FMMEA automation based on function flow modeling

Purpose: Omitting potential failure modes or inferencing inappropriate failure modes during failure mode and effects analysis (FMEA) implementation can lead to a lack of understanding of the causes of failures incurred or might lead to introducing inappropriate improvements. Therefore, it is critical to accurately infer the failure modes, which, in turn, requires selecting a suitable failure mode inference method because the results of such methods generally vary depending on the purpose and context of FMEA implementation.<BR>Methods: In this study, the advantages and disadvantages of functional and hardware approaches were compared and analyzed by applying them in mechanical and circuit components with respect to the implementation of component FMEA.<BR>Results: It was observed that the hardware approach can be more desirable for identifying the causes and mechanism of failures to prevent omissions and address the failure modes. However, when it becomes difficult to represent failure modes based solely on physical aspects, the functional approach can be used simultaneously with the hardware approach.<BR>Conclusion: Based on the aforementioned comparison and analysis, guidelines for selecting an effective FMEA method for implementing FMEA in organizations have been presented. However, this study focused only on component FMEA and did not consider system FMEA. Thus, the procedure for system FMEA, in terms of FMEA objectives and implementation, should also be examined.

This study aims to evaluate the risks that may arise during the production process in a plastic injection manufacturing enterprise with traditional Failure Mode and Effect Analysis (FMEA) and Grey Relational Analysis (GRA). Although it is a widely used analytical technique that helps to identify and reduce the risks of failure in a process, the Failure Mode and Effects Analysis (FMEA) has some drawbacks that the different risks can have the same risk priority values and the weight of risk factors is not take into consideration. This situation has been tried to be eliminated by integrating the FMEA with the GRA. As a result, it is seen that the order of risk priority values of the identified failure changes according to both methods.

There are lots of performance degradation failure modes in offshore wind turbine blade trailing edge, such as stiffness degradation, strength reduction, et al. The occurrence and severity of such failure modes can't be easily determined, and the traditional failure mode and effect analysis (FMEA) method is difficult to be applied. Considering the fact, an improved FMEA method based on performance degradation analysis is proposed for wind turbine blade trailing edge, in which the RPNs are calculated by new methods. Specifically, a performance parameter degradation model is established based on random processes, then the occurrence of each failure mode of that is assessed; The Pearson correlation coefficient of each failure mode and each performance parameter is calculated, then the calculation method of severity is given; Here the detection method of each performance degradation failure mode is assumed to be mature, thus the detections are considered equal. This research can provide reliability analysis methods for complex systems with performance degradation failure modes.

Objective To explore the effect of failure mode and effect analysis (FMEA) method in strengthening the management of critical value of blood specimens in hospital. Methods According to the procedures of FMEA, critical values management team were established, drew flow chart of generating test specimens and crisis value, explored the potential failure mode and failure cause, analyzed the severity, possibility and critical value, and formulate and implement countermeasures according to the research outcome. Results The crisis value of the failure mode fell from 1 320 to 151 after the implementation of FMEA, and rate of decline was 88.56% ; number of unqualified blood samples decreased from 510 cases to 78 cases, and the difference is statistically significant (χ2=407.27, P < 0.05); critical value processing defects decreased from 85 cases (a total of 236 cases) to 3 cases (a total of 229 cases), and the difference was statistically significant (χ2=91.26, P < 0.05). Conclusions FMEA method is helpful to improve the accuracy and precision of critical value management of clinical blood samples, so as to ensure the safety of patients in hospitals. Key words: Failure mode and effect analysis; Critical value management

