FMEA Using Uncertain Linguistic GRA-TOPSIS and Its Application to Endotracheal Suctioning
This chapter provides a novel risk priority method for FMEA, which can overcome some inherent drawbacks of the traditional FMEA in risk evaluation, risk factor weighting, and RPN computation. Considering experts’ vagueness and uncertainty in their evaluat
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FMEA Using Uncertain Linguistic GRA-TOPSIS and Its Application to Endotracheal Suctioning
This chapter provides a novel risk priority method for FMEA, which can overcome some inherent drawbacks of the traditional FMEA in risk evaluation, risk factor weighting, and RPN computation. Considering experts’ vagueness and uncertainty in their evaluations on failure modes, 2-dimensional uncertain linguistic variables (2DULVs) are advised to describe the risk evaluation of failure modes and the reliability of the evaluation results. The grey relational analysis technique for order preference by similarity to ideal solution (GRA-TOPSIS) is applied for determining the risk ranking of the identified failure modes. In particular, a maximizing deviation method is employed for calculating the optimal weights of risk factors in an objective way. Via a practical risk analysis case of endotracheal tube (ETT) suctioning, the new FMEA is proved to be appropriate and effective in coping with the risk evaluation problems with uncertain linguistic information. Furthermore, by comparing with existing methods, it is shown that the proposed integrated method excels in the risk evaluation and prioritization of failure modes in FMEA.
8.1 Introduction FMEA is known to be a systematic risk analysis tool for identifying and mitigating known and/or potential failures, problems, and errors from the system, design, process, and/or service before they occur (Liu 2016). As an extensively used preventive reliability analysis technique, FMEA possesses significant capabilities of identifying potential failure modes, evaluating their causes and effects so as to determine a list of prevention actions that can diminish the chance of failures (Wang et al. 2009; Liu et al. 2018c). The main goal is to identify the most critical failure modes to assign limited resources in implementing improvement efforts. Since it underlines the prevention of errors, FMEA is aimed at providing precautionary strategies instead of exploring a solution after the happening of failures. Although FMEA has proven to be an important early proactive tool in assessing insidious failures and preventing their occurrence, the conventional RPN method still suffers from many limitations © Springer Nature Singapore Pte Ltd. 2019 H.-C. Liu, Improved FMEA Methods for Proactive Healthcare Risk Analysis, https://doi.org/10.1007/978-981-13-6366-5_8
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(Chang et al. 1999; Braglia et al. 2003; Pillay and Wang 2003; Wang et al. 2009). The major problems this chapter aiming to address are summarized as follows (Huang et al. 2017; Zhao et al. 2017; Carpitella et al. 2018; Liu et al. 2018c; Tian et al. 2018; Liu et al. 2019): (1) It is difficult to obtain precise evaluations of risk factors due to the uncertainty and ambiguity of subjective information provided by FMEA team members. (2) The mathematical equation for computing RPN is debatable and has no complete scientific basis. (3) The weights of the risk factors O, S, and D are not taken int
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