Role of AI in enabling supply chain 5.0 : Drivers, barriers and critical success factors
Akash Raiakash.rainov30@gmail.comSwarrnim School of Management, Commerce & Liberal ArtsSwarrnim Startup & Innovation UniversityGandhinagar, Gujarat, 382422, India0009-0002-1326-3187View full profile → , Ruchita Mandliruchita.management@gmail.comDepartment of ManagementUnitedworld Institute of ManagementKarnavati UniversityGandhinagar, Gujarat, 384012, India0009-0004-4270-2219View full profile → , Kishan Patelpatelskishan8200@gmail.comDepartment of ManagementUnitedworld Institute of ManagementKarnavati UniversityGandhinagar, Gujarat, 384012, India0009-0005-8224-5072View full profile → , *Tripti SharmaCorresponding authortriptisharma.sharma369@gmail.comCenter of Management Studies and ResearchGanpat UniversityMehsana, Gujarat, 384012, India0000-0002-7054-0462View full profile → , Hetal Janihetal.jani10@gmail.comDepartment of ManagementSilver Oak Institute of Business Management (SOIBM)Silver Oak UniversityAhmedabad, Gujarat, 382481, India0009-0009-5742-3905View full profile → , Madhu Shuklamadhu.shukla@marwadieducation.edu.inDepartment of Artificial Intelligence, Machine Learning & Data ScienceMarwadi UniversityRajkot, Gujarat, 360003, India0000-0002-8023-7854View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 30 Sep 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JSMS-1710
- Pages:
- 1059–1072
Abstract
Supply Chain 5.0 combines advanced digital technologies with human-centric and sustainable operating principles, and Artificial Intelligence (AI) is central to this shift. This paper examines the drivers, barriers, and critical success factors (CSFs) shaping AI adoption in Supply Chain 5.0. Fifteen factors were identified through literature synthesis and expert consultation, and Interpretive Structural Modeling (ISM) combined with MICMAC analysis was used to establish their hierarchical and driving-dependence relationships. Results show that top management support and government policy are the strongest drivers, while implementation cost, workforce resistance, and integration complexity remain largely dependent outcomes. The resulting framework offers researchers, managers, and policymakers a structured basis for prioritising AI-adoption efforts in supply chain transformation.
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References
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