# 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  
Defining the weighting of criteria and scoring components across all criteria is fundamental to robust decision-making in MCDA. A variety of methodologies can be employed to define criteria weights. Analytical techniques like the AHP are widely used due to their structured approach [36]. AHP organizes criteria hierarchically, allowing experts to conduct pairwise comparisons to determine relative importance. The resulting pairwise comparison matrix is normalized, and weights are derived by averaging the rows. Simpler techniques like the weighted sum model involve distributing a fixed number of points among criteria based on their perceived importance [37]. These weights are then normalized and aggregated, providing a straightforward and intuitive approach for less complex decision contexts. Expert-driven approaches, such as the Delphi method, engage a panel of experts in multiple survey rounds to refine and validate weights [38]. The iterative feedback process ensures consensus and minimizes biases. Data-driven techniques, such as statistical analysis of historical data, offer objective alternatives [39]. For instance, regression or correlation analysis can identify criteria with the strongest influence on outcomes like safety incidents or maintenance costs, translating these relationships into weights. Advanced methods like ML can further refine weights by analyzing feature importance in predictive models, particularly when handling large datasets. Once weights are defined, the next step is to score components across all criteria. Scoring involves quantifying how well each component performs against specific factors. For quantitative criteria, normalization techniques are applied to standardize scores to a common scale. This ensures comparability across factors like safety impact (measured qualitatively) and implementation cost (measured in monetary terms). For qualitative criteria, such as stakeholder acceptance, Likert scales can be used, and scores are aggregated from multiple experts to ensure robustness [40]. Aggregating scores involves combining normalized values using weighted averages. At the criterion level, scores are aggregated within thematic categories, and category scores are further combined to produce a composite suitability score for each component with Equation (1). AHP is used in this study as a widely recognized MCDM method that provides a structured approach in a highly regulated and safety-critical environment, such as aviation. The AHP method is well-suited for this study due to its ability to handle complexity, prioritize safety-critical criteria, and integrate both quantitative and qualitative factors. Its structured and transparent process ensures that the decision-making framework aligns with aviation industry standards, balancing safety, cost-efficiency, and operational effectiveness. By incorporating expert judgment and validating consistency, AHP provides a robust foundation for evaluating and prioritizing components for predictive maintenance, ensuring informed, credible, and actionable decisions. AHP was implemented in this case study to establish and validate weights for 14 criteria across four main categories in predictive maintenance decision-making. Through pairwise comparisons from 12 aviation maintenance experts, the process revealed clear prioritization patterns. AHP was implemented in this case study to establish and validate weights for 14 criteria across four main categories in predictive maintenance decision-making. Through pairwise comparisons from 12 aviation maintenance experts, the process revealed clear prioritization patterns.