Agentic Insight Generation in VSM Simulations
For practitioners using value stream mapping simulations, this work provides a more reliable and automated way to generate insights, though it is incremental in nature.
The paper tackles the challenge of extracting actionable insights from complex value stream map simulations. The proposed decoupled, two-step agentic architecture achieves up to 86% accuracy with top-tier LLMs, showing high robustness.
Extracting actionable insights from complex value stream map simulations can be challenging, time-consuming, and error-prone. Recent advances in large language models offer new avenues to support users with this task. While existing approaches excel at processing raw data to gain information, they are structurally unfit to pick up on subtle situational differences needed to distinguish similar data sources in this domain. To address this issue, we propose a decoupled, two-step agentic architecture. By separating orchestration from data analysis, the system leverages progressive data discovery infused with domain expert knowledge. This architecture allows the orchestration to intelligently select data sources and perform multi-hop reasoning across data structures while maintaining a slim internal context. Results from multiple state-of-the-art large language models demonstrate the framework's viability: with top-tier models achieving accuracies of up to 86% and demonstrating high robustness across evaluation runs.