Real-Time Inventory Management: Reducing Stockouts and Overstocks in Retail

Amarnath Immadisetty

doi:10.70589/JRTCSE.2025.13.1.10

Authors

Amarnath Immadisetty Sr Manager, Software Engineering, Lowe's, United States of America Author

DOI:

https://doi.org/10.70589/JRTCSE.2025.13.1.10

Keywords:

Real-time inventory management, stockouts, overstocking, cost minimization, customer satisfaction, RFID, IoT, real-time analytics, supply chain optimization

Abstract

This will maximize inventory management effectiveness towards the goal of cost minimization, thereby enhancing customers' satisfaction. The paper discusses how real-time inventory management avoids stockouts and overstocking, two major issues that have taken the greatest toll on retail businesses. Advanced technologies, such as RFID, IoT, and real-time analytics, help retailers understand their inventory levels, work the supply chains with ease, and make decisions on actual data. This paper reviews causes and effects of both stockouts and overstocking, best practices in realtime inventory management, and the benefits involved: increase in operational efficiency, cost savings, and an improved consumer experience. Through various case studies and examples in various industries, one gets to understand from this article how the real-time inventory systems facilitate the retailer's job in satisfying demand with minimum wastage of stocks and loss of sale opportunities. Predictive analytics development will be examined for the integrative purposes of demand forecastinginventory optimization; additionally, views of what this technology could one day mean in a retail environment of inventory management will be assessed.

References

Badgujar, P. (2020). Real-Time Inventory Management in Retail. Journal of Technological Innovations, 1(4).

Jean, G. (2024). Inventory Management Strategies: Balancing Cost, Efficiency, and Customer Satisfaction.

Farooq, A., Abbey, A. B. N., & Onukwulu, E. C. (2024). Inventory Optimization and Sustainability in Retail: A Conceptual Approach to Data-Driven Resource Management. International Journal of Multidisciplinary Research and Growth Evaluation, 5(6), 1356-1363.

Purwasih, R., & Candana, D. M. (2024). Development of Inventory Management Information System in A Retail Company. Jurnal Sains Informatika Terapan, 3(2), 133-137.

Sekhar, C. (2022). Optimizing Retail Inventory Management with AI: A Predictive Approach to Demand Forecasting, Stock Optimization, and Automated Reordering. European Journal of Advances in Engineering and Technology, 9(11), 89-94.

Smith, H. K. (2024). Inventory Management for Entrepreneurs: Balancing Costs, Efficiency, and Customer Satisfaction.

D’Souza, P., Guo, M., Wang, D., & Lee, I. (2012). Real-time Inventory Management with RFIDash. Dept. of CIS-Senior Design, 2013.

Daniel, M., & Archie, O. (2024). Optimizing SME Inventory Operations: IoT and Automation for Cost-Effective Stock Management.

Ikpe, V., & Shamsuddoha, M. (2024). Functional Model of Supply Chain Waste

Reduction and Control Strategies for Retailers—The USA Retail Industry. Logistics, 8(1), 22.

Mittal, S. (2024). Framework for Optimized Sales and Inventory Control: A Comprehensive Approach for Intelligent Order Management Application. International Journal of Computer Trends and Technology, 72(3), 61-65.

Saillaja, V., Menaka, M., Kumaravel, V., & Machap, K. (2023, June). Development of an IoT-based Inventory Management System for Retail Stores. In 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) (pp. 954-958). IEEE.

Roosevelt, S. C., Veemaraj, E., & Kirubakaran, S. (2024, July). Real Time Stock Inventory Management System. In 2024 8th International Conference on Inventive Systems and Control (ICISC) (pp. 156-162). IEEE.

Verma, P. (2024). Transforming Supply Chains Through AI: Demand Forecasting, Inventory Management, and Dynamic Optimization. Integrated Journal of Science and Technology, 1(9).

Esrar, H., Zolfaghariania, H., & Yu, H. (2023). Inventory management practices at a big-box retailer: a case study. Benchmarking: An International Journal, 30(7), 2458-2485.'

Alahyane, L. (2024). Data-Driven Optimization of Inventory Management and Sales Strategies for Automotive Component Suppliers.

Singhal, K., Singh, V., & Kaul, A. (2024, December). Smart Retail: Utilizing Machine Learning for Demand Prediction, Price Strategy, and Inventory Management. In 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 485-489). IEEE.

Gérald, J. (2024). Streamlining Backroom Inventory Management in Big Box Retailers: a Comprehensive Study on Omni-Channel Strategies (No. 11718). EasyChair.

Bixler, M. J., & Honhon, D. (2021). Exploring the connections between backrooms, inventory record inaccuracies, and waste. Sustainability, 13(17), 9490.

Boleixa, I. S. (2023). Improving replenishment practices at the store level to minimize out-of-stock levels: a case study in a Portuguese grocery retailer (Master's thesis, ISCTE-Instituto Universitario de Lisboa (Portugal)).

Niaz, M. (2022). Revolutionizing Inventory Planning: Harnessing Digital Supply Data through Digitization to Optimize Storage Efficiency Pre-and Post-Pandemic. BULLET: Jurnal Multidisiplin Ilmu, 1(03).