Failure Mode And Effects Analysis Methodology Research Articles

Reducing overnight charges in the loading process of finished goods using FMEA method at PT. XYZ

As a part of the supply chain, the logistics department plays a crucial role in the operational activities of a company. Like other departments, the logistics department also incurs costs that do not add value, such as overnight charges. Failure in the loading of finished goods process can result in a dwell time exceeding 18 hours per vehicle, leading to overnight charges. This occurrence can be prevented by identifying risks and failure modes using Failure Mode and Effect Analysis (FMEA). The FMEA methodology is widely utilized across various industries, including manufacturing, for product development. Additionally, FMEA is frequently employed in failure analysis in service industries, food services, construction, mining, agriculture, and healthcare. The research was conducted to identify failure modes and potential causes in the loading of finished goods process. In PT. XYZ, there are five finished goods storage areas. From the process of weigh-in time, loading finish goods, and weigh-out time, 25 activities were analyzed, and seven activities with the highest Risk Priority Number (RPN) were selected for solutions. The activity with the highest RPN, scoring 512, was the queuing of trucks exceeding loading capacity. The why-why analysis technique was employed to assist in finding solutions. From the seven activities with the highest RPN, 9 solution plans were derived. The recommendation to reduce queue trucks is to organize and monitor the vehicles dispatched by the Freight Forwarder. As a result, the dwell time per vehicle decreased from 9.23 hours to 6.36 hours, and the overnight charges reduced from 148,145 USD to 12,345 USD

Risk Analysis of Operational Disruptions in Public Electric Vehicle Charging Stations Using the Failure Mode and Effects Analysis (FMEA) Method

The Indonesia government is actively promoting the adoption of electric vehicles, as detailed in the 2021-2030 Electricity Supply Business Plan. The state-owned electricity provider, PLN, is responsible for establishing Public Electric Vehicle Charging Stations (PEVCS). However, several of these stations have encountered malfunctions; notably, 82 of the 567 stations are classified as Unavailable, indicating they are non-functional. Research literature points to a financial loss of $34,000 from operational issues at PEVCSs. This research aims to helps management understand and prioritize disruption that leads to failures or damages at these stations. Method used is the Failure Mode and Effects Analysis (FMEA) method along with logistic regression to examine the disruptions at PEVCSs labeled as Unavailable. The data for this research comes from a six-month historical record of PEVCS disruptions. The variables utilized for logistic regression analysis include foundational variables from the FMEA methodology—Severity, Occurrence, and Disturbance—complemented by two supplementary variables: the speed and age of the PEVCS. Result was found that three out of twelve types of disruptions have a high likelihood of failure, specifically issues with Device Communication, Connectivity, and Emergency Stop functions. A disruption is deemed likely to cause failure if its probability exceeds 50%.

Evaluation of the effectiveness of defense measures during radiological risk assessment in gynecological brachytherapy

The implementation of risk analysis to all those practices that work with ionizing radiation is of paramount importance. Thanks to the studies published on risk assessment in radiation medicine, a culture on this subject is being created, which has led to the systematization of different methods created for this purpose. Such is the case of the Failure Mode and Effects Analysis (FMEA), the basis on which this research has been developed. The American Association of Physicist in Medicine (AAMP) TG-100 report, which provides all the details on the use of this technique, is used as main reference. On the other hand, the recommendations recently published by AAMP TG-275 report are also used, applying the FMEA methodology to the high dose rate gynecological brachytherapy (B-HDR-GYN), among other practices. The novelty of this research is the implementation of an operational algorithm that improves the analytical capacity of the traditional FMEA approach by allowing the measurement of the effectiveness of defense measures within it. In this study, when the defense measures interact in the FMEA, a decrease of four RPN values of the failure modes (FM) occurs; compared to reference values. This algorithm is based on a synergy of the risk matrix (RM) approach and the information on FMEA available in the TG-100 and TG-275.

Reducing the Risk of Fall among Oncology Patients using Failure Modes and Effects Analysis.

