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Kilimci, Z.H., Akyuz, A.O., Uysal, M., Akyokus, S., Uysal, M.O., Atak Bulbul, B. and Ekmis, M.A. (2019) An Improved Demand Forecasting Model Using a Deep Learning Approach and Proposed Decision Integration Strategy for the Supply Chain. Complexity, 2019, Article ID: 9067367.
https://doi.org/10.1155/2019/9067367

has been cited by the following article:

• TITLE: Intelligent Decision-Making in Warehouse Management: How AI Automation Improves Inventory Tracking, Order Fulfillment, and Logistics Efficiency Compared to Drone Technology

  AUTHORS: Somil Nishar

  KEYWORDS: Warehouse Management, Artificial Intelligence, Automation, Inventory Management, Order Fulfillment

  JOURNAL NAME: Intelligent Control and Automation, Vol.15 No.1, February 1, 2024

  ABSTRACT: This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.