Study on Risk Analysis of a Rotary Kiln-Based Activated Carbon Manufacturing Process Using Fuzzy-FMEA

Fuzzy Inference Based FMEA for MR Image Based HDR Brachytherapy

A Method for Evaluating Quality Assurance Needs in Radiation Therapy

Targeting specimen misprocessing safety events with failure modes and effects analysis.

The Improvement of a Novel Radiation Therapy Workflow by Failure Mode and Effects Analysis

Risk Management: It's Not Just FMEA

Overview of Failure Mode and Effects Analysis (FMEA): A Patient Safety Tool.

Though failure mode and effects analysis (FMEA) is becoming more widely adopted for risk assessment in radiation therapy, to our knowledge, its output has never been validated against data on errors that actually occur. The objective of this study was to perform FMEA of a stereotactic body radiation therapy (SBRT) treatment planning process and validate the results against data recorded within an incident learning system. FMEA on the SBRT treatment planning process was carried out by a multidisciplinary group including radiation oncologists, medical physicists, dosimetrists, and IT technologists. Potential failure modes were identified through a systematic review of the process map. Failure modes were rated for severity, occurrence, and detectability on a scale of one to ten and risk priority number (RPN) was computed. Failure modes were then compared with historical reports identified as relevant to SBRT planning within a departmental incident learning system that has been active for two and a half years. Differences between FMEA anticipated failure modes and existing incidents were identified. 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. Combining both methods yielded a total of 76 possible process failures, of which 13 (17%) were missed by FMEA while 43 (57%) identified by FMEA only. When scored for RPN, the 13 events missed by FMEA ranked within the lower half of all failure modes and exhibited significantly lower severity relative to those identified by FMEA (p = 0.02). FMEA, though valuable, is subject to certain limitations. In this study, FMEA failed to identify 17% of actual failure modes, though these were of lower risk. Similarly, an incident learning system alone fails to identify a large number of potentially high-severity process errors. Using FMEA in combination with incident learning may render an improved overview of risks within a process.

Clustering and visualization of failure modes using an evolving tree

In project-based learning, student projects benefit when they include the analysis and assessment of risk. However, students lack experience in risk assessment, which limits their ability to carry out this aspect of the work. In practice, the instructor often advises students to assess the risks because this would be a key feature of a professional project. But because of their inexperience, they are unable to assess risk properly. Failure mode and effects analysis (FMEA) has been used in project risk management. There is also a ‘fuzzy’ version of FMEA. It uses the fuzzy inference system to eliminate some of limitations of traditional FMEA. Fuzzy FMEA works better than the standard version when professionals undertake projects; however, it is not so easy for students to apply it perfectly because the risk priority number (RPN) in FMEA is subjectively weighed by experts. Expert opinion is required to ‘score’ each failure mode that may occur. Students lack the necessary experience to perform the ‘scoring’, and thus provide valid inputs to the system that provides the analysis and decisions about risk factors. In this study, a model is proposed for improving the fuzzy FMEA method so that it will provide better support for students as they identify and assess risks in their projects. We use two major approaches: 1) the membership function is constructed by agents. Expert opinions are simulated to help students gain a better appreciation of the concept of project risk and 2) fuzzy rule-based classification is developed by voting techniques to provide the values in the fuzzy rules table. It will be used to judge the risks using a fuzzy inference system. We illustrate the use of the proposed methods for supporting risk assessment by describing how they are applied during a student project.

PurposeTo propose a generic method to simplify the fuzzy logic‐based failure mode and effect analysis (FMEA) methodology by reducing the number of rules that needs to be provided by FMEA users for the fuzzy risk priority number (RPN) modeling process.Design/methodology/approachThe fuzzy RPN approach typically requires a large number of rules, and it is a tedious task to obtain a full set of rules. The larger the number of rules provided by the users, the better the prediction accuracy of the fuzzy RPN model. As the number of rules required increases, ease of use of the model decreases since the users have to provide a lot of information/rules for the modeling process. A guided rules reduction system (GRRS) is thus proposed to regulate the number of rules required during the fuzzy RPN modeling process. The effectiveness of the proposed GRRS is investigated using three real‐world case studies in a semiconductor manufacturing process.FindingsIn this paper, we argued that not all the rules are actually required in the fuzzy RPN model. Eliminating some of the rules does not necessarily lead to a significant change in the model output. However, some of the rules are vitally important and cannot be ignored. The proposed GRRS is able to provide guidelines to the users which rules are required and which can be eliminated. By employing the GRRS, the users do not need to provide all the rules, but only the important ones when constructing the fuzzy RPN model. The results obtained from the case studies demonstrate that the proposed GRRS is able to reduce the number of rules required and, at the same time, to maintain the ability of the Fuzzy RPN model to produce predictions that are in agreement with experts' knowledge in risk evaluation, ranking, and prioritization tasks.Research limitations/implicationsThe proposed GRRS is limited to FMEA systems that utilize the fuzzy RPN model.Practical implicationsThe proposed GRRS is able to simplify the fuzzy logic‐based FMEA methodology and make it possible to be implemented in real environments.Originality/valueThe value of the current paper is on the proposal of a GRRS for rule reduction to enhance the practical use of the fuzzy RPN model in real environments.

