Article contents
Real-Time Inventory Optimization in Retail Using Streaming Data
Abstract
This article examines how real-time streaming architectures transform inventory management in modern retail environments. Traditional batch-based inventory systems struggle with dynamic demand shifts, resulting in overstocking and stockouts that negatively impact financial performance and customer satisfaction. Real-time inventory systems integrate diverse data sources including point-of-sale systems, e-commerce platforms, warehouse management systems, and IoT sensors through event-driven architectures. These systems enable immediate visibility, continuous processing, automated actions, and cross-channel integration. Key components include data source integration, event-driven architecture using technologies like Apache Kafka and Flink, and event time processing for accurate demand forecasting. The article explores intelligent order fulfillment strategies such as ship-from-store optimization, split shipment decisions, markdown avoidance, and last-mile cost optimization. Implementation challenges discussed include data quality issues and scalability requirements, with solutions ranging from cycle counting integration to horizontal scaling approaches. A case study demonstrates how a major retailer transformed operations through real-time inventory optimization, achieving significant improvements in stock availability, carrying costs, fulfillment speed, and full-price sell-through rates. The article concludes by examining future directions including machine learning, edge computing, blockchain, augmented reality, and digital twins.
Article information
Journal
Journal of Computer Science and Technology Studies
Volume (Issue)
7 (3)
Pages
757-765
Published
Copyright
Open access

This work is licensed under a Creative Commons Attribution 4.0 International License.