Agentic Insight Generation in VSM Simulations

2026-04-14

AI Summary

This work addresses the challenges of extracting actionable insights from complex Value Stream Mapping (VSM) simulations, which are often time-consuming, error-prone, and hindered by difficulties in discerning subtle contextual differences. To overcome these limitations, the authors propose a decoupled two-stage agent architecture that separates orchestration from data analysis and integrates a domain knowledge–driven multi-hop reasoning mechanism. This approach enables precise data source selection and effective capture of contextual nuances within lightweight contexts. Evaluated across multiple state-of-the-art large language models, the method achieves a peak accuracy of 86%, demonstrating strong robustness and generalization capability across diverse data structures.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Web data generation and simulation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• value stream mapping
• insight generation
• large language models
• data analysis
• situational differences

Innovation

• agentic architecture
• value stream mapping
• multi-hop reasoning
• progressive data discovery
• large language models