Managing uncertainty of expert’s assessment in FMEA with the belief divergence measure

Abstract

Failure mode and effects analysis (FMEA) is an effective model that identifies the potential risk in the management process. In FMEA, the priority of the failure mode is determined by the risk priority number. There is enormous uncertainty and ambiguity in the traditional FMEA because of the divergence between expert assessments. To address the uncertainty of expert assessments, this work proposes an improved method based on the belief divergence measure. This method uses the belief divergence measure to calculate the average divergence of expert assessments, which is regarded as the reciprocal of the average support of assessments. Then convert the relative support among different experts into the relative weight of the experts. In this way, we will obtain a result with higher reliability. Finally, two practical cases are used to verify the feasibility and effectiveness of this method. The method can be used effectively in practical applications.

Challenges experienced during pharmacy automation and robotics implementation in JCI accredited hospital in the Arabian Gulf area: FMEA analysis-qualitative approach

“…It involves considering cost, timeframe, efficacy, and feasibility when prioritizing corrective actions ( Carlson, 2012 , Stamatis, 2003 ). However, its implementation can be challenging due to insufficient resources, lack of buy-in, difficulty in identifying failure modes, difficulty in prioritizing corrective measures, uncertainty, and lack of follow-up; henceforth, organizations shall commit to continuous monitoring and follow-up to ensure proper implementation and minimize the risk associated with failure modes ( Asan and Soyer, 2016 , Li et al, 2023 , Liu and Tang, 2022 , Stamatis, 2003 , Wu et al, 2023 , Yazdi, 2019 ). Misconceptions concerning FMEAs, such as their one-time nature, flexibility with organizational goals and resources, and difficulties for small enterprises, must be addressed for successful implementation.…”

“…Uncertainty can arise in FMEA when the severity or occurrence of a failure mode is difficult to predict or when the effects of a failure mode are unknown ( Asan and Soyer, 2016 , Liu and Tang, 2022 ). In such cases, the FMEA team may need to engage subject-matter experts, conduct additional tests or experiments, consult relevant literature to reduce uncertainty, gather additional data, perform expert reviews, use data-driven approaches, perform sensitivity analysis, apply statistical methods, and execute pilot studies ( Asan and Soyer, 2016 , Li et al, 2023 , Liu and Tang, 2022 , Wu et al, 2023 , Yazdi, 2019 , Yuan and Tang, 2022 ).…”

“…Uncertainty can arise in FMEA when the severity or occurrence of a failure mode is difficult to predict or when the effects of a failure mode are unknown ( Asan and Soyer, 2016 , Liu and Tang, 2022 ). In such cases, the FMEA team may need to engage subject-matter experts, conduct additional tests or experiments, consult relevant literature to reduce uncertainty, gather additional data, perform expert reviews, use data-driven approaches, perform sensitivity analysis, apply statistical methods, and execute pilot studies ( Asan and Soyer, 2016 , Li et al, 2023 , Liu and Tang, 2022 , Wu et al, 2023 , Yazdi, 2019 , Yuan and Tang, 2022 ). Consequently, to minimize bias in FMEA data collection, organizations must define clear criteria, train data collectors objectively, use multiple data sources, use blind data collection, use standardized measurement tools, monitor data quality, and conduct peer reviews ( Asan and Soyer, 2016 , Huang et al, 2020 , Liu et al, 2019 , Liu et al, 2020 , Liu et al, 2022 , Liu, 2019 , McDermott et al, 2009 , Stamatis, 2003 , Wu et al, 2023 ).…”

An Enhanced Pignistic Transformation-Based Fusion Scheme With Applications in Image Segmentation

“…Multi-source information fusion is a kind of technique that supports multi-sensors to make objective and accurate decisions, which is firstly applied in military fields [1]. In recent years, with the rapid development of information technology and knowledge-based artificial intelligence, the application of multi-source information fusion has been extended to civil decision-making [2], [3], [4]. Evidence theory is a widely accepted decision-making structure to solve multi-source information fusion problems [5], which has the capability to express uncertainty and ignorance when making decisions [6], [7], [8].…”

Application of Statistical Data and Methods to Establish RPN Ratings of Fmea Method for Construction Projects

“…According to the above-stated, the FMEA model should be a critical method which can reduce operating errors or delays and enhance building quality. In the FMEA model, the Risk Priority Number (RPN) can help engineering managers determine the problem-solving priority by multiplying 10 levels of Occurrence, Severity, and Detection each, and then problems can be solved by related improvement techniques and methods (Yuan & Tang, 2022;Liu & Tang, 2022;Chakhrit & Chennoufi, 2021;Jiang et al, 2019). Furthermore, some studies use the belief Jensen-Shannon divergence and entropy measure in the evidence theory to improve the science of FMEA method (Tang et al, 2023;Xie et al, 2020).…”