## 2 Background
### Digital Maintenance  
While digital capabilities are often initially associated with predictive maintenance, the reality is that the maintenance function, and by extension asset management (AM), encompasses more than just failure prediction. It involves complex tasks integrating different types of analysis and decisions that impact asset and system performance [13; 30]. Digital maintenance goes beyond predictive maintenance alone; it entails integrating strategic and operational perspectives into maintenance management processes through effective data and model management. Asset digitalization necessitates a good technical architecture that can include various systems and platforms. While digitalization solutions often originate from a technological perspective, incorporating a maintenance-focused approach is vital. This ensures that digitalized assets remain reliable and aligned with broader business goals by addressing maintenance requirements without delving deeply into architectural specifics. A maintenance-oriented approach prioritizes understanding and managing the digital functionalities of IT solutions. It focuses on overseeing end-to-end processes for data and model handling, tailored to maintenance needs. This perspective complements the technology-driven approach, emphasizing long-term asset performance and operational reliability. Information system providers design their technical and commercial offerings with the needs of end users in mind, often revolving around platforms and apps. Platforms integrate core digital services to support end-to-end processes, functioning similarly to IoT and cloud platforms. Apps, on the other hand, are tied to specific functionalities desired by users and rely on platforms for seamless integration in complex industrial environments. Intelligent Asset Management Platforms (IAMPs) play a central role in industrial systems by collecting and analyzing data from assets. These platforms are commonly categorized into Enterprise Asset Management (EAM), Asset Performance Management (APM), and Asset Investment Planning (AIP), offering comprehensive support for asset management strategies across different operational contexts. [PERSON] [13] proposes a visual tool (Figure 1) that integrates the Input-Process-Output (I-P-O) framework with the requirement to use multiple models and applications across different stages of the maintenance process. This tool highlights how raw data from various business systems are converted into specialized models, which are crucial for different facets of asset digitalization. The Input-Process-Output framework is structured in alignment with ISO 14224:2016, where each component represents a specific function ensuring a cohesive strategy for managing digital maintenance. These functions leverage models to produce outputs that interface with Intelligent Asset Management (IAM) applications, supporting activities across all stages of the maintenance and asset management lifecycle. The concept of digital maintenance operates on the premise that models and apps collaborate rather than operate in isolation. The results of one model serve as inputs for higher-level models, fostering interconnectedness. This interconnectedness is evident when the outcomes of models and apps from condition-based monitoring (CBM) contribute inputs to models focused on dynamic maintenance scheduling and investment optimization [13; 32; 33]. Similarly, the outputs of monitoring or CBM apps can be utilized as inputs for models such as the Asset Health Index (AHI) and Life Cycle Cost (LCC) models, as exemplified in this document. Figure 1 illustrates a concept that maps relevant applications across the full spectrum of management, from strategic to operational levels, while distinctly separating applications from IT functional blocks. This approach builds upon the asset and maintenance management framework originally outlined in references [34; 35; 36].