Failure Mode And Effect Analysis Analysis Research Articles

Quality improvement using lean six sigma in air conditioning products

Air Conditioners (AC) are essential electronic products for residential and industrial buildings in tropical countries or during warm weather in general. The penetration ratio of electronic Air Conditioners (AC) has been increasing every year. Data collection and processing were conducted directly by the author through observation, measurement, and calculation on the heat exchanger production of Air Conditioners (AC). In the production cost of Air Conditioners (AC), the heat exchanger accounts for the second highest cost after the compressor making up 19% of the total cost. The high cost is caused by pipe leaks, with a percentage of 73.7%. This research aims to analyze the improvement of Air Conditioner (AC) production quality using the Six Sigma method. The defect rate in the heat exchanger, evaluated through the Define, Measure, Analyze, Improve, Control (DMAIC) method originating from Six Sigma, was found to be 1,144.8 defects per million opportunities. Further analysis was conducted with the Failure Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) methods. This research was conducted at XYZ company by conducting interviews and observations for 3 months. FMEA and FTA analysis resulted in the top two failure risks based on the Risk Priority Number (RPN): flaring diameter too big and unstable brazing skill. In the manual brazing process, it was found that work by operators with level A certification produced 9% below standard outcomes. An improvement was obtained by changing the punch flaring size to 8.5mm and conducting skill certification and training for brazing operators. The results of the proposed improvement implementation led to a reduction in the DPMO value to 403.35, equivalent to a decrease of 64.7%. The defect ratio in production decreased by 38.1%, exceeding the previous target of 26.2%, equivalent to $31.96/month/unit. The results of this study are horizontal, allowing the implementation of improvements made in the heat exchanger production process to be expanded to other cooling electronic production processes.

Risk Management of Green Bean Farming Production in Gluranploso Village, Gresik District

Green beans are the most important food crop in Indonesia and production continues to increase to meet domestic demand. However, production is still insufficient due to several risks faced by farmers. This research analyzes the risk management of green bean production in Gluranploso Village using the Coefficient of Variation (KV) and Failure Mode and Effect Analysis (FMEA) methods. The results showed that yield variation was relatively low with a KV value of 17%, indicating good yield stability. The FMEA analysis identified four main sources of risk affecting production: seed aspect with an RPN value of 5.808, weather with an RPN value of 7.04, pests and diseases with an RPN value of 12.936, and technical aspects with an RPN value of 47.88, particularly delays in pest and disease control spraying. In addition, the market aspect is also a source of risk with the largest value in the increase in production input prices at 12.288. These sources require special attention in risk management. The study concludes that the implementation of appropriate risk management strategies, focusing on the identified key risk sources, can help farmers improve yield stability and farm profitability. These strategies include improving seed quality, mitigating weather risks, controlling pests and diseases, and improving technical procedures in cultivation and market risks. Thus, effective risk management can be key in increasing the productivity and sustainability of green bean farming in Gluranploso village.

A-188 Modal Failure and Effects Analysis, FMEA methodology, as a strategy to increase patient safety

Abstract Background Patient safety during the clinical care process is considered a priority in healthcare. Failure Mode and Effects Analysis (FMEA) is a proactive and systematic method of process evaluation and is used to identifying where and how each phase of the analytical process could fail allowing the detection of the causes and the effects they produce on the quality of the results that are issued. A regular use of this tool makes possible to identify the most critical steps, propose improvement strategies and evaluate their impact. The aim of the study is to evaluate applicability of the FMEA tool to identify errors in the pre analytical, analytical, and post analytical phase of a clinical laboratory as well as the main causes and effects, to reduce them by proposing improvement strategies Methods For each phase into which the laboratory activity is divided, the frequency and type of errors that are made and their final consequences on the patient are evaluated. A risk index (NPR=OxDxS) is calculated for each error considering the frequency with which it occurs (O), the ease with which it is detected (D) and the severity of the consequences on the patient (S). Values goes from 1 (rarely) to 10 (often). Improvement actions are proposed for all errors with a risk index greater than 100 and this index is re-evaluated after the implementation of that improvement measures. This same analysis is performed for three times over the last 10 years (in 2014, 2019 and 2023). Results In FMEA analysis carried out in 2014, a total of 40 errors were identified in the pre-analytical phase (63.5% of the total), 12 in the analytical phase and 11 in the post-analytical phase. 50% of the total errors detected required improvement strategies because they had an NPR≥100 value. In the 2023 evaluation, a total of 27 errors were identified in the pre-analytical phase, 10 in the analytical phase, and 5 in the post-analytical phase but only 6 required improvement strategies, which represents an 80% reduction in the most critical errors for patient safety. However, the pre-analytical phase continues to be the most critical, as it continues to account for 64% of the total errors identified, including the lack of identification of the patient or sample and the inadequate prior preparation of the patient. In analytical and post-analytical phases, the NPR values obtained in 2023 decrease significantly over analysis carried out in 2014 and its not needed to apply improvement strategies. Automation and the IT tools significantly reduce the number of errors with high NPR values, but it is undoubtedly the involvement of service professionals that makes the difference over the years. Conclusions The use of the FMEA methodological tool allows the design of appropriate indicators to detect and reduce errors in the clinical laboratory. Its application as part of a good quality control allows a continuous improvement in the laboratory process and consequently a guarantee of quality for the patient

