From a review of past research and literature, it was found that most of them applied fuzzy FMEA to various industrial sectors as well as automotive and industrial electronic and computer parts, industrial machines, electrical appliance manufacturing, the development and design of products, management processes within the organization, medical applications, etc. For example, Yeh and Hsieh  presented a new risk assessment system based on fuzzy theory in a sewage plant by ranking the action. This method can reduce or eliminate the respective effects of the failures. Chin et al.  presented a method of evaluation for the basic idea of new product development by using fuzzy FMEA to increase the reliability for the improvement of the evaluation, alternative designs, selection of materials and expense estimate. Liu and Tsai  offered prevention techniques and improvement of occupational hazards in the construction industry by using a fuzzy analytic network process (FANP) to specify the hazard type and hazard causes and using FMEA techniques in risk assessment of hazard causes on fuzzy inference. Dinmohammadi and Shafiee  proposed the risk assessment method and analyzed the errors by using fuzzy offshore wind turbine FMEA. Kumru and Kumru  studied fuzzy FMEA to improve the procurement processes of public hospitals to reduce the cost of products purchased, reduce the cost of the purchase process to resolve the service to the customer/patient and minimize inventory investment; this stabilized the process and thus increased the efficiency of the assurance process. Yeh and Chen  proposed the linguistic fuzzy variables to replace the severity, occurrence and detection for calculating and ranking the wafer processes yield change in semiconductor wafer manufacturing processes. Nuchpho et al.  studied the failure mode of defective products at this stage of the plating bath product category page (K-160) of sanitary ware by using fuzzy FMEA to prioritize the risk of failure and reduce the defects in the products. Maranate et al.  studied the application of fuzzy FMEA to the medical diagnostic severity of obstructive sleep apnea (OSA), which was caused by 19 failures for suspected OSA patients in the sleep laboratory center of Siriraj Hospital by prioritizing the risk of OSA and using specialist consensus in the evaluation of the unanimity on the violence level.
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A comprehensive risk management methodology including identification, assessment and response actions was presented and verified/demonstrated on a real-world case study of a road construction project in Iran. After identifying thirty risk events, they were ranked based on the RCN values using fuzzy FMEA and fuzzy AHP. Fuzzy FMEA technique was used to quantify parameters of risk events (i.e. P, C and CN) by a group of experts’ judgements. Fuzzy AHP technique was used to quantify and combine the three aspects of consequence (cost, time and quality). Appropriate risk response strategy for a risk event was then selected based on RF and CN values. When demonstrating strategy selection for the risk event of “increase in tar price”, two risk responses were finally selected and compared with the risk event of the BAU using the SED index. The results showed that the risk response of “change of paving technology into RCC pavement” can considerably mitigate the relevant risk event and provide the minimum deviations from the project targets.
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Local water utility companies are always required to produce the services and products with high quality in order to meet the customer desires. This desire encourages each company to a very complex problem in maintaining the quality and reliability of the product. Thus, the company faces challenges in designing the quality and reliability of the resulting product. In order to avoid all forms of failure in the process of clean water production and development, the method of Failure Mode Effect Analysis (FMEA) is used as a prevention effort. The method can also predict the form of failure and find the most economical way to stop the failure. FMEA techniques are implemented to identify potential forms of failure, determine their impact on production, and identify actions to reduce failures. Thus, this paper will analyze the risk factors of failure in clean water treatment by applying the Fuzzy FMEA method.
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Surgical cancelation rate is an important indicator of op- erating room inefficiency . Reduction of surgical cancel- ation rate is one of the major priorities of hospital manage- ment . Our results indicated that fuzzy FMEA can serve as an assisting tool to anticipate and the risk factors of sur- gical cancellation. While this study was limited to prioritiz- ing surgical cancellation factors, identifying the potential of fuzzy FEMA in exploring the risk factors in other healthcare services is an interesting ground for future studies.
Keskin and Özkan (2008) from Kocaeli University in Turkey conducted a study for a methodology that incorporates fuzzy ART neural network applied to FMEA that allowed the following targets to be reached: evaluation of failure modes with a more mathematical-based method; find solutions to the points at which the classical FMEA methods fail; separation of prioritization of failure modes from sensitivity of participants experience level; and finally the method can be applied simply and easily. Wang et al. (2009) pointed out the strength of fuzzy FMEA and compared fuzzy FMEA with traditional FMEA. Capture FMEA team members’ diversity opinions under different types of uncertainties, allowed risk factors and their relative importance weights to be evaluated in a linguistic manner (Hu-Chen et al., 2010). Failure Mode and Effect Analysis is one of the well–known techniques of quality management for continuous improvements of product and process designs, as it relies on determination of risk priority numbers, which indicates the level of risk associated with a potential problem and is a primary factor for its success (Kumru & Kumru, 2012).
