Abstract

SAP S/4HANA, as an intelligent ERP suite, has transformed enterprise operations by integrating real-time data processing, advanced analytics, and simplified system architecture. Despite its inherent advantages, organizations often encounter performance bottlenecks due to growing data volumes, complex transactional workloads, and suboptimal system configurations. This study explores performance optimization strategies within SAP S/4HANA environments while emphasizing the role of advanced data analytics in generating actionable business insights. The research investigates the integration of analytics tools, such as SAP Analytics Cloud and embedded predictive analytics, to enhance decision-making processes and operational efficiency. A mixed-method approach was employed, combining quantitative performance metrics analysis with qualitative case studies from enterprises across manufacturing, retail, and financial sectors. Key performance indicators, including system response time, transaction throughput, and data query latency, were measured before and after the implementation of optimization strategies, such as index tuning, data archiving, and real-time analytics deployment. The findings indicate that performance optimization not only reduces processing delays but also improves user experience, resource utilization, and data accuracy. Furthermore, advanced analytics enabled predictive forecasting, anomaly detection, and trend analysis, providing executives with timely insights for strategic planning. This study underscores the importance of aligning IT infrastructure with business objectives and adopting a continuous monitoring framework for performance management. In conclusion, integrating performance optimization techniques with advanced data analytics enhances SAP S/4HANA’s operational efficiency and empowers organizations to make data-driven decisions that drive competitive advantage. Recommendations for future research include exploring AI-driven automated performance tuning, expanding analytics integration, and assessing long-term ROI of optimization initiatives.