Failure Mode And Effect Analysis Method Research Articles

Analisis Pengendalian Kualitas Batako di CV. Berkah Pandaan dengan Menggunakan Metode FMEA (Failure Mode and Effect Analysist)

Kholifan fani, Engineering Study Program, Yudharta Pasuruan University Pasuruan, 02 July 2024. Batake Quality Control Analysis, At CV. BERKAH PANDAAN Using the FMEA (Failure Mode and Effect Analysis ) Method. The aim of this research data is to determinan the process of controlling the quality of bricks at CV. Berkah Pandaan using the FMEA (Failure Mode and Effect Analysis) Method. The approaches used are SWOT and FMEA. Failure Mode and Effect Analysis is defined as a method used to identify the causes and consequences of a failure. SWOT is the systematic identification of various factors to formulate company strategy. This analysis is based on logic that can maximize strengths and opportunities, but simultaneously minimize weaknesses and threats. Based on result of this research, the causes of defects that occur at CV. Be identified. BERKAH PANDAAN is caused by two thigs, namely in terms of humans and machiens, form the human perspective it is mosly caused by a lack of work concentration so that the quality of work decreases, this is triggered by working environmental conditions that are less supportive, including room temperatures that are too hot, insufficient lighting. Engine noise that is too loud in terms of the engine usually occurs because the engine is experiencing downtime.

Evaluation of physical risk factors by fuzzy failure mode and effects analysis: an apparel mill example

This study investigates the evaluation of risks faced by employees in a selected large-scale apparel mill using a risk assessment method with a fuzzy logic approach. The study found that risk assessment in the apparel industry is more accurate and reliable when using a fuzzy logic approach. Measurements were taken in three different time periods for noise, vibration, thermal comfort and lighting, which are physical risk factors. To evaluate the identified failure modes, the Failure Mode and Effects Analysis (FMEA) method was first applied. Then, for the same failure modes, the fuzzy FMEA method was applied using Fuzzy Logic Designer, an add-on to MATLAB. The study concludes that the fuzzy FMEA method can be a more useful method for risk assessment in the apparel industry.

An FMEA decision support model for hotel risk assessment based on the risk tolerance of multi-type travelers and online reviews

As a reliability management tool, failure mode and effect analysis (FMEA) is widely used to solve the problem of risk assessment in all trades and professions. Considering the opinions interaction and the diversity of risk tolerance of experts, this paper constructs a decision-support model of FMEA based on dynamic interaction of opinions and risk tolerance. Firstly, word frequency statistics and Word2Vec analysis are applied on 14,194 online reviews collected from TripAdvisor.com to identify major risk factors. Secondly, the consensus opinion of risk is reached through the dynamic interaction of opinions. Furthermore, the weight problem of risk factors is solved by a linear programming model with the goal of minimum individual regret, and the priority of risk is determined by an improved TODIM method considering risk tolerance. Through simulation experiments, sensitivity analysis and comparative analysis, the effectiveness and superiority of the proposed method are verified, and the theoretical research of FMEA method is enriched. This study provides a direction for the future improvement of hotel services based on understanding the preferences and interactions of multi-type travelers.

Application Of FMEA Method In Halal Risk Determination On Halal Poultry Slaughtering Operations

The importance of halal slaughtered chicken has become crucial in the era of globalization and complex supply chains. The rising awareness among Muslim communities regarding the integrity of slaughtered chicken products has led to a focus on adhering to halal compliance standards and Islamic law. This study aims to qualitatively analyze the halal risk points in chicken slaughtering operations, by using the Failure Mode and Effect Analysis (FMEA) method, in order to identify potential halal risks and ensure the integrity of halal chicken products. This qualitative study uses a analysis approach by examining secondary sources including journals, articles, guidelines and standards related to halal slaughtering process. In this study, seven Halal Risk Points (HRP) have been identified in the entire halal slaughtering process of broilers. Based on the results of the risk assessment through the FMEA method, each determined HRP will be assessed based on the severity, occurrence and detection rating scale. The final result of this study found that only six HRPs have a Risk Priority Number (RPN) value that exceeds 100. Therefore, corrective action recommendations are proposed to prevent any halal non-compliance from occurring on the process involved. Overall, the FMEA framework in this study is a new approach in guaranteeing the integrity of chicken slaughter products and is able to have a positive impact on the halal chicken slaughter industry players and also the implementers of the halal management system.

