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

Hao, Xinyue and Demir, Emrah ORCID: 2024. Artificial intelligence in supply chain management: Enablers and constraints in pre-development, deployment, and post-development stages. Production Planning and Control 10.1080/09537287.2024.2302482
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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 predevelopment 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.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Business (Including Economics)
Publisher: Taylor and Francis Group
ISSN: 0953-7287
Date of First Compliant Deposit: 18 December 2023
Date of Acceptance: 28 December 2023
Last Modified: 18 Jan 2024 16:38

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