A Framework for Creating a Function-Based Design Tool for Failure Mode Identification

Failure modes and effects analysis (FMEA) is a popular reliability tool in petroleum engineering. In FMEA, potential failure modes or corrective actions are evaluated, each assigned a Risk Priority Number (RPN) score, and prioritized for decision making. FMEA is also known as Failure Modes, Effects, and Criticality Analysis (FMECA), while focuses on failure modes prioritization. Despite of the popularity of FMEA and FMECA, it is not clear, how potential failure modes and corrective actions could be represented systematically, for effective decision making. In this paper, two new representations (i.e., a tree representation and a vector representation), for potential failure modes and corrective actions, are proposed. The tree representation for a potential failure mode allows its root cause(s), effect(s) and corrective action(s), together with their severity, occurrence and detection rating(s), to be represented as a three-layer tree model. The tree representation for a corrective action with similar contents is outlined too. The RPN model, together with its score, is represented as a node of the tree model. These tree models can also be represented as their associated equivalence layered-vector representations. In this paper, the usefulness of the proposed approaches is illustrated with benchmark FMEA worksheets pertaining to petroleum engineering.

Objective To investigate IMRT planning process using the combined application of failure modes and effects analysis (FMEA) and fault tree analysis (FTA) by reference to the report of Task Group 100 of the AAPM, and stablish and optimize the quality. Methods A multidisciplinary team detailed the process mapping of IMRT planning using Eclipse TPS. The team evaluated the potential failure modes (FMs) of every process step. The evaluation was divided into two groups according to whether quality management (QM) was considered. For every FM, occurrence (O), severity (S) and detectability (D) by consensus were evaluated, and the product of O, S and D yielded the risk priority number (RPN), which permitted the ranking of the FMs. Finally, FTA was used to determine the root factors contributing to the riskiest failure modes. Results The IMRT plan process consisted of 10 major sub-processes and 33 steps, which amounted to 47 failure modes. For the group without quality management, the RPN of FMs was between 13.2-271.8, 27 of which had RPN≥80, and 18 FMs had S≥8. For the group with quality management, the RPN of FMs was between 11.2-158.4, 11 of which had RPN≥80. The difference of RPN between the two groups was statistically significant (RPN of the group without QM=101.17±66.34, RPN of the group with QM=59.54±35.64, t=8.501, P<0.05). Finally, FTA was used to determine the root factors contributing to the FMs, i. e., prescription dose definition and importing images. Conclusions The FMEA and FTA methods are operable and practical, which can systematically and comprehensively analyze the potential failures and risks existing in the process of IMRT plan. And the FMEA and FTA can contribute to establish and optimize the quality management program in radiotherapy. Key words: Intensity modulated radiotherapy; Failure modes and effects analysis; Failure tree analysis; Quality management

Research has shown that nearly 80% of the costs and problems associated with product design are created during product development, and cost and quality are essentially designed into products during the conceptual design stage. Failure identification procedures (such as failure modes and effects analysis (FMEA), failure modes, effects and criticality analysis (FMECA) and fault tree analysis (FTA)) and design of experiments are currently being used for both quality control and for the detection of potential failure modes during the design stage or post‐product launch. Although all of these methods have their own advantages, they do not provide the designer with an indication of the predominant failures that should receive considerable attention while the product is being designed. The work presented here proposes a statistical clustering procedure to identify potential failures in the conceptual design stage. A functional approach, which hypothesizes that similarities exist between different failure modes based on the functionality of the product/component, is employed to identify failure modes. The various steps of the methodology are illustrated using a hypothetical design example. Copyright © 2004 John Wiley &amp; Sons, Ltd.

