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AI‐Driven Logistics: Understanding the Transformational Impact on Global Supply Chain Networks

This version is not peer-reviewed.

Submitted:

31 January 2025

Posted:

03 February 2025

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Abstract
The rapid evolution of artificial intelligence (AI) has significantly impacted various sectors, particularly in supply chain management (SCM). This study investigates the transformative effects of AI technologies on SCM, focusing on efficiency, operational resilience, and sustainable practices. By conducting a comprehensive literature review and analyzing recent advancements, the research identifies key AI applications such as predictive analytics, automation, and machine learning that facilitate improved decision-making processes and resource allocation. Furthermore, this study explores the challenges organizations face when implementing AI, including workforce adaptation, data security, and ethical concerns. Through qualitative research methodologies, including thematic analysis, the study synthesizes insights from industry professionals to uncover the practical implications of AI integration in supply chains. The findings reveal that companies leveraging AI technologies experience enhanced operational efficiency, reduced costs, and increased customer satisfaction. However, successful implementation necessitates a cultural shift within organizations, emphasizing the importance of training and reskilling employees to foster collaboration between humans and AI systems. Additionally, the study underscores the significance of establishing transparent practices to build trust among stakeholders. Ultimately, this research contributes to the growing body of knowledge on AI in SCM, providing a framework for organizations aiming to navigate the complexities of digital transformation. The implications of this study are vital for practitioners and researchers alike, as they highlight the critical role AI plays in shaping the future of supply chain dynamics.
Keywords: 
artificial intelligence; supply chain management; operational efficiency; predictive analytics; machine learning; digital transformation; sustainable practices
Subject: 
Business, Economics and Management  -   Business and Management
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

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