Strategic Supply Chain Transformation through AI: A Theoretical Model for Performance and Sustainability

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Published: 2026-03-31
DOI: https://doi.org/10.69980/terjq345
Keywords:
Artificial Intelligence, Supply Chain Transformation, Performance, Sustainability, Logistics

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Dr. Santanu Roy
Professor, IILM Institute for Higher Education, New Delhi, India
Dr. Shiny C M
Professor, Dept. of Management Studies, Jawaharlal College of Engineering and Technology, Kerala
S. Aishwarya
Research Scholar, Faculty of Management, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu -603 203

Abstract

The contribution of artificial intelligence to strategic supply chain transformation and its influence on the operational performance and sustainability results. Based on the quantitative method, the study leverages an energy-conscious supply chain dataset of 3,050 observations to test the connection between AI proxies, supply chain activities, performance measures, and environmental measures. Artificial intelligence is theorized on the basis of data-driven operational conditions including traffic intensity, speed, and delay pattern whereas transformation is theorized on the basis of logistics configurations. The results also indicate that AI-related variables do not have a statistically significant direct impact on performance; nevertheless, they have an indirect effect as they influence the operation environments. Conversely, there is a strong support of sustainability outcomes, especially the strong connection between CO 2 emissions and fuel use that emphasize resource efficiency as the major motivation of environmental performance. The research highlights the role of supply chain transformation as an interaction tool between AI capabilities and performance as well as sustainability. The findings give a subtle insight into the role of AI in decision-making and optimization of logistic systems. The study is relevant to the literature as it incorporates the AI, change, and sustainability as a single unit and provides practical knowledge on organizations intending to attain effective and sustainable supply chain management.

Article Details

Issue

Vol. 22 No. 3 (2026): The Journal of Theoretical Accounting Research

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Articles
Creative Commons License

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

How to Cite

Strategic Supply Chain Transformation through AI: A Theoretical Model for Performance and Sustainability. (2026). The Journal of Theoretical Accounting Research, 22(3), 71-77. https://doi.org/10.69980/terjq345

References

1. Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International journal of production research, 57(7), 2179-2202.

2. Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2024). Artificial intelligence in supply chain and operations management: a multiple case study research. International journal of production research, 62(9), 3333-3360.

3. Culot, G., Podrecca, M., & Nassimbeni, G. (2024). Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions. Computers in industry, 162, 104132.

4. Dora, M., Kumar, A., Mangla, S. K., Pant, A., & Kamal, M. M. (2022). Critical success factors influencing artificial intelligence adoption in food supply chains. International Journal of Production Research, 60(14), 4621-4640.

5. Dumitrascu, O., Dumitrascu, M., & Dobrotǎ, D. (2020). Performance evaluation for a sustainable supply chain management system in the automotive industry using artificial intelligence. Processes, 8(11), 1384.

6. Kassa, A., Kitaw, D., Stache, U., Beshah, B., & Degefu, G. (2023). Artificial intelligence techniques for enhancing supply chain resilience: A systematic literature review, holistic framework, and future research. Computers & Industrial Engineering, 186, 109714.

7. Kumar, A., Mani, V., Jain, V., Gupta, H., & Venkatesh, V. G. (2023). Managing healthcare supply chain through artificial intelligence (AI): A study of critical success factors. Computers & Industrial Engineering, 175, 108815.

8. Liu, K. S., & Lin, M. H. (2021). Performance assessment on the application of artificial intelligence to sustainable supply chain management in the construction material industry. Sustainability, 13(22), 12767.

9. Maghsoudi, M., Shokouhyar, S., Ataei, A., Ahmadi, S., & Shokoohyar, S. (2023). Co-authorship network analysis of AI applications in sustainable supply chains: Key players and themes. Journal of cleaner production, 422, 138472.

10. Naz, F., Agrawal, R., Kumar, A., Gunasekaran, A., Majumdar, A., & Luthra, S. (2022). Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions. Business Strategy and the Environment, 31(5), 2400-2423.

11. Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International journal of information management, 53, 102104.

12. Olan, F., Liu, S., Suklan, J., Jayawickrama, U., & Arakpogun, E. O. (2022). The role of Artificial Intelligence networks in sustainable supply chain finance for food and drink industry. International Journal of Production Research, 60(14), 4418-4433.

13. Programmer (2025). Energy-aware logistics scheduling dataset. Kaggle. https://www.kaggle.com/ data sets/ pro grammer3/energy-aware-logistics-scheduling-dat aset

14. Sanders, N. R., Boone, T., Ganeshan, R., & Wood, J. D. (2019). Sustainable supply chains in the age of AI and digitization: research challenges and opportunities. Journal of Business logistics, 40(3), 229-240.

15. Singh, A., Dwivedi, A., Agrawal, D., & Singh, D. (2023). Identifying issues in adoption of AI practices in construction supply chains: towards managing sustainability. Operations Management Research, 16(4), 1667-1683.

16. Smyth, C., Dennehy, D., Fosso Wamba, S., Scott, M., & Harfouche, A. (2024). Artificial intelligence and prescriptive analytics for supply chain resilience: a systematic literature review and research agenda. International Journal of Production Research, 62(23), 8537-8561.

17. Teixeira, A. R., Ferreira, J. V., & Ramos, A. L. (2025). Intelligent supply chain management: A systematic literature review on artificial intelligence contributions. Information, 16(5), 399.

18. Tsolakis, N., Schumacher, R., Dora, M., & Kumar, M. (2023). Artificial intelligence and blockchain implementation in supply chains: a pathway to sustainability and data monetisation?. Annals of operations research, 327(1), 157-210.

19. Walter, S. (2023). AI impacts on supply chain performance: a manufacturing use case study. Discover Artificial Intelligence, 3(1), 18.

20. Wang, T., & Shen, Y. (2025). Unlocking the potential of supply chain digitalization for enhancing enterprise green transformation performance: evidence from China. Humanities and Social Sciences Communications, 12(1), 1-16.

21. Yadav, A., Garg, R. K., & Sachdeva, A. (2024). Artificial intelligence applications for information management in sustainable supply chain management: A systematic review and future research agenda. International Journal of Information Management Data Insights, 4(2), 100292.

22. Zaid, M., Farooqi, R., & Azmi, S. N. (2025). Driving sustainable supply chain performance through digital transformation: the role of information exchange and responsiveness. Cogent Business & Management, 12(1), 2443047.

23. Zejjari, I., & Benhayoun, I. (2024). The use of artificial intelligence to advance sustainable supply chain: retrospective and future avenues explored through bibliometric analysis. Discover Sustainability, 5(1), 174.