Objective:This study was designed to reevaluate and improve the quality and safety of the chemotherapy preparation in a Central Chemotherapy Preparation Unit of a Public Hospital.Methods:A failure modes, effects, and criticality analysis (FMECA) was conducted by a multidisciplinary team. All potential failure modes at each stage of the chemotherapy preparation were recorded, and the associated risks were scored for their severity, occurrence, and detectability with a risk priority number (RPN). Corrective actions were suggested, and new RPNs were estimated for the modified process.Results:Failure modes, effects, and criticality analysis and priority matrix construction, revealed that the partial compliance of Unit’s premises with international standards (RPNstage: 307), the human errors throughout the compounding (RPNstage: 223)—labeling (RPNstage: 216)—prescribing (RPNstage: 198) steps, and the violation of working protocols by employees (RPNstage: 215), were the most important risks for which either urgent or immediate corrective actions had to be taken. Modifying the procedure through the proposed corrective actions is expected to lead to a significant (71.3%) risk containment, with a total RPNpreparation process reduction from 2102 to 604.Conclusions:Failure modes, effects, and criticality analysis and priority matrix development identified and prioritized effectively the risks associated with chemotherapy preparation allowing for the improvement of health services to cancer patients.

A failure mode and effect analysis (FMEA) helps to identify potential failure modes which can occur in any industry. These failures can lead to major quality defects in the product. In this study, we used a process FMEA approach in a maize flake producing company to enhance the quality and efficiency of the process. A field study approach was used to understand the process, potential failure modes present, and its causes in the process. We developed an FMEA model to evaluate the impact of these failure modes on the process. According to the effects of the potential failure mode on the process and product, a severity ranking was assigned to the respective potential failure mode. Using this severity ranking, detectability in the company and occurrences of the failure modes, a risk priority number (RPN) was assigned to these failure modes. By prioritizing the failure modes, some corrective actions were suggested to the company. These actions reduced the occurrences of the failure modes leading to a reduction in the RPN associated with the potential failure modes. These RPN reductions led to enhance the quality of product and overall increment in productivity and efficiency of the process.KeywordsFood industryFailure mode and effect analysis (FMEA)Risk priority number (RPN)Potential failure modeQuality enhancement

An alternative risk assessment method, known as failure mode effects and criticality analysis (FMECA), is demonstrated on the regional water supply systems (RWSS) in Tucson, AZ, USA that combines delivery of potable and reclaimed water and conveyance of wastewater to a developing area within the Tucson RWSS. The goal of FMECA is to examine the volumetric severity of a component failure on the overall system function by modeling the system under alternative failure modes. Within FMECA, the Risk Priority Number (RPN) is applied to compare the risk criticality between components' failures. To complete FMECA, the Tucson RWSS is represented in a network flow model that optimally allocates flows between sources and demand points to minimize operational costs. Potential failure mode consequences are evaluated from the flow model as the volume of water not delivered to users if the component is unavailable. The volumetric severity of the failure event is converted to an ordinal value using stakeholder judgment. Likelihood of each failure mode is similarly defined by stakeholders on a 1–10 scale. The RPN is then computed as the product of the severity and likelihood. RPN values for all failure modes are then ranked to assess the most critical elements. Alternative system configurations are examined to assess the value of redundancies on the Tucson RWSS resilience.

Failure mode, effects, and criticality analysis (FMECA) is a proactive quality tool that allows the identification and prevention of the potential failure modes of a process or product. In a conventional FMECA, for each failure mode, three risk parameters, namely frequency, non-detection, and severity are evaluated and a risk priority number (RPN) is calculated by multiplying these parameters to assess one signal criticality. However, in many cases, it suffers from some shortcomings regarding the decision-making and the situation where the information provided is ambiguous or uncertain. This paper describes a new fuzzy multi-criticality approach for improving the use of FMECA by treating FMECA as a fuzzy multi-criteria optimization model. The new approach bases on replacing the calculation of a single criticality with a fuzzy inference system for improving the criticality evaluations which offers five partial criticalities that efficiently and separately calculate the impact of a failure on the environment, personnel, production, equipment, and management. In addition, an analytical hierarchy method (AHP) is used to calculate the priorities weights for each partial criticality and construct a criticality matrix in order to improve the relevance of decision-making. Furthermore, a real case of LPG storage system for ZCINA Hassi Messaoud in Algeria is provided to illustrate the practical implementation of the suggested approach and extremely shows the pertinence of the suggested fuzzy model as decision-making tools in preventing industrial risks with providing encouraging results regarding the criticality estimation and improve decision-making by prioritizing “preventive –corrective actions” and determine the efficient action for each partial criticality to control the risk effectively.

