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This study aims to analyze the effectiveness of preventive maintenance on the Simplex machine at PT. XYZ by applying the Overall Equipment Effectiveness (OEE) method and identifying the primary causes of production losses through the Six Big Losses framework. Preventive maintenance is an important strategy to ensure machine reliability, reduce downtime, and improve production efficiency. OEE is a widely recognized performance measurement tool consisting of three key indicators: Availability, Performance, and Quality. These indicators collectively reflect the overall effectiveness of equipment in supporting the production process. The results of this study indicate that the OEE value of the Simplex machine is 79%, which remains below the world-class benchmark of 85% as recommended by the Japan Institute of Plant Maintenance (JIPM). This finding suggests that the machine’s performance has not yet reached the optimal standard and requires improvement efforts. Further analysis using the Six Big Losses approach reveals that the most significant contributors to reduced machine effectiveness are equipment failure and idling or minor stoppages. These two categories account for the majority of productivity losses, thereby affecting both machine utilization and production output. To further explore the underlying issues, a root cause analysis was conducted using a fishbone diagram, which enabled the identification of several critical factors related to human resources, methods, machines, materials, environment, and measurement systems. Based on this analysis, improvement proposals were developed through the 5W+1H method, providing a systematic strategy to enhance preventive maintenance practices. The recommended actions include scheduling more frequent inspections, improving operator training, upgrading spare parts management, and implementing stricter monitoring of machine performance. In conclusion, this study highlights the importance of continuous preventive maintenance to optimize machine productivity and reduce unplanned downtime. By adopting the proposed improvement strategies, PT. XYZ can increase the effectiveness of its Simplex machine, moving

The Paiton Steam Power Plant (PLTU) is one of the main sources of electrical energy in East Java, which plays a vital role in maintaining a sustainable electricity supply. The reliability of generator units is a key element in maintaining stable energy distribution. However, the high frequency of sudden generator failures poses serious challenges, such as increased downtime and increased maintenance costs. To address these challenges, this study aims to design a generator maintenance prediction model based on the Naive Bayes algorithm with a predictive maintenance approach. This study uses historical maintenance data and key sensor parameters such as temperature, oil pressure, and vibration as input. The data is analyzed through several stages, namely data preprocessing, selection of relevant features, and labeling generator conditions into three categories: Normal, Warning, and Critical. The Naive Bayes model is trained to classify the data probabilistically to generate predictions of future generator conditions. Model evaluation using accuracy metrics and a confusion matrix shows that the model successfully achieved an accuracy rate of 89% and was able to provide early warnings of potential failures up to 3 days before failure occurs. The implementation of this system is expected to support the shift in maintenance strategies from reactive and scheduled systems to data-driven predictive systems. Implementing failure predictions allows the technical team at the Paiton PLTU to conduct planned maintenance, avoid sudden disruptions, and extend equipment lifespan. Thus, this model has the potential to reduce operational downtime by up to 25%, while providing significant savings in operational and logistics costs. This research also shows that integrating machine learning technology into energy facility management can improve the efficiency and resilience of the overall electric power system.

The continuous operation of airlock machines in wheat milling facilities plays a critical role in the material handling system, especially in the transfer of grain from ships to silo storage. At PT XYZ, the airlock machine has been identified as the equipment with the highest frequency of downtime over a three-month observation period, leading to significant disruptions in production flow and increased corrective maintenance costs. This study aims to analyze the failure modes of the airlock machine using the Failure Mode and Effect Analysis (FMEA) method and to develop preventive maintenance recommendations based on the highest Risk Priority Number (RPN) values. The research adopts a quantitative descriptive approach, involving field observations, interviews with maintenance personnel, historical breakdown analysis, and machine technical documentation review. The FMEA results indicate that the seal, gear, and bearing are the most critical components, with RPN values of 224, 210, and 192, respectively. These components are prioritized for preventive actions such as regular seal replacement, scheduled lubrication, gear inspections, and motor monitoring. Simulation of the proposed maintenance strategy demonstrates a 66.7% reduction in downtime, from 18 hours to 6 hours per month, and a 43.7% reduction in total maintenance costs, from Rp 9,034,500 to Rp 5,087,500 monthly. These results validate the effectiveness of the FMEA method in identifying risk-prone components and optimizing maintenance planning. It is recommended that PT XYZ institutionalize periodic FMEA updates and establish a cross-functional analysis team to continuously monitor and improve equipment reliability.

