@misc{13291, abstract = {{The application of Large Language Models (LLMs) for the automated generation of assembly instructions shows significant potential for improving work preparation in production processes. However, challenges remain regarding the overall information quality and precision of the generated instructions. In light of these challenges, this study explores how the information quality of automatically generated assembly instructions can be enhanced through the targeted provision of structured input data, such as Assembly and Quantity BOMs (Bills of Materials), as well as the use of optimized prompt chaining techniques. The methodology employs ChatGPT-4o in combination with Retrieval Augmented Generation (RAG) within the Microsoft Azure environment. The results demonstrate that structured data inputs, particularly the use of Assembly BOMs with defined Tool-to-Component relations, significantly improve the precision and relevance of the generated instructions. Despite these advancements, achieving consistent information quality remains a barrier to broader practical implementation. Therefore, feedback loops should be integrated into the assembly instruction generation process to ensure continuous refinement and reliability. Future research should investigate the use of RAG or similar frameworks, focusing on optimizing data structures and implementing feedback mechanisms to enhance the automated generation of assembly instructions.}}, author = {{Herbort, Robin and Green, Dominik and Hinrichsen, Sven}}, booktitle = {{Intelligent Human Systems Integration (IHSI 2025): Integrating People and Intelligent Systems}}, editor = {{Ahram, Tareq and Karwowski, Waldemar and Martino, Carlo and Di Bucchianico, Giuseppe and Maselli, Vincenzo }}, isbn = {{978-1-964867-36-6}}, issn = {{2771-0718}}, keywords = {{Assembly Instruction, Retrieval Augmented Generation (RAG), Large Language Model (LLM)}}, location = {{Rome, Italy}}, pages = {{765--775}}, publisher = {{AHFE }}, title = {{{Automatic Creation of Assembly Instructions by Using Retrieval Augmented Generation}}}, doi = {{10.54941/ahfe1005883}}, volume = {{160}}, year = {{2025}}, } @misc{13292, abstract = {{The application of Large Language Models (LLMs) for the automated generation of assembly instructions shows significant potential for improving work preparation in production processes. However, challenges remain regarding the overall information quality and precision of the generated instructions. In light of these challenges, this study explores how the information quality of automatically generated assembly instructions can be enhanced through the targeted provision of structured input data, such as Assembly and Quantity BOMs (Bills of Materials), as well as the use of optimized prompt chaining techniques. The methodology employs ChatGPT-4o in combination with Retrieval Augmented Generation (RAG) within the Microsoft Azure environment. The results demonstrate that structured data inputs, particularly the use of Assembly BOMs with defined Tool-to-Component relations, significantly improve the precision and relevance of the generated instructions. Despite these advancements, achieving consistent information quality remains a barrier to broader practical implementation. Therefore, feedback loops should be integrated into the assembly instruction generation process to ensure continuous refinement and reliability. Future research should investigate the use of RAG or similar frameworks, focusing on optimizing data structures and implementing feedback mechanisms to enhance the automated generation of assembly instructions.}}, author = {{Herbort, Robin and Green, Dominik and Hinrichsen, Sven}}, booktitle = {{Intelligent Human Systems Integration (IHSI 2025): Integrating People and Intelligent Systems}}, editor = {{Ahram, Tareq and Karwowski, Waldemar and Martino, Carlo and Di Bucchianico, Giuseppe and Maselli, Vincenzo}}, isbn = {{978-1-964867-36-6}}, issn = {{2771-0718}}, keywords = {{Retrieval Augmented Generation, Large Language Model, Assembly Instructions}}, location = {{Rome}}, publisher = {{AHFE}}, title = {{{Automatic Creation of Assembly Instructions by Using Retrieval Augmented Generation}}}, doi = {{10.54941/ahfe1005883}}, volume = {{160}}, year = {{2025}}, } @misc{13293, abstract = {{The performance of large language models (LLMs) has improved significantly in recent years, with the result that they are now used in many companies in various industries. However, the design of a company-specific information system involving an LLM is associated with a large number of decisions. This leads to a high level of