Purpose:Though FMEA (Failure Mode and Effects Analysis) is becoming more widely adopted for risk assessment in radiation therapy, to our knowledge it has never been validated against actual incident learning data. The objective of this study was to perform an FMEA analysis of an SBRT (Stereotactic Body Radiation Therapy) treatment planning process and validate this against data recorded within an incident learning system.Methods:FMEA on the SBRT treatment planning process was carried out by a multidisciplinary group including radiation oncologists, medical physicists, and dosimetrists. Potential failure modes were identified through a systematic review of the workflow process. Failure modes were rated for severity, occurrence, and detectability on a scale of 1 to 10 and RPN (Risk Priority Number) was computed. Failure modes were then compared with historical reports identified as relevant to SBRT planning within a departmental incident learning system that had been active for two years. Differences were identified.Results:FMEA identified 63 failure modes. RPN values for the top 25% of failure modes ranged from 60 to 336. Analysis of the incident learning database identified 33 reported near‐miss events related to SBRT planning. FMEA failed to anticipate 13 of these events, among which 3 were registered with severity ratings of severe or critical in the incident learning system. Combining both methods yielded a total of 76 failure modes, and when scored for RPN the 13 events missed by FMEA ranked within the middle half of all failure modes.Conclusion:FMEA, though valuable, is subject to certain limitations, among them the limited ability to anticipate all potential errors for a given process. This FMEA exercise failed to identify a significant number of possible errors (17%). Integration of FMEA with retrospective incident data may be able to render an improved overview of risks within a process.

Application of failure mode and effect analysis in an assisted reproduction technology laboratory

Summary Such classic risk-assessment techniques as failure mode and effect analysis(FMEA) and fault-tree analysis (FTA) are in common use by engineers but have anumber of shortcomings. Dynamic simulation, a new computer technique, countersmany of these deficiencies by introducing a time element that accommodatesrealistic component-failure characteristics and testing and inspectionactivities. This paper demonstrates how FMEA and FTA have been used to helpdesign North Sea well completions and how dynamic simulation models complexsystems realistically to allow comparison of designs and to provide insightinto operating availability and component-testing frequencies. Introduction Both single- and dual-string gas-lift completions are commonly used in theNorth Sea, but it is not obvious which design provides greater safely andreliability. The single completion (Fig. 1) with annular safety valve issimpler to operate and to work over, but during normal production, it has alarge volume of high-pressure gas beneath the tubing hanger that acts on thehanger, tubing spool, production casing, and wellhead seals. The alternativedual-string design (Fig. 2), in which production is routed through the longstring and injection gas through the second or short string, reduces the gasinventory above the safety valve and removes the lift-gas pressure from theannulus. It is a more complicated system to operate and to maintain, however, and it has more component parts. To assist with the selection of completiontype, a conventional risk analysis was undertaken to rank the risks associatedwith each completion during both production and workover modes and to identifycritical components to allow for possible design changes. An FMEA provided aqualitative comparison of the two designs, and an FTA quantified the risks. While these static techniques successfully identified the critical components, they could not address such problems as changes in the well system withproblems as changes in the well system with time, undetected failures, effectsof inspection and maintenance, or components with failure distributions otherthan exponential. Use of a dynamic simulator, which simulates the performanceof the completion throughout many life cycles, provided a more realistic riskassessment and allowed sensitivity tests to be performed on maintenance andtest frequencies and undetected failures. We assumed that such conditions aswater cut and reservoir pressure were identical for both completion types. Theoretically, the influence of such conditions and the effect of varyingequipment sizes can be modeled by adjusting the reliability data, but eachspecific situation requires its own analysis. Reliability data were provided byRefs. 1 and 2, in addition to some proprietary data. Uncertainties are alwaysassociated with reliability data, and the application of the data isnecessarily based on judgment. Risks expressed numerically should be treated ascomparative rather than absolute. Note that, in the static and dynamic studiespresented here, different types of failure presented here, different types offailure distributions were used because more realistic distributions dmexponential ones were available for use in the dynamic simulation. Static Analysis FMEA. After definition of the two well systems to be compared (Figs. 1 and2), the first step in assessing the reliability of each system was to preparean FMEA. This involved identification of possible component failures and theirconsequences to provide a qualitative comparison of the inherent risks in eachsystem. To provide a basis for comparison of the FMEA for each well system, weprepared a list of very critical events (VCE's) prepared a list of verycritical events (VCE's) and critical events (CE's) to summarize each analysis. Events were defined as follows. VCE (Severity 1): loss of control of the well, possible loss of life, and evacuation necessary. CE (Severity 2): loss of oneor more safety barriers but not all barriers, evacuation possible, and majorworkover required. possible, and major workover required. Comparison of VCE'sand CE's for each system provided a qualitative assessment as to which systemwas likely to be more or less hazardous. FTA. FTA techniques previously applied to well-completion designs were usedin this study to provide a quantitative comparison of different designs. TheFTA is based on the component identification carried out in the FMEA, but theFTA differs from the FMEA because it is a top-down approach. Therefore, the topevent of interest (i.e., catastrophic wellhead-system failure) is defined and alogic tree is constructed of lower-level failures until the component-failurelevel is reached at the bottom of the tree. JPT P. 1374

