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Artificial Intelligence and Business Analytics Applications in Supply Chain Operations
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Artificial Intelligence and Business Analytics Applications in Supply Chain Operations

A special issue of Logistics (ISSN 2305-6290). This special issue belongs to the section "Artificial Intelligence, Logistics Analytics, and Automation".

Deadline for manuscript submissions: 16 October 2026 | Viewed by 4691

Special Issue Editors

Prof. Dr. Hokey Min

E-Mail Website
Guest Editor
Department of Management, College of Business, Bowling Green State University, Maurer Center 312, Bowling Green, OH 43403, USA
Interests: global supply chain management; supply chain technology; supply chain benchmarking; supply chain model design; operations management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Seong-Jong Joo

E-Mail Website
Guest Editor
Department of Operational Sciences, Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA
Interests: logistics and supply chain management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Logistics is pleased to announce a call for papers for a Special Issue titled “Artificial Intelligence and Business Analytics Applications in Supply Chain Operations”. It will focus on the relevance and managerial implications of two emerging technological tools, artificial intelligence (AI) and business analytics, for supply chain practices in the era of the fourth and fifth industrial revolutions. This Special Issue aims to encourage contributing scholars to conduct state-of-the-art theoretical and empirical research on the usefulness of AI and business analytics in enhancing supply chain productivity in the 21st century.

This Special Issue aims to gather cutting-edge research and innovative perspectives on various aspects of AI and business analytics. Topics of interest include supply chain analytics; machine learning algorithms for demand planning, inventory planning, logistics planning, and sourcing; smart warehousing and manufacturing; autonomous trucking; drone technology applications to last-mile delivery; delivery service providers for last-mile delivery; AI tools (e.g., ChatGPT and Co-Pilot) for supply chain ecosystem and resilience; and AI (e.g., neural network) applications in healthcare logistics and production scheduling.

Other topics related to generative AI issues or business analytics applications are welcome, provided they focus on the successful applications of AI and business analytics in logistics managerial problem-solving and innovative supply chain practices.

Prof. Dr. Hokey Min
Prof. Dr. Seong-Jong Joo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Logistics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • logistics management
  • operations management
  • purchasing management
  • marketing management
  • applied statistics
  • operations research