complexity in the design task. Against this background, companies need a structured approach that methodically supports the planning, development, implementation and long-term maintenance of LLM-based information systems so that domain- and company-specific requirements are taken into account as a result. This article therefore describes a method that supports the design, introduction and maintenance process of an LLM-based information system. The method consists of a process model and a list of design principles, which are also referred to as success factors. The process model developed is based on the proven six-stage REFA planning system. To identify and describe success factors, a systematic literature search was carried out. Based on an analysis of the contents of individual literature sources, success factors for the design of LLM-based information systems were identified. These success factors relate, for example, to the quality of the data provided, data security, user-centered system design and feedback mechanisms for improving information output.}}, author = {{Hinrichsen, Sven and Herbort, Robin and Green, Dominik and Adrian, Benjamin}}, booktitle = {{Human Interaction and Emerging Technologies (IHIET 2025)}}, editor = {{Ahram, Tareq and Motschnig, Renate }}, isbn = {{978-1-964867-73-1}}, issn = {{2771-0718}}, keywords = {{Large language model, Information system, Retrieval augmented generation}}, location = {{Vienna}}, publisher = {{AHFE}}, title = {{{How to Design an Operation-Specific LLM-Based Information System}}}, doi = {{10.54941/ahfe1006709}}, volume = {{197}}, year = {{2025}}, } @misc{13294, abstract = {{Die Leistungsfähigkeit von Large Language Models konnte in den letzten Jahren deutlich verbessert werden, so dass viele Unternehmen solche Modelle bereits einsetzen oder ihren Einsatz planen. Die Gestaltung eines betriebsspezifischen Informationssystems unter Einbeziehung eines Large Language Model (LLM) ist allerdings mit einer Vielzahl an Entscheidungen verbunden. Vor diesem Hintergrund wird in diesem Beitrag eine Methode beschrieben, die bei der Gestaltung und Einführung eines LLM-basierten Informationssystems unterstützen kann, um im Ergebnis eine möglichst anforderungsgerechte Lösung zu entwickeln. Diese Methode besteht dabei aus einem Vorgehensmodell und einer Liste mit Gestaltungsprinzipien, die auch als Erfolgsfaktoren bezeichnet werden.}}, author = {{Hinrichsen, Sven and Herbort, Robin and Green, Dominik and Adrian, Benjamin}}, booktitle = {{Arbeit 5.0: Menschzentrierte Innovationen für die Zukunft der Arbeit}}, isbn = {{978-3-936804-36-2}}, keywords = {{Large Language Model, Informationssystem, Methode}}, location = {{Aachen}}, pages = {{642--647}}, publisher = {{GfA-Press}}, title = {{{Vorgehensmodell zur Entwicklung und Implementierung von LLM-basierten Informationssystemen}}}, year = {{2025}}, } @misc{11330, abstract = {{With the increasing complexity in manual assembly and a demographic decline in skilled workforce, the importance of well-documented processes through assembly instructions has grown. Creating these instructions is a time-consuming and knowledge-intensive task that typically relies on experienced employees. Although various automation solutions have been proposed to assist in generating assembly instructions, they often fall short in providing detailed textual guidance. With the rise of generative artificial intelligence (AI), new potentials arise in this domain. Therefore, this paper explores these potentials by employing various large language models (LLMs), prompting techniques and input data in an experimental setup for generating detailed assembly instructions, including the planning of assembly sequences as well as textual guidance on tools, assembly activities, and quality assurance measures. The findings reveal promising opportunities in leveraging LLMs but also substantial challenges, particularly in assembly sequence planning. To improve the reliability of generating assembly instructions, we propose a multi-agent concept that decomposes the complex task into simpler subtasks, each managed by specialized agents.}}, author = {{Meyer, Frederic and Freitag, Lennart and Hinrichsen, Sven and Niggemann, Oliver}}, booktitle = {{2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}}, isbn = {{979-8-3503-6123-0}}, keywords = {{assembly instruction, GPT, large language model, LLM, prompt}}, location = {{Padova, Italy}}, publisher = {{IEEE}}, title = {{{Potentials of Large Language Models for Generating Assembly Instructions}}}, doi = {{https://doi.org/10.1109/ETFA61755.2024.10710806}}, volume = {{78}}, year = {{2024}}, }