###### Abstract  
This paper proposes a novel hybrid model that integrates failure mode and effects analysis (FMEA), Rank Order Centroid (ROC), and Combined Compromise Solution (CoCoSo) to improve risk assessment and prioritization of failure modes. A case study in the healthcare sector will be conducted to validate the effectiveness of the proposed model. ROC is used to assign weights to the FMEA criteria (severity, occurrence, and detectability). CoCoSo is then applied to create a robust ranking of failure modes by considering multiple criteria simultaneously. The results of the case study show that the hybrid FMEA-ROC-CoCoSo model improves the accuracy and objectivity of risk prioritization. It effectively identifies critical failure modes, outperforming traditional FMEA. The hybrid approach not only improves risk management decision making, leading to better mitigation strategies and higher system reliability, but also reduces the complexity typically found in FMEA hybrid models. This model provides a more comprehensive risk assessment tool suitable for application in different industries. hybrid FMEA; ROC; CoCoSo; risk assessment; failure mode prioritization Article  
A Hybrid FMEA-ROC-CoCoSo Approach for Improved Risk Assessment and Reduced Complexity in Failure Mode Prioritization  
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A Hybrid FMEA-ROC-Co  
Resenje (VIKOR), and Gray Relational Analysis (GRA) have been incorporated into FMEA to improve the accuracy and reliability of risk assessment. TOPSIS ranks alternatives based on their distance to an ideal solution, providing a more structured approach to prioritizing risks. However, it is insensitive to the weighting of the criteria and cannot fully capture the complexity of the interactions of failure modes. VIKOR, which focuses on finding a compromise solution between conflicting criteria, improves the balance in risk prioritization but can still cause problems in systems with strong dependencies. GRA addresses uncertainty in risk assessment, especially with incomplete information, but can also fail in environments with complex risk interactions [7; 8; 9]. Despite the improvements offered by these multi-criteria decision-making methods (MCDMs), challenges remain in overcoming the subjectivity and complexity of risk assessments. One of the main problems with traditional FMEA (Failure Modes and Effects Analysis) is its simplistic approach to weighting the three key factors: severity, occurrence, and detectability. In the traditional FMEA method, these factors are treated as equally important, and the Risk Priority Number (RPN) is calculated by simply multiplying the scores for each factor. This assumes that all failure modes and associated risks can be accurately assessed with the same weighting for each factor, which is rarely the case in real-life scenarios [10; 11; 12]. In practice, some risks are determined more by their severity (e.g., catastrophic failures with serious consequences), while others are influenced more by their detectability (e.g., hidden problems that are difficult to detect before they cause problems). In some contexts, where frequent failures, even of low severity, can significantly disrupt operations, the occurrence of failures could be the deciding factor. Therefore, equal weighting in traditional FMEA oversimplifies the complexity of risk assessment and leads to an inaccurate prioritization of risks. This can lead to certain risks being under- or overestimated, reducing the overall effectiveness of the risk management process [13]. Subjectivity is another important issue in FMEA, as expert judgments are often used to assign points for severity, occurrence, and detectability. These assessments can vary from expert to expert, leading to contradictory assessments. The lack of a structured method for dealing with these subjective inputs leads to further uncertainty, which can distort the prioritization of risks.