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

Failure mode and effect analysis is one of the QS-9000 quality system requirement supplements, with a wide applicability in all industrial fields. FMEA is the inductive failure analysis instruments which can be defined as a methodical group of activities intended to recognize and evaluate the potential failure modes of a product/ process and its effects with an aim to identify actions which could eliminate or reduce the chance of the potential failure before the problem occur. The purpose of this paper is to evaluate the FMEA research and application in the Thermal Power Plant Industry. The research will highlight the application of FMEA method to water tubes (WT) in boilers with an aim to find-out all the major and primary causes of boiler failure and reduce the breakdown for continuous power generation in the plant. Failure Mode and Effect Analysis technique is applied on most critical or serious parts (components) of the plant which having highest Risk Priority Number (RPN). Comparison is made between the quantitative results of FMEA and reliability field data from real tube systems. These results are discussed to establish relationships which are useful for future water tube designs.

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.

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.

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.

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

One of the prominent techniques in the field of Total Quality Management (TQM) is Failure Mode and Effects Analysis (FMEA). FMEA facilitates the recording of failures and analysing them to provide solutions for preventing their recurrence. During the early periods of FMEA evolution, there were two types namely Design FMEA and Process FMEA (PFMEA) in practice. In recent days, more types such as System FMEA, Service FMEA and Maintenance FMEA are being prescribed by the researchers. Meanwhile, a number of benefits of FMEA implementation have been reported. Yet, FMEA has not found its implementation in various fields. One among them is service field. This paper reports the examination of FMEA implementation in service industry. This direction of research led to the design of an improved model, named as 'Modified service FMEA'. Its implementation was examined in an Indian State Government owned passenger Transport Company. Despite certain practical hurdles, this exercise was successful in developing modified service FMEA table and pinpointing the seriousness of failures through the portrayal of Service Lost (SL) and Cost Lost (CL).

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

Reliability‐Centred Maintenance (RCM) is an economical maintenance approach that places more emphasis on sustaining system functionality than just fixing individual hardware parts. Failure Mode and Effects Analysis (FMEA), one of the main techniques in RCM, is used to determine possible failure modes, their underlying causes, and how they affect system performance. This study identifies the greatest risk that affects the system by using a higher risk priority number (RPN) and clearly illustrates the maintenance plan for each failure scenario. By putting in place a systematic RCM policy, the study seeks to reduce these risks. The results show that, in terms of the impact of failure, the machines in the spinning portion are the most crucial. By lowering risk through a focused FMEA approach, the RCM methodology's implementation shows notable economic benefits. The suggested approach improves maintenance efficiency by concentrating on the most important 20% of failure causes. According to the findings, implementing RCM resulted in a 31.5% decrease in labour expenses. Additionally, compared to the case company Bahir Dar Textile Share Company's (BDTSC) present run‐to‐failure maintenance policy, the proposed maintenance plan reduced the yearly spare parts costs by 28.12%. The results highlight RCM's potential for enhancing maintenance plans, cutting expenses, and raising overall operational effectiveness in the textile industry.

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

The apartment is a vertical building used for residential purposes. It is included in a high rise building category with high occupational risk accident level due to its work complexity, or high elevation factors. This research discussed the risk assessment of occupational accident at one of the apartment constructions in Surabaya in order to determine its dominant risk factors and mitigation. Failure Mode and Effect Analysis (FMEA) method was applied to this research. It is a risk assessment method based on severity, occurrence, and detection. Following the relevant assessment, it resulted in 3 types of occupational accidents with the highest Risk Priority Number, namely height work accident, falling material from tower crane, and risk of landslide excavation.

Quality control is a proactive of systematic effort to identify and eliminate the root causes of defects in Crude Palm Oil (CPO) by focusing resources on process points that have the highest risk of failure. The goal is to achieve optimal product quality consistency at PT. Kebun Ganda Prima. The application of the Failure Mode and Effect Analysis (FMEA) method in the processing of oil palm fresh fruit bunch (FFB) into CPO at PT. Kebun Ganda Prima PKS Kembayan can serve as an effective alternative to identify, evaluate, and analyze potential failures that may occur during the production process. This study aims to enhance CPO quality by minimizing product defects and optimizing production efficiency through the tracing of critical points in the process that are susceptible to failure. The results of the FMEA analysis indicate that the highest-risk failure mode occurs at the Fresh Fruit Bunch (FFB) reception stage, specifically poor oil palm fruit quality, with the highest Risk Priority Number (RPN) value of 128.85. This condition is characterized by a high proportion of overripe or rotten fruit, which is the primary cause of the increased Free Fatty Acid (FFA) content in the CPO. Additionally, other potential failures were identified in the form of imperfect stripping (threshing) and malfunction of key equipment, such as the sterilizer and thresher. Based on these findings, quality control efforts should be focused on strict supervision of initial raw material quality and the implementation of preventive maintenance on critical machinery. The proposed recommendations include tightening the Standard Operating Procedures (SOPs) at the FFB reception stage and optimizing the preventive maintenance schedule to ensure that the processing runs consistently and produces CPO that meets quality standards.

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

A good quality control system is important to be implemented to increase productivity and minimize defects in products. One of the quality control methods is failure mode and effect analysis (FMEA). This study uses the FMEA to identify the causes of the defects and recommend the prevention methods to overcome the causes of the defects in an Indonesian paper mill. The risk priority number (RPN) is calculated by multiplying the severity, occurrence, and detection of the failures that have been determined. Unsymmetrical and tainted products are the most dominant defects in the paper mill. An inappropriate machine setting is the cause of unsymmetrical products with the highest RPN of 343. The second highest RPN is problems with bleaching machines that caused tainted products with an RPN value of 216. This study offers suggestions to Indonesian paper mill to prevent and minimize defective products.

PT XYZ is a manufacturing company that produces electronics. During the production process, a significant issue has been identified - a high number of defective products due to dust contamination. This research is designed to analyze the factors contributing to these defects during the visual inspection process, using the Failure Mode and Effect Analysis (FMEA) method. This research can identify the factors causing failure, the severity level, and the probability of negative effects of the failure. Additionally, it allows us to determine the corrective actions or improvements needed to prevent or reduce the risk of product failure during the visual inspection process. The results of the research indicate that the primary cause of the NG dust-type products, based on the highest Risk Priority Number (RPN) value of 320, is operator skills. This factor is a potential cause of the NG dust-type products. Therefore, analysis and simulation of proposed improvements are being conducted, to reduce the number of NG products in the visual inspection process.