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FMEA was formally introduced in 1940s for military usage by the US Armed Forces. It develops a list of failure modes ranked according to their effect on the user. This ranking provides a measure for deciding which components or subsystems need further testing and/or redesign. Major factors include component or sub-system failure rate, type of failure (fail, degrade, etc.), severity of failure, and likelihood of detection .
In this study, the fuzzy FMEA is used to analyze and assess the risks of warehouse operations. Ten failure modes are determined for risk evaluation in warehouse operations of a food retail distributor. We classified the warehouse risks into two main categories, the first five cause permanent shrinkage of products, the second five are related to the processes. According to best of our knowledge, it is noticed that there has not been any published research article which considers risk factors under fuzzy environment for evaluation of warehouse operations. In this context, our research work has the originality of applying the fuzzy FMEA method through integration of fuzzy AHP-TOPSIS approaches that evaluates most serious failure modes considering risk factors for warehouse operations to address this research gap. The rest of the paper is organized as follows: The brief summary of problem statement by categorizing risks in warehouse operations is given in Section 2. An overview of fuzzy FMEA and combined fuzzy AHP-TOPSIS methodologies is explained in Section 3. Section 4 formulates the proposed risk-oriented assessment model in warehouse operations of food distributor company and Section 5 presents the application of the proposed methodology with numerical results and sensitivity analysis. Finally, conclusions and future work are provided in section 6.
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In the engineering education, the project-based learning (PBL) is widely used to enable students to gain knowledge, understanding, and skills from experience in the real world. Learning how to assess risk helps students understand the risks of particular courses of action. It also helps them to gain their knowledge about the project itself. Project managers often overlook students’ lack of experience, especially, in risk assessment. They often involve students as if they were profes- sional people although the students do not understand the concept of risk and are therefore unable to assess risks. To answer this question, the fuzzy failure mode and effects analysis method (Fuzzy FMEA) are currently used in project risk management as a way of avoiding the limitations of traditional FMEA. In particular, even in projects involving professionals, there are often problems with the accuracy of the RPN and even experts often assess the RPN subjectively. In the student projects, where team members lack experience, the problem is even greater.
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FMEA (Failure Mode and Effect Analysis) refers to a proactive quality tool that enables the identification and prevention of the potential failure modes of a product or process. However, in executing traditional FMEA, the difficulties such as vague information, relative importance ratings, decisions on same ratings, and opinion difference among experts arise which reduce the validity of the results. This paper presents a fuzzy logic based FMEA depending on fuzzy IF-THEN rules over traditional FMEA to make it precise and give proper maintenance decision. Here, the Risk Priority Number (RPN) is calculated and compared to the Fuzzy Risk Priority Number (FRPN) to give maintenance decision. Furthermore, the FMEA of Reach Stacker Crane (RST) is presented to demonstrate the proposed Fuzzy FMEA.
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4.1 Data Collection:-Before design and implementation of FMEA to core making process it is required to have careful knowledge of the process, therefore the same is studied by using process flow chart. The first phase of the work was to collect the core rejection data, information about cores, production lines and core making machines through visits to the production plant. Percent average Core rejection of three months is gathered from QC reports and the most common problems due to which cores are rejected are noted before the start of the study.
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systematic, proactive method for evaluating the process to identify were and it might be fail to assist the relevant impact of different failures in order to identify the part of processes that are most needed of the change. The FMEA process associated with step wise process starting from potential failure causes ,study existing and complete the working of mechanism , calculate the risk priority number (RPN)of existing and modified. The presented paper deals with the review of industrial case study and implementation of FMEA on them. This work discusses about implementation of Process Failure mode and effect analysis for improvement in welding process through better ment in various sub-processes . We considered various parameters and examined them. The parameters are discussed along with their rankings. Severity, Occurrence and Detection are detected to calculate the Risk Priority Number (RPN). The Risk Priority Number (RPN) can be obtained by multiplying Occurrence with Severity and Detection. RPN gives the idea about the most affecting parameters in the existing welding process. We detected how failure can occur and suggested the preventive action.