This project aimed to mitigate the risk of falls among oncology patients using Failure Modes and Effects Analysis (FMEA) in the outpatient setting.<br /><br /><br /><br />Methods: The project was conducted within outpatient settings, specifically encompassing outpatient clinics, daycare, radiology and radiotherapy, and rehabilitation at the SQCCCRC. The project employed an observational analytical design to assess the fall risk assessment procedure in outpatient settings. The project integrated a 7-step procedure for conducting an FMEA methodology, including defining the system or process, identifying potential failure mode, evaluating the effects of each failure mode, Assigning severity, likelihood, and detection of occurrence ratings, and identifying and implement corrective actions. In addition, Risk Priority Numbers (RPNs) were used to identify the impact of the interventions in reducing the risk of patient fall assessment and management.<br /><br /><br /><br />Result: In the patient fall screening process, interventions yielded substantial reductions in RPNs for failure modes like "Wrong assessment" (57% decrease) and "Complex risk assessment scale" (63% decrease), addressing knowledge gaps and simplifying risk assessment. Similarly, the "Missed fall assessment" failure mode saw an impressive 80% reduction in RPN, rectifying unclear processes and knowledge gaps. In the Fall risk precaution measures process, interventions led to noteworthy RPN reductions, such as 80% for "Unclear fall precaution measures-responsibilities" and 57% for "Missed bracelets for high risk," demonstrating successful risk mitigation. Moreover, interventions in the Patient Education process achieved significant RPN reductions (57% and 55%) for "No/improper education" and "Unuse of educational material and resources," enhancing staff education and patient awareness. The total reduction in RPNs was 62% in all failure modes in the fall assessment and management process. <br /><br /><br /><br />Conclusion: Overall, FMEA is a valuable strategy for reducing fall risks among oncology patients, but its success depends on addressing these limitations and ensuring the thorough execution and maintenance of the identified corrective actions.

Failure mode and effects analysis applied to the administration of liquid medication by oral syringes

ObjectiveTo carry out a Failure Mode and Effects Analysis (FMEA) to the use of oral syringes. MethodsA multidisciplinary team was assembled within the Safety Committee. The stages of oral administration process of liquid medication were analysed, identifying the most critical and establishing the potential modes of failure that can cause errors. The impact associated with each mode of failure was calculated using the Risk Priority Number (RPN). Preventive actions were proposed. ResultsFive failure modes were identified, all classified as high risk (RPN> 100). Seven of the eight preventive actions were implemented. ConclusionsThe FMEA methodology was a useful tool. It has allowed to know the risks, analyse the causes that cause them, their effects on patient safety and the measures to reduce them.

Failure modes and effects analysis of pediatric I-131 MIBG therapy: Program design and potential pitfalls.

There is growing interest among pediatric institutions for implementing iodine-131 (I-131) meta-iodobenzylguanidine (MIBG) therapy for treating children with high-risk neuroblastoma. Due to regulations on the medical use of radioactive material (RAM), and the complexity and safety risks associated with the procedure, a multidisciplinary team involving radiation therapy/safety experts is required. Here, we describe methods for implementing pediatric I-131 MIBG therapy and evaluate our program's robustness via failure modes and effects analysis (FMEA). We formed a multidisciplinary team, involving pediatric oncology, radiation oncology, and radiation safety staff. To evaluate the robustness of the therapy workflow and quantitatively assess potential safety risks, an FMEA was performed. Failure modes were scored (1-10) for their risk of occurrence (O), severity (S), and being undetected (D). Risk priority number (RPN) was calculated from a product of these scores and used to identify high-risk failure modes. A total of 176 failure modes were identified and scored. The majority (94%) of failure modes scored low (RPN <100). The highest risk failure modes were related to training and to drug-infusion procedures, with the highest S scores being (a) caregivers did not understand radiation safety training (O=5.5, S=7, D=5.5, RPN=212); (b) infusion training of staff was inadequate (O=5, S=8, D=5, RPN=200); and (c) air in intravenous lines/not monitoring for air in lines (O=4.5, S=8, D=5, RPN=180). Through use of FMEA methodology, we successfully identified multiple potential points of failure that have allowed us to proactively mitigate risks when implementing a pediatric MIBG program.

Use and effectiveness of the Failure Modes and Effects Analysis (FMEA) for identification of potential errors and failures in the process of root canal treatment

Background and Aim: Root canal treatment (RCT) has high technical sensitivity, and many human procedural errors can compromise the success of treatment and well-being of patients. The failure modes and effects analysis (FMEA) is a risk assessment and management tool that ensures the safety of patient care by a systematic approach to failures. This study aimed to identify the potential errors and failures in the process of RCT using the FMEA.Materials and Methods: This descriptive study evaluated the failure modes and their effects qualitatively and quantitatively using the FMEA methodology. For this purpose, a FMEA team was first established and the steps of RCT were described in a flowchart. Next, the potential errors and failures in each step were identified, and each failure mode was scored from 1 to 10, based on the severity of impact, likelihood of occurrence, and likelihood of detection. The three scores were multiplied to obtain the risk priority number (RPN). Corrective measures and preventive strategies were suggested for high-risk failure modes (RPN≥250).Results: The FMEA identified 19 steps and 48 potential failures in the process of RCT. The maximum RPN in the process of RCT was assigned to file fracture in the root canal (RPN=324), apical extrusion of irrigating solution (RPN=320), and inappropriate or no rubber dam isolation (RPN=315).Conclusion: The FMEA has high efficacy for detection and prioritization of improvable points in a complex dental procedure in a busy department such as the endodontics department.

