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This study aims to analyze the transformative impact of digitalization and technology in the manufacturing sector on improving operational efficiency, particularly in budgeting and resource utilization, as well as to identify the main barriers to technology adoption. Using a Literature Review and Case Study Analysis of secondary data (journals, company reports, and industry publications), it was found that digitalization and Automation supported by Artificial Intelligence (AI) fundamentally transform budgeting functions. This transformation has been shown to improve budget accuracy by up to 50% (reducing human errors) and process efficiency by up to 25%, turning budgets from static documents into adaptive and predictive control tools. Positive impacts are also observed in operations through increased production capacity (revenue surge) and the implementation of Predictive Maintenance, which reduces expenditure and asset downtime, in line with the principles Cost Efficiency and Lean Manufacturing. Nevertheless, the adoption of advanced technology faces significant obstacles, namely high initial capital investment and skill gaps among the workforce. It is concluded that the success of digitalization heavily depends on strategic budget planning to overcome capital barriers and adequate allocation of funds for Human Resource (HR) training to support effective collaboration between humans and machines.

This study aims to determine and analyze service waiting times, identify the root causes of long queues, and develop a strategy to improve service performance at the 5361137-gas station (SPBU) at the Surabaya-Gresik Toll Rest Area. The research method used is a mixed-methods approach with an exploratory sequential design. This study combines quantitative analysis using Queuing Theory to measure system performance (arrival rates and service times) and descriptive qualitative analysis using a Fishbone Diagram. Data were collected through direct observation, interviews, and g-form techniques. The results indicate that the current queuing system performance is in a critical or severe condition, indicated by a server utilization rate of 0.94 to 1.02 during peak hours. The average time spent by vehicles in the system is 14.3 minutes, of which 9.6 minutes (67%) is spent waiting in the queue. Fishbone diagram analysis revealed that the root cause of the main problem lies in the complex interaction of factors: Machine factors (EDC signal failure and pump repair downtime), Human and Method factors (implementation of static shifts and reactive maintenance), and Environmental factors (narrow layouts that hinder large vehicle maneuvers). As a solution, this study formulated a hybrid improvement strategy that includes short-term business process engineering (the use of Floating Staff and lane segregation) and long-term investment in additional pumps to change the queuing model from Single Channel to Dual Channel. This strategy is expected to reduce the utility level to a safe zone below 0.80 with a target waiting time of 3–5 minutes.

Mining operations rely heavily on the performance and reliability of heavy equipment used in the production process. One of the most important hauling units in open-pit mining is the dump truck, which functions to transport overburden and coal from the mining front to disposal areas. Due to high operational intensity, dump trucks require effective maintenance management to ensure equipment reliability and reduce unexpected downtime. However, maintenance activities are often carried out based only on routine service schedules without analytical planning based on historical data. This study aims to analyze the implementation of forecasting methods in maintenance management to improve the effectiveness of dump truck maintenance planning in mining operations. The research was conducted during field work practice at PT Putra Perkasa Abadi Jobsite BIB, Tanah Bumbu, South Kalimantan. The data used were historical maintenance records of dump truck units obtained from the maintenance department. The research method used a quantitative approach with time series forecasting analysis to identify maintenance patterns and estimate future maintenance needs. The results show that forecasting-based maintenance planning can help companies predict maintenance requirements more accurately and prepare maintenance resources more efficiently. Furthermore, the implementation of forecasting methods can reduce unexpected equipment failures and support operational efficiency in mining activities.

Unexpected downtime of a 2 kg/hour coffee grinder is crucial in cafe operations, thus less guaranteeing the availability of the grinder. The purpose of component replacement and repair planning is to obtain a prediction of the maintenance and repair schedule and costs in the 2026 period. The component replacement planning method includes collecting previous maintenance and repair data, applying the inspection-replace-repair-overhaul (IRRO) method, assessing component conditions, predicting component life, predicting technician costs, predicting supporting work equipment and supporting materials to be used in maintenance, predicting the time to replace spare parts or reinstall components after repair, estimating maintenance and repair costs for the 2026 period, and calculating the ratio of maintenance costs to profits. The results of component replacement and repair planning obtained maintenance costs for the 2026 period are IDR 2,350,000, - with an estimated coffee grinder rental rate of IDR 25,000/hour which has the potential to be rented for 1440 hours/year, and the ratio of maintenance costs to profits is 6.5% which implies that the coffee grinder with a capacity of 2 kg / hour is still suitable for use for the next few years and still has the opportunity to make a profit.

