Enhancing Bottleneck Analysis in Ship Manufacturing with Knowledge Graphs and Large Language Models

Abstract

Ship manufacturing is a critical backbone industry in China, where the nation leads on a global scale in terms of vessel completions and order volumes. However, the high volume of orders often imposes substantial processing loads, increases the risk of equipment failures, and exacerbates production bottlenecks. Despite the accumulation of significant amounts of data in this field, analyzing bottlenecks remains a persistent challenge, primarily due to the presence of heterogeneous, multi-source data and the lack of effective data integration mechanisms. The traditional approaches are largely limited to bottleneck detection, offering minimal capabilities in terms of deep analysis, traceability, and interpretability, which are crucial for comprehensive bottleneck resolution. Meanwhile, extensive knowledge remains underutilized, leading to analytical results that are overly reliant on expert experience and lacking in interpretability. To address these challenges, this research proposes a graph-retrieval-based bottleneck mining method for ship manufacturing, employing large language models and a knowledge graph. The approach integrates a data-driven “turning point” mechanism for dynamic bottleneck detection and the manufacturing process knowledge graph, consisting of process subgraphs and 5M1E (Man, Machine, Material, Method, Measurement, Environment) specification subgraphs. Furthermore, a question-answering chain is introduced to enhance the interaction between the LLMs and the knowledge graph, improving the retrieval and reasoning capabilities. Using practical production data from a Shanghai ship thin plate production line, our method demonstrates a superior performance compared to that of four existing models, validating its effectiveness in throughput bottleneck analysis. This approach provides a scalable and efficient solution for analyzing complex bottleneck issues in industrial production, contributing to enhanced manufacturing efficiency and digital transformation.

Keywords

• ship manufacturing process
• bottleneck mining
• knowledge graph
• large language models