Oluwadare Joshua Oyebode , Dr Soumyashree N Hegde, Dr Anil Tiwari , Dr. Shabnam Siddiqui , T A Raja , Dr. Ganesh Kumar R,


Advances in real-time data analysis, predictive maintenance, and resource optimization have been made possible by AI technologies, particularly big data analytics. Nonetheless, issues including poor data quality, inadequate infrastructure, and shortages of skilled agricultural workers continue to exist. Future developments in AI for agriculture will bring with them both benefits and difficulties, such as the need for fair access to AI technology and ethical problems. This study looks at how AI and big data analytics might enhance the sustainability of the manufacturing supply chain. Because of environmental concerns and the growing demand for healthcare, manufacturing supply chains are realizing the need of eco-friendly operations. It goes on to describe how AI and big data analytics may change these ramifications. Big data analytics is given top priority in this research for demand forecasting, inventory management, and procurement. In conclusion, big data analytics and AI are a huge help to sustainability, which is revolutionizing industrial supply chains for a greener future.

Keyword : AI-Driven, Sustainability, Revolutionizing, Supply Chains, Greener Future, Artificial Intelligence, Big Data.

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March 15, 2024
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1. Alshawi, A. (2022). Artificial Intelligence for Sustainability: Opportunities and Challenges. Artificial Intelligence for Renewable Energy and Climate Change, 1-31. 2. Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2021). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. Annals of Operations Research, 1-26. 3. Elufioye, O. A., Ike, C. U., Odeyemi, O., Usman, F. O., & Mhlongo, N. Z. (2024). AI-DRIVEN PREDICTIVE ANALYTICS IN AGRICULTURAL SUPPLY CHAINS: A REVIEW: ASSESSING THE BENEFITS AND CHALLENGES OF AI IN FORECASTING DEMAND AND OPTIMIZING SUPPLY IN AGRICULTURE. Computer Science & IT Research Journal, 5(2), 473-497. 4. Gupta, C. P., Kumar, V. R., & Khurana, A. (2023, December). Artificial Intelligence integration with the supply chain, making it green and sustainable. In 2023 7th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech) (pp. 1-5). IEEE. 5. Gupta, C., & Khang, A. (2024). Cultivating Efficiency-Harnessing Artificial Intelligence (AI) for Sustainable Agriculture Supply Chains. In Agriculture and Aquaculture Applications of Biosensors and Bioelectronics (pp. 372-388). IGI Global. 6. Hassan, M., Wahab, N. A. B. A., & Nor, R. B. M. (2023). The role of artificial intelligence in waste reduction in the beverage industry: a comprehensive strategy for enhanced sustainability and efficiency. AI, IoT and the Fourth Industrial Revolution Review, 13(11), 7. Kaledio, E., Russell, E., Oloyede, J., & Olaoye, F. (2023). Transformative Trends and Sustainable Practices in Modern Manufacturing: A Comprehensive Exploration. 8. Kumari, N., Chaudhary, D., Kaur, H., & Yadav, A. L. (2023). Artificial Intelligence in Supply Chain Optimization 2023 International Conference on IoT, Communication and Automation Technology (ICICAT), Gorakhpur, India, 2023, pp. 1-6, doi: 10.1109/ICICAT57735.2023.10263631. 9. Norgren, A., & Janzon Hägglund, W. (2023). Implementing Artificial Intelligence in Supply Chain Management: A Qualitative Study of How Manufacturing Companies Can Implement AI to Improve Supply Chain Management. 10. Rahman, A. (2023). AI revolution: shaping industries through artificial intelligence and machine learning. Journal Environmental Sciences and Technology, 2(1), 93-105. 11. Richey Jr, R. G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532-549. 