AUTOMATED WORKFLOW OPTIMIZATION SYSTEM FOR ENTERPRISE RESOURCE PLANNING

Abstract

Enterprise resource planning (ERP) systems generate large amounts of invoice processing data that can be leveraged to optimize related workflows and reduce costs. However, current manual and template-based methods for ERP workflow optimization are inefficient and limited. This paper proposes an automated invoice workflow optimization system that utilizes artificial intelligence to analyze ERP invoice data and dynamically optimize workflows. The system collects comprehensive invoice process data and applies techniques like machine learning, process mining, and statistical analysis to identify workflow optimization opportunities. It then generates an improved target workflow by removing redundant steps, automating tasks, adjusting risk-based approval orders, and implementing other enhancements. A continuous feedback loop enables the system to learn from results and progressively improve over time. By combining data-driven analytics with AI-powered optimization algorithms, this intelligent system delivers superior, adaptive business process enhancements. This approach represents a major advancement over manual analysis and static best practice workflows for ERP optimization.