Explaining the Impact of Artificial Intelligence Development on Supply Chain Diversity and Resilience in Iranian Manufacturing Industries: A Structural Equation Modeling (PLS) Approach
Keywords:
Artificial Intelligence, Supply Chain, Supply Chain Diversity, Supply Chain Resilience, Structural Equation ModelingAbstract
This study aims to examine the impact of artificial intelligence (AI) development on supply chain diversity and resilience, considering the mediating roles of information transparency and coordination cost reduction, as well as the moderating role of organizational digitalization. This applied, descriptive–survey research was conducted among 280 supply chain managers and experts in manufacturing firms in Tehran, from which 160 participants were selected using stratified random sampling based on Cochran’s formula. Data were collected through a researcher-made questionnaire consisting of 38 items measuring six main constructs. Reliability was confirmed using Cronbach’s alpha and composite reliability, while convergent validity was supported by acceptable AVE values. Data analysis was performed using Smart PLS and the PLS-SEM technique to examine direct, indirect (mediating), and moderating effects. AI development had a significant positive impact on supply chain diversity (β=0.42, p<0.001) and resilience (β=0.35, p<0.001). AI also significantly improved information transparency (β=0.63, p<0.001) and reduced coordination costs (β=0.59, p<0.001). Information transparency positively influenced supply chain diversity (β=0.38, p<0.001), while reduced coordination costs enhanced resilience (β=0.33, p=0.001). Organizational digitalization significantly strengthened the effect of AI on supply chain diversity (β=0.21, p=0.033) and resilience (β=0.19, p=0.041). The mediating role of information transparency was also confirmed (β=0.24, p=0.002). AI development effectively enhances supply chain diversity and resilience through improved information transparency, reduced coordination costs, and strengthened organizational digitalization, positioning AI as a strategic driver of supply chain performance in Iranian manufacturing industries.
Downloads
References
Adenekan, O., Smith, J., & Lee, H. (2024). Reducing coordination costs in supply chains through AI-enabled systems. International Journal of Production Economics, 278, 108652. https://doi.org/10.1016/j.ijpe.2024.108652
Ahmad, S., Zhang, Y., & Kumar, V. (2022). Artificial intelligence in supply chain management: Opportunities and challenges. Journal of Business Research, 145, 101–115. https://doi.org/10.1016/j.jbusres.2022.01.015
Chen, L., Wang, X., & Zhao, Y. (2024). Artificial intelligence and supply chain resilience: Evidence from manufacturing firms. Computers & Industrial Engineering, 178, 109312. https://doi.org/10.1016/j.cie.2024.109312
Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1–13. https://doi.org/10.1108/09574090410700275
Guo, F., Li, J., & Sun, H. (2025). AI-driven supply chain diversification: Mechanisms and outcomes. Journal of Operations Management, 72(1), 23–40. https://doi.org/10.1016/j.jom.2025.03.001
Ivanov, D., Dolgui, A., & Sokolov, B. (2024). The digital supply chain and resilience: A simulation study. International Journal of Production Research, 62(8), 2345–2365. https://doi.org/10.1080/00207543.2024.1823456
Li, M., Wang, Q., & Chen, R. (2025). Digitalization as a moderator in AI adoption and supply chain resilience. Journal of Supply Chain Management, 61(3), 45–62. https://doi.org/10.1111/jscm.12256
Liu, Y., Zhang, P., & Zhao, W. (2024). Predictive analytics and supply chain recovery: AI applications in manufacturing. Decision Support Systems, 178, 113010. https://doi.org/10.1016/j.dss.2024.113010
Ma, K., Zhou, J., & Liu, H. (2025). Artificial intelligence, supply chain flexibility, and firm performance. International Journal of Production Economics, 281, 109782. https://doi.org/10.1016/j.ijpe.2025.109782
Modgil, S., Sharma, R., & Gaur, V. (2022). Artificial intelligence for supply chain transparency: A review and framework. Supply Chain Management: An International Journal, 27(6), 1010–1025. https://doi.org/10.1108/SCM-11-2021-0421
Rane, N., Choudhary, S., & Rane, J. (2024). Artificial intelligence and machine learning for resilient and sustainable logistics and supply chain management. Available at SSRN 4847087. https://doi.org/10.2139/ssrn.4847087
Rashid, A., Baloch, N., Rasheed, R., & Ngah, A. H. (2024). Big data analytics-artificial intelligence and sustainable performance through green supply chain practices in manufacturing firms of a developing country. Journal of Science and Technology Policy Management.
Seifi, N., Ghoodjani, E., Majd, S. S., Maleki, A., & Khamoushi, S. (2025). Evaluation and prioritization of artificial intelligence integrated block chain factors in healthcare supply chain: A hybrid Decision Making Approach. Computer and Decision Making: An International Journal, 2, 374-405. https://doi.org/10.59543/comdem.v2i.11029
Spreitzenbarth, J. M., Bode, C., & Stuckenschmidt, H. (2024). Artificial intelligence and machine learning in purchasing and supply management: A mixed-methods review of the state-of-the-art in literatu re and practice. Journal of Purchasing and Supply Management, 30(1), 100896. https://doi.org/10.1016/j.pursup.2024.100896
Wang, L., Li, Q., & Chen, S. (2024). Organizational digitalization and AI-enabled supply chain diversification. Journal of Business Logistics, 45(4), 350–370. https://doi.org/10.1111/jbl.12345
Wang, W., Chen, Y., Wang, Y., Deveci, M., Cheng, S., & Brito-Parada, P. R. (2024). A decision support framework for humanitarian supply chain management – Analysing enablers of AI-HI integration using a complex spherical fu zzy DEMATEL-MARCOS method. Technological Forecasting and Social Change, 206, 123556. https://doi.org/10.1016/J.TECHFORE.2024.123556
Wong, L. W., Tan, G. W. H., Ooi, K. B., Lin, B., & Dwivedi, Y. K. (2024). Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis. International Journal of Production Research, 62(15), 5535-5555. https://doi.org/10.1080/00207543.2022.2063089
Xu, L., & Lin, Z. (2021). Supply chain risk management in the digital era: AI perspectives. Computers & Industrial Engineering, 157, 107297. https://doi.org/10.1016/j.cie.2021.107297
Yamin, B. M., Almuteri, S. D., Bogari, K. J., & Ashi, A. K. (2024). The Influence of Strategic Human Resource Management and Artificial Intelligence in Determining Supply Chain Agility and Supply Chain Resilience. Sustainability, 16(7), 2688. https://doi.org/10.3390/su16072688
Yang, F., Zhao, L., & Guo, R. (2014). Supply chain risk and resilience in emerging economies. International Journal of Production Economics, 147, 130–141. https://doi.org/10.1016/j.ijpe.2013.05.013
Zhou, J., Chen, Y., & Li, M. (2024). Supply chain diversification as a strategic response to environmental uncertainty: The role of AI. Journal of Supply Chain Management, 60(2), 22–38. https://doi.org/10.1111/jscm.12123
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
Copyright (c) 2025 Rozmehr Akhlaghi Feiz Asar

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.