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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.

The success of a production process is highly dependent on effective quality control. In this study, the focus is directed toward Pc Slab Type A products manufactured by PT. XYZ, a precast concrete company for highways and building structures. The evaluation emphasizes efforts to reduce chipping defects. Improvement priorities were determined using the Failure Mode and Effect Analysis (FMEA) method based on the Risk Priority Number (RPN). Root Cause Analysis (RCA) was applied to identify the underlying problems. The analysis revealed that the dominant factor comes from the human aspect, with the highest RPN value of 294, namely the lack of regular supervision. Additional contributing factors include improper mold opening methods (RPN 216), inconsistent worker performance (RPN 175), inter-division transfers (RPN 125), and delayed maintenance schedules (RPN 120). All causes were elaborated using the 5W+1H approach, covering reasons, locations, timing, implementation, and proposed corrective actions.

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 spice industry faces significant challenges in maintaining product weight consistency as part of quality assurance and compliance with production standards. A case at PT X revealed that a newly installed filling machine produced deviations from the target weight of 50 grams, with hypothesis testing showing that out of 30 samples, 17 samples fell outside the  confidence interval. To mitigate this issue, this study proposes the development of a real-time data-driven Decision Support sistem (DSS) combined with statistical approaches. The methodology includes two-tailed hypothesis testing to detect weight deviations and Failure Mode and Effects Analysis (FMEA) to identify dominant failure causes based on high Risk Priority Numbers (RPN), such as delayed machine calibration, operator error, and worn-out machine components. These findings serve as the foundation for designing the DSS architecture, which consists of sensor input modules, statistical data processing, risk mapping, and an automated corrective recommendation engine. The sistem is designed to enable early detection of deviations, accelerate response time to quality issues, and support data-driven decision-making on the production floor. The study concludes that a structured implementation of DSS can be an effective strategy to improve product weight consistency and enhance operational efficiency in spice manufacturing.  

The use of information technology (IT) in the government sector requires structured risk management to ensure the continuity of public services. The Department of Communication and Information Technology (Dinkominfo), as a digital service provider, faces various potential risks such as network disruptions, hardware failures, and cyberattacks that could interfere with daily operations. This study employs the Failure Mode and Effect Analysis (FMEA) method to identify, analyze, and formulate strategies to mitigate existing IT risks. Through the FMEA approach, each potential failure is evaluated based on its severity, occurrence, and detection capability, which are then used to calculate a Risk Priority Number (RPN). The analysis reveals that the highest RPN values are associated with information system errors, hardware failures, and network overloads. As mitigation measures, the study recommends conducting regular system audits, upgrading network capacity, and performing preventive maintenance on devices. This approach demonstrates that FMEA is an effective method for managing IT risks within government institutions.

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.

UD. Sarigut Bakery is known as one of several industries that focus on processing bread specifically in the Banda Aceh City area that seeks to produce with a variety of bread. The purpose of this study is to carry out efforts to identify, measure efforts, and analyzing efforts to the phase known to be the cause of the decline in quality of the production process of meses bread through a series of stages of the DMAIC SIX SIGMA methodology. It is known that the main problem is the discovery of disability with a total of 4,096 of the total production of the production of 21896 with a period of 3 months specifically in the dominant disability of Meses bread in the percentage of 81.29%. With the dominant of the disability itself the disability that comes in damaged bread with a percentage of 36.49% present from all of the total disability of related meses bread. After the assessment effort, the DPMO value was obtained by 26723.74 so that there were several levels of Sigma 3, which means it was still far from the world industry level which reached 6σ (3.4 DPMO). Efforts to analyze the data carried out by applying the Fishbone and FMEA (Failure Mode and Effect Analysis) diagrams are known to be related to the cause of the presence of disability from damaged bread with 3 modes of failure in potential, namely bread that is not filled with chocolate with a value of 20, the upper bread with a value of RPN of 80 and failure mode on broken bread with the highest RPN value of 90.

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.

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 advancement of information technology has driven companies to adopt technology-based systems to enhance operational efficiency while also increasing the complexity of information security risks. This study aims to analyse risk factors associated with information security assets, identify potential threats, assess risks, and provide mitigation recommendations. The OCTAVE method was applied to identify threats, vulnerabilities, and critical information technology assets, while FMEA was used to determine risk mitigation priorities based on the Risk Priority Number (RPN). The mitigation recommendations were developed in accordance with ISO 27001:2022 standards. Data collection was conducted through interviews with XYZ company representatives, identifying 34 information security asset risks, including 6 hardware failure potentials, 6 software failure potentials, 14 data failure potentials, 4 human resource failure potentials, and 4 network failure potentials. The risk categorisation results revealed 5 high-level risks, 6 moderate-level risks, 20 low-level risks, and 3 very low-level risks. The mitigation recommendations include three ISO/IEC 27001:2022 clauses: Human Resource Controls, Physical Controls, and Information Technology Controls.