An Enterprise Resource Planning (ERP) system delivers constant coordinated data and information to all key business units through a single application in today's hectic business environment, where organizations are extremely focused on minimizing the lead seasons of distinct business forms. The ERP System is becoming one of the most popular IT (Information Technology)solutions, but its use necessarily requires significant attention in terms of human resources, finances, time, and other resources. While the accurate and successful implementation of ERP is crucial, businesses must still deal with a variety of challenges. According to previous studies, 60 to 70 percent of ERP implementations either stop suddenly or completely fail. Studying the success and frustration elements for effective Erp system implementation. Quality-related issues are not looked into when implementing an ERP system. Given this, the current emphasis is on taking value-related concerns into account in the ERP usage model for the entire life cycle. Along with usability, simplicity of use, implementation, workability, effectiveness, familiarity, feasibility, and documentation, software reliability is a crucial component of software quality. Because software is typically highly adaptable, it is challenging to ensure software reliability. Because of this, big data simulation approaches are used by software companies. This analytical engine, which includes simulation techniques analytics, simulation techniques execution analytics, simulation techniques planning analytics, service analytics, and marketing analytics, is the mechanism of the intelligent system. Analytical engine is based on the master enterprise data base and enterprise knowledge database. From simulation results it can observe that it gives high precision, F1 Score, execution time, Accuracy, privacy and technology scaling.

Enterprise Resource Planning (ERP), Enterprise Information Systems (EIS), Simulation Techniques (ST), Big Data Analytics, Machine Learning, Supervised Learning, Unsupervised Learning Techniques, Reinforcement Learning, Deep Learning (DL), Knowledge Enterprise data base.