Reducing overnight charges in the loading process of finished goods using FMEA method at PT. XYZ

As a part of the supply chain, the logistics department plays a crucial role in the operational activities of a company. Like other departments, the logistics department also incurs costs that do not add value, such as overnight charges. Failure in the loading of finished goods process can result in a dwell time exceeding 18 hours per vehicle, leading to overnight charges. This occurrence can be prevented by identifying risks and failure modes using Failure Mode and Effect Analysis (FMEA). The FMEA methodology is widely utilized across various industries, including manufacturing, for product development. Additionally, FMEA is frequently employed in failure analysis in service industries, food services, construction, mining, agriculture, and healthcare. The research was conducted to identify failure modes and potential causes in the loading of finished goods process. In PT. XYZ, there are five finished goods storage areas. From the process of weigh-in time, loading finish goods, and weigh-out time, 25 activities were analyzed, and seven activities with the highest Risk Priority Number (RPN) were selected for solutions. The activity with the highest RPN, scoring 512, was the queuing of trucks exceeding loading capacity. The why-why analysis technique was employed to assist in finding solutions. From the seven activities with the highest RPN, 9 solution plans were derived. The recommendation to reduce queue trucks is to organize and monitor the vehicles dispatched by the Freight Forwarder. As a result, the dwell time per vehicle decreased from 9.23 hours to 6.36 hours, and the overnight charges reduced from 148,145 USD to 12,345 USD

Penilaian Risiko menggunakan Metode Failure Mode and Effects Analysis (FMEA) terkait Kontaminasi Silang pada Area Pengemasan di Industri Farmasi “XYZ”

Cross-contamination occurs due to other previously produced products, other rooms or areas producing different products, and particles from machinery or equipment. Quality risk management can be a preventive measure in reducing the potential for cross-contamination that occurs in the pharmaceutical industry. One part of quality risk management is risk assessment. One of the most common and effective methods is the Failure Mode and Effect Analysis (FMEA) method. This risk assessment is carried out using the stages of risk identification, risk analysis, and risk evaluation using categorization based on the Risk Priority Number (RPN). Based on the risk assessment using the FMEA method regarding cross-contamination in the Packaging area in the "XYZ" pharmaceutical industry, it can be concluded that the results of observations and interviews with operators and Packaging Supervisors identified 19 risks which were categorized as low, medium, high, and very high. Based on the results of the risk assessment obtained, the risk of cross-contamination that needs to be controlled is those with an RPN value of 30 ≤ RPN ≤ 60 (medium); 75 ≤ RPN ≤ 100 (high); RPN ≥ 150 (very high). Therefore, the "XYZ" pharmaceutical industry needs to determine and implement risk control measures. By adequately controlling the potential risk of cross-contamination, it will ensure the safety and quality of the products produced.

Development of modified fuzzy FMEA method in environmental risk assessment of earth dams

The investigations have shown that the construction of the dam and its related facilities have significant physical–chemical and ecological effects on the ecosystem. Failure Mode and Effects Analysis (FMEA) is a technique for ranking risks in projects to construct dams, but it has many deficiencies and ambiguities. Therefore, to prevent the shortcomings of the classical method, the modified fuzzy inference system (MFIS-FMEA) method has been used by creating a two-stage model to more accurately assess the risk of Eyvashan Dam. First, all the considered indicators are weighted using the Shannon entropy method, and the environmental risk is prioritized using the Fuzzy OWA method. In this study, two-stage fuzzy reasoning and a Max–Min combination rule are used. When severity (SEV) and occurrence (OCC) variables are combined, the critical risk index (RCI) values are predicted in the first stage. RCI and detection index (DET) input are then used to predict the MFIS-RPN in the second stage. The results of the risk priority number (RPN) in the MFIS-RPN method are much more accurate and serious than the FIS-RPN method due to the two-stage nature and the use of new language terms. The results of the proposed MFIS-RPN technique show that the highest RPN was obtained with immediate action in the dam construction phase for soil erosion and soil pollution and in the dam operation phase for aquatic and water pollution. Therefore, due to the increase in risk score, it is necessary to take immediate and more accurate monitoring during the construction and operation phases.

