Operational Efficiency in Retail: Using Data Analytics to Optimize Inventory and Supply Chain Management - Oluwakemi Famoti,; Ogechukwu Nwanneka Ezechi,; Chikezie Paul-Mikki Ewim,; Okiomah Eloho,; Titilayo Priscilia Muyiwa-Ajayi,; Abbey Ngochindo Igwe,; Alexsandra Ogadimma Ihechere, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
The retail industry faces constant challenges in managing inventory and optimizing supply chain processes to meet customer demand while minimizing costs. Data analytics emerges as a powerful solution to enhance operational efficiency in these areas. This paper explores how data-driven approaches can transform inventory management and supply chain operations. In inventory management, leveraging historical sales data, seasonal trends, and machine learning algorithms can significantly improve demand forecasting. Techniques such as ABC analysis, safety stock calculations, and economic order quantity (EOQ) optimization enable retailers to maintain optimal stock levels, reducing the risks of overstocking and stockouts. For supply chain management, data analytics facilitates effective supplier relationship management by tracking and evaluating performance through vendor scorecards. Logistics optimization, supported by geographic data for route planning and real-time shipment monitoring, enhances delivery efficiency and reduces costs. Integrating advanced warehouse management systems further streamlines operations. The implementation of robust inventory management software and supply chain analytics platforms, equipped with data visualization, reporting tools, and predictive analytics, empowers retailers to make informed decisions. Case studies of successful applications in both large-scale retailers and small to medium-sized businesses illustrate the tangible benefits and best practices in adopting data analytics. Despite the evident advantages, challenges such as data security and privacy concerns, and the need for seamless integration of Internet of Things (IoT) devices, remain. Future trends point towards greater utilization of artificial intelligence and machine learning to further revolutionize retail operations. In conclusion, embracing data analytics is imperative for retailers aiming to achieve operational efficiency. By harnessing data-driven insights, retailers can optimize inventory and supply chain management, ultimately enhancing customer satisfaction and profitability.
Keywords: Operational Efficiency; Retail; Data Analytics; Inventory; Supply Chain Management