A New Automation System for Equipment Status and Efficiency Detection with Machine Learning Based Image Processing

<div><p><em>This study aims to examine and analyze the impact of work attitude, Total Productive Maintenance (TPM) on the Overall Equipment Effectiveness (OEE) and production continuity and the other effect of Work Attitude and Total Productive Maintenance (TPM) on production continuity: The mediating role of Overall Equipment Effectiveness (OEE). The research was conducted at PT. Semen Tonasa i.e. units II to V unit which is the largest cement producer in eastern Indonesia. Withdrawal of respondents did with the criteria that only respondents yang having their respective sectors and who have work experience of at least one year were used as samples. Results of analysis Partial Least Square (PLS) Version 2.0.M3 used in analyzing the contribution of exogenous variables on endogenous variables directly, Sobel Test was used for pushing analyze the contribution of the indirect effect (mediation) whereas importance- performance analysis (IPA) to analogous interpret the relationship loading factor value and the average value. The results of this study provide evidence of work attitude has a positive and significant effect on the overall equipment effectiveness (OEE), total productive maintenance (TPM) has a positive and significant effect on overall equipment effectiveness (OEE), work attitude, total productive maintenance (TPM) and overall equipment effectiveness (OEE) positive and significant impact on production continuity.</em></p></div>

Overall Equipment Effectiveness (OEE) is an effective tool in identifying bottleneck. It can be integrated with other continuous improvement tools and techniques. OEE has been utilized through Lean Six Sigma DMAIC and the resulting framework is applied to a bottleneck process for a multi - national semiconductor company in the Philippines. The conceptual framework covered the integration of various approaches proven by related studies such as Total Productive Maintenance (TPM), FMEA, Visual Management, Mistake-Proofing, Single Minute Exchange of Dies (SMED) and DOE. Results showed that Overall OEE increased from 68% to 87% which indicated a 30% improvement. In terms of Hit Rate, it increased from 35% to 66% which is a 25% improvement. The highest improvement contributor is from the Performance component equivalent to a 24% improvement in OEE % and 67% improvement in Hit Rate. The conceptual framework derived from this study can be a guide in improving bottleneck processes for similar industries.

Overall Equipment Effectiveness (OEE) is the crucial tool of Total Productive Manufacturing (TPM). OEE evaluates the workability of the equipment in relation to its performance capacity provided that it should be operated in the optimal manufacturing conditions. It considers the whole manufacturing environment not merely the availability of the machine for performing the particular work, but also the efficiency of the product during the equipment availability together with losses in efficiency due to various factors such as yield waste, rework, scraps, etc. Such analysis provides a broad view of machine operation and manufacturing efficiency and assists to formulate efficient strategies to enhance the productivity of the manufacturing line. This paper implements the OEE tool to identify the root causes responsible for affecting system line efficiency. For this, a case study on a production line of VBNPL, one of the reputed beverage companies in Nepal working as the franchise of the international brand “PepsiCo. Int”, is done. Initially, the OEE of a single production line called “GRB 400” is calculated by recording the total planned breakdown, unplanned breakdown, and filler breakdown. Onwards, becoming specific the OEE of the Filler is computed and all possible reasons for the drop in the efficiency are identified, for instance falling of bottle, bottle burst, bottle jam, loose vent tube, etc. by using various techniques such as Pareto chart and Fishbone diagram. Finally, suitable recommendations are provided to mitigate the issues that hampered the efficiency of the production line.

Purpose: The purpose of this research is to design fundamental base for continuous improvement in maintenance that serves improving machine’s reliability and raises the productivity, to fix strong products in the market with perfect quality and competitive price. Zero breakdowns, zero defects and zero accidents are the target to possibly improve productivity, save costs by reducing wastes and equipment longevity. Making the workplace is healthy organizational environment and better performance, increases the work team spirit by appropriate follow instruction of the implementation and mutual respect and understanding between managers and employees. Design / Methodology / Approach: The data were collected using questionnaire, company’s documentation, interview and observations for 6 months at the lines of the company. The assessment is based on the overall equipment effectiveness (OEE), productivity and profitability approach. Cause-Effect diagram and Pareto chart techniques were used for identifying the problems in the production lines. The 8 pillars and others worksheets of TPM are used for 6 months period. Finally, OEE and its indexes were used as measuring of success the TPM implementation. Findings - The suggested scheme was effective in increasing the overall equipment effectiveness from 48 % to 85 % over a 6 months period. The parameters of OEE were a direct orientation of improvements in equipment availability, performance and product quality by highly recommendation on the pillars of TPM. This work may have exploits on the consistency of TPM nature to improve maintenance scheme for a generic system. The tools and worksheets presented can be helpful to managers and operators. Originality / value: Total productive maintenance (TPM) is one of the numerous diagnostic/prognostic maintenance strategies have been employed in this study. This research has implemented an integrated OEE and its parameters/managers and normal labors in order to clarify the cause / effect to make action for the critical/chronic lines to remedy the low production. This is the first time that the TPM in this company has been placed and its impact is gained after that specific and short period.