Environment Risk Assessment of Healthcare Waste Using Ensemble Learning Technique-Based EFMEA

Healthcare waste (HCW) affects the sustainable development of the environment greatly. The choice of medical waste treatment methods has become a significant concern for public health and safety due to the rapid surge in the volume and diversity of medical waste. As an important aspect of environmental risk assessment (ERA), HCW risk assessment plays a crucial role in environmental protection to develop sustainability strategies. Failure Mode and Effects Analysis (FMEA) has been extensively utilized in HCW risk assessment in the past few decades. In this paper, we aimed to address the limitations of traditional FMEA while incorporating the benefits of diverse FMEA methods and employ a novel ensemble learning technique-based FMEA method to perform risk assessment of HCW. A real-world case of HCW risk assessment is investigated for the verification of the performance and effectiveness of the ensemble learning technique-based Environment FMEA (EFMEA). Result of the case study shows that this ensemble learning technique-based EFMEA can provide a more reliable assessment result for HCW risk management.

Food Safety Risk Analysis of Songkem Duck Using Failure Mode and Effect Analysis (FMEA) Method

Food safety is an essential aspect of human health. Food processed without paying attention to food safety regulations can harm consumer health. This research aims to 1) identify activities with food safety risks, 2) determine the sequence of food safety risks that will be followed up, and 3) determine appropriate food safety risk mitigation. This research uses the Failure Mode and Effect Analysis (FMEA) method and fishbone diagrams. A case study was conducted on the Songkem pak Soleh Bangkalan duck supply chain. The research results show that activities that pose a food safety risk include receiving raw materials, storing raw material stocks, using raw materials and BTP, sanitizing production facilities, and storing finished products. The priority order of food safety risks in the Songkem duck supply chain is based on the FMEA method: storage of finished products, sanitation of production facilities, and storage of raw material stocks. Risk mitigation can be done to prevent food safety risks in storing raw material stocks and storing finished products, namely by controlling water activity (aw) and acidity factor (pH); and controlling the room's atmosphere. Meanwhile, to prevent risks due to sanitation hygiene, namely holding training to increase awareness of the importance of sanitation hygiene for restaurant employees monitoring and maintaining sanitation hygiene in the production environment.

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.

Risk analysis in bao bun production processes using FMEA and FTA methods: a case study

Risks are present in every activity and could cause damage if not properly addressed. This research discusses how to identify risks that occur in the production process of the food industry. The characteristics of the goods, such as flavor, price, appearance, and availability, are met through the production process by transforming raw materials into products. The XX Bakery business experiences several risks at the production stage that reduce the quality of the product which potentially causes financial losses. Failure Mode and Effect Analysis (FMEA) is used to identify and assess the risks and their level of importance. There were 20 risks that occurred in the production process obtained through observation and interviews. The risks were then assessed based on severity, occurrence, and detection through a questionnaire to determine the Risk Priority Number (RPN) value. The Pareto chart shows which risks would result in a significant impact if given treatment. There were top 5 risks in which RPN values resulted in 80% of the total score. The risks are broken mixer with a score of 360, fluctuation of raw material prices with a score of 320, power outages with a score of 240, workers experiencing fatigue with a score of 144, and water supply stopped with a score of 60. The risks were then further analyzed using Fault Tree Analysis (FTA) to find their root causes. The results of the risk management processes serve as a base for consideration in forming proposals for corrective actions that improve risks—the implementation of the proposed improvements is expected to reduce losses that potentially be experienced by the business.

Analysis of the Risk of Work Accidents in High-Story Building Construction Projects using the FMEA (Failure Modes and Effects Analysis) Method

Analysis of the Risk of Work Accidents in High-Story Building Construction Projects using the FMEA (Failure Modes and Effects Analysis) Method

A Novel Method for Failure Mode and Effect Analysis Based on the Fermatean Fuzzy Set and Bonferroni Mean Operator

Failure mode and effects analysis (FMEA) helps to identify the weak points in the processing, manufacturing, and assembly of products and plays an important role in improving product reliability. To address the shortcomings of the existing FMEA methods in terms of the uncertainty treatment of information and not considering the weights and correlations between risk factors, we propose a new FMEA method. In this paper, the Fermatean fuzzy Z-number (FFZN) is proposed by fusing the Fermatean fuzzy number and Z-number. Extending it to the Bonferroni mean (BM) operator, the Fermatean fuzzy Z-number-weighted Bonferroni mean (FFZWBM) operator is proposed. A new FMEA method is proposed based on this operator. In order to overcome the factors not considered in the FMEA method, two new risk factors are proposed and added. The ability of experts to express fuzzy information is enhanced by introducing the FFS. The weights and correlations between the influencing factors can be handled by aggregating the evaluation information using the FFZWBM operator. Finally, the proposed method is applied to an arithmetic example and the accuracy of the proposed method is proved by teaming it with other methods.