Goal: In this work, the application of FMEA (failure mode and effects analysis) in the execution of maintenance of water network in a medium size city in the state of Maranhão was approached. FMEA (failure mode and effects analysis) with occupational safety approach is applied in the execution of water supply network maintenance in a medium size city in the state of Maranhão. This work shows that FMEA is an effective tool for risk prioritization in work process. The FMEA application with use of index reference table can become faster than application without reference table - and thus spread the tool for risk analysis.
 Design / Methodology / Approach: A photographic records was carried out in loco and the main risks to the workers were enumerated and based on the obtained data, a risk analysis was elaborated with the application of FMEA (Failure modes and effects analysis).
 Results: The maintenance services teams of the supply system are subject to a high risk of accidents caused mainly by the working conditions precariousness. It was observed that the services of manual excavations and the lack of use of PPE (helmet, gloves, pants suitable for flooded environments and etc.) have high risk index (RPN) and that the mismanagement added to the unsafe behavior were the main factors to accident occurrence in this type of work.
 Limitations of the investigation: The study studied the reality found in a medium-sized city with precarious working conditions. Further studies can compare the work reality of other teams in cities of different sizes, with better working conditions. Other limitation of this work is the impossibility of work situation improvement and posterior tool application – a study with this magnitude is non-trivial and needs future research.
 Practical implications: The great achievement of this work is to demonstrate that FMEA - a tool that is widely used in maintenance management and product engineering - is able to identify and prioritize risks based on its preliminary risk index obtained - contributing to reduce the difficulty of index choices previously cited in literature and disseminate the FMEA utilization for employee safety and occupational health. Such a tool has a great capacity for quantitative description of the risks, showing that the FMEA is very useful in the work safety sector for the organization of correction plans.
 Originality / Value: The difficulties of using the tool mentioned in the available literature were minimized with the use of the reference table, showing that the use of FMEA can become faster and thus spread the tool for risk analysis.

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.

Purpose: Failure modes and effects analysis (FMEA) is a subjective risk mitigation technique that has not been applied to physics-specific quality management practices. There is a need for quantitative FMEA data as called for in the literature. This work focuses specifically on quantifying FMEA severity scores for physics components of IMRT delivery and comparing to subjective scores. Methods: Eleven physical failure modes (FMs) for head and neck IMRT dose calculation and delivery are examined near commonly accepted tolerance criteria levels. Phantom treatment planning studies and dosimetry measurements (requiring decommissioning in several cases) are performed to determine the magnitude of dose delivery errors for the FMs (i.e., severity of the FM). Resultant quantitative severity scores are compared to FMEA scores obtained through an international survey and focus group studies. Results: Physical measurements for six FMs have resulted in significant PTV dose errors up to 4.3% as well as close to 1 mm significant distance-to-agreement error between PTV and OAR. Of the 129 survey responses, the vast majority of the responders used Varian machines with Pinnacle and Eclipse planning systems. The average years of experience was 17, yet familiarity with FMEA less than expected. Survey reports perception of dose delivery error magnitudemore » varies widely, in some cases 50% difference in dose delivery error expected amongst respondents. Substantial variance is also seen for all FMs in occurrence, detectability, and severity scores assigned with average variance values of 5.5, 4.6, and 2.2, respectively. Survey shows for MLC positional FM(2mm) average of 7.6% dose error expected (range 0–50%) compared to 2% error seen in measurement. Analysis of ranking in survey, treatment planning studies, and quantitative value comparison will be presented. Conclusion: Resultant quantitative severity scores will expand the utility of FMEA for radiotherapy and verify accuracy of FMEA results compared to highly variable subjective scores.« less

