• Analyzed large-scale datasets of over 300+ Stock Keeping Units (SKUs) using SQL Server to extract, transform, and load (ETL) data for optimizing inventory management processes across multiple warehouses. • Performed weekly and monthly stock counting using Excel’s advanced formulas (VLOOKUP, INDEX-MATCH, and SUMIFS), improving accuracy and reducing discrepancies by 20%, ensuring precise stock levels and timely order placements. • Retrieved and managed expired stock data from SQL Server, implementing automated SQL queries to track and eliminate obsolete inventory, reducing stock wastage by 15%. • Created dynamic, data-driven reports and interactive dashboards in Power BI, enabling cross-functional teams to visualize stock movement and sales performance, reducing manual reporting efforts by 30%. ◇ Stock Sales Analysis: Designed detailed reports and dashboards to track stock turnover, sales velocity, and inventory aging, ensuring optimal stock availability and minimizing excess inventory. ◇ Future Trend Forecasting: Developed predictive models in Power BI and Excel to forecast stock trends based on historical sales data, improving stock procurement and reducing stockouts by 25%. ◇ Demand Prediction: Utilized time-series analysis and machine learning algorithms to predict future demand patterns, driving data-backed decisions on stock levels and procurement strategy. • Tools Used: Power BI, SQL Server, Excel (VLOOKUP, Pivot Tables, INDEX-MATCH, SUMIFS), Data Analysis Expressions (DAX), Time-series forecasting