This The maintenance objectives of a C6240A-type lathe involve a number of crucial aspects, including gears, tool bits, toolposts, lower sled and upper sled. This study was to investigate effective maintenance strategies for each of these components, focusing on preventive and predictive maintenance. Gears: Regular lubrication is also necessary to ensure smooth gear movement and prevent excessive friction that could lead to failure. Chisel Bits: Monitoring the condition of the tool blade needs to be done regularly. Toolpost: The toolpost needs to be checked periodically to ensure availability and safety of the cutting tool. Bottom and Top Slings: Maintenance of the lower and upper slings involves checking for tension and wear. C6240A type lathe users can minimize the risk of failure, improve operational efficiency, and extend the life of the machine. The method used in this research is a qualitative method, by means of observation and interviews. The results of this study are that the gears can be more durable because lubrication is always given and not easily thirsty, the tool blade is not easily blunted because the workpiece is fed little by little, and frequent honing is done to keep it sharp. The locking toolpost is not easily damaged if you use a rubber hammer when locking, the bottom row makes a change of ashock so that it can do automatic turning. This maintenance can increase the life of the lathe for longer operation.

Generator Set Service Management is the process of organizing maintenance, repair, and upkeep activities of generator sets, starting from planning, implementation, evaluation, control, and improvement of services to ensure the generator machine continues to operate effectively and efficiently. The research was conducted at PT. Sigma Quantum Insani, located in Balikpapan, East Kalimantan. The problem addressed in the research is the frequent delays in completing scheduled tasks within the generator set service management activities. The purpose of the study is to determine the critical path of activities and identify the duration of the critical path. From the analysis using the Critical Path Method (CPM) assisted by MS Project software, the identified critical path activities are: A1-B1-B2-B3-B4-B5-B6-B7-B8-B9-B10-B14-B16-B17-C1-C2-D1-D2-D3, which include DO approval, engineering scope, painting scope, and delivery scope. The total time obtained for the critical path is 38 days, resulting in a time efficiency of 8 days in the generator set service process.

The electronic medical record (EMR) is an important step in supporting the digitization of health services, with the aim of improving the effectiveness, efficiency and accuracy of the management of patient medical data in hospitals. This study aims to determine the readiness of the implementation of RME in BUDIASIH Hospital Trenggalek by using the Fishbone Diagram method as an analytical tool in identifying the factors that influence.This study uses a descriptive method with a qualitative approach to explore the implementation process in depth. The Data was obtained through in-depth interviews and direct observation to 20 informants who actively use the RME system in the implementation of daily tasks.The results showed that the readiness of RME implementation in Budi Asih Hospital was influenced by five main factors, namely man (Human Resources), matherial (tools), machine (data), method (methods), and money (budget). Health workers and administrative staff have been able to operate RME well, accompanied by regular training and workshops to improve understanding related to System updates. Equipment and support systems are available and functioning properly, along with scheduled maintenance to keep the system running stably. Data management has been done accurately and timely, supported by periodic audits to maintain the quality and validity of the data. The hospital also has a clear Standard Operating Procedure (SOP) I n the implementation of RME and has allocated a budget to support the sustainability of this system. Overall, Budi Asih Trenggalek Hospital has shown good readiness in the implementation of RME and is ready to develop this system to support the improvement of the quality of health services in the future.This study recommends increased maintenance of software and hardware as well as regular training of human resources to support the sustainability of the RME system.  

This study explores the development and application of a digital twin-driven cybersecurity risk assessment model for Industrial Internet of Things (IIoT) networks. The increasing complexity and interconnectivity of IIoT systems have expanded the attack surface, making them vulnerable to a wide range of cyber threats. The digital twin model addresses this challenge by creating real-time virtual replicas of physical systems, which can simulate and predict network vulnerabilities and attack vectors. The model uses machine learning algorithms and real-time data to simulate cyberattacks, including Distributed Denial of Service (DDoS), malware, and data breaches. By providing continuous monitoring and dynamic risk predictions, the digital twin model enhances the resilience of IIoT networks compared to traditional cybersecurity frameworks. The findings indicate that the model's ability to predict potential cyber threats and simulate various attack scenarios provides a more proactive and accurate approach to cybersecurity in IIoT environments. Additionally, the study highlights key mitigation strategies, including adaptive security mechanisms, real-time anomaly detection, and the use of lightweight encryption for resource-constrained devices. Despite its effectiveness, challenges such as computational requirements, integration with legacy systems, and scalability were identified. This research underscores the strategic importance of digital twin models in securing IIoT systems and advancing Manufacturing 4.0 ecosystems. Future research should focus on enhancing model accuracy, expanding its application to diverse industrial sectors, and improving interoperability with legacy systems to further strengthen the security posture of IIoT networks.

