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Leveraging Artificial Intelligence for Optimizing Logistics Performance: A Comprehensive Review

Author

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  • Hajar Fatorachian

    (Leeds Business School, Leeds, LS1 3HB, Leeds, United Kingdom Author-2-Name: Author-2-Workplace-Name: Author-3-Name: Author-3-Workplace-Name: Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:)

Abstract

" Objective - Artificial Intelligence (AI) has become a pivotal technology in transforming logistics performance. This paper aims to comprehensively understand how AI-enabled solutions improve efficiency, accuracy, and responsiveness in logistics operations. The study focuses on synthesizing current research to explore AI applications across various logistics domains, such as predictive analytics, autonomous vehicles, and smart warehousing. Methodology/Technique - A systematic review approach was used to gather and analyze existing literature on AI applications in logistics. The review covered studies published in recent years, highlighting the advancements and impact of AI on logistics processes. The methodology included selecting relevant sources, categorizing AI applications, and assessing their effects on different logistics functions. Finding - The findings reveal that AI adoption substantially improves logistics operations, including enhanced operational performance, cost reduction, and increased customer satisfaction. Specific AI applications, such as predictive analytics for demand forecasting, autonomous vehicles for transportation, and smart warehousing for inventory management, were identified as key contributors to these improvements. However, challenges such as data privacy concerns and integration complexities were also noted. Novelty - This study's novelty lies in its comprehensive analysis of AI applications across various logistics domains, offering a holistic view of AI's role in optimizing logistics performance. This paper highlights the benefits of AI adoption and addresses the associated challenges, providing insights into future research directions and practical implications for leveraging AI in logistics. Type of Paper - Review"

Suggested Citation

  • Hajar Fatorachian, 2024. "Leveraging Artificial Intelligence for Optimizing Logistics Performance: A Comprehensive Review," GATR Journals gjbssr655, Global Academy of Training and Research (GATR) Enterprise.
    • Handle: RePEc:gtr:gatrjs:gjbssr655
    DOI: https://doi.org/10.35609/gjbssr.2024.12.3(5)
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    References listed on IDEAS

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    1. ., 2024. "Global supply chain management for sustainability," Chapters, in: Concise Introduction to Global Supply Chain Management, chapter 7, pages 133-144, Edward Elgar Publishing.
    2. Govindan, Kannan & Soleimani, Hamed & Kannan, Devika, 2015. "Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future," European Journal of Operational Research, Elsevier, vol. 240(3), pages 603-626.
    3. George Baryannis & Sahar Validi & Samir Dani & Grigoris Antoniou, 2019. "Supply chain risk management and artificial intelligence: state of the art and future research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2179-2202, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Artificial Intelligence; Logistics; Performance Improvement; Predictive Analytics; Autonomous Vehicles; Smart Warehousing;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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