Reliability Analysis of Coal Feeder Instrumentation Using Failure Mode and Effect Analysis (FMEA) at PT. PLN Nusantara Power UP Tenayan

PLN Nusantara Power UP Tenayan is a coal-fired power plant supplying electricity to the Riau region with a capacity of 2 x 110 MW. Operating 24/7, the plant faces frequent failures, particularly in the instrumentation components of the coal feeder. This research employs the Failure Mode and Effect Analysis (FMEA) method to identify and analyze these failures. The Risk Priority Number (RPN), calculated by multiplying severity, occurrence, and detection ratings, helps determine the failure level. Research stages include a preliminary study, problem identification, observations and interviews, data collection and processing, FMEA analysis, and conclusion drawing. The results indicate that the instrumentation components are generally reliable, with RPN values below the critical threshold of 200. The speed sensor, with the highest RPN value of 160, is still categorized as reliable. Failures in these components can lead to operational failures, suboptimal coal feeder performance, and monitoring difficulties. The study recommends preventive maintenance every six months to enhance reliability and minimize disruptions.

A Risk Management Application by Failure Mode and Effect Analysis (FMEA) Method in A Road Drainage Channel Construction Project at H.M Ardan Street of South Sangatta District East Kutai, Indonesia

A construction project of road drainage channel in H.M Ardan Street, South Sangatta District (Multy Years) is included into type of construction project with high level of risk and work accidents since it has large number of workers, the use of sophisticated work tools and machines that require special methods and skills also strong supervision in its usage. These matters could bring undesirable impact potentiality in particular for the occupational safety and health aspect. A negligence of OSH implementation in the construction project can lead to work accident risk within the project. A work safety management is a part of management aspect with function to prevent any work accidents. Its prevention can be done by controlling the occurrence of high-potential work accidents whether in terms of consequences, occurrence probability, also simplicity in its detection. Many safety management methods have been introduced as method that able to be used for potential work accident identification, or measure the level of work accident’s risk, also for evaluating the work accidents; which leads to the Failure Mode and Effect Analysis (FMEA) method as the most appropriate method to achieve the objectives as described above. The selected method for this research was Failure Mode and Effect Analysis (FMEA) method, whereas the data source of this research were primary data obtained from interviews and questionnaires, and secondary data obtained from site plan drawings and identification data of the construction of drainage channels on H.M Ardan Street of South Sanggata District (Multi Years). According to FMEA analysis, there were 15 dominant risks with RPN values above the average value with 4 risks having a value of 48 as the highest RPN value found in this study. Type of risks with the highest RPN value were (a) stricken by heavy equipment and materials during loading and unloading construction materials; (b) fatigue due to an overly busy mobilization schedule; (c) workers fall while installing the formwork; (d) workers’ hand was pierced by nail or getting hit by hammer or wood. Furthermore, by the domino analysis conducted for this research, it produced several precautions and prevention recommendations that grouped into three main stages: Lack of Control, Basic Causes, and Immediate Causes.

