A Use-Case-Driven Digital Twin Framework for Traceable and Repeatable Decision Support in Regulated Environments

Digital twins are increasingly being considered as an enabling technology for data-driven decision-making in regulated aviation maintenance and operations. However, many digital twin implementations are data-led rather than requirement-led. Without a standardized approach, this can weaken trustworthiness and traceability and may reduce regulatory acceptability. This paper presents a conceptual, use-case-driven framework for creating digital twins through the combined use of ontologies and data flows to achieve a defined use case in a trustworthy, transparent, and repeatable manner. The central premise is that a digital twin creation should be requirement-driven: the intended use case establishes the required scope, frequency, and continuity of information, while the availability and integrity of data determine whether the twin can be created, realized, and operationalized. Ontologies provide the semantic infrastructure to represent domain knowledge, structure data relationships, and support transparent reasoning, while data flows and digital threads provide the connectivity needed to realize and sustain the twin. A five-step framework is proposed to define a logical progression from documented use-case definition through ontological description, data availability, data frequency, and operational continuity. The framework is illustrated using a hypothetical aviation maintenance use case involving runway rubber accumulation at London Heathrow, showing how requirements can be decomposed, linked to ontologies, mapped to data sources, and assessed within a repeatable process. The result is a structured method intended to support transparent, auditable, and repeatable digital twin development for decision-making in regulated environments.