Use of failure mode and effect analysis (FMEA) to improve active surveillance for methicillin-resistant Staphylococcus aureus (MRSA) at a university-affiliated medical center

The completeness and accuracy of the front pages of electronic medical records (EMRs) are crucial for evaluating hospital performance and for health insurance payments to inpatients. However, the quality of the first page of EMRs in China's medical system is not satisfactory, which can be partly attributed to deficiencies in the EMR system. Failure mode and effects analysis (FMEA) is a proactive risk management tool that can be used to investigate the potential failure modes in an EMR system and analyze the possible consequences. The purpose of this study was to preemptively identify the potential failures of the EMR system in China and their causes and effects in order to prevent such failures from recurring. Further, we aimed to implement corresponding improvements to minimize system failure modes. From January 1, 2020, to May 31, 2022, 10 experts, including clinicians, engineers, administrators, and medical record coders, in Zhejiang People's Hospital conducted FMEA to improve the quality of the front page of the EMR. The completeness and accuracy of the front page and the risk priority numbers were compared before and after the implementation of specific improvement measures. We identified 2 main processes and 6 subprocesses for improving the EMR system. We found that there were 13 potential failure modes, including data messaging errors, data completion errors, incomplete quality control, and coding errors. A questionnaire survey administered to random physicians and coders showed 7 major causes for these failure modes. Therefore, we established quality control rules for medical records and embedded them in the system. We also integrated the medical insurance system and the front page of the EMR on the same interface and established a set of intelligent front pages in the EMR management system. Further, we revamped the quality management systems such as communicating with physicians regularly and conducting special training seminars. The overall accuracy and integrity rate of the front page (P<.001) of the EMR increased significantly after implementation of the improvement measures, while the risk priority number decreased. In this study, we were able to identify the potential failure modes in the front page of the EMR system by using the FMEA method and implement corresponding improvement measures in order to minimize recurring errors in the health care services in China.