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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19 pages, 264 KB  
Article
AI Diffusion and the New Triad of Supply Chain Transformation: Productivity, Perspective, and Power in the Era of Claude, ChatGPT, Gemini, LLaMA, and Mistral
by Paul C. Hong, Young B. Choi and Young Soo Park
Logistics 2026, 10(2), 40; https://doi.org/10.3390/logistics10020040 - 5 Feb 2026
Viewed by 1008
Abstract
Background: The rapid diffusion of large language models (LLMs) such as Claude, ChatGPT, Gemini, LLaMA, and Mistral is reshaping logistics and supply chain management by embedding generative intelligence into planning, coordination, and governance processes. While prior studies emphasize algorithmic capability, far less [...] Read more.
Background: The rapid diffusion of large language models (LLMs) such as Claude, ChatGPT, Gemini, LLaMA, and Mistral is reshaping logistics and supply chain management by embedding generative intelligence into planning, coordination, and governance processes. While prior studies emphasize algorithmic capability, far less is known about how differences in diffusion pathways shape productivity outcomes, managerial cognition, and institutional control. Methods: This study develops and applies an integrative analytical framework—the AI Diffusion Triad—comprising Productivity, Perspective, and Power. Using comparative qualitative analysis of five leading LLM ecosystems, the study examines how technical architecture, access models, and governance structures influence adoption patterns and operational integration in logistics contexts. Results: The analysis shows that diffusion outcomes depend not only on model performance but on socio-technical alignment between AI systems, human workflows, and institutional governance. Proprietary platforms accelerate productivity through centralized integration but create dependency risks, whereas open-weight ecosystems support localized innovation and broader participation. Differences in interpretability and access significantly shape managerial trust, learning, and decision autonomy across supply chain tiers. Conclusions: Sustainable and inclusive AI adoption in logistics requires balancing operational efficiency with interpretability and equitable governance. The study offers design and policy principles for aligning technological deployment with workforce adaptation and ecosystem resilience and proposes a research agenda focused on diffusion governance rather than algorithmic advancement alone. Full article
(This article belongs to the Special Issue Artificial Intelligence and Business Analytics Applications in Supply Chain Operations)
23 pages, 639 KB  
Article
AI-Powered Tools for Supply Chain Resilience: A Dynamic Capabilities Perspective from Jordanian Manufacturing Firms
by Hazim Haddad, Luay Jum’a, Ziad Alkalha and Hilda Madanat
Logistics 2026, 10(1), 24; https://doi.org/10.3390/logistics10010024 - 19 Jan 2026
Cited by 1 | Viewed by 1325
Abstract
Background: In an increasingly volatile global business environment, supply chain resilience has become a strategic imperative, particularly for firms operating in developing economies. Guided by Dynamic Capabilities Theory (DCT), this study examines how AI-powered tools foster an innovation culture comprising communication, creativity, and [...] Read more.
Background: In an increasingly volatile global business environment, supply chain resilience has become a strategic imperative, particularly for firms operating in developing economies. Guided by Dynamic Capabilities Theory (DCT), this study examines how AI-powered tools foster an innovation culture comprising communication, creativity, and learning, and how these dimensions enhance supply chain resilience measured through flexibility, efficiency, and velocity. Methods: A quantitative research design was employed using survey data collected from 270 supply chain and operations managers in Jordanian manufacturing firms. Twelve direct hypotheses were tested using Partial Least Squares Structural Equation Modeling. Results: The findings indicate that AI-powered tools significantly influence communication, creativity, and learning. Communication and creativity positively affect all three dimensions of supply chain resilience. Learning significantly improves efficiency but shows no significant effect on flexibility or velocity, indicating that learning is mainly utilized for process improvement rather than rapid adaptation. Conclusions: The study demonstrates that AI adoption alone is insufficient to build resilient supply chains unless supported by innovation-oriented cultural capabilities. The findings extend DCT by clarifying the differentiated role of learning in resilience building and provide actionable guidance for managers seeking to align AI investments with cultural development in resource-constrained manufacturing contexts and long-term competitive advantage. Full article
(This article belongs to the Special Issue Artificial Intelligence and Business Analytics Applications in Supply Chain Operations)
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21 pages, 1222 KB  
Article
Artificial Intelligence-Driven Supply Chain Agility and Resilience: Pathways to Competitive Advantage in the Hotel Industry
by Ibrahim A. Elshaer, Alaa M. S. Azazz, Abdulaziz Aljoghaiman, Mahmoud Mansor, Mahmoud Ahmed Salama and Sameh Fayyad
Logistics 2026, 10(1), 5; https://doi.org/10.3390/logistics10010005 - 26 Dec 2025
Cited by 1 | Viewed by 1254
Abstract
Background: The extraordinary disturbances faced by the hotel industry, ranging from worldwide health problems to political instability and climate change, have highlighted the insistent need for more resilient and agile supply chain (SC) systems. This study explored how artificial intelligence (AI) capabilities [...] Read more.
Background: The extraordinary disturbances faced by the hotel industry, ranging from worldwide health problems to political instability and climate change, have highlighted the insistent need for more resilient and agile supply chain (SC) systems. This study explored how artificial intelligence (AI) capabilities can generate competitive advantage (CA) through supply chain agility (SCA) and supply chain resilience (SCR) as mediators and competitive pressure (CP) as a moderator. Methods: Drawing on the resource-based view (RBV) framework, we suggested and empirically tested the study model. Using data collected from 432 hotel managers and analyzed using Partial Least Squares Structural Equation Modelling (SEM-PLS). Results: the results reveal that AI-driven SC can significantly strengthen SCA and SCR. Furthermore, SCA and SCR can act as powerful mediators, and CP can strengthen the tested relationships (the links from AI adoption and CA) as a moderator. Conclusions: The study made several theoretical and practical contributions by integrating AI capabilities into SCR and SCA frameworks in the hotel and tourism context, and by providing practical evidence for professionals aiming to leverage AI-driven SC tools to navigate uncertainty and create sustainable CA. Full article
(This article belongs to the Special Issue Artificial Intelligence and Business Analytics Applications in Supply Chain Operations)
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Review

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25 pages, 2662 KB  
Review
Optimizing Biomass Feedstock Logistics Using AI for Integrated Multimodal Transport in Bioenergy and Bioproduct Systems: A Review
by Johanna Gonzalez and Jingxin Wang
Logistics 2026, 10(3), 54; https://doi.org/10.3390/logistics10030054 - 2 Mar 2026
Viewed by 619
Abstract
Background: The constant growth in demand for sustainable energy products and the development of the circular economy have created a critical need for an efficient supply chain for biomass. However, the inherent challenges of biomass make its harvesting, collection, storage, and transport [...] Read more.
Background: The constant growth in demand for sustainable energy products and the development of the circular economy have created a critical need for an efficient supply chain for biomass. However, the inherent challenges of biomass make its harvesting, collection, storage, and transport difficult, impacting logistical efficiency and the viability of bioenergy and bioproduct production. This study analyzes how combining artificial intelligence (AI) with multimodal transport can optimize and improve efficiency, as well as reduce costs, in biomass logistics. Methods: The study uses a tiered research framework that encompasses the physical domain (biomass limitations), the structural domain (mathematical modeling for multimodal transport), the intelligence domain (AI-based decision making), and the strategic approach. Results: The outcomes indicate that while truck transport is ideal for short distances, integrating rail and water transport through AI-driven optimization reduces costs and greenhouse gas emissions for long-distance travel. AI technologies, such as digital twins and machine learning, improve demand forecasting, real-time routing, and cargo consolidation, leading to enhanced prediction accuracy for transport costs. Conclusions: The integration of AI and multimodal networks builds resilient and sustainable biomass supply chains. However, full implementation requires addressing data fragmentation and investing in digital infrastructure to enable seamless coordination between supply chain stakeholders. Full article
(This article belongs to the Special Issue Artificial Intelligence and Business Analytics Applications in Supply Chain Operations)
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