CLOUD-ENABLED ERP MODERNIZATION THROUGH INTELLIGENT DATA MAPPING AND MIGRATION OPTIMIZATION

Abstract

Enterprise Resource Planning systems form the operational foundation of many organizations by integrating finance, procurement, inventory, manufacturing, human resources, supply chain, customer management, and reporting processes. However, a large number of enterprises continue to depend on legacy ERP platforms characterized by tightly coupled architectures, obsolete data models, duplicated records, proprietary interfaces, fragmented customizations, limited scalability, and expensive maintenance. Cloudenabled ERP modernization provides an opportunity to improve agility, interoperability, elasticity, analytics, and operational resilience, but migration from legacy environments remains complex because source and target systems frequently differ in schemas, semantics, identifiers, formats, business rules, master-data structures, and historical quality. This paper proposes a cloud-enabled ERP modernization framework based on intelligent data mapping and migration optimization. The framework integrates legacy source discovery, metadata extraction, data profiling, semantic schema matching, machine-learning-assisted field mapping, transformation rule generation, duplicate detection, quality scoring, dependencyaware migration sequencing, workload prediction, cloud resource optimization, secure API integration, secrets management, reconciliation, rollback control, and postmigration monitoring. The proposed methodology combines deterministic rules with similarity analysis, historical mapping knowledge, domain constraints, and human validation to identify reliable correspondences between heterogeneous ERP structures. Migration workloads are classified according to business criticality, data sensitivity, dependency, volume, transformation complexity, and downtime tolerance. An optimization layer schedules extraction, transformation, validation, and loading tasks across scalable cloud resources while controlling cost, energy consumption, and service disruption. Representative evaluation results indicate that the proposed framework can improve mapping accuracy, reduce manual mapping effort, decrease migration duration, lower transformation errors, improve duplicate detection, increase reconciliation success, reduce cloud resource waste, and strengthen rollback readiness compared with conventional manually driven migration approaches. The study provides a scalable foundation for enterprises seeking to modernize legacy ERP infrastructure through intelligent, secure, auditable, and cloud-aware data migration.