The Autonomous Supply Chain: AI, Robotics, and the Next Industrial Evolution

Authors

  • Mr. Venkata Surendra Kumar Settibathini Principal Architect- ERP, USA. Author

DOI:

https://doi.org/10.63282/3050-922X.ICRCEDA25-108

Keywords:

Supply Chain, Automation, Artificial Intelligence, Robotics

Abstract

The integration of Artificial Intelligence (AI) and robotics into supply chain management marks a pivotal shift in industrial operations, heralding what many term the next industrial evolution. These technologies have significantly transformed traditional supply chain models by enhancing efficiency, accuracy, and responsiveness through automation and predictive analytics. The adoption of AI and robotics is driven by the need to optimize logistics, reduce operational costs, and improve overall supply chain resilience. This paper delves into the transformative impact of AI and robotics in supply chains, highlighting their applications across procurement, warehousing, distribution, and production. We explore how AI-driven algorithms improve demand forecasting and supplier evaluation, while robotics streamline warehouse management through automated picking and packing. The study also examines the role of AI in optimizing route planning for distribution networks and improving production efficiency through predictive maintenance and autonomous assembly lines

References

[1] S. F. Wamba, A. Gunasekaran, S. Akter, S. J. Ren, R. Dubey, and S. J. Childe, "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, vol. 70, pp. 356-365, 2017.

[2] Puvvada, R. K. (2025). Optimizing Financial Data Integrity with SAP BTP: The Future of Cloud-Based Financial Solutions. European Journal of Computer Science and Information Technology, 13(31), 101-123.

[3] Puvvada, R. K. (2025). Enterprise Revenue Analytics and Reporting in SAP S/4HANA Cloud. European Journal of Science, Innovation and Technology, 5(3), 25-40.

[4] M. M. Queiroz and S. F. Wamba, "Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA," International Journal of Information Management, vol. 46, pp. 70-82, 2019.

[5] Puvvada, R. K. (2025). SAP S/4HANA Finance on Cloud: AI-Powered Deployment and Extensibility. IJSAT-International Journal on Science and Technology, 16(1).

[6] R. E. Bawack, S. F. Wamba, and K. D. A. Carillo, "A framework for understanding artificial intelligence research: Insights from practice," Journal of Enterprise Information Management, 2021.

[7] A. K. Dubey, P. J. Rimmer, and T. P. S. Akter, "Artificial intelligence and sustainable supply chain performance: A review and research agenda," Sustainable Production and Consumption, vol. 27, pp. 1208-1222, 2021.

[8] Vemulapalli, G., & Pulivarthy, P. (2025). Integrating Green Infrastructure With AI-Driven Dynamic Workload Optimization: Focus on Network and Chip Design. In Integrating Blue-Green Infrastructure Into Urban Development (pp. 397-422). IGI Global Scientific Publishing.

[9] Pulivarthy, P. (2022). Performance tuning: AI analyse historical performance data, identify patterns, and predict future resource needs. International Journal of Innovations in Applied Sciences and Engineering, 8(1), 139-155.

[10] O. K. Osei-Bryson and S. F. Wamba, "The impact of artificial intelligence on supply chain agility and resilience," Production Planning & Control, vol. 33, no. 16, pp. 1493-1511, 2022.

[11] R. Dubey, A. Gunasekaran, S. J. Ren, S. Childe, and S. F. Wamba, "Impact of AI-enabled supply chains on firm performance: Empirical evidence from emerging markets," Information & Management, vol. 58, no. 3, p. 103437, 2021.

[12] T. Ivanov, "Digital supply chain resilience: The role of artificial intelligence and blockchain technology," International Journal of Production Research, vol. 59, no. 1, pp. 1-17, 2021.

[13] C. Dubey, S. K. Paul, and R. Gunasekaran, "AI-powered risk management in global supply chains: Trends, challenges, and future research directions," Supply Chain Management: An International Journal, vol. 27, no. 5, pp. 567-590, 2022.

[14] K. M. Lee and S. W. Hsu, "Advancements in machine learning applications for supply chain optimization," IEEE Transactions on Engineering Management, vol. 69, no. 2, pp. 540-555, 2022.

[15] Pulivarthy, P. (2024). Gen AI Impact on the Database Industry Innovations. International Journal of Advances in Engineering Research (IJAER), 28, 1-10.

[16] B. Tiwari and P. K. Wadhwa, "Autonomous supply chain networks: Emerging trends and challenges," Journal of Supply Chain Management, vol. 58, no. 3, pp. 223-239, 2022.

[17] Arunkumar Thirunagalingam, “Enhancing Data Governance Through Explainable AI: Bridging Transparency and Automation”, International Journal of Sustainable Development Through AI, ML and IoT, vol 1, no.2, 2022.

[18] Mohanarajesh Kommineni, “Explore Knowledge Representation, Reasoning, and Planning Techniques for Building Robust and Efficient Intelligent Systems”, International Journal of Inventions in Engineering & Science Technology, vol 7.2021.

[19] Padmaja Pulivarthy, “Enhancing Dynamic Behaviour in Vehicular Ad Hoc Networks through Game Theory and Machine Learning for Reliable Routing”, International Journal of Machine Learning and Artificial Intelligence, vol 4, no. 4 pp. 13.

[20] Aragani, Venu Madhav and Maroju, Praveen Kumar and Mudunuri, Lakshmi Narasimha Raju, Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques (September 29, 2021). Available at SSRN: https://ssrn.com/abstract=5022841 or http://dx.doi.org/10.2139/ssrn.5022841.

[21] Swathi Chundru, “Seeing Through Machines Leveraging AI for Enhanced and Automated Data Storytelling”, International Journal of Innovations in Scientific Engineering, vol. 18 no.1, pp 47-57, 2023.

[22] Somanathan, S. (2023). Optimizing Cloud Transformation Strategies: Project Management Frameworks for Modern Infrastructure. International Journal of Applied Engineering & Technology, 5(1).

[23] Pulivarthy, P. (2024). Optimizing Large Scale Distributed Data Systems Using Intelligent Load Balancing Algorithms. AVE Trends in Intelligent Computing Systems, 1(4), 219-230.

[24] Muniraju Hullurappa, “Intelligent Data Masking: Using GANs to Generate Synthetic Data for Privacy-Preserving Analytics”, International Journal of Inventions in Engineering & Science Technology. Vol.9, pp.9, 2023.

[25] Sudheer Panyaram, “Digital Transformation of EV Battery Cell Manufacturing Leveraging AI for Supply Chain and Logistics Optimization”, International Journal of Innovations in Scientific Engineering. Vol 18 no.1. pp 78-87, 2023.

[26] Panyaram S. Intelligent Manufacturing with Quantum Sensors and AI A Path to Smart Industry 5.0. IJETCSIT [Internet]. 2025 May 18

[27] Venu Madhav Aragani, “Unveiling the Magic of AI and Data Analytics: Revolutionizing Risk Assessment and Underwriting in The Insurance Industry”, International Journal of Advances in Engineering Research vol. 24, no. 6, pp.1-13. 2022.

[28] Lakshmi Narasimha Raju Mudunuri, “AI-Driven Inventory Management: Never Run Out, Never Overstock”, International Journal of Advances in Engineering Research, vol. 26, no.6, pp. 24-36, 2023.

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Published

2025-07-02

How to Cite

1.
Settibathini VSK. The Autonomous Supply Chain: AI, Robotics, and the Next Industrial Evolution. IJERET [Internet]. 2025 Jul. 2 [cited 2025 Oct. 6];:50-6. Available from: https://ijeret.org/index.php/ijeret/article/view/178