Aplikasi metode FMEA sebagai Upaya Pengendalian Kecacatan Produksi Kain Weaving, Studi Kasus: PT X

This study aims to identify and analyze the factors causing defects using Failure Mode and Effect Analysis (FMEA) and formulate improvement plans to improve business processes. The research method used is FMEA analysis to find and reduce the factors causing defects. The results of the analysis show that human factors contribute 25%, material factors 26%, and method factors 24% to defects. The conclusion is that FMEA is effective in identifying the main causes of defects and providing a basis for improving the production process. Keywords: FMEA, Quality, Weaving

Implementation of Failure Mode and Effect Analysis (FMEA) for centrifugal pump maintenance in water supply distribution system

A centrifugal pump is an essential mechanical device in the Regional Drinking Water Company (PDAM) to distribute sterile water from the reservoir to local community houses by flowing the fluid through the pipeline. However, improper and inadequate maintenance of centrifugal pumps can result in component damage, including bearings that are broken, ripped, or damaged mechanical seals. Therefore, for the water supply to continue smoothly, proper maintenance needs to be carried out so that the engine's performance will be maintained and last longer. This study aims to conduct a maintenance analysis on centrifugal pumps utilized by the PDAM of Madiun City. The Failure Modes and Effects Analysis (FMEA) method was employed to assess the potential failure of a system, design, process, or service by implementing a systematic approach to analyze damage and devise maintenance strategies. The FMEA analysis revealed that the shaft of the centrifugal pump component had the highest Risk Priority Number (RPN) value, which amounts to 294. The wearing ring had the lowest RPN value among the centrifugal pump components, with a value of 54. The Pareto diagram results classified six components: shaft, clutch, stuffing box, motor, impeller, and mechanical seal to recommend preventative maintenance action. Additionally, three other components suggested for corrective maintenance were the bearing, packing, and wearing rings

Quality Control of Ceramic Wall Products Six Sigma Method with Dmaic Tools and Failure Mode and Effect Analysis (FMEA)

Considering the importance of increasingly rapid industrial development, business people must be aware of the orientation towards product quality. The manufacturing industry has an important role in industrial development. It is hoped that it will be able to grow and have advantages in industrial development, therefore it is necessary to improve and increase product quality. The company continues to strive to maintain and improve product quality and increase productivity so that customers are satisfied with the products produced. One way that needs to be done is to reduce the number of defective products in the production process. This effort is one way of continuous improvement carried out by PT. ABC and companies operating in the same field. The ceramic wall production process has relatively high demand but still has a high level of defects. Based on information on production data and data on the number of defects for the period January – September 2023, wall ceramic production has a defect rate of 3.86%. Companies must reduce the number of defects to achieve company targets. This research aims to improve the production process of wall ceramics by minimizing the number of defective products. This research uses the six sigma method with the DMAIC (Define, Measure, Analyze, Improve, Control) tool assisted by the FMEA (Failure Mode and Analysis) method. Corrective action to reduce defects based on the 5 Why analysis is to find out what the problem is and ask "why" and "what is the root of the problem". The research results show that there are 4 types of defects which have a large number of defects, namely Rupture Defects, Application Defects, Peeling Defects and Hole Defects. From the research results, solutions were obtained for the emergence of several types of defects in the wall ceramic production process, namely by providing planning suggestions and corrective actions which are discussed this time. At the defect calculation stage, Broken produces a DPMO value of 29.362 with a sigma level of 3.39 with a sigma level of 3.48, Defect Peeled produces a DPMO value of 28.044 with a sigma level of 3.38, and holes are defective. resulting in a DPMO value of 26,020 with a sigma value of 3.45 from the data calculation results, it is still not enough to meet the company's target at the sigma level, therefore the company must make quality improvements to achieve the main target at the sigma level.

Clinical effects of re-evaluating a lung SBRT failure mode and effects analysis in a radiotherapy department.

