Authors:
Lakshmi Narasimha Raju Mudunuri

Addresses:
Department of Refining Systems Information Services, Valero Energy Corporation, Texas, United States of America. rajumudunuri17@gmail.com

Abstract:

In today’s rapidly evolving industrial landscape, optimizing warehouse operations is paramount for maintaining competitive edge and operational efficiency. This research explores how Artificial Intelligence (AI) can revolutionize warehouse flow by enhancing space utilization, streamlining processes, and reducing operational costs. By integrating AI-driven solutions such as machine learning algorithms, real-time data analytics, and automated systems, warehouses can achieve unprecedented levels of efficiency and accuracy. The proposed method employs a mixed-methods approach, including semi-structured interviews and quantitative analysis of operational data from multiple warehouses that have implemented AI technologies over the past five years. The study uses tools such as regression analysis and hypothesis testing to evaluate the impact of AI on space utilization, inventory accuracy, order picking speed, and labor costs. Results demonstrate significant improvements in space utilization, reduction in labor costs, and enhanced overall productivity. The findings highlight the potential of AI to transform warehouse management, offering a blueprint for future implementations. The paper concludes with a discussion of the implications of AI integration, potential limitations, and avenues for future research.

Keywords: Artificial Intelligence; Warehouse Management; Space Utilization; Operational Efficiency; Inventory Control; Data Analytics; Labor Management; Warehouse Operations.

Received on: 21/11/2023, Revised on: 03/02/2024, Accepted on: 27/03/2024, Published on: 03/06/2024

DOI: 10.69888/FTSCS.2024.000198

FMDB Transactions on Sustainable Computing Systems, 2024 Vol. 2 No. 2, Pages: 64-73

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