FMEA Using IVIF-COPRAS and IVIF-ANP and Its Application to Hospital Service Diagnosing
In this chapter, a new FMEA method which integrates complex proportional assessment (COPRAS) and analytic network process (ANP) is proposed to assess and rank the risk of failure modes under interval-valued intuitionistic fuzzy context.
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FMEA Using IVIF-COPRAS and IVIF-ANP and Its Application to Hospital Service Diagnosing
In this chapter, a new FMEA method which integrates complex proportional assessment (COPRAS) and analytic network process (ANP) is proposed to assess and rank the risk of failure modes under interval-valued intuitionistic fuzzy context. The proposed risk priority method combines the advantages of interval-valued intuitionistic fuzzy sets (IVIFSs) in coping with uncertainty, vagueness, and incompleteness, and the merits of COPRAS and ANP in solving multiple criteria decision-making (MCDM) problems. Finally, a practical case in hospital service setting is presented to illustrate the accuracy, effectiveness, and flexibility of the proposed model.
10.1 Introduction FMEA is a perspective risk analysis tool in identifying and eliminating potential failures, problems, and errors to improve the reliability and safety of systems, designs, processes, and services (Stamatis 2003; Liu 2016). The main purpose of FMEA is to correct the most important failure modes before they reach customers, rather than solving them after failures happen. Although the traditional FMEA proves to be a useful risk assessment technique, it is criticized extensively for a variety of disadvantages in the literature (Liu et al. 2013; Liu 2016). The major limitations which we focus on in this chapter are summarized as follows (Huang et al. 2017; Liu et al. 2017; Wang et al. 2017; Anes et al. 2018; Carpitella et al. 2018; Catelani et al. 2018): (1) The risk factors are difficult to be precisely and completely evaluated because of the uncertainty and vagueness of FMEA team members’ judgments. (2) Only three risk factors O, S, and D are included for evaluating failure modes in the conventional FMEA, which may ignore other important risk factors. (3) The relative importance of risk factors is not taken into account, which is not reasonable in the real situation. (4) The use of RPNs to determine the risk ranking of failure modes is questionable and debatable.
© 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_10
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10 FMEA Using IVIF-COPRAS and IVIF-ANP …
In the literature, many researchers have applied fuzzy logic to address the uncertainty and improve the performance of FMEA (Liu et al. 2015a; Supciller and Abali 2015; Selim et al. 2016; Baykaso˘glu and Gölcük 2017; Geramian et al. 2018; Ilbahar et al. 2018; Park et al. 2018). However, the fuzzy set is characterized by only membership function and the initial risk assessment information may be distorted or lost in the prioritization of failure modes. Accordingly, intuitionistic fuzzy sets (IFSs) were used by some authors to express experts’ risk assessments in FMEA (Liu et al. 2015b; Sayyadi Tooranloo and Ayatollah 2016; Sayyadi Tooranloo et al. 2018). The IFS consisting of membership degree and non-membership degree can address the uncertainty of assessments in a more comprehensive manner. However, dec
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