Self-Adaptive ERP: Embedding NLP into Petri-Net creation and Model Matching
This addresses the problem of high costs and inefficiencies in ERP customization for businesses and consultants, though it appears incremental as it builds on existing methods like Petri nets and NLP.
The research tackled the resource-intensive and time-consuming process of ERP customization by introducing a Self-Adaptive ERP Framework that automates it using AI and NLP for Petri nets, reducing reliance on manual adjustments and improving efficiency and accuracy.
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.