Purpose- Quality activities should include all product life processes, starting from the determination of customers' demands and prejudices to the service activities provided to customers. By integrating quality-oriented practices, conducting FMEA, and employing Pareto analysis, the research aims to enhance the system's reliability, efficiency, and overall performance. This, in turn, can lead to improved customer satisfaction, reduced costs, increased productivity, and a more sustainable and successful operation in the packaging department. Methodology- In order to keep up with the rapidly developing technology, to be open to innovations with developing technologies, to be in an advantageous position against competitors and ultimately to satisfy customers and fulfil their satisfaction; it is necessary to meet the demands and needs of customers without error and at low cost. Failure Modes and Effect Analysis is applied to provide the sustainability of the production system without the failures. This study focuses on failure analysis, defining failure modes, and conducting error prevention studies within the context of businesses. The aim is to achieve zero failure and zero error by implementing effective prevention techniques. By identifying the root causes of failures and errors, businesses can develop strategies and procedures to prevent them from occurring in the first place. This proactive approach helps ensure a higher level of quality, reliability, and customer satisfaction. By emphasizing the importance of error and failure prevention, businesses can strive for continuous improvement, minimize disruptions in the production system, reduce costs associated with rework or product recalls, and maintain a strong competitive position. Implementing zero failure-error and zero error-failure prevention techniques is crucial for sustaining a reliable and efficient production system. Findings- This research shows that quality is a science that people meet in reality, not simply a theoretical concept. All organizations in the universe should take their quality management systems one step further with their approach to quality processes. In this context, the concept of zero failure-error in production or service lines has become important. By applying FMEA and considering sustainability aspects, the research aims to enhance the manufacturing process, minimize risks and failures, and contribute to sustainable practices in the food industry. Conclusion- The mentioned sample study on Failure Modes and Effects Analysis (FMEA) in food packaging within the production department of a food sector enterprise in the Marmara region highlights the importance of preventing possible failure modes and their impact on the sustainability of production. One key aspect emphasized in the study is the potential reduction of food and packaging waste by minimizing packaging or substituting oil-based materials with renewable resources. By utilizing environmentally friendly food packaging, various advantages can be achieved, including a decrease in food loss and waste, as well as a reduction in the negative environmental impact associated with managing packaging materials and waste. To accomplish this, it is crucial to address food demands throughout the entire food supply chain, with particular attention to transportation and consumption stages. In order to optimize the sustainability of food and packaging systems as a whole, it is necessary to consider the product and its packaging as an integrated system. By applying FMEA and considering the potential failure modes in food packaging, the study aims to identify areas for improvement and implement measures that enhance sustainability throughout the production process. Keywords: Failure prevention, failure mode and effect analysis, quality, sustainable production. JEL Codes: M10, L15, O14

Criticality analysis is a technique for the assessment of criticality rating for every constitutive part. Failure mode effect and criticality analysis (FMECA) are broadly utilized for characterizing, distinguishing and dispensing with potential failures from system, design, or process for the criticality analysis. The determination of the critical ranking of failure modes for criticality analysis is a vital issue of FMECA. The traditional method of FMECA determines the critical ranking of failure modes using the risk priority numbers, which is the product of evaluation criteria like the occurrence, severity and detection of each failure mode but it may not be realistic in some applications. The practical applications reveal that the criticality analysis using traditional FMECA has been considerably criticized for several reasons. In this paper, first, a detailed FMEA to find out the various failure modes of each component of a conventional lathe machine is performed and thereafter, the Fuzzy FMECA approach is used to perform the criticality analysis. A comparative analysis of fuzzy FMECA with traditional FMECA is also done to find out the most superior approach for the criticality analysis. It was concluded that the fuzzy FMECA approach is the most superior approach for the criticality analysis of a system.

Failure mode, effects and criticality analysis (FMECA) is a well-known reliability analysis tool for recognizing, evaluating and prioritizing the known or potential failures in system, design, and process. In conventional FMECA, the failure modes are evaluated by using three risk factors, severity (S), occurrence (O) and detectability (D), and their risk priorities are determined by multiplying the crisp values of risk factors to obtain their risk priority numbers (RPNs). However, the conventional RPN has been considerably criticized due to its various shortcomings. Although significant efforts have been made to enhance the performance of traditional FMECA, some drawbacks still exist and need to be addressed in the real application. In this paper, a new FMECA model for risk analysis is proposed by using an integrated approach, which introduces Z-number, Rough number, the Decision-making trial and evaluation laboratory (DEMATEL) method and the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method to FMECA to overcome its deficiencies in real application. The novelty of this paper in theory is that the proposed approach integrates the strong expressive ability of Z-numbers to vagueness and uncertainty information, the strong point of DEMATEL method in studying the dependence among failure modes, the advantage of rough numbers for aggregating experts’ diversity evaluations, and the strength of VIKOR method to flexibly model multi-criteria decision-making problems. Based on the integrated approach, the proposed risk assessment model can favorably capture and aggregate FMECA team members’ diversity evaluations and prioritize failure modes under different types of uncertainties with considering the failure propagation. In terms of application, the proposed approach was applied to the risk analysis of failure modes in offshore wind turbine pitch system, and it can also be used in many industrial fields for risk assessment and safety analysis.