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.