This study analyzes the maintenance process of the Roll Bending machine used in the wind tower production line at PT Kenertec Power System, Cilegon, Indonesia. The Roll Bending machine plays a crucial role in shaping steel plates into cylindrical shell components, which are later assembled into wind tower sections. The objective of this research is to identify maintenance patterns, types of failures, and improvement strategies to ensure machine reliability and operational efficiency. The research employed observation, interviews with maintenance personnel, and documentation review to collect relevant data. The findings show that the machine experienced multiple failures, mostly related to hydraulic system leaks, PLC programming errors, and component wear such as cylinders, seals, and gear pumps. A significant increase in corrective maintenance activities occurred between August 2023 and April 2024, particularly in February 2024, indicating the need for a more consistent predictive maintenance strategy. The implications of this study highlight that optimized maintenance scheduling and monitoring are essential to reduce downtime, avoid production delays, and maintain product quality. This research is expected to support maintenance decision-making and contribute to the improvement of industrial machine reliability in wind tower manufacturing operations.

Damage to rotable axle components on CAT KT4 Loaders in the mining industry is often the main cause of operational downtime. This study aims to analyze the frequency of damage to axle components and evaluate the effectiveness of preventive maintenance to reduce downtime duration and repair costs. Damage data collected from January to August 2024 were quantitatively analyzed to identify the most frequent damage patterns, especially seal leaks and brake modules. The results show that the implementation of preventive maintenance, such as regular seal replacement and hydraulic pressure checking, can significantly reduce the frequency of breakdowns and improve operational efficiency. The research also provides recommendations on maintenance strategies that can be implemented to extend component life and reduce overall downtime.

This study analyzes failures in the bending machine at PT. XYZ and determines maintenance priorities to reduce downtime and improve production efficiency. The company often faces repeated breakdowns, especially in hydraulic and control components, which negatively impact productivity. To address these issues, the research applies Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA). The study employs a descriptive qualitative approach using downtime and repair data from September 2024 to February 2025. FMEA was conducted to identify failure modes, effects, and causes, and to calculate the Risk Priority Number (RPN) as a basis for prioritization. FTA was then applied to trace root causes by mapping logical relationships among contributing factors leading to the top event. Recommendations were formulated with the 5W+1H method to propose preventive maintenance actions. The results indicate that the hydraulic valve is the most critical component, with an RPN value of 504 due to oil contamination. The main causes include damaged filters, improper oil usage, and lack of a cooling system. The hydraulic cylinder seal and back gauge were also found to contribute significantly to machine failures. FTA analysis revealed root causes such as inadequate maintenance procedures, unsuitable materials, and insufficient inspections. The proposed improvements involve regular replacement of oil filters, structured lubrication schedules, installation of oil coolers, and technician training to strengthen compliance with standard procedures. Overall, the integration of FMEA and FTA provides a systematic approach to identify critical components and root causes, enabling PT. XYZ to implement preventive strategies that minimize failures, reduce downtime, and improve bending machine performance sustainably.

In the modern computing era, servers face significant challenges in data storage due to hardware failures, cyber attacks, or human errors. The problem highlighted focuses on the impact of file systems on three critical aspects: data integrity (accuracy and consistency of data without corruption), data recovery (the ability to restore data after a failure), and failure resilience (fault tolerance, such as redundancy and journaling to prevent downtime). The main issue is that traditional file systems like FAT32 or NTFS are often susceptible to fragmentation, metadata loss, or long recovery times, which can lead to data loss of up to 20-30% on enterprise servers, especially in high-traffic environments like cloud computing.A simple problem-solving process is conducted through a straightforward comparative analysis approach: (1) A literature review of popular file systems (ext4, ZFS, Btrfs); (2) Failure simulations using tools like fsck and stress testing on virtual servers (e.g., via KVM or Docker); and (3) Measuring performance metrics with benchmarking tools like Bonnie++ for I/O throughput, recovery time, and error rates. This process is designed to be simple, requiring only a virtual lab setup without expensive hardware, and is analyzed quantitatively with descriptive statistics.The solution to the problem indicates that advanced file systems like ZFS or Btrfs provide significant improvements: data integrity is up to 95% more secure through automatic checksums, data recovery is achieved in minutes through snapshots and RAID integration, and failure resilience is higher with copy-on-write features. The main recommendation is to migrate to journaling-based file systems for servers, combined with automated backups, which can reduce the risk of downtime by up to 50%. This research provides practical guidance for system administrators to enhance server reliability without excessive additional costs.

