# Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies
## 3 Results
### Methodology of Selecting Categories and Factors for PM in Aviation
Figure 7: Economic and feasibility factors analysis. Regulatory compliance emerged as the most critical factor with exceptionally high ratings (9.1 overall score, 92% expert consensus, 89% implementation feasibility), followed by data privacy and security (8.6 overall score, 86% consensus, 84% implementation) and environmental influence (8.3 overall score, 83% consensus, 81% implementation). A clear threshold was established at the 8.3 score mark, with three factors scoring above this level being selected as primary factors for the framework. The remaining five factors, while important for regulatory considerations, showed lower but still significant scores, ranging from safety regulations (8.2) to audit compliance (7.7). This quantitative differentiation provided a robust foundation for factor selection, ensuring focus on the most critical regulatory and compliance aspects in predictive maintenance implementation. The analysis of organizational and human factors examined nine distinct criteria through survey responses from 78 aviation maintenance experts, evaluating each factor through three key metrics: overall score, expert consensus, and implementation feasibility (Figure 9). Operational impact emerged as the highest-rated factor (8.8 overall score, 88% expert consensus, 86% implementation feasibility), followed closely by training requirements (8.7 overall score, 87% consensus, 85% implementation), technological integration (8.5 overall score, 85% consensus, 83% implementation), and stakeholder acceptance (8.4 overall score, 84% consensus, 82% implementation). A natural threshold was established at the 8.4 score mark, with four factors scoring above this level being selected as primary factors for the framework. The remaining five factors, while relevant to organizational considerations, showed lower scores, ranging from change management (8.0) to communication framework (7.6). This clear differentiation in scores provided a quantitative basis for factor selection, ensuring focus on the most impactful organizational and human aspects in predictive maintenance implementation. While the data collection process was designed for robustness, several constraints were encountered. First, variations in data availability across different aviation maintenance organizations posed challenges in standardizing responses. Some experts had access to more comprehensive predictive maintenance datasets, while others relied on qualitative assessments. Additionally, integrating historical maintenance records required careful  
Figure 8: Regulatory and compliance factors analysis. Figure 9: Organizational and human factors analysis. filtering to exclude outdated or incomplete data. Finally, ensuring regulatory alignment in data collection required careful adherence to aviation safety and compliance frameworks, limiting the ability to include proprietary datasets in the analysis. Despite these challenges, methodological adjustments, such as cross-validation of expert responses and sensitivity analysis, were implemented to enhance the reliability of findings.