Failure Mode and Effect Analysis (FMEA) was first developed as a formal design methodology in the 1960s by the aerospace industry with their noticeable reliability and safety requirements. The FMEA is used to analyse concepts in the early stages before hardware is defined (most often at system and subsystem). It focuses on major failure modes linked with the proposed functions of a concept proposal. The cause and effect diagram is used to discover all the possible or real causes (or inputs) that result in a single effect (or output) (1). Causes are set according to their level of significance, resulting in a portrayal of relationships and hierarchy of events. This can help us to search for root causes, identify areas where there may be problems, and compare the relative importance of different causes.
Reference , defines the FMEA as a preventive approach for failures locating and keeping the reliability. Furthermore, describes the FMEA as a crucial tool to improve the design of manufacturing and process. Moreover, it can be used to improve reliability, reduce life cycle risk of organisations, and develop a preventive maintenance plan for in-service machinery. In contrast,  defines it as a method uses to address the potential failure modes, their causes, and the effects of each failure on the system (product or process). Reference , utilises the FMEA as a tool for non- technical risk, for example, the lack of interaction between the five project management processes will affect the overall progress hence, FMEA can help in solving this risk.
included in discussions of those steps in the process in which they are involved. For example, a hospital may utilize couriers to transport medications from the pharmacy to nursing units. It would be important to include the couriers in the FMEA analysis of the steps that occur during the transport itself, which may not be known to personnel in the pharmacy or on the nursing unit.
transforms the given problem in to another FLFPP with fewer fuzzy constraints. A relationship between these two problems, which ensure that the solution of the original problem can be recovered from the solution of the transformed problem. A simple numerical example explains the procedure of the proposed method.
The FMEA procedure assigns a numerical value to each risk associated with causing a failure, using Severity, Occurrence and Detection as metrics. As the risk increases, the values of the ranking rise. These are then combined into a risk priority number (RPN), which can be used to analyze the system. By targeting high value RPNs the most risky elements of the design can be addressed. RPN is calculated by multiplying the Severity by the Occurrence by the Detection of the risk. Severity refers to the magnitude of the End Effect of a system failure. The more severe the consequence, the higher the value of severity will be assigned to the effect. Occurrence refers to the frequency that a Root Cause is likely to occur, described in a qualitative way. That is not in the form of a period of time but rather in terms such as remote or occasional.
To address this problem, a consortium of aerospace companies 1 shared their experiences and produced a best practice guide. In parallel, the same companies were involved in specifying a Bayesian model to support estimation of reliability during design. This Bayesian model required inputs about the prior number of engineering concerns with a new design and the times at which these concerns are likely to be realised in use. The latter data is usually instantiated using relevant in-service or test data, while the former is captured through the elicitation of structured engineering judgement in order to gather insight into the state of engineering knowledge (Hodge et al, 2001). During the evaluation of Bayesian modelling with the industrial consortium, the synergies between elements of statistical modelling and FMEA have been developed into a process for eliciting beliefs about engineering concerns that has been referred to as a focussed FMEA in its implementation.
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An FMEA is usually carried out by a team consisting of design and maintenance personnel whose experience includes all the factors to be considered in the analysis. The causes of failure are said to be Root Causes, and may be defined as mechanisms that lead to the occurrence of a failure. While the term failure has been defined, it does not describe the mechanism by which the component has failed. Failure Modes are the different ways in which a component may fail. It is vitally important to realize that a Failure Mode
industry. The productivity of an industry can be improved by giving importance to the maintenance. Failure Mode Effect Analysis (FMEA) is a powerful preventive method for the risk management, which aims in eliminating potential failures associated with each component of machines in an industry. FMEA is a component level analysis. From the study the failure modes with high risk are found out by calculating the risk priority number (RPN).
FMEA (Failure Mode and Effects Analysis) and FMECA (Failure Mode and Effects Criticality Analysis) methods have theirs beginning in 50s years of the last century, when they were elaborated for the purpose of reliability analysis of weaponry and are used till now in e.g. aircraft industry, space and electronic industry. The essence of FMEA/FMECA is analysis of impact of every potential defect on functionality of the whole system and order of potential defects according to the level of its severity. FMECA method additionally introduces analysis of the degree of defect severity and examines whether it has critical character for functionality of the whole evaluated system. Those methods are quite laborious, require knowledge and experience of persons who apply them, they are supported with specialist tools, using elements of knowledge engineering and fuzzy logic [1, p. 83].