Artificial intelligence in supply chain management: enablers and constraints in pre-development, deployment, and post-development stages

Abstract

This study presents a comprehensive investigation into the AI supply chain journey, combining a systematic literature review (SLR) and empirical interviews with supply chain experts. The objective is to identify and analyze key enablers and constraints influencing AI in the pre-development, deployment, and post-development stages. The research integrates empirical data with a Technology-Organization-Environment (TOE) framework, revealing the interactions between technological, organizational, and environmental factors. The thematic analysis uncovers six axial themes for the pre-development stage and one theme for the deployment and post-development stages respectively, providing valuable insights into factors influencing successful AI integration. Moreover, industry-specific insights are unveiled for the Airline, Agri-food, Retail, and Logistics sectors, emphasizing the importance of contextual factors and tailored AI strategies. The study contributes to the existing knowledge by offering practical implications for AI integration in supply chains, highlighting the significance of managing constraints and industry heterogeneity. By identifying and understanding the key constraints, this research provides a deeper understanding of the constraints faced during different stages of AI in supply chains. This study makes a substantial contribution to the current socio-technical discourse on the successful journey of AI in supply chains by deriving eight propositions that offer valuable insights. These propositions delve into the practical implications of addressing constraints and transforming them into enablers for achieving enhanced supply chain performance. The propositions offer guidance to both academic researchers and industry professionals, equipping them with actionable strategies to navigate the complexities and intricacies of integrating AI technologies into the supply chain. By embracing these propositions, stakeholders can effectively harness the power of AI to optimize various aspects of the supply chain, leading to improved efficiency, agility, and competitiveness. Ultimately, this research contributes to advancing the understanding of the AI journey in supply chains and offers practical solutions to drive the successful embracing of AI technologies in real-world supply chain environments.

Keywords:

• Artificial intelligence
• enablers and constraints
• supply chain management
• Technology-Organisation-Environment
• industry 4.0

SUSTAINABLE DEVELOPMENT GOALS:

• SDG 9: Industry, innovation and infrastructure

Acknowledgements

The authors gratefully acknowledge support provided by the Cardiff University and Khalifa University. Thanks are due to the editors and referees for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Xinyue Hao

Xinyue Hao is a PhD candidate in the Logistics and Operations Management Section of the Cardiff Business School. She holds an MSc degree in Digital Innovation Built Asset Management from University College London and a BSc in Management Science from Dongbei University of Finance and Economics. Xinyue’s main research interest is positioned within the field of AI decision-making in the supply chain domain.

Emrah Demir

Emrah Demir (PhD) is a professor of operational research in the Logistics and Operations Management Section of the Cardiff Business School and a visiting professor in the Department of Engineering Systems and Management of Khalifa University. He holds BEng and MSc degrees in Industrial Engineering from Baskent University, and a PhD in Management Science from the University of Southampton. Professor Demir’s main research interest is positioned within the field of logistics and emerging technologies. Currently, he is the Area Editor of the Journal of Heuristics and Associate Editor of OR Spectrum, IMA Journal of Management Mathematics and Frontiers in Future Transportation – Freight Transport and Logistics.