Risk Priority with Fuzzy Logic: Application of a Textile Factory

The most important reality in the business life that has not changed in the last fifty years is perhaps the necessity for the “customer satisfaction to be sustainable.” Every failure that adversely affects product quality also causes customer dissatisfaction. In this study, the Failure Mode and Effects Analysis (FMEA) was used to analyze the potential quality failures of the production system in a textile factory. By using this method, the probability, severity and detectability of quality faults (quality risks) which could lead to customer dissatisfaction were determined. In this method, the risk magnitudes are found by multiplying the probability, severity and detectability values of risks. These risks with high priority, which are also called the Risk Priority Numbers (RPN), are the risks which need to be considered as priority, and for which more resources are needed to be allocated. These three components are equally effective when determining the Risk Priority Number because of the multiplication operation. However, when ranking the risks, the role of the severity component is more important than the others. This is because a risk of low severity may rank low in the priority order even if it occurs very frequently (even if it has a high probability). Similarly, in the exact opposite condition, even if the probability is low, a risk with a high severity needs to be placed higher in the priority order, and more resources are needed to eliminate such risks. Due to this uncertain situation, prioritization has also been made by creating a rulebased fuzzy logic in MATLAB, with the assumption that it is more meaningful to use fuzzy expressions instead of definite expressions when determining the magnitudes of risks.

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