FMEA Using Cloud Model and PROMETHEE Method and Its Application to Emergency Department
In this paper, we explore two key issues inherent to the FMEA practice: the representation of diversified risk assessments of FMEA team members and the determination of priority ranking of failure modes. Specifically, a framework integrating cloud model,
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FMEA Using Cloud Model and PROMETHEE Method and Its Application to Emergency Department
In this paper, we explore two key issues inherent to the FMEA practice: the representation of diversified risk assessments of FMEA team members and the determination of priority ranking of failure modes. Specifically, a framework integrating cloud model, a new cognitive model for coping with fuzziness and randomness, and preference ranking organization method for enrichment evaluation (PROMETHEE) method, a powerful and flexible outranking decision-making method, is developed for managing the group behaviors in FMEA. Moreover, FMEA team members’ weights are objectively derived taking advantage of the risk assessment information. Finally, we apply the new risk priority method to analyze the working process in an emergency department, and further validate its effectiveness via sensitivity and comparison discussions.
9.1 Introduction FMEA is a prospective risk-management tool used for assessing and eliminating potential failure modes of processes, systems, products, and services (Stamatis 2003). The FMEA implementation procedure was first formalized in 1980 in the Military Standard (MIL-STD 1629A) (Braaksma et al. 2012). In 1990, FMEA was recommended by the International Organization for Standardization (ISO) for design review in the ISO 9000 series. Later, formal processes of FMEA were established in 1994 by General Motors, Ford and Chrysler and in the standard J1739_199407 developed by the Society of Automotive Engineers (SAE). Nowadays, FMEA is viewed as a critical enabler to achieve continuous quality improvement in Lean/Six Sigma projects (Guerrero and Bradley 2013). The conventional RPN approach, however, has been reported as suffering from a lot of deficiencies, which make it lack repeatability and capability for improving maintenance routines continuously. The major drawbacks are stated as follows (Guerrero and Bradley 2013; Liu et al. 2016; Huang et al. 2017; Hu et al. 2018; Liu et al. 2019a, b): First, the three risk factors are supposed to be of equally importance in the RPN method. But, in real-life applications, the rela© 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_9
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tive weights of risk factors are often different. Second, the same RPN value can be derived with different combinations of O, S, and D scores, but the corresponding risk implications maybe dissimilar. Third, the risk factors are subjective and linguistic in nature, which cannot be determined precisely using a scale of numbers from 1 to 10. Fourth, the RPN score is obtained by multiplying three ordinal scale values, which is problematic in line with the measurement theory. As highlighted in many studies, FMEA is a group-oriented risk analysis approach normally applied by an inter-functional and multidisciplinary expert team (Guerrero and Bradley 2013; Liu et al. 2015a, 2017). For example
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