Machine learning-driven optimization of enterprise resource planning (ERP) systems: a comprehensive review

Abstract

Abstract In the dynamic and changing realm of technology and business operations, staying abreast of recent trends is paramount. This review evaluates the progress in the development of the integration of machine learning (ML) with enterprise resource planning (ERP) systems, revealing the impact of these trends on the ERP optimization. In recent years, there has been a significant advancement in the integration of ML technology within ERP environments. ML algorithms characterized by their ability to extract intricate patterns from vast datasets are being harnessed to enable ERP systems to make more accurate predictions and data-driven decisions. Therefore, ML enables ERP systems to adapt dynamically based on real-time insights, resulting in enhanced efficiency and adaptability. Furthermore, organizations are increasingly looking for artificial intelligence (AI) solutions as they actually try to make ML models within ERP clear and comprehensible for stakeholders. These solutions enable ERP systems to process and act on data as it flows in, due to the utilization of ML models, which enables enterprises to react effectively to changing circumstances. The rapid insights and useful intelligence offered by this trend have had a significant impact across industries. IoT (Internet of Things) and ML integration with ERP are continuously gaining significance. These algorithms allow for the creation of adaptable strategies supported by ongoing learning and data-driven optimization, which has a number of benefits for ERP system optimization. In addition, the Industrial Internet of Things (IIoT) was investigated in this review to provide the state-of-the-art and emerging challenges due to ML integration. This review provides a comprehensive analysis of the integration of machine learning algorithms across several ERP applications by conducting an extensive literature assessment of recent publications. By synthesizing the latest research findings, this comprehensive review provides an in-depth analysis of the cutting-edge techniques and recent advancements in the context of machine learning (ML)-driven optimization of enterprise resource planning (ERP) systems. It not only provides an insight into the methodology and impact of the state-of-the-art but also offers valuable insights into where the future of ML in ERP may lead, propelling ERP systems into a new era of intelligence, efficiency, and innovation.

Keywords

• Enterprise resource planning
• Machine learning
• Optimization
• IIoT state-of-the-art techniques