SmartSync: Machine Learning for Seamless SAP RAR Data Migration from Legacy ERP Systems

Migrating to SAP Revenue Accounting and Reporting (RAR) from legacy ERP systems like Oracle is a costly, error-prone process, often delaying compliance with IFRS 15. This study leverages machine learning to automate data mapping for invoices, contracts, and revenue schedules, streamlining SAP RAR transitions. Using a realistic dataset simulating Oracle-to-RAR migration, k-means clustering and random forest models achieve 92% mapping accuracy, reducing errors by 55% compared to manual ETL methods. Visualizations highlight error patterns, guiding seamless integrations. This blueprint accelerates ERP transitions, ensuring compliance and cutting costs for enterprises worldwide, offering a scalable solution for modern revenue accounting.