## 2 Background Research
In addition, the quality of the data can have a significant impact on the accuracy of the model, making data management a critical issue [24]. Fuzzy logic, Bayesian methods, and machine learning algorithms are computationally intensive and require advanced hardware and software infrastructure. For example, Bayesian networks can become computationally intensive as the complexity of the system increases [25]. The integration of hybrid FMEA models into existing industrial systems represents a further technical hurdle. Most industries have implemented quality management systems based on traditional FMEA methods. Switching to hybrid models may require a significant reconfiguration of these systems as well as new software tools that can handle fuzzy logic or AI [26]. In addition to the technical challenges, organizational and cultural barriers often hinder the introduction of hybrid FMEA models. Successful implementation requires a change in mindset and practice, which can be difficult to achieve in entrenched organizational cultures [27]. Many industries lack the necessary expertise to implement and maintain hybrid FMEA models. Specialists in fuzzy logic, AI, and Bayesian analysis are required to design, implement, and interpret the results of these models. Without proper training and education, the complexity of hybrid FMEA can overwhelm employees and lead to improper implementation [27]. Employees who are accustomed to traditional FMEA methods may resist the introduction of hybrid models, especially if they perceive them to be too complex or unnecessary. Organizational inertia can slow down the transition and lead to partial or incorrect implementations [28]. Implementing hybrid FMEA models requires significant investment in training programs to ensure that employees know how to use these advanced tools effectively. Industry is often reluctant to allocate resources for training, especially when the perceived benefits of hybrid FMEA are not immediately clear [27]. One of the main barriers to the adoption of hybrid FMEA models is cost. Traditional FMEA is relatively inexpensive to implement, whereas hybrid models require investment in specialized software, hardware, and expertise. In addition, the maintenance of hybrid systems, which involves continuous data collection and model updating, adds to ongoing operational costs [29]. Hybrid FMEA models produce complex results that can be difficult for non-experts to interpret. For example, fuzzy FMEA results are not as easy to interpret as the RPN in traditional FMEA and often require in-depth knowledge of fuzzy logic to interpret the risk assessment results. This complexity in interpretation can hinder decision-making processes as managers may not fully understand or trust the results generated by the hybrid models. As a result, decision makers may resort to traditional, simpler methods, even if hybrid FMEA models provide better accuracy and precision [30]. Scaling hybrid FMEA models to large industrial systems can be difficult. For example, constructing Bayesian networks for highly complex systems involves modeling numerous dependencies, which can lead to overly complicated and computationally intensive models [24]. It can be difficult for industries to maintain these models over time, particularly as system configurations change. Instead of implementing hybrid FMEA models for the entire system, companies can start with an incremental implementation and focus on high-risk areas or subsystems where hybrid methods will bring the most benefit. This allows for a smoother transition and reduces resistance to change as employees can gradually familiarize themselves with the new techniques [31]. Collaboration with academic institutions can help industries overcome the technical and organizational barriers to adopting hybrid FMEA models. Research institutions often have the necessary expertise to develop and test new methods, and joint projects can give industries access to cutting-edge tools and techniques [27].