@misc{13334, abstract = {{Retrieval-augmented generation (RAG) based on large language models (LLMs) has established itself as a key technology for combining domain-specific information with generative language skills, thereby providing transparent, up-to-date information. Many firms are already piloting such LLM-based information systems, but report a high degree of complexity in planning and implementation. A generally accepted regulatory framework that consistently maps key decisions is not yet available to companies. This article therefore presents a multi-level system that organizes design decisions throughout the configuration process. This framework is intended to support users in the planning, realizing, evaluation, and further development of an LLM-based information system. To achieve this goal, a qualitative-empirical research design was chosen. First, publications from the period 2022 to 2025 were identified and selected using a systematic literature search in accordance with the PRISMA guideline. The selected publications were then evaluated using a qualitative content analysis. The result is a system that was reviewed, revised and finalized at an expert workshop.}}, author = {{Ullrich, Dominik and Wallys, Jens and Hinrichsen, Sven}}, booktitle = {{Intelligent Human Systems Integration (IHSI 2026): Disruptive and Innovative Technologies}}, editor = {{Ahram, Tareq and Karwowski, Waldemar and Giraldi , Laura and Benelli , Elisabetta}}, isbn = {{978-1-964867-76-2}}, issn = {{2771-0718}}, keywords = {{Retrieval-Augmented Generation, LLM-Based Information System, Conceptual Framework}}, location = {{Florence}}, pages = {{63--73}}, publisher = {{AHFE International}}, title = {{{Conceptual Framework for Designing Domain-Specific LLM-Based Information Systems}}}, doi = {{10.54941/ahfe1007065}}, volume = {{200}}, year = {{2026}}, } @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{10585, abstract = {{Low-code programming allows the creation of software applications using a graphical user interface with minimal classical programming code ("low code") and without requiring extensive programming knowledge. This puts it in contrast to previous generations of programming languages. The advantages of low-code development are manifold, including the increase of software development capacities through a partial decentralization of the development process, speeding up software development through the low-code approach, and designing software with a strong user-centric focus. Using a low-code development platform can help companies adapt their own business processes to changing requirements more quickly and to make complexity resulting, for example, from heterogeneous customer wishes, manageable. Since many low-code development platforms are available, it is not easy for companies to select and successfully introduce a platform that meets their requirements. For this reason, this article presents a procedure model that assists in the process of selecting and implementing a platform.}}, author = {{Hinrichsen, Sven and Nikolenko, Alexander and Becker, Kai Leon and Adrian, Benjamin}}, booktitle = {{Human Systems Engineering and Design (IHSED 2023): Future Trends and Applications}}, editor = {{Karwowski, Waldemar and Ahram, Tareq and Milicevic, Mario and Etinger, Darko and Zubrinic, Krunoslav}}, isbn = {{978-1-958651-88-9}}, issn = {{2771-0718}}, keywords = {{Complexity Management, Low-Code Development Platform, Process Model for Selection and Implementation}}, location = {{Dubrovnik}}, publisher = {{AHFE International}}, title = {{{How to select and implement a suitable Low-Code Development Platform}}}, doi = {{10.54941/ahfe1004155}}, year = {{2023}}, } @misc{10782, abstract = {{With the trend towards shorter product lifecycles, smaller batch sizes, and more product variants, the complexity of manual assembly activities is increasing. To support employees in carrying out complex assembly tasks, the use of assembly instructions is indispensable to ensure high process capability and work productivity. However, the creation of assembly instructions is often time-consuming. Thus, the use of automation approaches can be a way to simplify the creation of assembly instructions. Therefore, this paper introduces a promising automation concept for applying robotic process automation (RPA) to generate assembly instructions automatically. Finally, the automation concept is demonstrated in a practical use case that illustrates the associated automation potential of RPA.}}, author = {{Meyer, Frederic and Hinrichsen, Sven and Niggemann, Oliver}}, booktitle = {{Human Interaction & Emerging Technologies (IHIET 2023): Artificial Intelligence & Future Applications}}, issn = {{2771-0718}}, keywords = {{Digital Assembly Instruction, Industrial Engineering, Manual Assembly, Robotic Process Automation, RPA, Work Instruction}}, location = {{NIzza}}, pages = {{629--638}}, publisher = {{AHFE International}}, title = {{{How to Generate Assembly Instructions with Robotic Process Automation}}}, doi = {{10.54941/ahfe1004070}}, volume = {{111}}, year = {{2023}}, } @misc{10783, abstract = {{The development trend in manual assembly towards increasing demands in terms of quality, variety, and cost pressure makes the transition for people with cognitive disabilities to the general labor market extremely difficult. Nevertheless, this employment sector is a central component of many activities in a sheltered workshop. Therefore, this paper investigates the use of an informational assistance system for persons with cognitive impairments to close the gap between the characteristics of this group and the operational requirements. In this way, the transition from the sheltered workshop to the general labor market will be facilitated and promoted.}}, author = {{Bendzioch, Sven and Hinrichsen, Sven}}, booktitle = {{Human Interaction & Emerging Technologies (IHIET 2023): Artificial Intelligence & Future Applications}}, issn = {{2771-0718}}, keywords = {{Manual Assembly, Informational Assistance System, Image Processing System, People with Disabilities}}, location = {{NIzza}}, pages = {{548--556}}, publisher = {{AHFE International}}, title = {{{Informational Assistance System – a Key to Self-Empowerment of Persons with Cognitive Disabilities in Manual Assembly?}}}, doi = {{10.54941/ahfe1004061}}, volume = {{11}}, year = {{2023}}, }