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FMEA Using Uncertain Linguistic GRA-TOPSIS and Its Application to Endotracheal Suctioning

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Improved FMEA Methods for Proactive Healthcare Risk Analysis

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Abstract

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.

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Authors and Affiliations

  1. School of Economics and Management, Tongji University, Shanghai, China

    Hu-Chen Liu

  2. College of Economics and Management, China Jiliang University, Zhejiang, China

    Hu-Chen Liu

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  1. Hu-Chen Liu
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Liu, HC. (2019). FMEA Using Uncertain Linguistic GRA-TOPSIS and Its Application to Endotracheal Suctioning. In: Improved FMEA Methods for Proactive Healthcare Risk Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-13-6366-5_8

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  • DOI: https://doi.org/10.1007/978-981-13-6366-5_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6365-8

  • Online ISBN: 978-981-13-6366-5

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