The increasing complexity of radiation treatments can hinder its clinical success. This study aimed to better understand evolving risks by re-evaluating a Failure Mode and Effects Analysis (FMEA) in lung SBRT. An experienced multidisciplinary team conducted an FMEA and made a reassessment 3years later. A process map was developed with potential failure modes (FMs) identified. High-risk FMs and their possible causes and corrective actions were determined. The initial FMEA analysis was compared to gain a deeper perspective. We identified 232 FMs. The high-risk processes were plan approval, target contouring, and patient evaluation. The corrective measures were based on stricter standardization of plan approval, pre-planning peer review, and a supporting pretreatment checklist, which substantially reduced the risk priority number in the revised FMEA. In the FMEA reassessment, we observed that the increased complexity and number of patients receiving lung SBRT conditioned a more substantial presence of human factors and communication errors as causal conditions and a potential wrong dose as a final effect. Conducting a lung SBRT FMEA analysis has identified high-risk conditions that have been effectively mitigated in an FMEA reanalysis. Plan approval has shown to be a weak link in the process. The increasing complexity of treatments and patient numbers have shifted causal factors toward human failure and communication errors. The potential of a wrong dose as a final effect augments in this scenario. We propose that digital and artificial intelligence options are needed to mitigate potential errors in high-complexity and high-risk RT scenarios.

Optimization of treatment workflow for 0.35T MR-Linac system.

This study presents a novel and comprehensive framework for evaluating magnetic resonance guided radiotherapy (MRgRT) workflow by integrating the Failure Modes and Effects Analysis (FMEA) approach with Time-Driven Activity-Based Costing (TDABC). We assess the workflow for safety, quality, and economic implications, providing a holistic understanding of the MRgRT implementation. The aim is to offer valuable insights to healthcare practitioners and administrators, facilitating informed decision-making regarding the 0.35T MRIdian MR-Linac system's clinical workflow. For FMEA, a multidisciplinary team followed the TG-100 methodology to assess the MRgRT workflow's potential failure modes. Following the mitigation of primary failure modes and workflow optimization, a treatment process was established for TDABC analysis. The TDABC was applied to both MRgRT and computed tomography guided RT (CTgRT) for typical five-fraction stereotactic body RT (SBRT) treatments, assessing total workflow and costs associated between the two treatment workflows. A total of 279 failure modes were identified, with 31 categorized as high-risk, 55 as medium-risk, and the rest as low-risk. The top 20% risk priority numbers (RPN) were determined for each radiation oncology care team member. Total MRgRT and CTgRT costs were assessed. Implementing technological advancements, such as real-time multi leaf collimator (MLC) tracking with volumetric modulated arc therapy (VMAT), auto-segmentation, and increasing the Linac dose rate, led to significant cost savings for MRgRT. In this study, we integrated FMEA with TDABC to comprehensively evaluate the workflow and the associated costs of MRgRT compared to conventional CTgRT for five-fraction SBRT treatments. FMEA analysis identified critical failure modes, offering insights to enhance patient safety. TDABC analysis revealed that while MRgRT provides unique advantages, it may involve higher costs. Our findings underscore the importance of exploring cost-effective strategies and key technological advancements to ensure the widespread adoption and financial sustainability of MRgRT in clinical practice.

The risk-based operational safety strategy for Perkin-Elmer/Cetus DNA Thermal Cycler PCR systems

Introduction: this article was conducted in the field of polymerase chain reaction (PCR) test safety, where it is essential to understand the contribution and impact of risk analysis methods to improve test safety medical diagnosis at the institutional level. In this context, the current study focuses on failure modes and effects analysis (FMEA) applied to the Perkin-Elmer/Cetus deoxyribonucleic acid (DNA) thermocycler.Objective: to describe the application of FMEA to improve the operational safety of Perkin-Elmer/Cetus DNA thermal cyclers used in medical diagnostics in ataxic patients. Methods: to carry out this study, a methodology based on an exhaustive review of the scientific literature available in academic databases and relevant sources was used. Previous studies on FMEA analysis of DNA thermocyclers in common and expanded forms were collected and analyzed.Results: the results obtained demonstrated that FMEA is an effective tool to identify and evaluate possible failure modes of Perkin-Elmer/Cetus DNA thermal cyclers. Several defects were identified and their consequences, causes and classification based on their severity and frequency were determined. Likewise, corrective actions are proposed to reduce the identified risks.Conclusions: early identification of possible failure modes and implementation of corrective actions can reduce risk and guarantee the quality of the diagnoses carried out and therefore improve operational safety

Identification of Advantages and Limitations of Current Risk Assessment and Hazard Analysis Methods when Applied on Autonomous Agricultural Machineries

