# Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies
## 3 Results  
### Methodology of Selecting Categories and Factors for PM in Aviation  
The data collection process was designed to ensure comprehensive and unbiased insights from multiple sources. This study integrates primary data gathered through structured expert surveys and secondary data from historical maintenance records, industry reports, and regulatory guidelines. Expert surveys were conducted with 78 aviation maintenance professionals, ensuring a diverse representation of stakeholders from MRO providers, airline operators, and regulatory bodies. The survey design followed best practices in structured decision-making research, employing both closed and open-ended questions to balance quantitative scoring with qualitative insights. The identification and  
Figure 2: Conceptual framework for predictive maintenance study. validation of key categories and factors for aviation predictive maintenance implementation emerged through a comprehensive analysis of expert survey responses. The composition and expertise of survey participants were carefully analyzed to ensure comprehensive representation across the aviation maintenance sector. The expert role distribution analysis revealed a balanced representation of key stakeholders, with MRO professionals forming the largest group (Figure 3). The experience distribution analysis demonstrated a strong foundation of expertise among participants, with the majority having extensive industry experience (Figure 4). This diverse representation across roles and depth of experience enhanced the reliability and comprehensiveness of the survey findings, ensuring that the insights gathered encompassed both practical industry knowledge and regulatory perspectives. Initially, eight thematic categories were considered: technical and operational, economic and feasibility, regulatory and compliance, organizational and human, data-driven, risk and resilience, environmental and sustainability, and innovation and technological development. Each category underwent rigorous evaluation based on three fundamental metrics: overall score, expert consensus percentage, and implementation feasibility. This multi-dimensional assessment approach ensured a balanced consideration of both theoretical importance and practical applicability. Through statistical analysis and validation, four primary categories distinguished themselves with consistently superior performance across all evaluation metrics (Figure 5). Figure 4: Expert experience distribution in survey. Figure 3: Expert role distribution in survey. The top four categories show significantly higher scores across all metrics: the technical and operational category, the economic and feasibility category, regulatory and compliance category, the organizational and human factors category. The technical and operational category achieved the highest overall rating (9.2), with remarkable expert consensus (92%). The economic and feasibility category followed, with an overall score of 8.8 and 87% expert consensus. Regulatory and compliance category secured strong validation, with an 8.7 score and 86% consensus. The organizational and human factors category, while ranking fourth, maintained robust metrics (8.6 score, 84% consensus). The selection methodology incorporated multiple validation techniques to ensure result reliability. Consensus analysis examined expert agreement levels, response distributions, and variance patterns. Implementation feasibility assessment evaluated practical challenges, resource requirements, and integration capabilities. Cross-category correlation analysis identified relationships, dependencies, and synergies between factors. Strict validation criteria required minimum thresholds of 8.0 for overall scores, 80% for expert consensus, and 80% for implementation feasibility. Secondary categories, including data-driven (7.8), risk and resilience (7.6), environmental (7.4), and innovation factors (7.2), demonstrated relevance but fell below the established thresholds.