Objective To analyze the risks of hypertonic glucose injection ( 20%) (hypertonic glucose) in clinical application using failure mode and effect analysis (FMEA) method and improve the relevant management measures. Methods A study group on risk prevention strategies of hypertonic glucose was established in the First People′s Hospital of Hefei. Domestic and foreign literature on medication error cases, preventive measures or management strategies, and data from questionnaires and on-the-spot investigations were collected. The failure modes in the process of hypertonic glucose application were analyzed and assessed. Severity (S), frequency of occurrence (O), and likelihood of detection (D) of the risks related to these failure modes were scored and the risk priority numbers (RPN) were determined. The failure modes with higher RPN were screened out and the corresponding prevention strategies were formulated. The incidence of medication errors and potential hazards, cognition rate of knowledge on drugs, qualification rate of drug storage, and satisfaction rate for management measures before and after the improvement were analyzed comparatively. Results According to the results from literature search, questionnaires, and on-the-spot investigations, a total of 24 failure modes were found in the 6 links relevant with hypertonic glucose application in clinical, which included links of prescription writing and delivery, pharmacist dispensing, nurse dispensing, post-administration monitoring, drug management, and information technology. Ranking the RPN from high to low, 7 major failure modes with higher RPN were selected from the 24 failure modes, including the existence of drugs with similar name and drugs with similar package, wrong drug dispensing, absence of post-administration monitoring, lack of marked labels for drugs, garbles caused by lack of medication knowledge, and untimely record and report of medication errors. The incidences of medication errors and potential safety hazards before and after management improvement were 3.2% (7/216) and 0.4% (1/234), respectively and the difference had no statistical significance (P=0.058). The average cognitive rates of physicians, nurses and pharmacists before and after the improvement were 65.2%(15/23), 54.2%(13/24), 67.9%(19/28) and 95.8%(23/24), 91.7%(22/24), 95.5%(21/22), respectively and the differences were statistically significant (P<0.05 for all). The qualification rates of drug storage before and after management improvement were 70.2%(151/215) and 98.7%(230/233), respectively and the difference was statistically significant (P<0.05). The average satisfaction degrees of medical staff with hyperosmotic glucose management were 81.3%(61/75) and 95.7%(65/70), respectively before and after the management improvement and the difference was statistically significant (P<0.05). Conclusion Failure modes in the process of hypertonic glucose use can be effectively identified using the FMEA method, and the risk events can be effectively reduced after the corresponding improvement measures. Key words: Healthcare failure mode and effect analysis; Glucose solution, hypertonic; Hospital administration; High-alert medicine

Background Hospital construction and renovation works cause derangement, increasing mistake rate in healthcare workers and resulting in dust contamination and dispersal of fungal spores that could improve morbidity and mortality in severely immune suppressed patients. The necessary renovation of hematology ward at IRCCS Policlinico San Martino in Genoa, scheduled for February 2020 required a risk assessment to avoid logistical, organizational and clinical problems. Two possible scenarios were proposed: (1) displacement of patients in another building previously used as hematological unit or (2) department split in 2 sections to carry out renovation works in different periods. Risk management of the choice relies on failure mode and effects analysis (FMEA) method, a proactive tool based on risk priority number (RPN) that ranks failure modes in terms of occurrence, severity and detection. Aim of this work is to analyze and compare the two scenarios; a team of experts in Public health with specific skills in risk management and hospital governance used FMEA to single out failure modes and to compute RPN. Methods Thirteen failure modes were combined together in four major possible risks: (a) lengthening waiting times and delay haematopoietic stem cell transplant procedures, (b) exposure to airborne contamination, (c) cross contamination and water contamination, (d) wrong behaviors. RPN was computed for each failure mode. Results Relevant failure modes were: reduction of bedsides in both scenarios (7 bedsides lost in first scenario RPN1 540 vs 9 bedsides lost in second scenario RPN2 630), less air exchange/hour in the first scenario (4/h RPN1 500 vs 6/h RPN2 400) balanced by closeness to the work site in the second scenario(RPN2 500). First scenario total RPN was 2430, second scenario was 3250. Conclusions Using FMEA, we assessed the different risks profile of two scenarios. Although some residual risks persisted, the first scenario shows a lower risk profile for all major risks. Key messages Risk assessment is a key element to better understand hospital problems and improve environmental health in healthcare setting FMEA represents a useful tool to guide hospital manager in difficult choices concerning severely immune suppressed patients.

