# Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies
## 3 Results
### MCDA-Based Decision-Making Framework for PM Implementation
#### 3.3.4 Methodology for Defining Weighting of Criteria and Scoring Components
Expert pairwise comparisons matrix is presented at Figure 12.  
Figure 12: Expert pairwise comparisons matrix with legend: FSI—flight safety impact, RP—reliability predictability, DP—degradation progression, DS—data sufficiency, EF—economic feasibility, RC—regulatory compliance, TI—technological integration, EI—environmental influence, OI—operational impact, SC—scalability, WT—workforce training, DPS—data privacy and security, ELM—end-of-life management, SA—stakeholder acceptance. In the pairwise comparison matrix, the blue color intensity indicates the relative importance between criteria:  
* Darker blue indicates a stronger relative importance (higher ratio). * Lighter blue indicates weaker relative importance (lower ratio). * White cells (no blue) indicate either equal importance (1.0) or where the row criterion is less important than the column criterion (values < 1). The final pairwise comparison matrix in AHP for this study was derived by systematically aggregating individual matrices provided by 12 aviation maintenance experts. Each expert contributed a pairwise comparison matrix evaluating the relative importance of criteria for implementing PM of aircraft components. This process ensures that diverse expert judgments are combined into a single, coherent decision-making matrix while maintaining the integrity of individual assessments. Each expert's pairwise comparison matrix represents their evaluation of criteria using a standard scale (e.g., 1 = equal importance, 3 = moderate importance, 5 = strong importance, up to 9 = extreme importance). The elements of these matrices adhere to the reciprocal property, where the importance of criterion \(i\) compared to \(j\) is inversely proportional to the importance of \(j\) compared to \(i\). For instance, if an expert rates flight safety impact as strongly more important than economic feasibility with a value of 7, then the reciprocal value of 1/7 is assigned for the reverse comparison. As an example, Figure 13 shows pairwise comparison matrices of two different experts. The expert-rating distribution analysis (Figure 14) presents the consensus patterns and variations in ratings across 14 predictive maintenance criteria, based on evaluations from 12 aviation experts. Figure 14: Expert-rating distribution analysis. Figure 13: Examples of pairwise comparison matrices of two different experts. (**a**) Matrix of one expert; (**b**) Matrix of another expert. Figure 14 displays three key metrics for each criterion: median rating (green line), 25 th percentile (blue line), and 75 th percentile (red line). Table 4 provides detailed statistics, including median values, standard deviations, and importance weights for each criterion. Flight safety impact shows the highest median rating (9.2) with the strongest consensus, while environmental and end-of-life management criteria display wider distributions, indicating more varied expert opinions. This AHP-based weighting system in Section 3.3.3 was successfully applied to evaluate two case components--a turbofan engine (achieving a score of 0.9225) and an FMC (scoring 0.635)--demonstrating its practical applicability in discriminating between components suitable and unsuitable for predictive maintenance implementation.