Intelligent Resource Allocation in ERP with Machine Learning

Efficient resource allocation is a critical component of Enterprise Resource Planning (ERP) systems. Existing approaches often rely on static allocation methods that fail to adapt to dynamic business environments, leading to inefficiencies. This paper proposes an intelligent, Machine Learning (ML)-based solution leveraging reinforcement learning to dynamically optimize resource allocation in ERP systems. We review resource allocation challenges, present our dynamic ML-based framework, and validate its effectiveness through simulated scenarios. Results demonstrate significant improvements in resource utilization, adaptability, and overall system performance. This study evaluates eight MLbased resource allocation methods for ERP systems across six metrics: efficiency, cost reduction, scalability, implementation time, integration complexity, and energy consumption. Using normalized data and weighted analysis, the research identifies Automated Resource Allocation System as the optimal solution, with Machine Learning based Scheduling as a strong alternative.