Introduced by Seiichi Nakajima, one of the creators of Total Productive Maintenance (TPM), the indicator OEE – Overall Equipment Effectiveness – is used as a metric for operational performance, being a productive management tool increasingly used in the industry. Study, lacks an effective indicator to indicate the efficiency of its equipment. Thus, this work applied the Overall Equipment Effectiveness (OEE) indicator in order to identify the overall efficiency of the most critical equipment in the manufacturing process of dental implants in a machining company located in Joinville-SC. access to machine data in the company which served as the study environment, collect the main equipment data, perform the calculation of the OEE indicator with the information obtained and direct the improvement teams with the results of indicators obtained. Therefore, the objective of the referred work is to implement the OEE indicator in a dental implant machining machine. Among the performance, quality and availability indicators, only the performance indicator was above the recommended global average, which is 95%. For the other two factors, incremental measures had to be studied and recommended to the improvement teams, since the value obtained for quality was 98% and for availability was 85%, against the recommended 99% and 95% respectively. The work shows that the adapted methodology was efficient to identify the losses of the process, to measure the real use of the equipment and to apply improvement actions in the two indicators that presented values ​​below the globally recommended.

Overall equipment effectiveness (OEE) is the key metric of total productive manufacturing (TPM). OEE monitors the actual performance of a tool relative to its performance capabilities under optimal manufacturing conditions. OEE looks at the entire manufacturing environment measuring, not only the equipment availability, but also, the production efficiency while the equipment is available to run product, as well as the efficiency loss that results from scrap, rework, and yield losses. Analysis of the equipment effectiveness loss mechanisms provides the user with improvement opportunities for the operation. This paper focuses on the use of OEE and major equipment loss analysis to optimize the performance of constraint tools at Fairchild Semiconductor, South Portland, Maine. Factory modeling is a key element of the OEE management process, defining the equipment and process capabilities at the workstation (micro) level, then identifying constraint tools and OEE performance requirements through macro level factory capacity modeling. Also discussed in this paper, is the SEMI productivity metrics standard proposal which defines OEE and loss analysis methods which are consistent with SEMI E10-96 concepts.

Globalization and highly competitive business environment are pushing many organizations to realize numerous productivity improvement efforts in order to continue to survive. To gain competitive advantage, manufacturing organizations are compelled to adopt productivity enhancement programs such as total quality management (TQM), total productive maintenance (TPM), lean manufacturing concept to achieve operational excellence. Overall Equipment Effectiveness (OEE) is a well-accepted production performance measure to gauge the equipment effectiveness. OEE is defined as how much the equipment is performing to meet the production planned target. OEE, in manufacturing is merely a percentage figure to evaluate how effective the manufacturing equipment is utilized. OEE is not directly linked to profitability. Most companies do not associate increases or decreases in OEE to financial performance. This paper presents the relationship between OEE, production throughput and production part cost in order to express and translate OEE in monetary units.

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

Modern production systems must guarantee high performance. Increasingly challenging international competition, budget reductions for the health sector and constant technological evolution are just three of the many aspects that drive pharmaceutical companies to continuously improve the productivity of their lines. The scientific literature has for many years been proposing calculation models for estimating the productivity of a machine. One of the most famous, and still used, is overall equipment effectiveness (OEE). This allows the calculation the valuable output considering the six ‘big losses’. The limitations of this approach are noticeable when considering a production line instead of a single machine. Numerous researchers have proposed alternative methods or changes in OEE, to be able to cover the widest spectrum of possible cases. In this study, we wanted to evaluate how such theoretical models related to OEE are actually able to represent the world of tight production flows or whether, in these cases, a more complex type of simulation should be preferred. To do this, we carried out a case study of a production line in the pharmaceutical industry, and the results showed that the simulation approach gives better results because of the peculiarities not considered by the theoretical models.

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.