Highlights The three main types of risk assessment and hazard analysis techniques applied on autonomous agricultural machines are: (1) Informal Group Analysis; (2) Hazard Analysis and Risk Assessment (HARA); and (3) Failure Mode and Effects Analysis (FMEA). Replicability is the main advantage of FMEA and HARA, while cost effectiveness is the main advantage of Informal Group Analysis. Subjectivity and the requirement for prior knowledge (data) are the main weaknesses of FMEA, HARA, and Informal Group Analysis when applied to novel and revolutionary autonomous agricultural machines. Abstract. In the last ten years, the development of automated agricultural machinery has seen noteworthy advancements. Nevertheless, the successful commercialization of these technologies depends critically on their ability to operate safely. This study evaluated the advantages and limitations of current risk assessment and hazard analysis methods currently used to ensure the safety of autonomous agricultural machines. An online survey containing 18 questions was distributed to 711 participants identified as potential individuals who are currently working or have worked on autonomous agricultural machines to determine the type and frequency of risk assessment and hazard analysis methods applied on autonomous agricultural machines, examine the advantages and limitations of each method, and investigate the perceived effectiveness of each method. Frequency analysis was used to determine the most and least utilized risk assessment and hazard analysis methods. The advantages and limitations of each risk assessment and hazard analysis approach were compared. Descriptive statistics (counts, means, medians, percent) and frequency analysis of the variables were used. The three main types of risk assessment and hazard analysis techniques applied to autonomous agricultural machines. The methods are (a) Informal Group Analysis (e.g., Brainstorming), (b) Hazard Analysis and Risk Assessment (HARA), and (c) Failure Mode and Effects Analysis (FMEA). Replicability is perceived as the main advantage of FMEA and HARA, while cost-effectiveness is the main advantage of Informal Group Analysis. The need to have pre-existing data of the autonomous agricultural machine at hand to be able to perform risk assessment and subjectivity are the main limitations of FMEA, HARA, and Informal Group Analysis dealing with novel and revolutionary autonomous agricultural machines. Industry experts do not believe that the risk assessment and hazard analysis procedures now used are reliable and efficient enough to guarantee the safety of autonomous agricultural tractors. This study reveals important information about the current state of risk assessment and hazard analysis methods in the context of autonomous agricultural machinery. This knowledge can inform future research, policy development, and industry practices to ensure the safety of autonomous agricultural machines. Keywords: Agricultural machine, Autonomous, FMEA, HARA, Hazard analysis, Informal Group Analysis, Risk assessment, Safety, Survey.

Evaluasi Kinerja Mesin Bending Hidrolik Menggunakan Metode Overall Equipment Effectiveness (OEE), Failure Mode and Effect Analysis (FMEA), dan Fault Tree Analysis (FTA)

PT Hari Mukti Teknik is a company engaged in the Manufacturing Industry that produces laundry machines and other mechanical engineering. The problem that occurs in the production process at PT Hari Mukti Teknik is the performance of the bending machine which has a high downtime value of an average of 994 minutes per month. The purpose of this study is to evaluate the performance of the bending machine by knowing the level of effectiveness of the performance of the bending machine using the OEE method, knowing the causes of losses that affect the level of effectiveness of machine performance, using the six big losses method and analyzing losses using the Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) methods to propose improvement efforts. Based on the results of data processing, the average Overall Equipment Effectiveness (OEE) value of bending machines in one year is 84.21%, with the largest losses being equipment failure losses of 7.34%. Proposed improvements based on FMEA and FTA analysis are to carry out periodic maintenance, make a predictive maintenance list, carry out periodic re-checks, carry out periodic lubrication, check environmental cleanliness, and replace damaged components.

Failure Mode and Effects Analysis Prior to the Introduction of AI Generated GTVs for Brain Metastases in the Clinical Workflow

