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Enhancing Inventory Management through Real-Time Power BI Dashboards and KPI Tracking

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  • Srikanth Yerra

Abstract

Effective inventory management is essential for op- timizing supply chain operations, reducing costs, and ensuring seamless product availability. Traditional inventory tracking methods often lead to inefficiencies due to delayed data updates and a lack of real-time insights. This study explores how Power BI dashboards and Key Performance Indicator (KPI) tracking can revolutionize inventory management by providing real-time visibility, data-driven decision-making, and predictive analytics. Power BI integrates with databases like SQL Server and cloud- based platforms such as Microsoft Azure, enabling businesses to monitor stock levels, forecast demand, and optimize ware- house operations through interactive dashboards. By leveraging essential KPIs—including stock turnover ratio, demand forecast accuracy, and order fulfillment rate—organizations can proac- tively manage inventory levels, reduce holding costs, and improve supply chain responsiveness. Additionally, this study examines the challenges associated with real-time inventory tracking, such as data integration complexities, cybersecurity risks, and system scalability. The findings highlight that companies using Power BI for inventory management enhanced efficiency, improved decision-making, and reduced operational risks. This research underscores the significance of real-time business intelligence tools in modern inventory management and proposes future enhancements, including AI-driven forecasting and IoT-based monitoring, to further optimize supply chain operations.

Suggested Citation

  • Srikanth Yerra, 2025. "Enhancing Inventory Management through Real-Time Power BI Dashboards and KPI Tracking," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(2), pages 944-951, March.
  • Handle: RePEc:jbh:ijsrcs:v11:y2025:i2:id:1161
    DOI: 10.32628/CSEIT25112458
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112458
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