Research Background: The Imperative for Intelligent ERP Systems The past two decades have witnessed a profound digital transformation that has reshaped how organizations operate and compete. Enterprise Resource Planning (ERP) systems, as a core component of Business Information Systems, play a vital role in integrating diverse business functions—such as finance, supply chain management, and human resources—into a unified platform. By consolidating organizational data, ERP systems enhance transparency, streamline operations, and support managerial decision-making. Despite their centrality, traditional ERP systems remain predominantly transactional in nature, designed to process structured data such as sales figures, purchase orders, and inventory levels. They lack the ability to provide advanced, intelligent insights that reflect dynamic market conditions and evolving customer needs. In addition to internal business data, organizations are now increasingly exposed to vast streams of social and behavioral data from platforms like Facebook, WhatsApp, and website visits, which offer deep insights into customer preferences and emerging market trends. Integrating structured ERP data with unstructured customer feedback and behavioral engagement metrics enables the development of a comprehensive "Customer 360°" perspective. This research proposes a hybrid, AI-enhanced ERP framework that unifies these multiple data streams, relying on open-source systems (like ERPNext) and AI libraries (like TensorFlow and Hugging Face) to provide an innovative and practical solution.
Mahmoud Abdelbassir ElGarhy (Thu,) studied this question.