This research was conducted at PTPN IV Regional II Adolina Palm Oil Plantation and Mill Unit with the aim of evaluating the effect of Ripple Mill machine efficiency on palm kernel impurity levels through a multiple linear regression analysis approach. Ripple Mill is a vital piece of equipment in the palm kernel cracking process, which plays a role in determining the quality of the palm kernel. This machine works by breaking the kernels to produce kernels that are separated from the shells. However, when machine efficiency decreases due to technical and operational factors, palm kernel impurity levels tend to increase, characterized by an increase in shell fragments, fibers, and other foreign materials. This condition can have an impact on decreasing product quality and increasing further processing costs. Data collection was carried out over five days of observations covering the variables of machine efficiency and kernel impurity levels obtained from production results. The study found a strong negative correlation between machine efficiency and impurity levels in palm kernels. This means that the higher the efficiency of the Ripple Mill, the lower the resulting palm kernel impurity levels. This finding also confirms that quality control in the production process is not only determined by raw material factors but also highly dependent on the performance of the processing machine. Furthermore, the research results demonstrate the importance of implementing preventive maintenance strategies, including regular inspections of the rotor bar, square bar, and drive motor, as well as regulating operating parameters such as rotational speed and machine load. Proper preventive maintenance and operational control are essential to sustain machine efficiency and product quality. Therefore, continuous improvement in technical and managerial aspects is essential to maintain palm kernel quality and support optimal palm oil mill productivity.

The Ripple Mill machine is one of the vital components in a Palm Oil Mill (PKS) that functions to separate the palm kernel from its shell. This process is very important because the quality and quantity of the palm kernel produced will directly affect the economic value and production efficiency in the palm oil industry. At PTPN IV Regional II Plantation Unit and Adolina PKS, various technical problems were found that caused a decrease in machine performance, including rotor bars and square bars that experienced wear due to age and improper machine settings. In addition, other damage that often occurs is a broken van belt due to age and excessive tension, feeder motor dysfunction caused by excessive load, and motor overheating that occurs due to age and high workload. To overcome these problems, this study uses the Failure Mode and Effect Analysis (FMEA) method. This method aims to identify various potential failures, assess the severity, frequency of occurrence, and detection capability, so that the Risk Priority Number (RPN) value can be calculated as a basis for repair priorities. The results showed that worn rotor bars and worn square bars had the highest RPN values, each at 280, equivalent to 40.23% of the total identified risks. This indicates that these two components are critical points requiring immediate repair and maintenance attention. Applying FMEA in this context provides tangible benefits, including helping the company formulate a more targeted maintenance strategy, reducing the risk of recurring damage, and minimizing downtime that impacts production. With more systematic maintenance, operational efficiency can be improved while extending the lifespan of the Ripple Mill machine.

Background: The rapid advancement of digital technologies in the Industry 4.0 era has transformed industrial mechanical systems into highly interconnected and data driven environments through the integration of sensors, the Internet of Things (IoT), data analytics, and cyber physical systems. This increasing complexity requires more adaptive and accurate monitoring and prediction methods than conventional simulation approaches, which often face limitations in capturing real time dynamic system behavior. Objective: This study aims to develop a predictive performance model for industrial mechanical systems by integrating Digital Twin technology with Physics Informed Machine Learning in order to improve monitoring accuracy and support predictive maintenance strategies. Methods: This research adopts a data driven modeling and simulation approach by developing a digital representation of an industrial mechanical system that is connected to real time sensor data. The prediction model is constructed using a Physics Informed Neural Network (PINN), which integrates operational data with physical principles governing system dynamics. The research process includes the development of a Digital Twin model, integration of sensor data, training of the PINN model, model validation using experimental data, and evaluation of prediction performance using statistical metrics. Results: The results indicate that the integration of Digital Twin technology and PINN significantly improves the prediction accuracy of industrial mechanical system performance compared with conventional simulation methods and purely data driven machine learning models. The proposed model is capable of representing system dynamics more consistently, accurately following sensor data patterns, and providing strong potential for supporting machine condition monitoring and predictive maintenance strategies in modern industrial environments.

This study aims to determine the working system of the Qpro Ex Muratec Winding Machine at PT Budi Texindo Prakarsa. This study focuses on knowing the working system of the muratec Qpro EX winidng machine. As for what will be explained about the working system of the sorting and structuring winding machine, how the system works, and about the outside of the muratec qpro ex winding machine. In addition, it explains about special maintenance and control on the muratec qpro ex winding machine. The research method with the observation method where in this method makes direct observations to be able to find out the process of rolling yarn, literature studies sourced from references to journals, articles, books, and so on, the documentation method used by taking sample images of the observed machine parts.

