# Data Conversion Strategies for Effective Aviation Technical Support as a Service
## 1 Introduction
### Research Gap, Contributions and Paper Structure  
Despite extensive research on aviation technical support, digital platforms, and maintenance optimization, there remains a significant gap in how small airlines access and integrate technical support services in a cost-effective and scalable manner. While existing studies have explored various aspects of aviation maintenance, data standardization, and predictive maintenance systems, they do not comprehensively address the fragmented and resource-constrained operational environment of small airlines.  
Several key areas of prior research provide valuable insights but lack integration into a unified service-oriented framework like ATSaaS:  
* Studies on predictive maintenance and AHMS focus on large airlines and high-resource environments (e.g., Airbus Skywise and Boeing AHM) but do not consider the financial and technical constraints faced by small carriers.
* Research on data exchange protocols in aviation (e.g., ATA Spec 2000 and iSpec 2200) provides structured frameworks but does not offer implementation strategies that accommodate the heterogeneous IT capabilities of small aviation service providers.
* Literature on digital platforms and cloud-based aviation solutions highlights the potential of shared services models but lacks an in-depth exploration of how these platforms can be optimized for cost-efficiency, scalability, and regulatory compliance in small airline operations.  
This study bridges these gaps by introducing the ATSaaS model, a novel service-based digital platform designed to optimize technical support for small airlines. The main contributions of this research are as follows:  
* Identification of the challenges faced by small airlines in accessing reliable and cost-effective technical support, emphasizing the need for an ATSaaS framework.
* Development of a structured ATSaaS model, analyzing both centralized and decentralized architectures for data conversion and integration, including their respective advantages and limitations.
* Mathematical modeling and life cycle cost analysis, evaluating the trade-offs between initial investments, operational costs, and long-term scalability of centralized vs. decentralized ATSaaS architectures.
* Introduction of a multi-criteria decision-making framework that considers cost, performance, security, scalability, and sustainability, providing a structured approach for selecting the optimal ATSaaS architecture.
* Addressing real-world implementation challenges, including data standardization, cybersecurity risks, and regulatory compliance, offering practical strategies for integrating ATSaaS into small airline operations.  
This paper is structured as follows. Section 2 presents alternative ATSaaS platform architectures, examining centralized and decentralized approaches and their implications for data conversion. Section 3 introduces mathematical models for life cycle cost analysis and multi-criteria decision making. Section 4 discusses the results through numerical examples, analyzes the extended decision-making model with dynamic indicators, and explores the integration of additional factors for comprehensive ATSaaS evaluation. This section also addresses study limitations and suggests future research directions. Finally, Section 5 concludes by summarizing key findings and their implications for implementing ATSaaS in the aviation industry.