Harnessing system-focused simulation, debriefing and FMEA to inform healthcare blood transfusion safety and policy

Healthcare systems improvement using simulation and debriefing is an increasingly employed, yet underutilized quality improvement tool to enable user-centred design. This approach allows users to experience real-life systems and processes through simulation and then provide feedback on how a system supports them within their role. Understanding this interaction of people and their systems is critical to safe, quality, reliable and efficient care and bridges the gap between how we think a system is working and how it is working. This novel project was collaboratively developed and led by simulation, human factors and patient safety experts and used existing organizational safety data to target further high-risk safety threats surrounding administering, cross-checking and labelling blood products for transfusion. A system-focused simulation-based approach was used to identify system issues for a large healthcare organization’s transfusion policy redesign. A Failure Mode and Effects Analysis (FMEA) was then used to apply a risk score to the findings from the simulation user feedback to inform a large high-risk policy redesign. Multiple recommendations were provided to the participating units and policy and procedure redesign teams surrounding environmental issues, standards, interpretation and usability of the policy. Our collaborative patient safety, simulation and human factors project was successful in proactively identifying both active and latent factors contributing to adverse events and identifying recommendations using FMEA methodology to improve patient safety, including revisions to the physical space within the lab, and the provincial blood transfusion policy and procedure.

Implementation of the failure modes and effects analysis in a Hospital Radiopharmacy Unit

AimThe aim of this study is the implementation in a Hospital Radiopharmacy Unit of a risk analysis methodology in order to proactively identify possible failure modes and prioritize corrective measures. Materials and methodsBy means of the failure modes and effects analysis (FMEA), the possible failure modes of each of the stages of the processes of prescription, preparation, and administration of radiopharmaceuticals for diagnostic and therapy were identified. From the variables of severity, probability and detectability, the risk was quantified using the Risk Priority Number (RPN) for each failure mode, sub-process, and type of radiopharmaceutical. Improvement measures were established and the reduction in the RPN value was calculated. ResultsA total of 96 failure modes were identified (58 for diagnostic radiopharmaceuticals and 38 for therapy). Biunivocal identification of the patient with the radiopharmaceutical is the failure mode with the highest RPN (60) and the radiolabeling cell sub-process the one that has the highest risk (RPN 286). As a result of the improvement measures, the overall RPN was reduced by 22% for diagnostic radiopharmaceuticals and 20% for therapy. This reduction would be 46% and 31% respectively if radiopharmacy software and a barcode technology in the administration were implemented. ConclusionsThe application of the FMEA methodology as a risk analysis tool allows to identify the critical points of the processes related to radiopharmaceuticals and prioritize measures to reduce the risk.

Implementación del análisis modal de fallos y efectos en una Unidad de Radiofarmacia Hospitalaria

AimThe aim of this study is the implementation in a Hospital Radiopharmacy Unit of a risk analysis methodology in order to proactively identify possible failure modes and prioritize corrective measures. Materials and methodsBy means of the failure modes and effects analysis (FMEA), the possible failure modes of each of the stages of the processes of prescription, preparation, and administration of radiopharmaceuticals for diagnostic and therapy were identified. From the variables of severity, probability and detectability, the risk was quantified using the Risk Priority Number (RPN) for each failure mode, sub-process, and type of radiopharmaceutical. Improvement measures were established and the reduction in the RPN value was calculated. ResultsA total of 96 failure modes were identified (58 for diagnostic radiopharmaceuticals and 38 for therapy). Biunivocal identification of the patient with the radiopharmaceutical is the failure mode with the highest RPN (60) and the radiolabeling cell sub-process the one that has the highest risk (RPN 286). As a result of the improvement measures, the overall RPN was reduced by 22% for diagnostic radiopharmaceuticals and 20% for therapy. This reduction would be 46% and 31% respectively if radiopharmacy software and a barcode technology in the administration were implemented. ConclusionsThe application of the FMEA methodology as a risk analysis tool allows to identify the critical points of the processes related to radiopharmaceuticals and prioritize measures to reduce the risk.