Operational efficiency and product quality are crucial for enhancing the competitiveness of MSMEs in the era of globalization and digital transformation. This study explores the application of Six Sigma to reduce product defects at MSME X, a footwear manufacturer in Bandung. Using a quantitative approach with interviews, documentation, and observation, the research applied the DMAIC (Define, Measure, Analyze, Improve, Control) framework to identify and address quality issues. Key defects included joint cracks, under-fill, and press marks. Analysis using Pareto charts and cause-and-effect diagrams revealed root causes such as inadequate operator training, inconsistent raw materials, and irregular machine maintenance. To address these, the company implemented retraining programs, improved standard operating procedures, and adopted digital monitoring systems. As a result, the defect rate was reduced by 40%, and the sigma level improved from 3.9 to 4.45. The study confirms that Six Sigma can significantly enhance both product quality and operational performance. These findings offer valuable insights for other MSMEs aiming to adopt structured quality improvement methods to remain competitive in the global market.

This study aims to analyze the failure risks of UV digital printing machines used at PT XYZ using the Failure Mode and Effects Analysis (FMEA) method. In the digital printing industry, the continuity of machine operations heavily depends on effective maintenance to prevent downtime that could disrupt the production process. The FMEA method is employed to identify potential failure modes and evaluate the severity, occurrence, and detection of each failure. This research adopts a quantitative approach, with data collected through observations, interviews, and historical documentation. The analysis reveals that the printhead, cooling system, and UV lamp are the components with the highest Risk Priority Numbers (RPN), at 504, 441, and 288 respectively. Based on these findings, it is recommended that preventive and predictive maintenance strategies be enhanced to avoid recurring failures.

The development of digital printing technology requires machines with high reliability and efficiency. The AccurioPress 6100 is a widely used digital printing machine that offers a speed of up to 100 pages per minute. However, the machine’s Overall Equipment Effectiveness (OEE) at PT. OctoPrint only ranged from 45% to 53%, which is below the industry standard of 85%. This study identifies the gap in machine efficiency and proposes solutions based on OEE analysis involving Availability, Performance, and Quality indicators. The study adopts a descriptive quantitative method, collecting data through direct observation and interviews over a 14-week period. Preventive maintenance strategies were implemented to improve machine performance. The results showed an increase in OEE to 60–63%, supported by improvements in Mean Time To Failure (MTTF) and reductions in Mean Time To Repair (MTTR).

Draft Sizing quality is a crucial issue in the sizing department. There were 14,881 total warp breaks woven during the November to December period. This led to low draft sizing quality. This research aims to improve the quality of draft sizing by applying Grey Theory in traditional FMEA. This type of research uses descriptive qualitative research. Data collection techniques using questionnaires, observations and interviews at PT Delta Merlin Dunia Tekstil V. The data processing method in this study uses Grey Theory and FMEA methods. The results of this study indicate that from the sizing process flow there are 7 sizing areas that are potential failures of draft sizing and show that out of 26 priority sequences there are 10 different priorities and 16 the same priority. Determination of improvement priorities is based on the value of the smallest degree of relationship, namely Potential causes PIV is not stable in the size box area with the result of the degree of relationship value of 0.356, Potential causes PIV is not stable in the head stock area with the result of the degree of relationship value of 0.382, Potential Causes Kampas wear in the creel beam stand area with the result of a degree value of 0.477, Potential Causes Gear and Chain wear in the dryer cylinder area with the result of a degree value of 0.453 and Potential Causes The break regulator hole is not symmetrical (pneumatic piston is not aligned) in the creal beam stand area with the result of a degree value of 0.466. This research can recommend the sizing improvement process based on proposed improvements or maintenance schedules on the sizing machine, using Grey Theory in the FMEA method to improve the quality of the sizing draft.