Risk management in the beverage production industry using FMEA and fuzzy cognitive map in an uncertain environment

Proper and systematic management of food industry failures can improve the quality of products and save a lot on the costs of organizations and people’s health. One of the conventional methods for risk assessment is the Failure Modes and Effects Analysis (FMEA) which is often performed in a phase or stage. Compared to the combined methods, this method is less accurate due to similar priorities of failure in the evaluation and the lack of consideration of the interaction between risks. The current research has applied an integrated approach based on two techniques, FMEA and Fuzzy Cognitive Map (FCM), in a multi-stage manner to increase assessment accuracy and ranking of failures. By considering the risks of an industry in an uncertain environment and the causal relationships between failures, this approach can evaluate the industry’s risks better than conventional methods. In the research method, the initial prioritization of failures by the FMEA method is used as the input of the multi-stage FCM. The cause-and-effect relationship between the failures is determined by experts and the functional records of the processes, and the FCM is prepared. Since no research evaluates the risks of the malting industry step by step and considers the causal relationships between the risks, the present study has improved risk evaluation in the malting industry by using a multi-stage FCM. The ranking results with the proposed hybrid approach and its comparison with the conventional methods showed that the rating became more accurate, and the multiple priorities were improved. Managers of the malt beverage industry can make effective investment decisions to reduce or better control the risks of this industry by using the results of applying the proposed approach.

Reliability analysis of intelligent manufacturing systems based on improved FMEA combined with machine learning

Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears to be becoming more significant. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Machine learning can handle large amounts of data and has merits in reliability analysis and prediction, which can help in failure mode classification and risk management under limited resources. Therefore, this paper devises a method for complex systems based on an improved FMEA model combined with machine learning and applies it to the reliability management of intelligent manufacturing systems. First, the structured network of failure modes is constructed based on the knowledge graph for intelligent manufacturing systems. Then, the grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes. Hereafter, the k-means algorithm in unsupervised machine learning is employed to cluster failure modes into priority classes. Finally, a case study and further comparative analysis are implemented. The results demonstrate that failure modes in system security, production quality, and information integration are high-risk and require more resources for prevention. In addition, recommendations for risk prevention and monitoring of intelligent manufacturing systems were given based on the clustering results. In comparison to the conventional FMEA method, the proposed method can more precisely capture the coupling relationship between the failure modes compared with. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems.

Proposed Improvements to Reduce the Risk Work Accidents on CV HB with FMEA and HAZOP Method

A company called CV HB makes socks based on what its customers want. Bandung is home to CV HB. Several machines, including Santoni, Dakong, Obrass, Stock Steam, and others, are employed in CV HB's sock production process. Numerous personnel who are in charge of running the machinery and performing other production tasks are also a part of the production process. The execution of health and safety protocols has yet to be well-executed in the production sector. The organization saw 24 workplace accidents between 2019 and 2023, including injuries from needles and blades, hands stuck in machinery, brain injuries from machines, slipping down stairs, and many more incidents. In order to address health and safety concerns at CV HB, the research attempts to pinpoint the root causes of workplace mishaps and offer suggestions for enhancement. Hazard and Operability Review (HAZOP) and Failure Mode and Effects Analysis (FMEA) were the methodologies used in the investigation. A few suggestions for improvement for the chosen departments are using material handling tools during the material retrieval process, posting warnings about machines with sharp parts, requiring protective gear like masks and gloves, designing foam padding for sharp machine parts, and installing fans.

An integrated QFD and FMEA method under the co-opetitional relationship for product upgrading

Improving customer satisfaction is as important as reducing risk associated with product upgrades. The quality function deployment (QFD), as a customer-centric tool, is widely used for product improvement. The failure mode and effect analysis (FMEA) method is recognized as a reliability management tool. Considering the need to eliminate risks and meet customer expectations, building an integrated QFD-FMEA framework is useful for product upgrading. To enhance the performance, the QFD and FMEA methods are improved. First, the base criterion method (BCM) is used to derive the CR weights in the QFD method and RFs in the FMEA method. Second, the combined compromise solution (CoCoSo) method and interactive multi-criteria decision making (TODIM- Portuguese acronym) methods are utilized to derive the priority of TCs and FMs, respectively. Third, the interval-valued intuitionistic fuzzy set (IVIFS) is used to express the expert evaluation. Moreover, given that co-opetitional relationship exist in firms and lead to opinion interactions, the improved Hegselmann-Krause (HK) model is utilized in our improved method to stimulate opinion interaction under different competitive states in different phases. Finally, a case study is presented to validate the improved method, and its superiority is demonstrated through sensitivity and comparative analysis.