REORGANIZATION OF THE ASSEMBLY STATION IN THE PRODUCTION PROCESS OF THE SLIDING FLOOR FOR RELOADING RAMPS IN THE CONTEXT OF IMPROVING THE QUALITY OF THE FINISHED PRODUCT

The paper presents the definitions of the term quality quoted in the literature on the subject. FMEA (Failure Mode and Effects Analysis) methodology was presented. A reloading ramp was characterized as the subject of the study. A cause-and-effect analysis of non-compliance in the finished product was performed using the Pareto-Lorenz diagram, Ishikawa diagram and brainstorming. The FMEA analysis of the assembly table indicated the elements of the workstation causing the most frequently occurring non-conformities. Based on the obtained results, corrective actions were proposed to reorganize the assembly station. The implemented activities made it possible to reduce the critical RPN coefficients for the elements of the assembly table and to shorten the time necessary to make one piece of the finished product.

Validation plan of bone marrow collection, processing and distribution using the failure mode and effect analysis methodology: a technical report

Background aimsBone marrow (BM) is commonly used in the pediatric and adult setting as a source of hematopoietic stem cells (HSCs). The standards of the Joint Accreditation Committee of the International Society for Cell & Gene Therapy & European Society for Blood and Marrow Transplantation (JACIE) include specific requirements regarding BM collection, processing and distribution. To run this process, each transplant team develops a series of JACIE-compliant procedures, customizing them with regard to local settings and paths. Moreover, JACIE standards require that transplant teams validate and periodically revise their procedures to keep the entire process under control. In this article, the authors describe the methodology adopted in our center to fulfill the aforementioned JACIE requirements. MethodsThe authors developed a validation plan based on the failure mode and effect analysis (FMEA) methodology. According to the FMEA approach, the authors carefully revised activities and procedures connected to BM collection, processing and distribution at our institution. The entire process was initially divided into five main phases (assessment of donor eligibility, perioperative autologous blood donation, preparation of BM collection kit, BM harvesting and BM processing and distribution), comprising 17 subphases and 22 activities. ResultsFor each activity, one or more failure modes were identified, for a total of 28 failure modes, and a risk priority number (RPN) was then assigned to each failure mode. Although many procedures were validated, others were subjected to substantial changes according to the RPN rating. Moreover, specific indicators were identified for subsequent monitoring to contain the risk of failure of steps emerging as critical at FMEA. ConclusionsThis is the first study describing use of the FMEA methodology within an HSC transplant program. Shaping the risk analysis based on local experience may be a trustworthy tool for identifying critical issues, directing strict monitoring of critical steps or even amending connected procedures. Overall, the FMEA approach enabled the authors to improve our process, checking its consistency over time.

Improving reliability engineering in product development based on design theory: the case of FMEA in the semiconductor industry

In industry, the failure mode and effect analysis (FMEA) methodology is one of the main tools used for reliability management in product design and development. However, the academic literature highlights several shortcomings of the FMEA methodology. Therefore, the main purposes of this paper are the analysis of the weaknesses of FMEA, the improvement of the method, and the implementation of a new methodology able to support quality and reliability management in a more efficient way. Motivated by these objectives, a formal new methodology is proposed by extending the classic FMEA methodology through C-K design theory. To test the effectiveness of the proposed approach and analyze the acceptance of this method by users, a case study is conducted in STMicroelectronics, one of the European leaders in the semiconductor industry.

A failure mode and effect analysis (FMEA)-based approach for risk assessment of scientific processes in non-regulated research laboratories

Nowadays, Quality Management tools such as failure mode and effect analysis (FMEA) are widely used throughout the aeronautical, automotive, software, food services, health care and many other industries to sustain and improve quality and safety. The increasing complexity of scientific research makes it more difficult to maintain all activities under control, in order to guarantee validity and reproducibility of results. Even in non-regulated research, scientists need to be supported with management tools that maximize study performance and outcomes, while facilitating the research process. Frequently, steps that involve human intervention are the weak links in the process. Risk analysis therefore gives considerable benefit to analytical validation, assessing and avoiding failures due to human error, potential imprecision in applying protocols, uncertainty in equipment function and imperfect control of materials. This paper describes in detail how FMEA methodology can be applied as a performance improvement tool in the field of non-regulated research, specifically on a basic Life Sciences research process. We chose as “pilot process” the selection of oligonucleotide aptamers for therapeutic purposes, as an example of a complex and multi-step process, suitable for technology transfer. We applied FMEA methodology, seeking every opportunity for error and its impact on process output, and then, a set of improvement actions was generated covering most aspects of laboratory practice, such as equipment management and staff training. We also propose a useful tool supporting the risk assessment of research processes and its outputs and that we named “FMEA strip worksheet.” These tools can help scientists working in non-regulated research to approach Quality Management and to perform risk evaluation of key scientific procedures and processes with the final aim to increase and better control efficiency and efficacy of their research.

