Advances in Machine Learning-driven Quality Control within ERP Systems: A Comprehensive Survey

Enterprise Resource Planning (ERP) systems have become essential instruments in contemporary corporate operations, offering significant benefits in process optimization and efficient data management. The assurance of quality and dependability in these intricate systems is of utmost significance given that errors or inconsistencies might yield extensive ramifications. This study highlights the importance of quality control within ERP systems, emphasizing the crucial role of accuracy and integrity in facilitating efficient decision-making processes and optimizing operational efficiency. This analysis explores the increasing importance of upholding ERP quality as firms become increasingly dependent on these systems to traverse intricate business environments. In addition, this study explores the capacity of machine learning to enhance quality control procedures. Machine learning techniques, owing to their ability to examine large datasets and identify subtle patterns, provide a transformative shift away from the traditional rule-based approaches. Machine learning enhances the capacity to anticipate problems, reduce interruptions, and maintain consistent operational performance by employing real-time anomaly detection, predictive modeling, and adaptation to shifting ERP settings. This study explores the complex confluence of technology and business imperatives, highlighting the transformative impact of the collaboration between machine learning and quality control on the evolution of ERP systems.