Supplementary Data for: Digital Twins in Manufacturing: A Systematic Literature Review with Retrieval-Augmented Generation

Abstract The paper presents a systematic literature review on the use of digital twins in manufacturing, with the goal of developing a comprehensive taxonomy that synthesizes existing categorizations. Given the increasing complexity and volume of literature in this domain, conventional review methods are becoming insufficient. To address this challenge, the study applies Retrieval-Augmented Generation, a technique that combines large language models with real-time information retrieval, enabling the automated identification and summarization of typologies across a broad corpus of publications. A total of 1,354 publications were initially screened, leading to 144 distinct categorizations relevant to digital twins in industrial contexts. The resulting taxonomy classifies digital twins along multiple dimensions, including life cycle stages, physical domain and hierarchy levels, model characteristics, digital thread connectivity, deployment strategies, and functional autonomy. This multidimensional framework distinguishes between digital templates, digital twin prototypes, and operational digital twins or shadows, and includes specific domains such as product, process, environment, and human interaction. Furthermore, it accounts for varying levels of model fidelity and autonomy, from descriptive to fully autonomous systems. This work provides both researchers and practitioners with a structured approach to understanding and implementing digital twins in manufacturing environments. The taxonomy serves as a foundation for future research and as a practical tool for industrial applications. Supplementary Data This Supplementary Data contains an .xlsx file, which gives Details about the Liteature Research. The Sheet "Summary" contains the AI Categorization and the AI Summary for every paper. Since four different hyperparameter combinations where used, this sheet summarizes the distinguished sheets, which where created by the RAG-Framework. Furthermore, it contains the results of the Summary Check, where the paper was checked by a human while only considering the AI-Summary. During the Detail Check, the complete paper was considered and a preliminary dimension and categories for the categorization created in the paper are stated. Both Checks contain inlude- and exclude criteria. The Column "Validation" contains a check, where 10 random papers in which the AI has not found a Categorization, whre checked in Detail if they really don´t contain a categorization. "Comparison to Fulltext" Contains the result of a fulltext-search for the keywords "Taxonomy", "Categorization", "Classification", and "Typology", while only papers not found by the AI are considered. It furthermore contains a detail check, if the paper contains a categorization of digital twins. The Sheet "Include - Exclude" contains a Summary of the include- and exclude-criteria.