Ships as the main mode of transportation that has a strategic role in trade transportation, the trade supply chain really needs proper operations. The operational of the ship is greatly influenced by the performance of the main engine. One of the critical components of the main engine is the exhaust valve which functions to regulate the release of exhaust gas from the combustion chamber to the exhaust manifold. The research is qualitative, collecting data through observation, documentation, and interviews. The event tree analysis (ETA) method is implemented the sequence of impacts produced by exhaust valve, such as high working temperatures, low engine performance, and standard functions described in the manual book so that leaks can be identified. So, From this analysis, the impacts that are traced sequentially to the performance of the exhaust valve itself can also be found. In addition, a fishbone diagram is used to describe the factors that cause leaks, including human error, managerial deficiencies, neglected inspections, and errors in maintenance techniques. Thus, the right handling can be found to overcome exhaust valve leaks, such as repairs required while the engine is stopped, repeated spare part submissions from ship management, conducting spindle thickness inspections, and improving maintenance techniques as specified in the engine manual book. Proper and effective preventive and handling actions based on the root causes outlined are essential to ensure proper operation of the machine.

The home industry partner for the tofu business is located in Kalidoh Langensari Barat Village, Ungaran Barat District, Semarang Regency. The partner's obstacle is the failure to meet the increasing market demand. This obstacle is caused because the production process is still carried out manually or traditionally so that tofu production is limited to human power. The right solution offered through this community service is the application of process technology in the form of a soybean grinding machine with a rotary system to increase tofu productivity. The purpose of this community service is to make an electric soybean grinding machine, apply a rotary grinding machine to the tofu production process, and conduct evaluation and assistance to maintain the sustainability of the program. The initial stage carried out in this service consists of planning and making the machine. Planning is based on market needs and partner conditions related to the provision of electrical energy. The design results are made through machine work in the Mechanical Department workshop. The next stage is the application of the machine including assembly and installation of the machine at the partner's location, as well as operational training and machine maintenance. The final stage of the service is evaluation and assistance. Evaluation is carried out by directly assessing the partner's operational practices on the machine that has been assisted. The assistance provided includes supervision, work control, and direction when the partner produces tofu using the production machine. It is expected that the rotary soybean grinding machine can increase the production of partner tofu.

Osha Snack UMKM faces issues related to product defects, which are suspected to arise due to a lack of quality control in the production process. This study aims to identify the causes of product defects, provide improvement suggestions, and determine effective ways to control product quality. Data were collected through interviews with relevant parties and field surveys to gather information on production volume, product defects, and production activities. The data were processed by organizing it into check sheets, performing regression calculations, and analyzing it using scatter diagrams and fishbone analysis. The Statistical Process Control (SPC) method was applied to analyze, control, and improve the production process. The results show a significant relationship between production volume and the number of defective products, with the regression equation Y = 0.0764X and a coefficient of determination R² = 0.9625. The most common type of defect is broken snacks before packaging. In addition, waste products also come from product returns by buyers, which leads to raw material waste and hinders the achievement of production targets. The analysis results suggest that the UMKM should implement quality control measures in the form of process improvements, enhancement of work skills, proper machine maintenance, implementation of SOPs, and improvement of working conditions.

Background: Structural Health Monitoring plays a critical role in ensuring the safety, reliability, and sustainability of high performance composite structures used in aerospace, civil infrastructure, and mechanical systems. Conventional externally mounted sensors often face challenges related to environmental interference, maintenance complexity, and long term stability. Objective: This study aims to develop and validate an integrated smart composite monitoring system with embedded sensing capabilities that enhances damage detection accuracy and operational durability under varying mechanical stress conditions. Method: Smart composite specimens were fabricated by embedding fiber optic and piezoelectric sensors within fiber reinforced polymer laminates, followed by tensile, fatigue, and vibration testing. Signal processing techniques including time frequency analysis were applied to extract damage sensitive features, which were then classified using machine learning algorithms to distinguish healthy and damaged structural states. Results: The experimental findings demonstrate high damage detection capability, stable sensor performance under cyclic loading, improved reliability compared to conventional monitoring approaches, and consistent monitoring accuracy throughout the fatigue life of the specimens. The integration of embedded sensing and data driven analytics significantly enhances structural response interpretation and supports predictive maintenance strategies.

This study aims to analyze the causes of bottlenecks in lip balm production at PT Rumah Rumput Laut by using a survey approach and fishbone diagram analysis method. The research method used is a descriptive method with survey approach which was conducted at PT Rumah Rumput Laut, Bogor Regency, West Java. Data were collected through observations and interviews with related parties in the production process based on previously identified problems. A fishbone diagram was used to identify the factors causing bottlenecks, which were grouped into six main categories, namely: manpower, machine, method, material, measurement, and environment. The results of the fishbone diagram analysis show that bottlenecks in lip balm production are caused by the irregular flow of material transfer, long waiting time for the quality control process, and limited production capacity. To overcome this problem, it is necessary to improve the operator supervision system, implement a regular machine maintenance schedule, rearrange the production space layout, and use technology to monitor machine conditions in real time.