用AI和NLP自动生成可适配的流程模型，提升ERP定制效率。
Self-Adaptive ERP: Embedding NLP into Petri-Net creation and Model Matching
- 通过NLP将业务描述转为可动态调整的佩特里网模型。
- 结合系统使用分析实现结构与功能的自动匹配。
- 适合需要快速迭代ERP系统的中大型企业
企业资源规划（ERP）顾问在根据特定业务需求定制系统时，需处理大量数据并不断调整功能。该研究提出一种自适应ERP框架，利用人工智能（AI）与自然语言处理（NLP）技术，基于企业流程模型和系统使用分析，自动完成定制化配置。框架通过设计科学研究（DSR）与系统文献综述（SLR）构建，将业务流程转化为可自适应的佩特里网（Petri-net）模型，实现结构与功能的自动匹配。该方法显著减少对人工调整的依赖，提升ERP定制的效率与准确性，降低对顾问的长期需求。
原文摘要 · Abstract (English)
Enterprise Resource Planning (ERP) consultants play a vital role in customizing systems to meet specific business needs by processing large amounts of data and adapting functionalities. However, the process is resource-intensive, time-consuming, and requires continuous adjustments as business demands evolve. This research introduces a Self-Adaptive ERP Framework that automates customization using enterprise process models and system usage analysis. It leverages Artificial Intelligence (AI) & Natural Language Processing (NLP) for Petri nets to transform business processes into adaptable models, addressing both structural and functional matching. The framework, built using Design Science Research (DSR) and a Systematic Literature Review (SLR), reduces reliance on manual adjustments, improving ERP customization efficiency and accuracy while minimizing the need for consultants.
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