Högskolan i Skövde

As manufacturing systems become increasingly complex and data-rich, there is an opportunity to identify predictive patterns in the data, including deeper patterns and more general, broad principles for effective use of our production infrastructure. Simulation-based multi-objective optimization provides additional options and predictions for industrial decision-makers; however, traditional analytics approaches struggle to provide the necessary insights. More sophisticated methods, such as data mining, provide greater insight; however, they require technically proficient users to achieve results. This paper will examine an example application in which data mining methods were applied to the results of multi-objective optimization and modeled as a knowledge graph. Our study presents the development and evaluation of an LLM-based Graph Retrieval-Augmented Generation (GraphRAG) tool that can translate natural language queries into Neo4j graph database queries for manufacturing data analysis. We present both successful query generations and identify failure modes, providing insights into the current capabilities and limitations of this approach. The paper includes a detailed use case description, documenting specific manufacturing analytics requirements and the corresponding graph query patterns needed to extract meaningful insights.