Researchers have unveiled a pioneering framework showing how generative AI can transform supply chain management, boosting forecasting, logistics, and resilience while paving the way for sustainable global operations.
Research: The transformative power of generative AI for supply chain management: Theoretical framework and agenda. Image Credit: Aun Photographer / Shutterstock
With the growing complexity of global supply chains, enterprises face significant challenges in resource coordination, demand forecasting, and dynamic process optimization. Traditional supply chain management (SCM) methods are often inflexible, reactive, and inefficient, which may lead to missed opportunities and revenue losses. Although technological progress has played a crucial role in addressing these challenges, and Generative Artificial Intelligence (GAI) has emerged as a transformative force with numerous advantages for SCM, existing literature on the role of GAI in enhancing supply chain performance still lacks a comprehensive theoretical framework for the construction of GAI applications and their empowerment mechanisms in SCM.
Collaborative Research on GAI in SCM
Therefore, Huamin WU from the School of Economics and Management, China University of Petroleum-Beijing, Guo LI from the School of Management, Beijing Institute of Technology, and Dmitry IVANOV from the Department of Business and Economics, Berlin School of Economics and Law have jointly conducted a research entitled "The Transformative Power of Generative AI for Supply Chain Management: Theoretical Framework and Agenda".
Key Capabilities and Empowerment Mechanisms
This study first identifies the core GAI capabilities required for constructing the SCM framework, which are classified into five categories: learning and creativity, perception and prediction, expression and communication, collaboration and interaction, and adjustment and adaptation. It then explores the empowerment mechanisms of GAI in SCM, including driving improvements in demand forecasting, procurement management, inventory management, logistics management, and risk management.
Challenges and Solutions
Meanwhile, the study analyzes the challenges faced by GAI in SCM application, such as data quality and availability issues, ethical and social implications, integration difficulties, high computational costs, and problems of accuracy and consistency, and proposes corresponding solutions.
Future Research Agenda
After that, the study points out the apparent gaps in existing research and puts forward a comprehensive research agenda, focusing on the SCM framework empowered by GAI, which is divided into technology-driven directions (including intelligent supply chain design and risk prediction, and the intersection of GAI with emerging technologies) and management innovation practices (including ethical and social implications of GAI, and sustainable supply chain design and low-carbon transition). The research results provide a theoretical basis and practical guidance for enterprises to effectively apply GAI in SCM and build flexible, robust, and sustainable supply chains.
Access the Full Study
The paper "The Transformative Power of Generative AI for Supply Chain Management: Theoretical Framework and Agenda" is authored by Huamin Wu, Guo Li, and Dmitry Ivanov. Full text of the paper: https://link.springer.com/article/10.1007/s42524-025-4240-x
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