# Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies
## 3 Results
### MCDA-Based Decision-Making Framework for PM Implementation
#### 3.3.3 Case Studies  
The case studies demonstrate the practical application of PM framework, validating its effectiveness through real-world examples. This phase involves selecting a diverse set of aircraft components, applying the framework to calculate their PM suitability scores, and conducting sensitivity analysis to evaluate the robustness of the results. By comparing the outcomes with historical maintenance data and expert opinions, this phase ensures that the framework aligns with industry realities and supports data-driven decision-making. The selection of components for analysis is crucial to validate the framework across various scenarios. For example, choose turbofan engine and FMC. For each component, we will calculate a PM suitability score based on the expanded decision-making framework using all 14 factors. The weights \(w_{i}\) for each criterion \(k_{i}\) are chosen based on importance, and scores \(S_{ij}\) are assigned based on the component's characteristics by experts in aircraft maintenance (Table 3). The thresholds for determining whether a component is suitable for PM are not directly set at calculated scores. These thresholds are established based on operational needs, historical data, expert insights, and industry standards. A structured approach to defining thresholds involves several key steps. First, an initial threshold is set based on a scoring scale. In this case, scores range from 0 to 1, with higher scores indicating better alignment with PM criteria. A typical starting point for a threshold might be 0.75, reflecting the level of alignment needed to justify PM adoption. Components scoring above this threshold are likely to benefit from PM, while those scoring below may not meet the necessary criteria. \begin{table}
\begin{tabular}{c c c c} \hline \(i\) & **Criterion \(k_{i}\)** & **Weight \(w_{i}\)** & **Score for Engine** & **Score for FMC** \\ \hline
1. & Flight Safety Impact & 0.15 & 0.95 & 0.95 \\ \hline
2. & Reliability Predictability & 0.1 & 0.9 & 0.4 \\ \hline
3. & Degradation Progression & 0.1 & 0.85 & 0.3 \\ \hline
4. & Data Sufficiency & 0.1 & 0.9 & 0.6 \\ \hline
5. & Economic Feasibility & 0.1 & 0.8 & 0.5 \\ \hline
6. & Regulatory Compliance & 0.05 & 0.9 & 0.8 \\ \hline
7. & Technological Integration & 0.05 & 0.85 & 0.6 \\ \hline
8. & Environmental Influence & 0.05 & 0.8 & 0.6 \\ \hline
9. & Operational Impact & 0.1 & 0.9 & 0.7 \\ \hline
10. & Scalability & 0.05 & 0.9 & 0.6 \\ \hline
11. & Workforce Training & 0.05 & 0.8 & 0.7 \\ \hline
12. & Data Privacy and Security & 0.05 & 0.9 & 0.5 \\ \hline
13. & End-of-Life Management & 0.05 & 0.85 & 0.6 \\ \hline
14. & Stakeholder Acceptance & 0.05 & 0.9 & 0.6 \\ \hline \end{tabular}
\end{table} 
Table 3: Criteria weights and scoring for use cases. Next, the threshold is refined using expert input and industry standards. Regulatory guidance, safety requirements, and insights from maintenance professionals help determine appropriate thresholds for different component types. For instance, aviation regulations may necessitate higher thresholds (e.g., 0.80-0.85) for safety-critical components like engines, while maintenance experts can identify specific criteria--such as degradation predictability or data sufficiency--that make PM viable. Historical data and benchmarking further refine thresholds. By analyzing past performance, such as comparing PM-managed components' scores to their failure rates or cost savings, more accurate thresholds can be established. Industry benchmarking provides additional context, with thresholds varying for low-criticality components (e.g., 0.70) versus high-criticality systems (e.g., 0.85). Conducting a sensitivity analysis is another critical step. Testing different thresholds (e.g., 0.65, 0.70, 0.75, 0.80) helps evaluate their impact on component selection and cost-benefit outcomes. Higher thresholds may reduce the number of components included in the PM program but enhance overall reliability and cost-effectiveness for those selected. Thresholds can also vary by component criticality.