Abstract

Failure mode and effect analysis has been applied in manufacturing and service industries but can still be improved. Failure mode and effect analysis is a common tool used to evaluate risk priority number; however, numerous scholars have doubted the effectiveness of failure mode and effect analysis and have thus proposed methods for correcting failure mode and effect analysis from its conventional formula. Because implemented actions can determine or influence resource allocation and its effects, completing one corrective action can occasionally simultaneously improve various failure modes. In this study, failure mode and effect analysis and decision-making trial and evaluation laboratory were integrated to correct failure modes and increase their effectiveness. First, failure mode and effect analysis was employed to identify the items for improvement. Second, decision-making trial and evaluation laboratory was adopted to examine the reciprocal influences and causality among these items. Finally, the priority for improving the items was proposed. By combining the advantages of failure mode and effect analysis and decision-making trial and evaluation laboratory, this research method complemented the shortcomings of the two techniques. According to the empirical research of this case study in which decision-making trial and evaluation laboratory was employed to analyze the causality among the items of the failure modes, the malfunction of production lines can be solved faster and more effectively compared with merely considering the size of risk priority number values.

Highlights

  • BackgroundFailure mode and effect analysis (FMEA) is a widely employed systematic analysis technique for evaluating the probability of failure points occurring and their subsequent effects

  • We considered that risk priority number (RPN) can filter critical failure modes

  • After the corrective actions are employed, the correction values must be recalculated to determine whether risk has been reduced and to detect the effectiveness of the corrective measures applied to the failure modes

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Summary

Background

Failure mode and effect analysis (FMEA) is a widely employed systematic analysis technique for evaluating the probability of failure points occurring and their subsequent effects. At all stages of a product life cycle, FMEA is commonly employed in the manufacturing industry and is gradually being used in the service industry. Numerous studies have applied risk priority number (RPN) to solve failure mode problems. RPN is a critical decision index, and its numerical size influences the priority in solving problems. Several studies have focused on calculating and adjusting RPN. Some studies have further proposed the effects of failure mode to adjust the RPN weight through the failure mode effects.[1] Other studies have employed estimated RPN to conduct a highly accurate and reasonable risk evaluation.[2] We considered that RPN can filter critical failure modes. Because causality exists among failure modes, we recommend first identifying the causal relationships among the failure modes before determining the priority for improving them

Motivation and objective
Literature review
Calculate the normalized direct-relation matrix
Draw the causal diagram
Calculate direct or indirect-relation matrix
Results of FMEA
D RPN 3 18 5 225 2 42
D RPN 3 90 3 36 5 200
Causal diagram
Interactive relationships of the criteria
Conclusion
Full Text
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