Authors: Sagar Gupta
Abstract: Enterprise Resource Planning (ERP) systems have long served as the backbone of organizational information systems, integrating finance, operations, human resources, supply chains, and customer-facing processes into unified platforms. Traditionally, ERP implementations relied on rule-based configurations and deterministic workflows. However, the evolution of neural networks has introduced adaptive, data-driven intelligence into ERP ecosystems. Neural architectures are increasingly being deployed to enhance demand forecasting, anomaly detection, process optimization, and user personalization within ERP systems. This paper traces the evolution of neural networks in ERP implementations, from early adoption in predictive analytics to contemporary applications in autonomous process automation and decision intelligence. It also explores case studies, challenges, and future research directions, highlighting the transformative potential of neural networks in reshaping the ERP landscape
DOI: http://doi.org/10.5281/zenodo.17075766