BackgroundMedical ultrasound device has been more and more widely used in the hospital and Its safety risk is significantly increased when failures occur. However, there is a lack of quantitative risk assessments of different types of failure modes for medical ultrasound device. This study utilizes a failure mode, effect and criticality analysis (FMECA) approach for quantitative risk evaluation of different failure modes for ultrasound devices.MethodsThe 4216 medical ultrasound device failure records at various hospitals were investigated. A failure mode and effect analysis method was developed for the quantitative evaluation of the risks of different failure modes. Visual correlation analysis was conducted for all categories to identify the causes of various failure modes. Based on the severity, occurrence and detectability of the failure causes determined, the risk priority number (RPN) for each failure mode was back-calculated through the obtained tracking diagram.ResultsThe failure modes of unclear images, unable to power on and dark shadows on an image had the highest RPNs. One failure mode could be caused by various factors, and the failure location was not necessarily related to the cause of the failure.ConclusionsThis quantitative approach more accurately evaluated the risks of different failure modes, and the results of the corresponding analysis of failure modes and causes could support the rapid determination of the causes of failures in clinical practice.

Evidence theory is a useful tool for modeling and reasoning uncertain information inherent in experts’ evaluations, which is not handled efficiently in traditional failure mode and effects analysis (FMEA). This study proposes an integrated FMEA method that incorporates evidence theory and is applied to human reliability assessment. The human error information of a human-machine system in FMEA is described as a directed graph by a Bayesian network (BN) to assess the dependence among potential human-related failure modes. The BN is extended to propagate the epistemic uncertainty of FMEA team members, where belief mass is applied to model uncertainties in team members’ knowledge and to convert their subjective cognition into varying levels of uncertainty. Risk indexes for occurrence, severity and detection from multiple sources are defined as a special assessment state. The combination of the belief mass of different failure modes is performed using extended Dempster’s rules to avoid the influence of conflicting evidence. Finally, an application in the healthcare system is provided to verify the effectiveness of our model. A comparison with other fuzzy FMEA methods is also conducted, demonstrating the advantages of the proposed model in dealing with decision-makers’ epistemic uncertainty and potential failure mode interdependencies.

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.

PT. DALWA ANUGRAHHASANIAH, a company that produces Packaged Drinking Water (AMDK) using ozone technology, this company also produces various sizes of glasses, bottles, and uses two types of raw materials - mineral water and demineralized water. This study aims to ensure that the AMDK production process does not experience defects. In an effort to reduce product defects, the Failure Mode and Effect Analysis (FMEA) method is used. FMEA is a proactive risk management method used to identify potential failures in a system, design, process, or service before it reaches the consumer. The goal is to prevent failure, maintain product quality, and reduce associated costs. The Failure Mode and Effect Analysis (FMEA) method is detailed, focusing on identifying failure modes, effects, causes, severity, occurrence, detection, and risk prioritization. Successful FMEA implementation is defined by a 30%-50% reduction in the new RPN value compared to the initial RPN value.

Purpose – The purpose of this paper is to develop a new failure mode and effect analysis (FMEA) framework for evaluation, prioritization and improvement of failure modes. Design/methodology/approach – A hybrid multiple criteria decision-making method combining VIKOR, decision-making trial and evaluation laboratory (DEMATEL) and analytic hierarchy process (AHP) is used to rank the risk of the failure modes identified in FMEA. The modified VIKOR method is employed to determine the effects of failure modes on together. Then the DEMATEL technique is used to construct the influential relation map among the failure modes and causes of failures. Finally, the AHP approach based on the DEMATEL is utilized to obtain the influential weights and give the prioritization levels for the failure modes. Findings – A case study of diesel engine’s turbocharger system is provided to illustrate the potential application and benefits of the proposed FMEA approach. Results show that the new risk priority model can be effective in helping analysts find the high risky failure modes and create suitable maintenance strategies. Practical implications – The proposed FMEA can overcome the shortcomings and improve the effectiveness of the traditional FMEA. Particularly, the dependence and interactions between different failure modes and effects have been addressed by the new failure analysis method. Originality/value – This paper presents a systemic analytical model for FMEA. It is able to capture the complex interrelationships among various failure modes and effects and provide guidance to analysts by setting the suitable maintenance strategies to improve the safety and reliability of complex systems.