PT. YGI, an automotive manufacturing company, uses the Press Injection GB-36 machine in its production process. However, this machine frequently experiences failures, causing high downtime and reduced production efficiency. This study aims to identify the types of failures, determine the most critical components, and propose a maintenance system using the Reliability Centered Maintenance (RCM) approach. The method used is descriptive qualitative, involving FMEA (Failure Mode and Effect Analysis), Logic Tree Analysis (LTA), and maintenance action selection. Data were collected through observations, historical failure documentation, and interviews. The analysis results indicate that components such as the injection nozzle, heating, and clamping are the most critical, contributing over 80% of total failures based on Pareto analysis and the highest RPN values in FMEA. Proposed maintenance actions include Condition Directed and Time Directed approaches. Additionally, the maintenance system is supplemented with standard operating procedures (SOP) and routine inspection schedules to improve machine reliability and reduce production downtime. This study is expected to enhance the efficiency and productivity of the Press Injection GB-36 machine at PT. YGI through the appropriate implementation of RCM.

The reliability of power distribution systems is a crucial factor in ensuring stable electricity supply for industrial, commercial, and household users. Conventional protection systems often face limitations in terms of real-time monitoring, remote control, and adaptive responses to fault conditions, which can result in longer outage durations and higher operational costs. This research aims to develop a smart protection system for power distribution using Internet of Things (IoT) technology to enhance system reliability. The proposed method integrates IoT-enabled sensors, microcontrollers, and communication modules to monitor critical parameters such as voltage, current, and frequency in real time. Data are transmitted to a cloud-based platform for analysis and decision-making, enabling rapid detection of abnormalities and remote tripping of circuit breakers. The prototype was tested under various fault scenarios, including short circuits and overloads, and demonstrated faster response times compared to conventional systems. Results show that the IoT-based protection system improved fault detection accuracy, reduced downtime, and provided predictive maintenance insights through data analytics. The synthesis of these findings highlights that integrating IoT into protection mechanisms not only increases operational reliability but also supports the transition toward smart grids. In conclusion, the developed system proves effective in addressing the limitations of traditional protection systems by offering real-time monitoring, automation, and enhanced decision-making for modern power distribution networks.

The increasing integration and digitization of smart grid systems have exposed them to a variety of security threats, necessitating robust security measures to ensure their reliability and efficiency. This paper proposes a novel Digital Twin-Based Cyber-Physical Security Framework, incorporating AI-driven predictive maintenance and zero-trust architecture to address the evolving challenges of securing smart grids. By leveraging digital twin technology, this framework creates a real-time virtual representation of physical systems, enabling continuous monitoring and simulation for enhanced security and operational performance. Zero-trust security principles are integrated to ensure that no entity, whether inside or outside the network, is trusted by default, thus significantly reducing the risk of cyber-attacks. Additionally, AI-driven predictive maintenance enhances the framework’s reliability by proactively identifying potential failures before they occur, reducing downtime and improving system resilience. Through the development and simulation of this framework, including attack and failure scenarios, the paper demonstrates that the proposed system outperforms traditional methods in terms of anomaly detection, system downtime, and response times. The integration of predictive maintenance allows for early identification of component failures, thus enhancing the overall resilience of the grid. The zero-trust architecture further strengthens the cybersecurity posture, preventing unauthorized access and attacks. The study also identifies challenges, such as data synchronization and scalability, which must be addressed for broader implementation in large-scale smart grid systems. The findings suggest that the proposed framework could play a critical role in the future evolution of smart grid security, offering valuable insights for researchers and practitioners.  

