The Age of AI in the Management of Businesses and Supply Chains

Dear Colleagues,

The Special Issue “The Age of AI in the Management of Businesses and Supply Chains” aims to explore the transformative role of artificial intelligence (AI) in reshaping modern business practices and supply chain management. As organizations increasingly adopt AI-driven tools and data-centric strategies, new opportunities and challenges emerge in decision-making, operational efficiency, resilience, and sustainability.

This Special Issue seeks to bring together cutting-edge research that advances theoretical understanding and provides practical insights into how AI technologies—such as machine learning, predictive analytics, generative AI, and autonomous systems—are integrated into business processes and supply chain operations.

The scope includes, but is not limited to:

• AI-driven decision-making in operations and strategic management;
• Intelligent supply chain planning, forecasting, and optimization;
• Applications of AI in logistics, procurement, and inventory management;
• Human–AI collaboration and organizational transformation;
• Ethical, governance, and transparency issues in AI adoption;
• AI for supply chain resilience, risk management, and disruption mitigation;
• Sustainability and circular economy enabled by AI technologies;
• Digital twins and real-time analytics in supply chain ecosystems;
• Generative AI applications in business and operations management.

Both empirical and conceptual contributions are welcome, including interdisciplinary approaches that bridge business, engineering, and data science perspectives.

Brief review of the literature

The rapid advancement of AI has significantly influenced business and supply chain management research over the past decade. Early work focused on analytical models and decision support systems, while recent studies emphasize autonomous decision-making and real-time optimization enabled by big data and machine learning.

In supply chain management, AI has been widely applied to demand forecasting, inventory optimization, and logistics planning. Machine learning models have demonstrated superior performance compared to traditional statistical methods, particularly in handling complex, non-linear, and high-dimensional data (Choi et al., 2018; Carbonneau et al., 2008). Similarly, predictive analytics and AI-driven optimization tools have enhanced supply chain visibility and responsiveness (Ghobakhloo et al., 2026).

Recent literature highlights the growing importance of AI in improving supply chain resilience. AI-enabled simulation models and digital twins allow firms to anticipate risks and dynamically adapt to changing conditions (Ivanov, D., & Dolgui, A. (2020). At the organizational level, AI adoption is reshaping decision-making processes and business models. Studies emphasize the shift toward data-driven cultures and the integration of human expertise with algorithmic intelligence (Brynjolfsson & McAfee, 2017). However, challenges remain regarding trust, explainability, and ethical considerations in AI deployment (Rai, 2020). These developments signal a new phase in which AI is not only supporting but actively shaping business strategies and supply chain ecosystems.

We request that, prior to submitting a manuscript, interested authors initially submit a proposed title and an abstract of 300–500 words summarizing their intended contribution. Please send it to the Administrative Sciences/ editorial office (admsci@mdpi.com) or / to the guest editors (sule.edit@sze.hu, buics.laszlo@sze.hu). Abstracts will be reviewed by the guest editors for the purposes of ensuring proper fit within the scope of the Special Issue. Full manuscripts will undergo double-blind peer review.

Reference list:

• Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harward Business Review, https://hbr.org/2017/07
• Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. Vol. 184(3) European Journal of Operational Research 1140-1154 https://doi.org/10.1016/j.ejor.2006.12.004

• Choi, T.-M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management Vol. 27(10) https://doi.org/10.1111/poms.12838

• Ghobakhloo, M., Okwir, S., Iranmanesh, M., Fathi, M., & Foroughi, B. (2026). Artificial intelligence-enabled antifragility in production and supply chain operations. Production Planning & Control, 1–21. https://doi.org/10.1080/09537287.2026.2665127
• Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450
• Rai, A. (2020). Explainable AI: from black box to glass box. J. of the Acad. Mark. Sci. 48, 137–141 https://doi.org/10.1007/s11747-019-00710-5

Dr. Edit Süle
Dr. László Buics
Prof. Dr. Marco Cucculelli
Guest Editors

Manuscript Submission Information

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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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Administrative Sciences 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 1600 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.

• artificial intelligence
• digital supply chains
• Industry 4.0 and 5.0
• hybrid decision making
• human-machine collaboration