用大模型自动分析流程中的无效步骤，提升企业优化效率。
Automated Business Process Analysis: An LLM-Based Approach to Value Assessment
- 分两阶段：先拆解流程步骤，再按精益原则分类
- 在50个流程模型上实现可复现的定性分析结果
- 适合需要快速评估流程价值的企业或咨询人员
业务流程是组织运营的核心，但其优化因手动分析耗时、主观性强而困难。本文利用大语言模型（LLMs）自动化价值增值分析——一种识别流程中非增值环节的定性分析方法。该方法分为两个阶段：首先将高层活动分解为详细步骤以支持细粒度分析；其次基于精益原则对每一步进行分类。我们基于50个业务流程模型构建并公开了人工标注的基准标签。评估表明，结构化提示相比零样本基线有稳定提升，且两项任务均表现良好。研究揭示了大模型在增强人类专家判断的同时，显著降低手动分析的时间成本与主观偏差。
原文摘要 · Abstract (English)
Business processes are fundamental to organizational operations, yet their optimization remains challenging due to the timeconsuming nature of manual process analysis. Our paper harnesses Large Language Models (LLMs) to automate value-added analysis, a qualitative process analysis technique that aims to identify steps in the process that do not deliver value. To date, this technique is predominantly manual, time-consuming, and subjective. Our method offers a more principled approach which operates in two phases: first, decomposing high-level activities into detailed steps to enable granular analysis, and second, performing a value-added analysis to classify each step according to Lean principles. This approach enables systematic identification of waste while maintaining the semantic understanding necessary for qualitative analysis. We develop our approach using 50 business process models, for which we collect and publish manual ground-truth labels. Our evaluation, comparing zero-shot baselines with more structured prompts reveals (a) a consistent benefit of structured prompting and (b) promising performance for both tasks. We discuss the potential for LLMs to augment human expertise in qualitative process analysis while reducing the time and subjectivity inherent in manual approaches.
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