Real-Time Analytics for Strategic Inventory Optimization in E-commerce

  • Authors

    • Divya Sharma HR Manager, Cognizant, India. Author
    • Vikram Sethi Senior Consultant, Deloitte, India. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2021PI8T5C

    Published 05-05-2021

  • Real-Time Analytics, Inventory Optimization, E-Commerce Logistics, Machine Learning, Time-Series Forecasting, Lstm, Reinforcement Learning, Iot In Retail, Demand Prediction, Smart Warehousing, Streaming Data, Supply Chain Intelligence, Reorder Point Optimization, Stockout Reduction, Operational Efficiency

    Issue

    Vol. 4 No. 1 (2021)

    Section

    Articles

    How to Cite

    [1]
    D. Sharma and V. Sethi, “Real-Time Analytics for Strategic Inventory Optimization in E-commerce”, IJMLPA, vol. 4, no. 1, pp. 01–12, May 2021, doi: 10.67228/3142788X/IJMLPA-2021PI8T5C.

  • Abstract

    In the rapidly evolving landscape of e-commerce, inventory optimization plays a crucial role in ensuring customer satisfaction, operational efficiency, and cost minimization. Traditional models such as Economic Order Quantity (EOQ) or periodic review systems are increasingly inadequate for addressing the real-time complexities of today’s multi-channel, high-velocity retail environments. This paper proposes a real-time analytics framework to enhance strategic inventory optimization in e-commerce settings. By leveraging live data streams from point-of-sale systems, customer behavior analytics, IoT-enabled warehouses, and external variables like seasonality or promotional activity, we design a hybrid methodology combining time-series forecasting (LSTM), machine learning models, and reinforcement learning-based optimization. This integrated system enables dynamic adjustment of reorder points, safety stock, and replenishment frequency based on current and predictive demand signals. We present a modular implementation architecture suitable for cloud and edge deployment and evaluate its effectiveness through simulations and industry-specific case scenarios. Key performance indicators such as stockout frequency, carrying cost, and service level improvement are used to validate the model. Finally, the paper discusses the operational challenges, scalability trade-offs, and ethical considerations of real-time data utilization. The findings contribute to both academic discourse and practical implementation strategies, positioning real-time analytics as a cornerstone for the next generation of intelligent inventory management in e-commerce.

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