Evaluating the academic scientific laboratories' safety by applying failure mode and effect analysis (FMEA) at the public university in Lebanon

PT The lathe is one of the essential machines in production activities in this company. The amount of downtime caused by machine failure will reduce product quality and quantity. Therefore, this research aims to identify the factors that most influence the risk of lathe failure so that repair priorities and maintenance strategies can be determined. This can be achieved by using the Failure Mode and Effect Analysis (FMEA) method. FMEA is a method that can be used to identify the causes and impacts of each possible failure mode on machine components by systematically explaining the levels of failure so that appropriate prevention or repair can be carried out. The FMEA method shows the Risk Priority Number (RPN) value as a reference in determining the choice of maintenance strategy, namely, predictive maintenance, preventive maintenance or corrective maintenance. The research results show that the highest RPN value is found in the motor shaft pulley component at 320 and Nut Screw at 210. Maintenance priorities are determined based on the Pareto diagram principle. The appropriate maintenance strategy carried out by PT. Keywords: FMEA, Lathe, Maintenance, RPN

This paper introduces a formulation of appropriate risk estimation methods that can be used for improving of processes in the electronics area. Two risk assessment methods have been chosen with regard to the specifics of adhesive joining based on electrically conductive adhesives. The paper provides a combination of a failure mode and effect analysis (FMEA) and fault tree analysis (FTA) for optimizing of the joining process. Typical features and failures of the process are identified. Critical operations are found and actions for avoiding failures in these actions are proposed. A fault treehas been applied to the process in order to get more precise information about the steps and operations in the process, and the relations between these operations. The fault tree identifies potential failures of the process. Then the effects of the failures have been estimated by the failure mode and effect analysis method. All major differences between failure mode and effect analysis and fault tree analysis are defined and there is a discussion about how to use the two techniquescomplement each other and achieve more efficient results.

Purpose: Failure Mode and Effects Analysis (FMEA) provides a proactive method of improving the quality and safety of treatments by identifying and correcting hazards points in the process of clinical care. FMEA, however, is often thought to be prohibitively labor intensive. This study outlines a method of streamlined FMEA, conducted with limited resources, and assesses its feasibility and effectiveness. Methods: FMEA was performed on the external beam service of a clinic treating approximately 650 patients per year on three linear accelerators. A facilitator and local team leader were identified, and a plan was developed to complete the exercise in four one‐ hour meetings as follows: 1 (core group), introduction and process mapping, 2 (all staff) identification of failure modes from expert user input, 3 (core group) scoring of failure modes according to an FMEA risk priority number, RPN, i.e. the product of severity, occurrence and detectability scores, and 4 (all staff) error proofing of the top‐five ranked failure modes. Results: Fifty‐ two failure modes were identified, 43 of which were scored and ranked. Specific interventions were developed for the five highest ranked failure modes. FMEA scoring after intervention indicated that the average RPN score for the top five modes decreased from 273 to 161 (p=0.03) while FMEA scoring of a control group of failure modes with no intervention did not show a significant change in RPN (p=0.07). The exercise was accomplished within the expected timeline and required 55 total hours of staff time and 20 hours of facilitator effort. Conclusion: Streamlined FMEA analysis is feasible with a relatively modest effort, and can reduce the risk profile of a facility. This may open the possibility of performing repeat FMEA on a regular basis.