Self-Adaptive ERP: Embedding NLP into Petri-Net creation and Model Matching

Ahmed Maged; Gamal Kassem

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arXiv · 2501.03795

Self-Adaptive ERP: Embedding NLP into Petri-Net creation and Model Matching

Ahmed Maged·Gamal Kassem

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Abstract

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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Ahmed Maged, Gamal Kassem. 2025-01-07. Self-Adaptive ERP: Embedding NLP into Petri-Net creation and Model Matching. https://arxiv.org/abs/2501.03795

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