Increasing productivity is very important for companies to achieve success in their business processes. The company studied in this study was in Batam, a manufacturing company engaged in the manufacture of plastic products. The purpose of this study was to determine the value of OEE from machines that produce Hard Disc Drive products in order to increase the effectiveness of the machine. Knowing the main root causes that often occured damage to the machine and to find out how to solve problems that can increase machine productivity. This study uses the Overall Equipment Effectiveness (OEE) and Fuzzy Failure Mode and Effects Analysis methods. The results of the calculation and discussion get the OEE (Overall Equipment Effectiveness) value for April 2018, which is 63.87%. The difference in OEE values obtained is still below the standard value of the OEE classification that makes OEE not reach the target. The main cause of damage to DC machines is 60 tons, namely the low Performance Rate with an average value of 71.71%, this is due to the value of Reduced Speed Losses which has the largest contribution in the six big losses (losses) with a loss of 91.97 the clock which resulted in a DC 60 ton machine not working optimally. The FRPN value of 277 was obtained in the DC 60 ton machine factor so that repairs were needed.

This article discusses how to improve the overall equipment effectiveness (OEE) of the chicken bowl printing machine. OEE is important to measure machine performance in the production process. OEE will affect the costs incurred during production activities, such as product defects, how effective the performance of machines is, and how much time is available for production. The failure-tags analysis was used to identify the category factors causing engine failure. The why-why analysis was used to identify the root cause of the problem, and the theory of change was used to explain what should be implemented by the company to improve the OEE value. In 2020, only December had a value above standard by 95%, based on the Seiichi Nakajima OEE has a good value if the value >85%. The OEE values From January to May were fell from 73% to 34% and then kept increasing to 73% in November. The increase of the OEE value can be achieved by implementing a communication schedule, monitoring and evaluating the performance of maintenance staff to achieve a fast maintenance machine process, and applying maintenance rules and guidelines.

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

The manufacturing industries are among the focused industries of any country according to their contribution to nations’ economies. Therefore, the appropriate utilization of all manufacturing/processing equipment is a high priority. The problem is the complexity to keep all the activities, operations, and use of equipment in synchronization with the reduction of all possible losses and defects. Such losses are becoming the real cause of the investment in the maintenance section of the industrial units. The different versions of established concepts define ‘Maintenance’ as ‘repair of the equipment,’ but this concept covers old and limited dimensions. Therefore, this notion relates the maintenance strategies only as a function of preventive maintenance, predictive maintenance and corrective maintenance. At the basic level of OEE implementation, various process industries of the country are yet unaware of the potential benefits of this strategic tool. The quality and performance-enhancing approaches are being developed based on time management and production loss analysis to get the optimum output from available resources. This study is to mark all the critical strategies associated with sustainable performance and to trace all possible drawbacks while measuring effectiveness through OEE (Overall Equipment Effectiveness). OEE is a tool of Total Productive Maintenance (TPM) without whom the effective utilization of resources is a difficult task. OEE supports the entire perspective on effectiveness uniquely and logically but also improves the life span of the machinery through improvements and monitoring in the maintenance operations and activities. This paper aims to identify the role of different strategies associated with OEE for unceasable production and optimum use of resources. This paper arranges the literature in an organized way and assists to find promising decisions to implement OEE with integrated concepts.

In the face of pandemic-induced emergencies and unpredictable natural disasters, industries are compelled to implement rescue plans to mitigate unexpected risks. In this context, Overall Equipment Effectiveness (OEE) is considered as a key metric, followed by sustainability efforts to manage unforeseen risks, encompassing social, environmental, and economic aspects. OEE is considered as a lean tool to determine the efficiency of equipment or processes on par with the world class OEE standard, i.e., 85%. Performance, Availability and Quality as three main drivers of OEE. This research study explores the implementation of OEE in conjunction with sustainability principles in an auto sector manufacturing firm, aiming to enhance operational efficiency and sustainability practices. The research involves a 12-week initial session from April to June 2022, followed by an analysis of July to September 2022, resulting in an impressive OEE value of 48%. Notable improvements in Availability (89.75%), Performance (72.68%), and Quality (73.82%) contribute significantly. The analysis reveals enhancements in scrap rework (17%), training (16%), maintenance (13%), material availability (12%), and production capability (11%). Achievements include improvements in green profile (25%), health and safety (20%), and energy efficiency (25%), along with reductions in carbon dioxide emissions (21%), waste management (17%), and scrap (15%). This research underscores the commitment of the case study industry to sustainable development and economic growth, showcasing significant enhancements in product quality and efficiency. The integration of sustainability principles into OEE initiatives is pivotal for modern industrial optimization. The study results highlight the profound significance of this synergistic relationship, particularly within the blending section, driving substantial positive outcomes in manufacturing processes and operational excellence. The implementation of sustainability efforts not only mitigates risks and fosters growth for automotive manufacturers but also yields environmental benefits. Based on findings of this study, a roadmap for automotive manufacturers is devised to achieve robust OEE while concurrently reaping economic and environmental rewards by employing sustainability principles.