is presented, overall process effectiveness (OPE), which measures the effectiveness of an operation process. The action research (AR) methodology was used; collaborative work was done between researchers and management team participation. The framework was developed with the researchers’ and practitioners’ experiences, and the data was collected and analyzed; some improvements were applied and finally, a critical reflection of the process was done. This new metric contributes to measuring the unloading process, identifying losses, and generating continuous improvement plans tailored to organizational needs, increasing their market competitiveness and reducing the non-value-add activities. The OEE framework is implemented in a new domain, opening a new line of research applied to logistic process performance. This framework contributes to recording and measuring the data of one unloading area and could be extrapolated to other domains for lean performance. It was possible to generate and validate knowledge applied in the field. This study makes collaborative participation providing an effectiveness indicator that helps the managerial team to make better decisions through AR methodology.

In this global era where the level of competition is higher, in addition to the influence of the suitable marketing strategy is also required strategy from the side of production/productivity. PT. XYZ is a company engaged in the manufacturing of snacks especially biscuits. The problem that often occurs in this company is the number of biscuits that are not in accordance with the standard and the production does not reach the target set because the machine suddenly breaks down frequently. To overcome the problems PT. XYZ then choose to implement the Total Productive Maintenance (TPM) strategy. This study aims to evaluate the implementation of 8 TPM Pillars and measure the effects on manufacturing performance in the form of Overall Equipment Effectiveness (OEE) and Waste. This study uses questionnaire-based survey method. The number of samples distributed is 40 units. Which returned and filled 33 questionnaires and which is worth to be processed as many as 30 samples. Then tested the validity and reliability of data using SPSS program. Validity critical value R = 0.361 for n = 30 and error rate 5%. For reliability test, R value = 0.60 was selected. From the validity test, there are 7 items of questions that are not valid so it is not included in the next process. For the reliability test of the questionnaire is quite reliable with the value of Cronbach's alpha of 0.811. From the CFA analysis, only 6 of 8 TPM pillars are significant while for manufacturing performance only OEE variable is significant. Correlation between 8 Pillars of TPM and manufacturing performance is Strong enough with a value of R = 0.862, which also means 74.3% (R2) variable manufacturing performance can be explained/influenced by variable 8 Pillar TPM and 26.7% the rest by other variables.

The initial adoption phase of new production technologies is the period between the first production run or technology reconfiguration and the achievement of a stable target output. This time frame is generally characterized by productivity unsteadiness, quality performance variability, and unexpected machine failures together with increasing production volumes due to the process setup and instability, which inevitably affects production output. In this context, human performance represents an additional source of variability and process instability that is dependent on the workers’ productivity, learning curve and related training activities. Hence, to effectively assess the ramp-up phase of new production technologies, an appropriate evaluation of human performance is required. This paper proposes a comprehensive framework and criteria to perform a consistent assessment of the initial adoption phase of new production technologies by introducing two OEE measurement methodologies that distinguish between human performance, process configuration and technical features of the production technology. The proposed framework is then applied to and validated by a case study concerning the introduction of a semi-automatic packaging machine in a primary multinational company in the logistics industry. This case study shows the difference between the two OEE measures, along with the values interpretation and useful insights for achieving a stable production output.

Counterfactual impact evaluation (CIE) is a scientific quantitative approach mainly based on experiments and quasi experiments. CIE is trying to prove a causal relationship between outputs and outcomes. CIE does not take into account coherence of external incentives of companies with internal incentives that have or may have an impact on the behaviour of enterprises. The paper sets up internal evaluation indicators for businesses, counterfactuals useful for creating a more complex metrics evaluating businesses in the area of performance. The aim of the paper is to present model situation using the elementary principle of counterfactual impact evaluation based on “the Overall Equipment Effectiveness (OEE)”.

PT XYZ is one manufacturing company engaged in the production of animal feed. In the production process sometimes product results does not comply with the requests received by the company. There are many factors which could this problem happened. One of them is less than the maximum engine performance production caused by high downtime on a production machine. To find out how well the performance of a production machine, then it is held a performance assessment by using Overall Throughput Effectiveness (OTE) method.Overall Throughput Effectiveness calculations performed on the line pellet machine was obtained 48.97%. This value is still far to the standard value desired by the company which is 70%. For performance improvement can be done by improving the engine damaged and analyzed by using Failure Mode and Effect Analysis (FMEA). From the analysis showed that the failure modes of root dies breaking up with the value of the RPN 224 is a damage that must be repaired immediately because obtaining the highest RPN value compared to other failure modes.

Every company wishes to provide the best possible service to their customers bydelivering their products on time, for which quality and production lead time are the key factorsthat need continuous improvements by way of adopting the right internal strategy. This researchapplies the concept of lean manufacturing to identity wastes along the value stream to find outnon added value activities. In the initial stage, Big Picture Mapping is used to describe thewhole stream entirely, than Process Activity Mapping is used to find value added, non valueadded and necessary-but-non-value added activities, whilst Quality Filter Mapping is used tofind the defects of the quality of defect type. In the last stage, Total Product Maintenance(TPM) is applied to minimize defects by means of Overall Equipment Effectiveness (OEE). TheOEE Sensitivity analysis is applied to the operating time, cycle time and defects to find out themost influential factor for the improvement of OEE. The result of this research has identifiedcycle time as the influential factor that affects the OEE value. The 10% decrease in cycle timewill improve OEE by 10,20%