Table 16: Adaptive risk identification with scenario of risk weight threshold: hetero-hetero.Elements in level 1Risk factor Risk weight Risk (푆푙푠)
푆푂 퐷 푤푆 푤푂 푤퐷 GRPN (푅푙푠푒) 푇푙푠 Identify risk
푅 111 5 8 1 0.6 0.3 0.1 0.69 0.55 Yes
푅 112 5 8 4 0.3 0.6 0.1 0.81 0.78 Yes
푅 113 2 5 8 0.3 0.1 0.6 0.70 0.50 Yes
푅 114 2 9 3 0.6 0.1 0.3 0.42 0.62 No
푅 115 9 4 2 0.6 0.1 0.3 0.72 0.75 No
푅 116 5 9 5 0.3 0.6 0.1 0.85 0.61 Yes
푅 117 5 2 4 0.6 0.3 0.1 0.57 0.55 Yesanalysis on correlation and regression to compare it to the
traditional (TRPN-based) approach. To understand how the
proposed model works, we use a healthcare example as a
potential application of the proposed GRPN-based FMEA
model. An illustrated example of Down syndrome test is
given, and the computation of GRPNs is explained in detail.
We introduce two application modes based on experience
and preference. The experience-based mode allows the user
to choose a risk factor combination arbitrarily. This mode
can be used in different organizations, departments, or
processes, by estimating historical data of failure modes for
each of the risk factors (SOD). Under the preference-based
mode, we assume that the organization always defines a risk
management policy to identify failure modes. Therefore, the
weight combination is determined by the policy, for example,
(H,L,M).Afterselectingtheweightcombination,wesetthe
GRPN threshold to determine if the failure modes exist.
However, this paper only discusses two of various stochastic
models for the risk factor distribution, that is, uniform and
normal. In fact, there are numerous distributions in real
world. More realistically, future work can pay more attention
to testing and validation for various distributions.
Conflict of Interests
The authors declare that there is no conflict of interests
regarding the publication of this paper.
Acknowledgment
This paper is sponsored by the National Science Council of
Taiwan (NSC 98-2410-H-227-004).
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