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GenAI Technology Approach for Sustainable Warehouse Management Operations: A Case Study from the Automative Sector

Author

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  • Sorina Moica

    (Faculty of Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540139 Târgu Mureș, Romania)

  • Tripon Lucian

    (Logistics of Innovations Group, Bosch Automotive SRL, 515400 Blaj, Romania)

  • Vassilis Kostopoulos

    (Faculty of Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540139 Târgu Mureș, Romania
    Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece)

  • Adrian Gligor

    (Faculty of Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540139 Târgu Mureș, Romania)

  • Noha A. Mostafa

    (Department of Industrial Engineering, Zagazig University, Zagazig 44519, Egypt
    Department of Mechanical Engineering, The British University in Egypt, El-Sherouk 11837, Egypt)

Abstract

The emergence of Generative Artificial Intelligence (GenAI) is reshaping logistics and supply chain operations, offering new opportunities to improve efficiency, accuracy, and responsiveness. In the automotive manufacturing sector, where high-volume throughput and precision are critical, the integration of AI technologies into warehouse management represents a strategic advancement. This study presents a case analysis of the implementation of AI-driven reception processes at an Automotive facility in Blaj, Romania. The research focuses on the transition from manual operations to automated recognition using industrial-grade imaging systems integrated with enterprise resource planning platforms. The integrated approach used combines Value Stream Mapping, quantitative performance analysis, and statistical validation using the Wilcoxon Signed-Rank Test. The results reveal a substantial reduction in reception time up to 79% and significant cost savings across various operational scales with improved data accuracy and minimized logistics failures. To support broader industry adoption, the study proposes a Cleaner Logistics and Supply Chain Model, incorporating principles of sustainability, ethical compliance, and continuous improvement. This model serves as a strategic framework for organizations seeking to align AI adoption with long-term operational resilience and environmental responsibility. The findings validate the operational and financial advantages of AI-enabled warehousing management in achieving sustainable digital transformation in logistics.

Suggested Citation

  • Sorina Moica & Tripon Lucian & Vassilis Kostopoulos & Adrian Gligor & Noha A. Mostafa, 2025. "GenAI Technology Approach for Sustainable Warehouse Management Operations: A Case Study from the Automative Sector," Sustainability, MDPI, vol. 17(20), pages 1-25, October.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:20:p:9081-:d:1770616
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    References listed on IDEAS

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