A Decision Support Model for Mitigating Supply Chain Risks Based on a Modified FMEA, Multi-Objective Optimization and Multi-Criteria Decision-Making Approach

Abstract Recent years have seen a huge development in the subject of supply chain risk management. In this increasingly uncertain world, the use of practical and effective tools for decision making and risk mitigation has become more necessary than ever. In this research, mitigation strategies for a tier one multinational company operating in the automotive industry and providing an assembly operation to final customer Renault Tanger and Renault SOMACA were prioritized according to their effectiveness, as well as their implementation costs. Based on research in the literature and the opinions of experts in the field. 44 risks and 55 mitigation strategies were identified. FMEA (Failure Modes and Effects Analysis) method was used based on the latest AIAG 2019 edition to filter and identify the risks to be prioritized, we used then a multi-objective optimization approach to identify the mitigation strategies that constitute the Pareto front for each of the risks and finally used the EDAS method for the final ranking of the strategies. Our case revealed that strategies like ensuring elaborating a contingency planning and defining the responsibilities, imposing contractual obligations on subcontractors, applying a flexible supply contract were found to be relevant risk mitigation strategies for the company. Managers interested in mitigating risk can deploy this model to prioritize risk mitigation strategies.

Analisis Pengendalian Kualitas Produk Tiang Pancang di PT. X dengan Menggunakan Metode Failure Mode and Effect Analysis (FMEA)

This research was conducted at a company engaged in the precast concrete industry. The company produces and distributes piling products both domestically and abroad. The quality of pile products made by the company is one of the considerations of consumers. For this reason, the company must provide the best quality by utilizing all production factors as efficiently as possible. This is done to maintain consumer confidence and reduce the number of products that must meet company standards. This research aims to find the leading cause of defects in pile products. The method used is failure mode and effect analysis (FMEA). The FMEA method will look at the factors that cause defects based on the highest risk priority number (RPN). From the research results obtained, four factors cause defects in pile products, namely human factors, machine factors, material factors and method factors. Based on calculating the priority risk value in the FMEA method, the RPN value results are Machine Factors of 192, Material Factors of 180, Human Factors of 120, and Method Factors of 75. The factor causing the highest pile product defects is the machine factor, with an RPN value of 192. Based on this, it is known that the spinning machine used is too old and many machine components need to be replaced, so the proposed improvements given are to carry out regular and scheduled machine checks and maintenance and immediately repair or replace damaged machine components.

An integrative approach to medical laboratory equipment risk management.

Medical Laboratory Equipment (MLE) is one of the most influential means for diagnosing a patient in healthcare facilities. The accuracy and dependability of clinical laboratory testing is essential for making disease diagnosis. A risk-reduction plan for managing MLE is presented in the study. The methodology was initially based on the Failure Mode and Effects Analysis (FMEA) method. Because of the drawbacks of standard FMEA implementation, a Technique for Ordering Preference by Similarity to the Ideal Solution (TOPSIS) was adopted in addition to the Simple Additive Weighting (SAW) method. Each piece of MLE under investigation was given a risk priority number (RPN), which in turn assigned its risk level. The equipment performance can be improved, and maintenance work can be prioritized using the generated RPN values. Moreover, five machine learning classifiers were employed to classify TOPSIS results for appropriate decision-making. The current study was conducted on 15 various hospitals in Egypt, utilizing a 150 MLE set of data from an actual laboratory, considering three different types of MLE. By applying the TOPSIS and SAW methods, new RPN values were obtained to rank the MLE risk. Because of its stability in ranking the MLE risk value compared to the conventional FMEA and SAW methods, the TOPSIS approach has been accepted. Thus, a prioritized list of MLEs was identified to make decisions related to appropriate incoming maintenance and scrapping strategies according to the guidance of machine learning classifiers.