Design and development of modified service failure mode and effects analysis model

<div class="htmlview paragraph">Fault tree analysis (FTA) evolved from the aerospace industry in the 1960’s. A fault tree is deductive logic model that is generated with a top undesired event in mind. FTA answers the question, “how can something occur?” as opposed to failure modes and effects analysis (FMEA) that is inductive and answers the question, “what if?” FTA is used in risk, reliability and safety assessments. FTA is currently being used by several industries such as nuclear power and chemical processing. Typically the automotive industries uses failure modes and effects analysis (FMEA) such as design FMEAs and process FMEAs. The use of FTA has spread to the automotive industry. This paper discusses the use of FTA for automotive applications. With the addition automotive electronics for various applications in systems such as engine/power control, cruise control and braking/traction, FTA is well suited to address failure modes within these systems. FTA can determine the importance of these failure modes from various perspectives such as cost, reliability and safety. A fault tree analysis of a car starting system is presented as an example.</div>

With the publication of the quality guideline ICH Q9 "Quality Risk Management" by the International Conference on Harmonization, risk management has already become a standard requirement during the life cycle of a pharmaceutical product. Failure mode and effect analysis (FMEA) is a powerful risk analysis tool that has been used for decades in mechanical and electrical industries. However, the adaptation of the FMEA methodology to biopharmaceutical processes brings about some difficulties. The proposal presented here is intended to serve as a brief but nevertheless comprehensive and detailed guideline on how to conduct a biopharmaceutical process FMEA. It includes a detailed 1-to-10-scale FMEA rating table for occurrence, severity, and detectability of failures that has been especially designed for typical biopharmaceutical processes. The application for such a biopharmaceutical process FMEA is widespread. It can be useful whenever a biopharmaceutical manufacturing process is developed or scaled-up, or when it is transferred to a different manufacturing site. It may also be conducted during substantial optimization of an existing process or the development of a second-generation process. According to their resulting risk ratings, process parameters can be ranked for importance and important variables for process development, characterization, or validation can be identified. Health authorities around the world ask pharmaceutical companies to manage risk during development and manufacturing of pharmaceuticals. The so-called failure mode and effect analysis (FMEA) is an established risk analysis tool that has been used for decades in mechanical and electrical industries. However, the adaptation of the FMEA methodology to pharmaceutical processes that use modern biotechnology (biopharmaceutical processes) brings about some difficulties, because those biopharmaceutical processes differ from processes in mechanical and electrical industries. The proposal presented here explains how a biopharmaceutical process FMEA can be conducted. It includes a detailed 1-to-10-scale FMEA rating table for occurrence, severity, and detectability of failures that has been especially designed for typical biopharmaceutical processes. With the help of this guideline, different details of the manufacturing process can be ranked according to their potential risks, and this can help pharmaceutical companies to identify aspects with high potential risks and to react accordingly to improve the safety of medicines.

Failure Modes and Effects Analysis (FMEA) is an effective technique for identifying and eliminating possible failures. FMEA has been widely used in the manufacturing industry and in various phases of the life cycle of a product. The aim of FMEA is to ensure high quality and reliability. Design FMEA (DFMEA) is used to identify and analyze potential failure modes and their associated causes for product design. It is a verification activity that can help avoid a large percentage of product design problems before the design is finalized. When dealing with new generations of a product including design changes on the basis of existing ones, DFMEA of earlier generations can be used as a reference for such design modifications. Traditionally, criticality or risk assessment in FMEA is performed by developing a risk priority number (RPN). Failure modes that have high RPN values are assumed to be more critical and deserve more attention. In this paper, we propose a method to assess failure modes in DFMEA considering two RPNs for diesel engines. In addition to the traditional RPN, a new RPN is proposed. A diesel engine cylinder head is used as an example to illustrate the application of the two RPNs in a design FMEA.

PurposeThis paper proposes an integrated IT2F-FMEA model under a group decision-making setting. In risk assessment models, experts' evaluations are often aggregated beforehand, and necessary computations are performed, which in turn, may cause a loss of information and valuable individual opinions. The proposed integrated IT2F-FMEA model aims to calculate risk priority numbers from the experts' evaluations and then fuse experts' judgments using a novel integrated model.Design/methodology/approachThis paper presents a novel failure mode and effect analysis (FMEA) model by integrating the fuzzy inference system, best-worst method (BWM) and weighted aggregated sum-product assessment (WASPAS) methods under interval type-2 fuzzy (IT2F) environment. The proposed FMEA approach utilizes the Mamdani-type IT2F inference system to calculate risk priority numbers. The individual FMEA results are combined by using integrated IT2F-BWM and IT2F-WASPAS methods.FindingsThe proposed model is implemented in a real-life case study in the furniture industry. According to the case study, fifteen failure modes are considered, and the proposed integrated method is used to prioritize the failure modes.Originality/valueMamdani-type singleton IT2F inference model is employed in the FMEA. Additionally, the proposed model allows experts to construct their membership functions and fuzzy rules to capitalize on the experience and knowledge of the experts. The proposed group FMEA model aggregates experts' judgments by using IT2F-BWM and IT2F-WASPAS methods. The proposed model is implemented in a real-life case study in the furniture company.

With increasing deployment, complexity, and sophistication of equipment and related processes within the clinical imaging environment, system failures are more likely to occur. These failures may have varying effects on the patient, ranging from no harm to devastating harm. Failure mode and effect analysis (FMEA) is a tool that permits the proactive identification of possible failures in complex processes and provides a basis for continuous improvement. This overview of the basic principles and methodology of FMEA provides an explanation of how FMEA can be applied to clinical operations in a radiology department to reduce, predict, or prevent errors. The six sequential steps in the FMEA process are explained, and clinical magnetic resonance imaging services are used as an example for which FMEA is particularly applicable. A modified version of traditional FMEA called Healthcare Failure Mode and Effect Analysis, which was introduced by the U.S. Department of Veterans Affairs National Center for Patient Safety, is briefly reviewed. In conclusion, FMEA is an effective and reliable method to proactively examine complex processes in the radiology department. FMEA can be used to highlight the high-risk subprocesses and allows these to be targeted to minimize the future occurrence of failures, thus improving patient safety and streamlining the efficiency of the radiology department.