AI autosegmentation of organs-at-risk (OARs) is common practice at many radiotherapy clinics. Despite the abundance of gross tumor volume (GTV) autosegmentation algorithms, adoption in clinical care has been slow due to the high risk associated with errors in GTV delineation. Here we present a failure mode and effects analysis (FMEA) to evaluate the risk associated with introducing AI derived GTVs in patients treated with stereotactic radiosurgery (SRS). An AI GTV autosegmentation algorithm for brain metastases was developed in-house based on a V-Net 3D CNN. Registered CT and MR images and a contour of the brain are input into the software and all identified lesions are returned in a DICOM-RT structure set. Following algorithm evaluation, a workflow was developed to enable AI GTV autosegmentation to be introduced clinically for every SRS patient. The following steps were added to existing procedures: 1) workflow to send CT/MR and brain structure to external server, 2) autosegmentation run on the server, 3) AI GTV structures with a standard nomenclature added to existing OAR structure set, and 4) MD review, editing, and approval of AI GTVs. After successfully completing the physics evaluation testing of the new process, we formed a team of 10 faculty and staff including physicists, residents, physicians, and planners to perform the FMEA prior to clinical implementation. The team met to map the process, identify potential failure modes, and score their frequency of occurrence, severity, and detectability. A 3-point scale (1, 3, or 5) was used to simplify the scoring process. Occurrence was defined as rare, sometimes, or often; severity as low, medium, or high; and detectability as obvious, possible, or challenging. The risk probability numbers (RPNs) were calculated and the steps in the process with the highest RPNs were flagged for further discussion. The FMEA team completed their process map and analysis primarily in 4 meetings. The process map began with acquisition of the patients CT simulation scan and ended with physician approval of final volumes for treatment planning. We identified 17 process steps and 72 possible failure modes, of which 26 were associated with the new workflow. Eighteen failure modes had an RPN greater than 30 (highest risk score in at least one category) and were flagged to assess mitigation strategies. Five were unique to the new AI GTV workflow and mitigation strategies will be designed prior to clinical use. Those involved risks related to inaccurate AI GTV contours, false positives, and an incomplete review stemming from over-reliance by team members on AI. AI is increasingly being employed at every step of radiotherapy to automate and streamline processes. The FMEA analysis resulted in the identification of the riskiest parts of using AI GTV autosegmentation. This can be an effective tool in the development of checks to ensure that GTV autosegmentation methods can be safely introduced in support of patient care.

Challenges experienced during pharmacy automation and robotics implementation in JCI accredited hospital in the Arabian Gulf area: FMEA analysis-qualitative approach

BackgroundPharmacy automation and robotics implementation are essential aspects of healthcare facilities. It streamlines the medication dispensing process and significantly reduces medication errors. However, implementing automation and robotics in pharmacies comes with its challenges. We aim to detect and rectify potential dangers in the pharmacy workflow by utilizing the Failure Mode and Effects Analysis (FMEA) methodology; this is expected to augment performance and increase profitability. Materials and methodsIn this study, we conducted an FMEA analysis using a qualitative approach to identify the challenges experienced during pharmacy automation and robotics implementation in a Joint Commission International(JCI) accredited hospital in the Arabian Gulf area. The pharmacy processes and procedures were mapped in a Flow chart to visualize the pharmacy workflow, including highlighting the risks that were found. Then these risks were arranged as Potential failure modes and added to the table as 9 main points for each RPNs were calculated, and then the 9 points were prioritized for the action plans. ResultsVia applying traditional Risk Priority Number (RPN) FMEA, the Pharmacy board identified the process stages marked risky failure modes through several FMEAs, calculating the total RPNs at the implementation phase. It revealed several challenges, including staff training, technical issues, and inadequate communication. Furthermore, the study resulted in corrective and intervention steps. ConclusionPharmacy automation and robotics implementation is a complex process that requires proper planning, preparation, and execution. The FMEA approach effectively identifies potential problems and evaluates their impact on the pharmacy system. Nine major failure modes appeared to be risky stages with high RPN scores. Therefore, multiple interventions were done during the implementation to enhance the knowledge of challenges faced during the implementation of the automation process and solve it. Future studies should address the identified challenges and develop strategies to mitigate them.

A risk-based soft sensor for failure rate monitoring in water distribution network via adaptive neuro-fuzzy interference systems

Water Distribution Networks (WDNs) are considered one of the most important water infrastructures, and their study is of great importance. In the meantime, it seems necessary to investigate the factors involved in the failure of the urban water distribution network to optimally manage water resources and the environment. This study investigated the impact of influential factors on the failure rate of the water distribution network in Birjand, Iran. The outcomes can be considered a case study, with the possibility of extending to any similar city worldwide. The soft sensor based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) was implemented to predict the failure rate based on effective features. Finally, the WDN was assessed using the Failure Modes and Effects Analysis (FMEA) technique. The results showed that pipe diameter, pipe material, and water pressure are the most influential factors. Besides, polyethylene pipes have failure rates four times higher than asbestos-cement pipes. Moreover, the failure rate is directly proportional to water pressure but inversely related to the pipe diameter. Finally, the FMEA analysis based on the knowledge management technique demonstrated that pressure management in WDNs is the main policy for risk reduction of leakage and failure.