4317 Using Failure Modes and Effects Analysis to Guide Adaptation of an Evidence-Based Parenting Program for Mothers with Substance Use Disorders

OBJECTIVES/GOALS: To identify possible failures that could occur in the delivery of an evidence-based parenting program for mothers with substance use disorders (SUD) through existing home-visiting services, and to develop solutions to the most significant failures. METHODS/STUDY POPULATION: Using failure modes and effects analysis (FMEA) methodology, we conducted two 2-hour advisory panel discussions with 15 people from a variety of disciplines and life experiences related to SUDs. The intervention delivery process included five steps: (1) Recruitment, (2) Screening, (3) Matching, (4) Enrollment in person, and (5) Intervention delivery. Participants collectively determined possible failures, causes, and consequences. Participants then agreed on three scores (Likert Scale 0-10) for the likelihood of occurrence, detection, and severity of the failure, with 10 being the highest likelihood, difficulty detecting, or severity. A risk priority number (RPN) was calculated as the product of the 3 scores (maximum RPN = 1,000). The group then identified possible solutions for failures with higher RPNs. RESULTS/ANTICIPATED RESULTS: For each step in the process we identified the following number of failure nodes and RPN scores: (1) recruitment: 13 failures; RPN = 800, (2) screening: 102 failures; RPN = 10, (3) matching: 4 failures: RPN = 490, (4) enrollment: 6 failures; RPN = 80, (5) delivery: 11 failures; RPN = 80. The most critical failures related to recruitment and were perceived as being caused by potential development of mistrust in the community. Participants strongly encouraged the use of “strengths-based language,” clear referral plans for mothers that did not qualify, and inclusion of mothers that did not have custody of their children. These findings resulted in changes to the screening script, enrollment procedures, and inclusion criterial for the program. DISCUSSION/SIGNIFICANCE OF IMPACT: FMEA methodology was particularly effective in identifying possible failures for the integration of an evidence-based parenting program into existing home-visiting services as they related to the psychological safety of mothers with SUDs. The process resulted in direct changes to procedures for the anticipated program integration and study.

A Novel Failure Mode and Effects Analysis Model Using Triangular Distribution-Based Basic Probability Assignment in the Evidence Theory

Failure mode and effects analysis (FMEA) is a typical risk assessment and prevention technology, in which different experts provide different assessments on the target system to identify the risk grades of its components. Sometimes, the assessments contain conflict information. How to manage and fuse the conflict assessment information is an open issue. We propose a triangular distribution-based basic probability assignment (TDBPA) method to model and fuse the conflict risk level coming from different experts' assessments in the frame of Dempster-Shafer evidence theory. First, the subjective assessments of risk analysis from domain experts are modeled with belief structure in Dempster-Shafer evidence theory. Then, the assessments are transformed as the TDBPA function. Thirdly, the conflict risk assessments from the FMEA team for failure analysis can be fused with Dempster rule of combination. After that, the modified risk priority number (RPN) model based on fused assessment can be calculated for ranking of failure modes. Finally, recommended actions should be taken for prevention of potential risk items. We verify the rationality and efficiency of the proposed method with a case study in the blades of an aircraft turbine. In short, the presented FMEA methodology procedure in this paper is well organized so that we can apply it in a more simple and understandable way. Utilizing the character of triangular distribution, taking adjacent values into account, TDBPA method can smooth the conflict assessment for information fusion. In addition, the shortcoming of repeating values in classical RPN is eliminated in the proposed method, which improves the ability for risk assessment of FMEA.