Background: Maritime engineering has traditionally relied on reactive and preventive maintenance strategies, often leading to operational inefficiencies, unplanned downtime, and excessive costs. With the rise of smart ship technologies, predictive maintenance (PdM) has emerged as a data-driven solution, leveraging sensor-based monitoring and real-time diagnostics to optimize ship maintenance. However, its integration into maritime education remains underexplored, particularly in training vessels used for vocational learning. Original Value: This research contributes new insights into the feasibility, effectiveness, and educational relevance of predictive maintenance in maritime vocational training. Unlike previous studies that focus on commercial ship applications, this study examines PdM within the context of training vessels at Poltekpel SULUT, bridging the gap between academic training and industry expectations. Objectives: The study seeks to answer: How does predictive maintenance improve the efficiency, cost-effectiveness, and reliability of naval auxiliary systems in training vessels? Methodology: A qualitative approach was employed, integrating sensor-based performance analysis, structured interviews, and questionnaire surveys involving cadets, instructors, and industry professionals. Data were analyzed through thematic categorization, cross-group comparisons, and narrative synthesis. Results: PdM demonstrated high effectiveness in reducing downtime (92/100), optimizing maintenance efficiency (91/100), and aligning with industry practices (89/100). However, challenges in sensor accuracy (85/100) and training integration were identified. Conclusions: The findings highlight the necessity of incorporating predictive maintenance into maritime training curricula to equip future engineers with the skills required for Industry 4.0 maintenance solutions, ensuring better operational efficiency and sustainability in the maritime sector.

Background: Traditional naval maintenance strategies rely on centralized supply chains and pre-manufactured spare parts, leading to long repair downtimes and logistical inefficiencies, particularly for vessels operating in remote maritime regions. Additive manufacturing (3D printing) offers a disruptive alternative by enabling on-demand production of spare parts, reducing dependence on external suppliers, and enhancing fleet self-sufficiency. However, material durability, operational feasibility, and cost-effectiveness remain underexplored for naval applications. Original Value: This research advances the study of AM in naval engineering, assessing its practical viability beyond theoretical potential. Unlike previous studies focusing on commercial maritime applications, this study evaluates 3D printing’s impact on naval fleet readiness, supply chain resilience, and sustainability. Objectives: The study investigates how 3D printing can optimize naval maintenance efficiency, specifically analyzing its feasibility, material performance, cost implications, and logistical advantages. Methodology: A qualitative-empirical approach was used, combining material performance testing, expert interviews, and operational case studies to evaluate mechanical durability, economic feasibility, and AM integration challenges. Results: Findings indicate that AM reduces repair downtime by 40%, lowers part procurement costs by 30–50%, and enhances supply chain resilience. However, material limitations and infrastructure readiness remain key adoption challenges. Conclusions: Hybrid AM adoption—where 3D printing supplements rather than replaces traditional manufacturing—offers the most practical near-term approach for naval fleets. Strategic investment in material research, onboard AM training, and fabrication infrastructure will enhance fleet efficiency, reduce environmental impact, and future-proof maritime maintenance strategies.

Port logistics efficiency is determined not only by the adequacy of infrastructure and the advancement of technology but also by the motivation of the workforce and the overall quality of the work environment. This study investigates how these two factors influence employee performance in the Warehouse Division of PT Yusen Logistics Indonesia. The research employed qualitative methods, gathering data through semi-structured interviews, field observations, and document analysis, with participation from warehouse workers, supervisors, and safety officers. Through thematic analysis, the study found that employee recognition and active supervisory engagement were key contributors to workforce motivation. The work environment, characterized by equipment reliability, safety culture, and yard capacity, directly impacted employee productivity. When both motivation and work environment were favorable, employees exhibited greater procedural compliance, increased throughput, and improved safety practices. In contrast, inadequate motivation and unfavorable work conditions resulted in inefficiencies, downtime, and higher risk-taking behaviors. This study’s findings provide insights into three key areas: maritime economics by highlighting labor’s critical role in port operations, social management by establishing the link between environmental quality and workforce productivity, and vocational education by shaping the training of cadets and practitioners. It underscores that sustainable port operations require human-centered strategies, in addition to infrastructure development. For better performance, companies should prioritize motivating their workforce and improving the work environment to foster a more efficient and safer operational setting.