Risk prioritization using a modified FMEA analysis in industry 4.0

To assess the reliability of production systems, a Failure Mode and Effects Analysis (FMEA) is usually performed to determine how reliable a system is in its entirety. In order to ensure smooth production operations and timely delivery of orders, it is necessary to ensure continuity of operations without failure and interruption. Industry 4.0 (I4.0) can provide so many benefits, but it also introduces new challenges because of its technical components such as sensors and actuators. In this paper, we propose an FMEA model and structure quantitative risk assessment metrics for a yogurt-filling system in an I4.0 environment. We use the risk priority number (RPN), which is usually a product of three factors: severity (S), occurrence (O), and detection (D) ratings. In this study, we use also a fourth factor, namely, dependency (D2). We compare RPN scores for traditional components and I4.0 components. The application method of the FMEA model was presented in detail, and its practicality was verified and demonstrated. To get valid results, a team of experts was utilized. Then Pareto method was used together with the FEMA approach to prioritize the risks that need more attention. After that, risk prevention steps were introduced. Results showed that four components are the most important ones. Results also showed that some of the components of I4.0, such as proximity sensors and color detection sensors were found with the largest priority. A better performance was achieved following the enhancements suggested by the experts' team. A reduction in RPN of 55% has been achieved by implementing the upgrades, indicating an increase in system reliability.

ANALISIS KUALITAS PRODUK PENYAMAKAN KULIT DENGAN METODE SQC dan FMEA

PT Adi Satria Abadi is a company engaged in tanning. The problem faced by the company is the presence of defective leather during the goat and sheep tanning production process. From these problems, the purpose of this study was to identify the level of product defects, analyze the factors that cause defects, and make recommendations for improvements to improve the quality of goat and sheep tanning products. Data processing in this study uses the SQC (Statistical Quality Control) and FMEA (Failure Mode and Effect Analysis) methods. Based on the identification results, it was found that there were 1,507 holes for holes, 1,066 for thick defects, 1,061 for louse defects, and 1,086 for size defects. The results of the SQC analysis still have uncontrolled data based on the control chart which is on size defects with a total of 1,086 defects, while for hollow defects, too thick defects, and tick defects are under control. The results of the FMEA analysis show that the order of completion with the first priority is spotting bugs with an RPN value of 36. The suggested improvements to the company are by directly selecting raw materials and supervising employees.
 
 Keywords: Quality, Product Defects, Seven Tools, SQC, FMEA.

Risk management in humanitarian supply chain based on FMEA and grey relational analysis

The COVID-19 pandemic has disrupted the humanitarian supply chain management (HSCM) necessary for delivering emergency items during the disaster. The combined effects of climate change and the pandemic uncover the vulnerabilities of humanitarian supply chain operations and highlight the importance of risk management. This study aimed to identify priority risk factors and proposed mitigating risk strategies of a local government that is at the forefront of relief operations. It used Grey Relational Analysis (GRA) method to validate the Failure Mode and Effect Analysis (FMEA) approach in identifying priority issues relating to the supply chain risks. This paper reveals that the results of FMEA and GRA are almost similar.

Analysis of Quality Control Using the Failure Mode And Effect Analysis (FMEA) Method in the Welding Process in the Feed Drum Project

PT. Xyz is a steel fabrication company located in Gresik. One of their projects undertaken by PT. XYZ is a Feed drum Project, the welding process is the most important stage in this project. At the time of product testing or testing there were still many welding points that were defective so that the product did not pass the trial, the trial was carried out by hydrotest where this test was carried out with water as the test medium to determine the strength of a material and find out whether there was a leak or not and as well as a visual check. The types of defects found in the feed drum project are spatter, overlap, porosity, undercut, incomplete fusion, slag, bad String Bead (Weaving Fault), start stop, pin hole. The research uses the FMEA (Failure Mode Effects and Analysis) the method which aims to identify and analyze the occurrence of defects. In the results, the highest RPN value is found in the Incomplate Fusion type of defect with a value of 384 so that it becomes a priority in carrying out control and repair .