Objective To consummate the cleaning process of medical power tool so as to improve the quality of cleaning of medical power tool. Methods The failure mode and effect analysis (FMEA) project group was built up in April 2016. The cleaning process of medical power tool was analyzed with the method of FMEA. The intervention was carried out aiming at high risk factor. Besides, the cleaning process of medical power tool was consummated and improved continually combining the detection results of ATP bioluminescence assay. The risk assessment of the cleaning process of medical power tool was carried out to determine high risk factors and formulate as well as implement improvement measures. The control effect of risk priority number (RPN) of cleaning on medical power tool and cleaning effect were compared before (March 2016) and after (September 2016) FMEA. Results The RPN of the cleaning process of medical power tool decreased from (218.17±89.14) before implementing FMEA to (33.26±9.54) after implementing FMEA. The qualified rate of ATP bioluminescence assay in washing quality increased from 68.6% to 98.5% before and after implementation with a significant difference (χ2=22.94, P<0.01) . Conclusions The application of FMEA can effectively control the risk factors of the cleaning process of medical power tool, improve the cleaning quality of power tool and reduce the opportunity for infection in patients. Key words: Failure mode and effect analysis; Cleaning; Medical power tool; Management

Failure mode and effects analysis (FMEA) is a widely used, powerful tool to identify and assess potential failure modes in products and to make products more reliable. Due to the complexity of products and lack of knowledge, FMEA involves many uncertainties in practice. In previous studies, numerous modified FMEA methods based on fuzzy logic and Dempster-Shafer (D-S) evidence theory have been employed to address these uncertainties. These studies focus on how to handle uncertainties and to identify a more reliable prioritization of risk priority numbers (RPNs). However, studies have not sufficiently examined how many uncertainties are present in resulting RPNs. To better model and process various types of uncertainties in FMEA, two new area-based metrics are constructed in this paper. One is the interval area metric (IAM), which is used in RPN representation. The other is the dimensionless uncertainty metric (DUM), which is used to measure how many uncertainties there are in RPN. IAM is used to rank the risks in failure modes, and DUM is used to rank the uncertainties in failure modes. Then, an expert system is presented to qualitatively evaluate the DUM, which can help FMEA users intuitively judge whether further investigation should be performed to alleviate the epistemic uncertainties in each failure mode. Finally, a practical risk evaluation case regarding the grinding wheel system of a numerically controlled (NC) machine is provided to demonstrate the application and effectiveness of the proposed FMEA. The case study shows that the calculation programs of IAM and DUM do not require any assumptions or need to address conflict among experts. In addition, proposed method can not only give a more accurate rating of each failure mode, but also help designers intuitively see the uncertainty grade of each RPN, which is useful to help them understand FMEA 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.

Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory

The ship-to-ship (STS) transfer of cargo operations are increasingly safe procedures, nevertheless they remain difficult and complex operations. The adverse effects of a potential accident demands a detailed and thorough work on risk and safety analysis for the transfer operations. In the light of the above, the Process Failure Mode and Effects Analysis (PFMEA) which is a branch of the traditional Failure Mode an d Effects Analysis (FMEA), is applied in combination with a Fuzzy Inference System (FIS) methodology to evaluate different risk scenarios of STS transfer of cargo operations. Although PFMEA is currently applied mainly to manufacturing processes, the aim of the paper is to implement this methodology to the ship-to-ship transfer of cargo. A significant advantage of the PFMEA is the analytical review of the STS operations. Nevertheless, serious drawbacks such as the fact that different combinations of the three implemented risk constituents may lead to the same Risk Priority Number (RPN) or the determination of the relative importance among the three risk factors of the PFMEA namely the likelihood of occurrence (O), the severity of a failure (S), and the ability to detect the potential failure (D), make the PFMEA weak and ineffective. To deal with the aforementioned weaknesses a fuzzy inference system (FIS) based on Mamdani's methodology, is applied as a remedy. The objective of this paper is to assess and evaluate different hazardous scenarios in an STS transfer operation implementing the PFMEA in combination with a FIS. To do so, a team of experts with relative experience with respect to STS transfer operations evaluated certain hazardous scenarios that have derived by the study of several guidelines and recommendations regarding these operations. The paper concludes with interesting insights emerged by the aforementioned tasks.

Failure Mode and Effect Analysis: An Application in Reducing Risk in Blood Transfusion

Design FMEA of electrical systems is a costly and labour intensive process. Ideally it would be done when the electrical system is first designed, and repeated whenever any change is made to the design. Because of the cost, this has not been possible in the past. This paper describes how an existing tool for automating electrical design failure mode and effects analysis (FMEA) can be augmented to make incremental design FMEA much less of a burden for the engineer. The tool is able to generate the effects for each failure mode and to assign significance values to the effects. The first time that it is run on a design, the engineer still has quite a lot of work to do, examining the results and deciding what actions need to be taken because of the FMEA. When a change is made to the circuit, the engineer runs the FMEA tool again and receives a new report. Because of the uniformity of the reports provided by the FMEA tool, it has proved possible to write software which sorts out the failure effects which have changed from the previous analysis and only report those results to the engineer. This makes examination of the repercussions of the incremental FMEA much less effort for the engineer, and makes it feasible to perform an incremental FMEA every time the design is amended.

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

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

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