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

  1. GOPICHAND BANDARUPALLI
  2. Vijaya Kanaparthi
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Abstract

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.

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  1. Version published to 10.21203/rs.3.rs-6459008/v1 on Research Square