INVENTORY MANAGEMENT OPTIMIZATION USING BUSINESS ANALYTICS AT FLIPKART

Abstract

Inventory management is a critical operational function that directly impacts profitability, customer satisfaction, and supply chain efficiency. Flipkart, India's leading ecommerce platform, manages millions of SKUs across diverse categories including electronics, fashion, groceries, and home appliances. This paper examines how Flipkart leverages advanced business analytics techniques—including demand forecasting, ABC-XYZ classification, safety stock optimization, Economic Order Quantity (EOQ) modeling, and real-time data analytics—to optimize inventory management across its vast distribution network. Primary data was collected through structured interviews with supply chain professionals, while secondary data was sourced from industry reports, Flipkart annual disclosures, and academic literature. The study reveals that analytics-driven inventory decisions reduce stockout rates, minimize carrying costs, and improve order fulfillment accuracy. Findings indicate that machine learning-based demand forecasting achieves over 85% accuracy, ABC analysis effectively prioritizes high-value SKUs, and real-time dashboard monitoring significantly reduces overstock situations. Recommendations include deeper AI integration, vendor-managed inventory partnerships, and regional demand modeling to further enhance inventory performance.