Inventory Optimization & Demand Forecasting

Industry Retail – Multi-location Consumer Goods ________________________________________ Organization Overview A mid-to-large retail enterprise operating over 50 stores across multiple regions. The company manages a wide portfolio of fast-moving consumer products with varying demand patterns and supplier lead times. ________________________________________ Business Challenges • Excess inventory accumulation in low-demand locations • Frequent stock-outs during peak demand periods • High inventory carrying and warehousing costs • Limited visibility into store-level inventory health • Reactive replenishment planning ________________________________________ Business Objectives • Improve demand forecasting accuracy • Optimize inventory levels across locations • Reduce inventory holding costs • Maintain high product availability • Enable data-driven replenishment decisions ________________________________________ Problem Statement How can historical sales, inventory, promotions, seasonality, and supplier data be leveraged to predict demand accurately and optimize inventory across all stores? ________________________________________ Data Model Overview The solution is built using a star schema architecture. Fact Tables • Sales transactions • Inventory snapshots • Promotional activity Dimension Tables • Product master • Store master • Calendar • Supplier details ________________________________________ Key Performance Indicators • Demand volume and trends • Inventory turnover ratio • Stock-out rate • Excess inventory percentage • Product availability rate • Forecast accuracy metrics ________________________________________ Analytical Approach Demand Analysis • Product-level demand distribution • Store and region-wise sales performance Tools: SQL, Power BI ________________________________________ Trend & Seasonality Analysis • Monthly and quarterly demand patterns • Festival and peak-period impact Tools: Power BI, Python ________________________________________ Promotion Effectiveness • Incremental demand during promotions • Comparison of promotion types Tools: SQL, Power BI ________________________________________ Inventory Optimization • Identification of overstocked and understocked products • Inventory turnover evaluation Tools: SQL, Power BI (DAX) ________________________________________ Stock-out Analysis • Stock-out frequency by product and store • Lost sales identification Tools: SQL, Power BI ________________________________________ Demand Forecasting • Short-term demand forecasting (30/60/90 days) • Accuracy measurement Tools: Python (Time Series Models) ________________________________________ Business Impact • Inventory carrying cost reduced by 30% • Product availability improved to 97% • Improved forecasting accuracy enabling proactive planning • Better visibility for management through dashboards ________________________________________