AI Driven Decision Support System for Enterprise Resource Planning in SME’S

Abstract

Small and medium enterprises (SMEs) increasingly depend on Enterprise Resource Planning (ERP) systems to integrate business operations, improve resource utilization, and enhance organizational productivity. However, conventional ERP platforms primarily generate descriptive reports and often lack intelligent decision support capabilities that can assist managers in responding to rapidly changing business conditions. This paper presents an AI Driven Decision Support System (AI-DSS) designed to strengthen ERP environments by combining machine learning, predictive analytics, and data-driven recommendations for operational decision making. The proposed framework gathers data from multiple ERP modules, including finance, inventory, procurement, sales, and human resources, and transforms them through preprocessing and feature engineering techniques before applying intelligent prediction models. A hybrid learning approach is employed to forecast business trends, identify operational risks, optimize inventory levels, and recommend suitable actions for managers. The framework also incorporates explainable decision mechanisms that improve transparency and user confidence in AI-generated recommendations. Experimental evaluation demonstrates that the proposed model improves prediction accuracy, reduces decision latency, and enhances overall resource utilization compared with conventional ERP decision-making approaches. Furthermore, the system supports scalable deployment for SMEs by minimizing computational overhead while maintaining high analytical performance. The proposed AI-enabled ERP decision support framework contributes to improved business agility, operational efficiency, and strategic planning, making it a practical solution for organizations seeking intelligent digital transformation in highly competitive business environments.