Authors : Md Raisul Islam Khan; Ayan Barua; Fazle Karim; Niropam Das

Volume/Issue : Volume 10 - 2025, Issue 6 - June

Google Scholar : https://tinyurl.com/y888m7sk

DOI : https://doi.org/10.38124/ijisrt/25jun1480

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : The transformation of supply chain management has been made possible by this revolution as well as developments in artificial intelligence (AI) and business analytics. A careful balancing act between efficiency and flexibility is necessary for such integration. The goal of this project is to investigate how AI-driven technologies, including automation, machine learning, predictive analytics, and the Internet of Things, might improve supply chains' perception, reaction, and adaptation to changing market conditions. This study highlights the ways AI-powered supply chains achieve cost efficiency, resilience, and agility by leveraging Dynamic Capabilities Theory and organizational flexibility models. Case studies of companies like Amazon, Walmart, and Tesla effectively illustrate AI's ability to drive real-time decision-making, automation-based efficiencies, and flexible supply chain strategies. Nevertheless, despite the transformative potential of AI, there are a number of obstacles that must be resolved. These encompass the seamless integration of technology, data security, and workforce adaptability. The results indicate that artificial intelligence is essential for the successful operation of modern, adaptable supply chain management, enabling businesses to prosper throughout periods of uncertainty. To further enhance supply chain flexibility and performance, future research should examine AI’s role in autonomous supply networks, human- AI collaboration, and governance frameworks.

Keywords : Artificial Intelligence (AI), Supply Chain Flexibility, Business Analytics, Dynamic Capabilities Theory, Automation & IoT.

References :

