# Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies
## 3 Results
### Methodology of Selecting Categories and Factors for PM in Aviation
This clear differentiation between primary and secondary categories reinforced the robustness of the selection methodology and the significance of the chosen factors. The analysis revealed several crucial insights into aviation predictive maintenance implementation. Safety and technical considerations consistently dominated priority rankings across all expert groups, while economic factors showed strong correlation with implementation feasibility. Regulatory compliance emerged as a critical enabler for successful implementation, and organizational factors demonstrated significant impact on practical adoption potential. This methodological approach successfully identified and validated categories and factors that not only demonstrated high importance scores but also showed strong expert consensus and practical implementation potential. The resulting framework provides a structured, comprehensive approach to evaluating predictive maintenance opportunities in aviation components, balancing theoretical importance with practical implementation considerations. The selected categories and their constituent factors represent a robust foundation for predictive maintenance decision-making, addressing both technical requirements and organizational dynamics in aviation maintenance implementation. The comprehensive analysis of technical and operational factors in predictive maintenance reveals a clear hierarchy among ten evaluated criteria (Figure 6). Figure 5: Category analysis in predictive maintenance. The assessment, based on survey responses from 78 aviation maintenance experts, measured each factor across three key metrics: overall score, expert consensus, and implementation feasibility. Flight safety impact emerged as the most critical factor, scoring highest across all metrics (9.2 overall score, 94% expert consensus, 91% implementation feasibility), followed by reliability predictability (8.8 overall score, 88% consensus, 86% implementation) and data sufficiency (8.7 overall score, 87% consensus, 84% implementation). The analysis shows a natural threshold around the 8.0 score mark, with four factors scoring above this level being selected as primary factors for the framework. The remaining six factors, while still relevant, showed lower scores and consensus levels, ranging from component criticality (8.0) to system complexity (7.4). This clear differentiation in scores and consensus levels provided a quantitative basis for factor selection, ensuring that the most critical and implementable factors were prioritized in the final framework. The analysis of economic and feasibility factors across eight criteria reveals a clear prioritization pattern based on survey responses from aviation maintenance experts. Each factor was evaluated using three key metrics: overall score, expert consensus, and implementation feasibility (Figure 7). Economic feasibility emerged as the most significant factor, achieving the highest ratings (8.9 overall score, 89% expert consensus, 87% implementation feasibility), followed by scalability (8.4 overall score, 84% consensus, 82% implementation) and end-of-life management (8.2 overall score, 82% consensus, 80% implementation). A natural threshold appears around the 8.2 score mark, with three factors scoring above this level being selected as primary factors for the framework. The remaining five factors, while demonstrating relevance, showed lower scores and consensus levels, ranging from return on investment (ROI) potential (8.1) to investment timeline (7.5). This quantitative differentiation provided a clear basis for factor selection, ensuring focus on the most impactful and implementable economic considerations in predictive maintenance. The analysis of regulatory and compliance factors encompassed eight distinct criteria, evaluated through comprehensive survey responses from 78 aviation maintenance experts using three key metrics: overall score, expert consensus, and implementation feasibility (Figure 8). Figure 6: Technical and operational factors analysis.