# Data Conversion Strategies for Effective Aviation Technical Support as a Service
## 4 Discussion
### Extended Decision-Making Model with Adaptive Indicators for the System Life Cycle  
This section presents a numerical case study to demonstrate the application of the extended decision-making model with Adaptive indicators for the system life cycle. This model evaluates centralized and decentralized architectures for ATsaaS platform by incorporating multiple time-dependent criteria. Unlike traditional cost-focused models, this extended framework considers a comprehensive set of factors: cost, performance, reliability, security, flexibility and scalability, control and management, and sustainability. This case study presents a comprehensive evaluation methodology for comparing centralized and decentralized IT architectures across a 15-year operational period. The analysis employs seven key performance indicators with a dynamic weighting system that evolves over time, reflecting changing priorities between early deployment (\(\leq\)5 years) and long-term operation (>5 years). The evaluation framework considers cost efficiency, performance, reliability, security, scalability, control and management, and sustainability. Each criterion is assessed using specific mathematical functions described in Section 3.3 that model the behavior of both architectural approaches over time. The analysis integrates initial deployment conditions with a 300-unit system scale and incorporates time-dependent factors affecting each criterion. The dynamic weight distribution shifts at the 5-year mark, acknowledging that organizational priorities and system requirements change as the deployment matures. This approach enables a more nuanced understanding of each architecture's strengths and weaknesses across different operational phases. Tables 3 and 4 present the initial parameters, weighting distributions, and scoring functions used in the analysis. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Criterion** & **Parameter Type** & **Centralized (U1)** & **Decentralized (U2)** \\ \hline Cost & Base Cost & 1,200,000 & 800,000 \\  & Time Factor & 70,000 \(\times\) t\({}^{2}\)/2 & Growth rate: 0.08 \\  & Scale Factor & 50,000 \(\times\) ln(300) \(\times\) t & (100,000 \(\times\) 300/0.08) \\ Performance & Response Time & 0.5 & 1.0 \\ Reliability & Time Degradation & 0.01 t & 0.02 t \\  & Scale Impact & - & 0.001 \(\times\) 300 \\ Security & Fixed Score & 0.9 & 0.8 \\ Scalability & Scale Factor & ln(300)/10 & 300 \(\times\) [exp(0.08 \(\times\) t)]/5000 \\ Control & Complexity Factor & 0.5 & 1.0 \\ Sustainability & Base Impact & 10,000 \(\times\) 300 & - \\  & Time Impact & 5000 t & 20,000 \(\times\) 300 t \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Initial parameters by criterion. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Criterion** & **Weight (\(\leq\)5 Years)** & **Weight (>5 Years)** \\ \hline Cost (w1) & 0.35 & 0.20 \\ Performance (w2) & 0.10 & 0.25 \\ Reliability (w3) & 0.10 & 0.20 \\ Security (w4) & 0.05 & 0.15 \\ Scalability (w5) & 0.30 & 0.10 \\ Control and Management (w6) & 0.05 & 0.05 \\ Sustainability (w7) & 0.05 & 0.05 \\ \hline Total & 1.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Weight distribution by time period. The Figure 6 illustrates the comparative performance of both architectures over the entire evaluation period. The calculation demonstrates how individual criteria contribute to the overall assessment scores, ultimately revealing the conditions under which each architectural approach proves more advantageous. The Figure 6 illustrates the comparative performance evaluation of centralized and decentralized architectures over a 15-year period using a dynamic weighting method.