article · International Journal of Management & Entrepreneurship Research
Artificial intelligence offers powerful tools for strengthening supply chain resilience amidst growing global volatility. By reviewing technologies such as predictive analytics, real-time monitoring, optimisation algorithms, and autonomous systems, the research evaluates solutions for critical logistics hurdles. These challenges encompass demand variability, unexpected disruptions, inventory allocation, transportation inefficiencies, and supplier relationships. Examining practical case studies highlights both successful adoptions and lessons from past failures. The integration of artificial intelligence improves risk management, sharpens decision-making, and allows operations to adapt dynamically to emergent disruptions. Nonetheless, widespread adoption faces substantial barriers, notably high implementation expenses, data privacy risks, and internal resistance to organisational change. Actionable recommendations provide a practical guide for enterprises seeking to harness these digital capabilities to build adaptable, robust logistics networks capable of withstanding future uncertainties.
Global supply chains frequently face unexpected shocks, from transport delays to sudden demand shifts. Understanding how artificial intelligence can mitigate these disruptions helps organisations safeguard the continuous flow of goods. Highlighting both the practical benefits and operational hurdles provides industry leaders with realistic expectations for modernising their logistics networks.
The review targets logistics managers and enterprise operations teams seeking to deploy predictive analytics, real-time tracking, and automated optimisation tools. While artificial intelligence tools are already applied and tested across commercial supply chains through real-world deployments, broader adoption remains tempered by high setup costs, organisational resistance, and data governance considerations.
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In an increasingly complex and unpredictable global economy, supply chain resilience has emerged as a critical priority for organizations striving to adapt to disruptions and maintain operational continuity. Artificial intelligence (AI) has proven to be a transformative force in addressing the challenges of logistics and supply chain management, offering innovative solutions for forecasting, decision-making, and operational efficiency. This research explores how AI-driven approaches can solve specific supply chain challenges, including demand variability, disruption management, inventory optimization, transportation inefficiencies, and supplier relationship management. The review proposes a comprehensive analysis of AI technologies such as predictive analytics, real-time monitoring systems, optimization algorithms, and autonomous systems, highlighting their contributions to enhancing supply chain resilience. By leveraging AI, organizations can improve their ability to predict disruptions, optimize resource allocation, and respond dynamically to real-time challenges. The research incorporates case studies and real-world applications to illustrate successful implementations of AI in logistics, as well as lessons learned from failed attempts. Additionally, the review examines the benefits of integrating AI into supply chain operations, such as improved risk management, enhanced decision-making, and increased adaptability to uncertainties. However, it also addresses the challenges of AI adoption, including high implementation costs, data privacy concerns, and organizational resistance to change. By presenting actionable insights and recommendations, this research aims to provide a roadmap for organizations seeking to harness AI to strengthen their supply chain resilience. The findings underscore the transformative potential of AI technologies in fostering robust, adaptive, and efficient supply chain systems capable of withstanding future uncertainties and disruptions. Keywords: Supply Chain, Artificial Intelligence, Logistics Management, Review.
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DOI: 10.51594/ijmer.v6i12.1745
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