Evaluating radiotherapy treatment delay using Failure Mode and Effects Analysis (FMEA)

PurposeThis study identified and evaluated the factors that are responsible for delay in the clinical workflow of radiation therapy, starting from the CT simulation (CT-Sim) to the first fraction of treatment delivery using the Failure Mode and Effects Analysis (FMEA) methodology. Materials and methodsA total of 1106 patient cases were retrospectively analyzed using FMEA methodology. For each failure mode (FM), the following factors were rated and discussed by the group: occurrence (O), severity (S), detectability (D), and methods of improvement or mitigation. In addition, two new factors, namely social effect (SE) and economic effect (EE), were introduced to evaluate the impact of FM on the department or hospital. Risk priority number (RPN) and the product of RPN, SE, and EE (i.e. RPNSE-EE) were calculated for each FM. ResultsAverage delay caused by identified FM was 8 days while 76% of the FMs resulted in delay of less than 5 days. The RPN of all the FMs ranged from 4 to 60 with an average value of 18. “Tumor volume, prescription and objective” had the highest average RPN of 23. The majority of FMs with high RPN were identified in “CT-Sim” (RPN: 21.5 ± 11.1; RPNSE-EE: 97.0 ± 46.4) and “treatment planning” (RPN: 20.1 ± 8.1, RPNSE-EE: 152.9 ± 76.5) stages. ConclusionThe FMEA enabled identification of the factors that caused delay in the pre-treatment process of radiation therapy. “CT-Sim” and “treatment planning” stages had more FMs with high RPN values which have higher priority for future improvement. Two new factors, SE and EE, were introduced and appeared to be valuable in evaluating the impact of FMs on radiation oncology department or hospital in general.

Improving safety in blood transfusion using failure mode and effect analysis.

One of the medical areas where errors can have more serious consequences is the process of blood transfusion. We used failure mode and effect analysis (FMEA) for evaluating potential failures and improving transfusion safety in a medium-size urban hospital with a highly complex transfusion service. Each failure mode was evaluated using the likelihood of occurrence, severity of the effect, and probability of detection. The obtained results allowed each failure to be prioritized and decisions to be made in an organized manner to determine solutions. We define measures and indicators that allow the comparison of their results in a longer time period than most of the previous studies. The most important failures were those regarding 1) transmitting information about the transfusion request, 2) patient identification, 3) sample identification, 4) cross-matching ordered tests, 5) transfusing blood components, 6) completing and sending the transfusion control document, and 7) reporting of transfusion reactions. The application of the FMEA methodology allowed implementation of safety measures and monitoring of the measures using indicators, including the mandatory records of the hemovigilance system. There was a 56% improvement in the risk prioritization numbers in the second stage of the FMEA. FMEA allows for identification of factors that reduce safety in this hospital, analysis of the causes and consequences of these errors, design of corrective measures, and establishment of indicators to monitor their application. The FMEA methodology can help other institutions to identify their own specific vulnerabilities.

An FMEA-based integrated framework to quantify and improve operational performance in advanced manufacturing environments: a case study

The failure mode and effect analysis (FMEA) aims to improve the operational performance in manufacturing processes of a product. Current FMEA practice does not define a specific method for quantifying achievement in operational performances. Risk priority number (RPN) is independent of operational performance. The capability of current FMEA to improve operational performance cannot be validated unless a quantifiable operational performance improvement is demonstrated. This study offered guidelines to quantify the operational performance within the FMEA methodology. Different from traditional FMEA, the new FMEA-based integrated framework ascertains the priority set by RPN with operational performance indicators (OPI) for ensuring the priority is relevant to operational performance. Design for manufacturing and assembly (DFMA) and poka-yoke provide systematic guidelines for optimising design and developing a highly robust error-proofing system. Application of the new FMEA-based integrated framework in a medical device manufacturer in Malaysia demonstrated that the overall defect rate was reduced by 87.2%.

A novel evidential FMEA method by integrating fuzzy belief structure and grey relational projection method

Failure mode and effects analysis (FMEA) has been applied extensively in reliability engineering domain. Risk priority number (RPN), which is the product of occurrence (O), severity (S) and detection (D) of a failure is the most important measure used in FMEA for prioritizing risk. In this paper, a novel evidential FMEA integrating fuzzy belief structure and grey relational projection method(GRPM) is presented to avoid the use of traditional RPN, for it has been criticized with some weaknesses. First, a new distribution form of assessments applying fuzzy belief structure to represent the experts’ opinions in a more flexible and reasonable manner is constructed. Second, a new method to transform the experts’ fuzzy opinions into basic probability assignments (BPAs) is proposed. Third, to address the problems existing in conflicting evidence combination, the differences of experts’ authorities are taken into consideration, which is represented by the discounting coefficient. Besides, GPRM is adopted to address a more comprehensive assessments by fusing the 3 diverse criterion with weights respectively, which can overcome the limitations in traditional FMEA. An example is illustrated to show the practical application of the proposed FMEA methodology in engineering area.