Can traceability of gametes and embryos be ensured during IVF? The use of a simple and comprehensive traceability system that includes the most susceptible phases during the IVF process minimizes the risk of mismatches. Mismatches in IVF are very rare but unfortunately possible with dramatic consequences for both patients and health care professionals. Traceability is thus a fundamental aspect of the treatment. A clear process of patient and cell identification involving witnessing protocols has to be in place in every unit. To identify potential failures in the traceability process and to develop strategies to mitigate the risk of mismatches, previously failure mode and effects analysis (FMEA) has been used effectively. The FMEA approach is however a subjective analysis, strictly related to specific protocols and thus the results are not always widely applicable. To reduce subjectivity and to obtain a widespread comprehensive protocol of traceability, a multicentre centrally coordinated FMEA was performed. Seven representative Italian centres (three public and four private) were selected. The study had a duration of 21 months (from April 2015 to December 2016) and was centrally coordinated by a team of experts: a risk analysis specialist, an expert embryologist and a specialist in human factor. Principal investigators of each centre were first instructed about proactive risk assessment and FMEA methodology. A multidisciplinary team to perform the FMEA analysis was then formed in each centre. After mapping the traceability process, each team identified the possible causes of mistakes in their protocol. A risk priority number (RPN) for each identified potential failure mode was calculated. The results of the FMEA analyses were centrally investigated and consistent corrective measures suggested. The teams performed new FMEA analyses after the recommended implementations. In each centre, this study involved: the laboratory director, the Quality Control & Quality Assurance responsible, Embryologist(s), Gynaecologist(s), Nurse(s) and Administration. The FMEA analyses were performed according to the Joint Commission International. The FMEA teams identified seven main process phases: oocyte collection, sperm collection, gamete processing, insemination, embryo culture, embryo transfer and gamete/embryo cryopreservation. A mean of 19.3 (SD ± 5.8) associated process steps and 41.9 (SD ± 12.4) possible failure modes were recognized per centre. A RPN ≥15 was calculated in a mean of 6.4 steps (range 2-12, SD ± 3.60). A total of 293 failure modes were centrally analysed 45 of which were considered at medium/high risk. After consistent corrective measures implementation and re-evaluation, a significant reduction in the RPNs in all centres (RPN <15 for all steps) was observed. A simple and comprehensive traceability system was designed as the result of the seven FMEA analyses. The validity of FMEA is in general questionable due to the subjectivity of the judgments. The design of this study has however minimized this risk by introducing external experts for the analysis of the FMEA results. Specific situations such as sperm/oocyte donation, import/export and pre-implantation genetic testing were not taken into consideration. Finally, this study is only limited to the analysis of failure modes that may lead to mismatches, other possible procedural mistakes are not accounted for. Every single IVF centre should have a clear and reliable protocol for identification of patients and traceability of cells during manipulation. The results of this study can support IVF groups in better recognizing critical steps in their protocols, understanding identification and witnessing process, and in turn enhancing safety by introducing validated corrective measures. This study was designed by the Italian Society of Embryology Reproduction and Research (SIERR) and funded by the Italian National Transplant Centre (CNT) of the Italian National Institute of Health (ISS). The authors have no conflicts of interest. N/A.

Surface image guided, Linac-based radiosurgery (SIG-RS) is a modern approach for delivering radiosurgery that utilizes optical stereoscopic imaging to monitor the surface of the patient during treatment in lieu of using a head frame for patient immobilization. Considering the novelty of the SIG-RS approach and the severity of errors associated with delivery of large doses per fraction, a risk assessment should be conducted to identify potential hazards, determine their causes, and formulate mitigation strategies. The purpose of this work is to investigate SIG-RS using the combined application of failure modes and effects analysis (FMEA) and fault tree analysis (FTA), report on the effort required to complete the analysis, and evaluate the use of FTA in conjunction with FMEA. A multidisciplinary team was assembled to conduct the FMEA on the SIG-RS process. A process map detailing the steps of the SIG-RS was created to guide the FMEA. Failure modes were determined for each step in the SIG-RS process, and risk priority numbers (RPNs) were estimated for each failure mode to facilitate risk stratification. The failure modes were ranked by RPN, and FTA was used to determine the root factors contributing to the riskiest failure modes. Using the FTA, mitigation strategies were formulated to address the root factors and reduce the risk of the process. The RPNs were re-estimated based on the mitigation strategies to determine the margin of risk reduction. The FMEA and FTAs for the top two failure modes required an effort of 36 person-hours (30 person-hours for the FMEA and 6 person-hours for two FTAs). The SIG-RS process consisted of 13 major subprocesses and 91 steps, which amounted to 167 failure modes. Of the 91 steps, 16 were directly related to surface imaging. Twenty-five failure modes resulted in a RPN of 100 or greater. Only one of these top 25 failure modes was specific to surface imaging. The riskiest surface imaging failure mode had an overall RPN-rank of eighth. Mitigation strategies for the top failure mode decreased the RPN from 288 to 72. Based on the FMEA performed in this work, the use of surface imaging for monitoring intrafraction position in Linac-based stereotactic radiosurgery (SRS) did not greatly increase the risk of the Linac-based SRS process. In some cases, SIG helped to reduce the risk of Linac-based RS. The FMEA was augmented by the use of FTA since it divided the failure modes into their fundamental components, which simplified the task of developing mitigation strategies.