1. Sanders, N. R. (2019). The Humachine: Humankind, Machines, and the Future of Enterprise. Routledge. https://www.routledge.com/The-Humachine-Humankind-Machines-and-the-Future-of-Enterprise/Sanders-Wood/p/book/9781138571341
2. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/smj.4250180702
3. Volberda, H. W. (1997). Building flexible organizations for fast-moving markets. European Management Journal, 15(2), 169–183. https://doi.org/10.1016/S0263-2373(97)00010-6
4. Alalade, E. O. (2025). Digital Transformation and Supply Chain Efficiency: A Case of Amazon Inc. International Journal of Research and Innovation in Social Science, IX(I), 3931-3944. https://doi.org/10.1016/j.jbusres.2023.01.002
5. 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. https://doi.org/10.1080/00207543.2018.1530476
6. Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Wamba, S. F., & Roubaud, D. (2021). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59(1), 110-128. https://doi.org/10.1080/00207543.2020.1736726
7. Gunasekaran, A., Subramanian, N., & Papadopoulos, T. (2017). Information technology for competitive advantage within logistics and supply chains: A review. Transportation Research Part E: Logistics and Transportation Review, 99, 14-33. https://doi.org/10.1016/j.tre.2017.06.008
8. Lin, Y., Kumar, A., Shankar, R., & Banerjee, P. (2024). AI in Logistics: Improving Route Efficiency and Sustainability. Journal of Supply Chain Management, 60(2), 45-59. https://doi.org/10.1016/j.jscm.2023.08.009
9. Seifert, R., & Markoff, R. (2022, January 27). Tesla’s supply chain: From cautionary tale to role model. IMD Business School Insights. https://doi.org/10.1016/j.ijpe.2021.08.017
10. Stevenson, M., & Spring, M. (2007). Flexibility in supply chains: A framework and systematic review of research. International Journal of Operations & Production Management, 27(7), 685-713. https://doi.org/10.1108/01443570710756956
11. Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21(10-11), 1105-1121. https://doi.org/10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E​
12. Gunasekaran, A., Subramanian, N., & Papadopoulos, T. (2017). Information technology for competitive advantage within logistics and supply chains: A review. Transportation Research Part E: Logistics and Transportation Review, 99, 14-33. https://doi.org/10.1016/j.tre.2017.06.008​
13. Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829-846. https://doi.org/10.1080/00207543.2018.1488086​
14. Palan, K. (2024). Driving supply chain flexibility in an uncertain and volatile world. Supply Chain Management Review, 28(2), 45-53.​ https://www.scmr.com/article/driving_supply_chain_flexibility_in_an_uncertain_and_volatile_world
15. Bag, S., Gupta, S., Kumar, S., & Sivarajah, U. (2021). Big data analytics and machine learning in supply chain management: A systematic literature review and future research agenda. International Journal of Production Research, 59(11), 3423-3452. https://doi.org/10.1080/00207543.2021.1884317
16. Chopra, S., & Meindl, P. (2023). Supply Chain Management: Strategy, Planning, and Operation (8th ed.). Pearson.
17. Kamble, S. S., Gunasekaran, A., & Sharma, R. (2020). Modeling the internet of things adoption barriers in food supply chains: A TISM approach. Computers & Industrial Engineering, 140, 106229. https://doi.org/10.1016/j.cie.2019.106229
18. Kshetri, N. (2018). Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80-89. https://doi.org/10.1016/j.ijinfomgt.2017.12.005
19. Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440. https://doi.org/10.1016/j.bushor.2015.03.008
20. Wang, Y., Zhang, Q., & Wang, J. (2022). The role of artificial intelligence in sustainable logistics: A review and future research agenda. Transportation Research Part E: Logistics and Transportation Review, 162, 102455. https://doi.org/10.1016/j.tre.2022.102455
21. Wu, L., Yue, X., Jin, A., & Yen, D. C. (2021). Smart supply chain management: A review and implications for future research. International Journal of Information Management, 57, 102298. https://doi.org/10.1016/j.ijinfomgt.2020.102298
22. Cui, R., Allon, G., Bassamboo, A., & Van Mieghem, J. A. (2015). Information sharing in supply chains: An empirical and theoretical valuation. Management Science, 61(11), 2803-2824. https://doi.org/10.1287/mnsc.2014.2061
23. Elmachtoub, A. N., & Levi, R. (2016). Supply chain management with online customer selection. Operations Research, 64(2), 458-473. https://doi.org/10.1287/opre.2015.1457
24. Hernández, C., Bharatheesha, M., Ko, W., Gaiser, H., Tan, J., van Deurzen, K., ... & Wisse, M. (2016). Team Delft's robot winner of the Amazon Picking Challenge 2016. arXiv preprint arXiv:1610.05514. Retrieved from https://arxiv.org/abs/1610.05514
25. Wamba, S. F., & Queiroz, M. M. (2020). Blockchain in the operations and supply chain management: Benefits, challenges and future research opportunities. International Journal of Information Management, 52, 102064. https://doi.org/10.1016/j.ijinfomgt.2019.102064
26. Wamba, S. F., & Queiroz, M. M. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67, 102544. https://doi.org/10.1016/j.ijinfomgt.2022.102544
27. Wuest, T., Kusiak, A., Dai, T., & Tayur, S. R. (2020). Impact of COVID-19 on manufacturing and supply networks—The case for AI-inspired digital transformation. Journal of Manufacturing Systems, 60, 936-943. https://doi.org/10.1016/j.jmsy.2020.05.008
28. Bandara, K., Shi, P., Bergmeir, C., Hewamalage, H., Tran, Q., & Seaman, B. (2019). Sales demand forecast in e-commerce using a long short-term memory neural network methodology. arXiv preprint arXiv:1901.04028. Retrieved from https://arxiv.org/abs/1901.04028
29. Taghizadeh, E. (2017). Utilizing artificial neural networks to predict demand for weather-sensitive products at retail stores. arXiv preprint arXiv:1711.08325. Retrieved from https://arxiv.org/abs/1711.08325
30. Elder, S. D. (2020). The impact of supermarket supply chain governance on smallholder farmer cooperatives: The case of Walmart in Nicaragua. Agriculture and Human Values. Retrieved from https://doi.org/10.1007/s10460-020-10102-4
31. Wang, S. C., Tsai, Y. T., & Ciou, Y. S. (2021). A hybrid big data analytical approach for analyzing customer patterns through an integrated supply chain network. Journal of Business Logistics. Retrieved from https://doi.org/10.1111/jbl.12225
32. Dehghanimohammadabadi, M. (2021). A novel iterative optimization-based simulation (IOS) framework: An effective tool to optimize system performance. Simulation Modelling Practice and Theory. Retrieved from https://doi.org/10.1016/j.simpat.2021.102355
33. AlMahri, S., Xu, L., & Brintrup, A. (2024). Enhancing supply chain visibility with knowledge graphs and large language models. arXiv preprint arXiv:2408.07705. https://arxiv.org/abs/2408.07705
34. Bhattacharya, L. (2024). 3M: Rethinking regionalization to adapt to supply chain disruptions. Singapore Management University, Centre for Management Practice, 11(2), 48. https://scholar.google.com/citations?hl=en&user=lzlcE-cAAAAJ
35. Cheong, T. (2024). Impact of supply chain power and drop-shipping on a manufacturer's optimal distribution channel strategy. European Journal of Operational Research. https://scholar.google.com/citations?hl=en&user=M0PUKeYAAAAJ
36. John, J. (2021). Tesla's supply chain innovation: A case study on digital transformation. Operations and Supply Chain Management: An International Journal, 14(4). https://scholar.google.com/citations?hl=en&user=cE6O0d4AAAAJ
37. Koskinen, K. (2024). With software updates, Tesla upends product life cycle in the car industry. Journal of Business Research. https://scholar.google.com/citations?hl=it&user=2gkpamMAAAAJ
38. Fosso Wamba, S., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
39. Singh, R., & Singh, S. (2020). Role of AI in supply chain management: A dynamic capabilities perspective. Computers & Industrial Engineering, 144, 106476. https://doi.org/10.1016/j.cie.2020.106476
40. Sheffi, Y. (2015). The power of resilience: How the best companies manage the unexpected. MIT Press. https://doi.org/10.7551/mitpress/9780262029797.001.0001
41. Wamba, S. F., & Queiroz, M. M. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67, 102544. https://doi.org/10.1016/j.ijinfomgt.2022.102544