This study analyzes the effectiveness of implementing predictive maintenance (PdM) on the final drive components of the Komatsu PC200-8 unit at PT. Antareja Mahada Makmur, Site PT. Multi Harapan Utama, East Kalimantan, in an effort to reduce downtime and operational losses. Before the implementation of PdM in 2022, there were 12 repair cases for the final drive with a total downtime of 772.1 hours, repair costs amounting to IDR 310.6 million, rental income loss of IDR 208.03 million, and total losses of IDR 518.63 million. In 2023, during the PdM transition phase, the number of cases decreased to 4, with a total loss of IDR 252.05 million, although downtime remained high (714.6 hours) due to the limited scope of PdM implementation on certain units and components. In 2024, with full PdM implementation, the number of repair cases decreased to 5, with total downtime of only 96 hours and losses of IDR 45.75 million. The cost of PdM implementation for the year was only IDR 21.9 million. As of July 2025, no further damage to the final drive has been recorded, demonstrating a significant improvement in equipment reliability. The reduction in total losses from 2022 to 2024 amounted to IDR 472.88 million, indicating PdM’s effectiveness in avoiding significant costs through condition monitoring methods such as oil analysis, magnetic plug rating, thermal inspection, and oil leak testing (floating seal). The findings of this study confirm that PdM is effective in reducing downtime, repair costs, and enhancing asset management in the mining sector. It also improves equipment reliability and overall operational efficiency, proving PdM to be a successful strategy in reducing losses, increasing productivity, and supporting the sustainability of company operations.

This study aims to analyze the causes of component failure in the Power Train system of unit OHT773E CO2278 at PT. Cipta Kridatama, Samarinda, using the Root Cause Failure Analysis (RCFA) method. The Power Train system is responsible for transferring power from the engine to the final drive and other components, making it critical for the operational success of heavy equipment. Therefore, optimal maintenance is essential to prevent fatal failures that could impact the unit's performance. Based on the analysis, the dominant cause of failure is human factors, particularly technician negligence during component installation. This negligence results from a lack of understanding of the procedures and specifications recommended by the manufacturer, leading to incorrect installation of components. This failure impacts the achievement of the component’s expected lifetime, thus shortening the operational life of the components and increasing the risk of more severe damage. This also leads to higher repair costs and reduced unit productivity, resulting in longer downtime. To address this issue, several preventive measures are recommended, such as regular training for technicians to enhance their understanding of correct procedures and specifications, as well as the importance of following manufacturer guidelines during every maintenance and installation process. Additionally, it is advised to conduct routine discussions between technicians and supervisors to ensure that every maintenance step and installation complies with the established procedures. Increased oversight of the installation and maintenance process is also necessary, along with periodic rejuvenation of components to ensure the optimal performance of the Power Train system. Strengthening Preventive Maintenance (PM) practices is also crucial to minimize future damage potential. Implementing these solutions is expected to enhance the reliability of the Power Train system, extend component lifespan, and reduce failure frequency, ultimately improving the overall efficiency and productivity of the company.

Dr. Soedirman Kebumen Regional General Hospital has implemented PACS to replace traditional film, but limitations in IT infrastructure, RME integration, and human resource readiness require an integrated implementation model that combines cloud-hybrid, DICOM/HL7 with SSO, continuous training, and managerial support. To evaluate the implementation of the PACS system in the Radiology Department of Dr. Soedirman General Hospital in Kebumen and to analyze the factors that support and hinder the effectiveness of the PACS system in improving the quality of radiology services. The research used a qualitative approach with an interactive model. Data collection was conducted through in-depth interviews, Focus Group Discussions (FGD), direct observation, and documentation. The data obtained were analyzed using ATLAS.ti software to explain the PACS implementation and its impact on the effectiveness of radiology services. The PACS implementation improved the quality of radiology services by accelerating access to medical images and enhancing workflow efficiency. Some challenges, such as system downtime, integration with other systems, and technical limitations, need to be addressed. Integration of artificial intelligence (AI) and telemedicine technologies needs to be enhanced to achieve optimal radiology services. Factors supporting successful implementation include the adoption of advanced technologies (cloud computing and AI), adequate infrastructure, technical support from the IT team, and strong managerial commitment.  Barriers to success include imperfect system integration, power outages, downtime, storage capacity limitations, and a shortage of trained human resources. Proposed implementation models include improving PACS system infrastructure, developing ongoing training for staff, improving PACS system integration with other hospital systems, and improving interdepartmental communication to streamline workflows and reduce obstacles in the diagnostic process.

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.

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 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.