AI and Smart Logistics in Supply Chains

Dear Colleagues,

This Special Issue, invites original research exploring how artificial intelligence and emerging digital technologies are transforming logistics and supply chain management in the era of Logistics 4.0 and Industry 5.0 (Cohen and Tinglong, 2026; Helo and Thai, 2024; Shamsuddoha et al., 2023). The issue aims to advance theoretical and practical understanding of AI-enabled smart logistics systems, emphasizing intelligent automation, real-time analytics, cyber–physical systems, digital twins, IoT, blockchain, augmented reality and human–machine collaboration (Nicoletti, 2026; Wamba-Taguimdje et al., 2020). Recent studies highlight the growing role of AI integration, generative AI, predictive analytics and collaborative digital ecosystems in enhancing agility, resilience, sustainability and operational efficiency across global supply chains (Fosso Wamba et al., 2024; Lin, 2025; Soomro & Khan, 2026). At the same time, important challenges remain concerning organizational adaptation, transparency, trust, governance and adoption barriers (Booyse & Scheepers, 2024; Von Eschenbach, 2021; Lemos et al., 2025). Contributions may address machine learning, large language models, intelligent decision-support systems, smart transportation, real-time information flow and scalable digital twin implementation in logistics operations (Ivanov & Gusikhin, 2026; Shamsuddoha et al., 2025). Interdisciplinary, empirical, conceptual and case-study-based submissions are encouraged to provide insights into the future of AI-driven supply chains and smart logistics innovation.

All submissions should contribute to the body of scientific literature and practice primarily in logistics and supply chain contexts, with insights for both academia and industry. Reflecting logistics’ evolution, this Special Issue particularly encourages contributions that integrates the following:

• AI generations serving logistics and supply chain management
• Data-based decision support systems in logisitcs
• Expert-based decision support systems in logistics
• Enhancing sustainability with AI tools across the supply chain
• Human-Centricity: Logistics 0 reintroduces the human role in decision-making and operations
• Robustness: A logistics system's proactive ability to resist and endure disruptions
• Resilience: Through adaptive supply chain design, real-time AI analytics and human–machine synergy, logistics systems gain flexibility to handle disruptions
• Anti-fragility: How to design supply networks and logistics systems in single companies that gain value from stress, volatility or chaos
• Ethics and risk mitigation in the application of smart solutions and artificial intelligence in logistics
• Sustainability: Green logistics practices, optimized routing, energy‑efficient warehouses and reverse‑logistics align with environmental goals

Related literature

Booyse, D., & Scheepers, C. B. (2024). Barriers to adopting automated organisational decision-making through the use of artificial intelligence. Management Research Review, 47(1), 64-85. doi: https://doi.org/10.1108/MRR-09-2021-0701

Eschenbach, V. W. J. (2021). Transparency and the black box problem: Why we do not trust AI. Philosophy & Technology, 34(4), 1607-1622. https://doi.org/10.1007/s13347-021-00477-0

Fosso Wamba, S., Guthrie, C., Queiroz, M. M., & Minner, S. (2024). ChatGPT and generative artificial intelligence: an exploratory study of key benefits and challenges in operations and supply chain management. International Journal of Production Research, 62(16), 5676–5696. https://doi.org/10.1080/00207543.2023.2294116

Helo, P., Thai, Vinh, V. (2024) Logistics 4.0 – digital transformation with smart connected tracking and tracing devices International Journal of Production Economics Vol 275 https://doi.org/10.1016/j.ijpe.2024.109336

Ivanov, D., Gusikhin, O. (2026) Supply chain digital twin design and implementation at scale: A case study at the Ford Motor Company and generalizations. Omega, Vol. 139. https://doi.org/10.1016/j.omega.2025.103447

Lemos, S. I., Ferreira, F. A., Zopounidis, C., Galariotis, E., & Ferreira, N. C. (2025). Artificial intelligence and change management in small and medium-sized enterprises: an analysis of dynamics within adaptation initiatives. Annals of Operations Research, 353(1), 197-223. https://doi.org/10.1007/s10479-022-05159-4

Lin, K. Y. (2025). Generative artificial intelligence–driven sustainable supply chain management: a UNISONE framework for smart logistics and predictive analytics under Industry 5.0. International Journal of Logistics Research and Applications, 1–32. https://doi.org/10.1080/13675567.2025.2540855

Cohen, Maxime C., Tinglong Dai (2026) AI in Supply Chains. Perspectives from Global Thought Leaders. Springer Series of Supply Chain Management Springer Cham https://doi.org/10.1007/978-3-032-07054-8

Nicoletti, B. (2026) Artificial Intelligence for Logistics 5.0. From Foundation Models to Agentic AI Palgrave Macmillan Cham https://doi.org/10.1007/978-3-031-94046-0

Shamsuddoha, M., Kashem, M.A., Nasir, T. (2023). Smart Transportation Logistics: Achieving Supply Chain Efficiency with Green Initiatives. In: Ibne Hossain, N.U. (eds) Data Analytics for Supply Chain Networks. Greening of Industry Networks Studies, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-031-29823-3_10

Shamsuddoha, M., Khan, E. A., Chowdhury, M. M. H., & Nasir, T. (2025). Revolutionizing Supply Chains: Unleashing the Power of AI-Driven Intelligent Automation and Real-Time Information Flow. Information, 16(1), 26. https://doi.org/10.3390/info16010026

Soomro, M. A., & Khan, A. N. (2026). Collaborative networks, agility and AI integration: advancing supply-chain innovation in global logistics. International Journal of Logistics Research and Applications, 1–20. https://doi.org/10.1080/13675567.2026.2671442

Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411

Dr. Edit Süle
Prof. Dr. Péter Földesi
Dr. Adrián Horvath
Guest Editors

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