Enterprise Resource Planning (ERP) systems have become crucial for modern businesses to manage various aspects of operations effectively. With the increasing complexity and volume of data, incorporating machine learning (ML) capabilities in cloud-based ERP systems has gained attention. This research critically reviews the applications of ML in cloud ERP systems, focusing on benefits, challenges, and implications. The advantages of ML include automation, better decision- making, and real-time insights. However, challenges such as data integration, security, and scalability must be addressed. The paper provides insights into the potential of ML in ERP implementations, contributing to a better understanding of its role in enhancing performance, efficiency, and intelligence. Purpose The purpose of this research is to critically review the applications of machine learning in cloud-based ERP systems and identify potential benefits, challenges, and implications. The paper aims to assess the effectiveness of ML in enhancing the performance, efficiency, and intelligence of cloud ERP solutions. Methodology This research utilizes a critical review approach to analyze the applications of machine learning in cloud- based ERP systems. Various aspects, such as data integration, decision-making, predictive analytics, security, and scalability, are examined to evaluate the effectiveness of ML. The review includes an analysis of research papers, case studies, and reports related to ERP implementation and ML.  Findings Machine learning offers advantages such as task automation, improved decision-making, and real-time insights. It has the potential to revolutionize ERP systems and optimize business processes. However, challenges include data integration, security, and scalability. Data integration strategies need to be implemented to ensure smooth functioning. ML systems must be robust enough to detect and prevent security threats, protecting sensitive business information. Scalability strategies are essential to handle the growing volume of data efficiently and maintain competitiveness. Overall, this research article provides valuable insights into the potential implications of machine learning for ERP implementations. It highlights the advantages of integrating ML into ERP systems, such as improved decision-making, production planning, and control. However, it also identifies the challenges and limitations associated with ML implementation and emphasizes the importance of data integration and scalability strategies. The findings contribute to a better understanding of the role of machine learning in ERP implementations and offer insights for businesses considering the adoption of ML capabilities in their cloud ERP systems. Enterprise Resource Planning (ERP) systems have become an essential component of modern businesses, providing integrated solutions for managing various aspects of operations, such as finance, human resources, and supply chain. With the advancement of technology, cloud-based ERP systems have emerged as a popular choice due to their flexibility, scalability, and cost- effectiveness. However, the increasing complexity and volume of data in these systems have posed challenges in terms of performance, efficiency, and intelligence. To address these challenges, the incorporation of machine learning (ML) capabilities in cloud ERP implementations has gained significant attention. By analyzing various aspects such as data integration, decision-making, predictive analytics, security, and scalability, we aim to assess the effectiveness of ML in enhancing the performance, efficiency, and intelligence of cloud ERP solutions. Machine learning offers several advantages in the context of ERP implementations. Firstly, it enables the automation of complex tasks, reducing manual efforts and improving overall efficiency. ML algorithms can learn from historical data and make accurate predictions, leading to better decision-making capabilities. Additionally, ML can enhance the intelligence of ERP systems by providing real-time insights and recommendations based on data analysis. These capabilities have the potential to revolutionize the way businesses operate and optimize their processes. However, the integration of machine learning into ERP systems also brings forth challenges and implications that need to be addressed. Data integration is a critical aspect, as MLalgorithms require access to diverse and high-quality data for effective learning and prediction. Strategies for data integration need to be implemented to ensure the smooth functioning of cloud ERP systems. Moreover, the use of predictive analytics in cloud ERP systems raises concerns regarding data privacy, security, and ethical considerations. ML systems need to be robust enough to detect and prevent security threats, ensuring the protection of sensitive business information. Scalability is another important factor to consider when incorporating machine learning capabilities into cloud ERP implementations. As the volume of data grows, ERP systems need to scale efficiently to handle the increased workload. Scalability strategies must be implemented to ensure the performance and intelligence of cloud ERP systems, enabling businesses to adapt to changing demands and maintain a competitive edge. In conclusion, this research article aims to provide a comprehensive review of the applications of machine learning in cloud-based ERP systems. By critically analyzing the potential benefits, challenges, and implications, we seek to assess the effectiveness of ML in enhancing the performance, efficiency, and intelligence of cloud ERP solutions. The findings of this research will contribute to a better understanding of the role of machine learning in ERP implementations and provide insights for businesses considering the adoption of ML capabilities in their cloud ERP systems.

Keywords : Machine Learning, Artificial Intelligence, Cloud, ERP.