12. Shobhana, N. (2024). AI-Powered Supply Chains Towards Greater Efficiency. In Complex AI Dynamics and Interactions in Management (pp. 229-249). IGI Global. 13. Singh, P. K. (2023). Digital transformation in supply chain management: Artificial Intelligence (AI) and Machine Learning (ML) as Catalysts for Value Creation. International Journal of Supply Chain Management, 14. 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. 15. Vishwakarma, L. P., & Singh, R. K. (2022). Application of Artificial Intelligence (AI) in Supply Chain: An Overview. Artificial Intelligence of Things for Smart Green Energy Management, 191-212. 16. Momin, U. (2023). NREGA-Catalyst for Fostering Inclusive Growth. International Journal for Multidimensional Research Perspectives, 1(4), 63-72. 17. Momin, M. U. An Analysis of the Challenges and Opportunities Encountered by Small and Medium Enterprises (SMES) in the Context of the Indian Economy. 18. Momin, U., Mehak, S. T., & Kumar, M. D. (2023). Strategic Planning and Risk Management in the Stratup, Innovation and Entrepreneurship: Best Practices and Challenges. Journal of Informatics Education and Research, 3(2). 19. Mahajan, T., Momin, U., Khan, S., & Khan, H. ROLE OF WOMEN’S ENTREPRENEURSHIP IN SOCIAL AND ECONOMIC DEVELOPMENT OF INDIA. 20. Yadav, S., Sudman, M. S. I., Dubey, P. K., Srinivas, R. V., Srisainath, R., & Devi, V. C. (2023, October). Development of an GA-RBF based Model for Penetration of Electric Vehicles and its Projections. In 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) (pp. 1-6). IEEE. 21. Sakthivel, M., Sudman, M. S. I., Ravishankar, K., Avinash, B., Kumar, A., & Ponnusamy, M. (2023, October). Medical Image Analysis of Multiple Myeloma Diagnosis Using CNN and KNN Based Approach. In 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) (pp. 92-97). IEEE. 22. Faisal, L., Rama, V. S. B., Roy, S., & Nath, S. (2022). Modelling of electric vehicle using modified sepic converter configuration to enhance dc–dc converter performance using matlab. In Smart Energy and Advancement in Power Technologies: Select Proceedings of ICSEAPT 2021, Volume 2 (pp. 643-653). Singapore: Springer Nature Singapore. 23. Faisal, L., Rama, V. S. B., Yang, J. M., Wajid, A., & Ghorui, S. K. (2022, May). Performance and simulation analysis of ipmsyncrm (internal permanent magnet synchronous reluctance motor) for advanced electric vehicle design. In 2022 3rd International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE. 24. Mohd, R., & Faisal, L. (2022). Smart Agricultural Practices using Machine Learning techniques For Rainfall Prediction: A case Study of Valkenburg station, Netherlands. Mathematical Statistician and Engineering Applications, 71(4), 8451-8462. 25. Wani, A. A., & Faisal, L. (2022). Design & development of novel hybrid set of rules for detection and type of malignant or non-malignant tumor in human brain based on svm using artificial intelligence classifier. Mathematical Statistician and Engineering Applications, 71(4), 10253-10276. 26. Mohammed, A. H. (2021). Fish schooling and sorensen trust based wireless sensor network optimization. International Journal, 9, 6. 27. Mohammed, A. H. DDoS Malicious Node Detection by Jaccard and Page Rank Algorithm in Cloud Environment. 28. Mohammed, A. H. (2021). Invasive Weed Optimization Based Ransom-Ware Detection in Cloud Environment. 29. Choudhuri, S. S., Bowers, W., & Siddiqui, M. N. (2023). U.S. Patent No. 11,763,241. Washington, DC: U.S. Patent and Trademark Office. 30. Zanzaney, A. U., Hegde, R., Jain, L., Choudhuri, S. S., & Sharma, C. K. (2023, September). Crop Disease Detection Using Deep Neural Networks. In 2023 International Conference on Network, Multimedia and Information Technology (NMITCON) (pp. 1-5). IEEE.