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AI and Machine Learning in Predictive Analytics for Supply Chain Optimization in the Global Economy

Chief Editors:  Mr. Irshadullah Asim Mohammed, Dr. Yogesh Mohan Gosavi, and Prof. (Dr.) Vineeta Kaur Saluja

Associate Editor: Mrs. Sruthi S

Co-Editors: Dr. S. Rajeswari, Dr. Nikhil Saini, and Ms. Atreyee Banerjee

ISBN:  978-81-985805-1-1

Chapter: 18

DOI: https://doi.org/10.59646/mrnc18/321

Author: Irshadullah Asim Mohammed

Abstract

This research explores the role of Artificial Intelligence (AI) and Machine Learning (ML) in predictive analytics for optimizing supply chains within the global economy. It examines how these technologies enhance efficiency, reduce operational risks, and improve decision-making, ultimately fostering resilience and competitiveness in supply chain networks. The study adopts a comprehensive review approach, analyzing recent literature, industry reports, and case studies to assess the application of AI and ML in supply chain predictive analytics. It evaluates different algorithms, models, and frameworks utilized for demand forecasting, risk management, inventory optimization, and logistics planning. The study highlights that AI and ML significantly improve supply chain performance by enabling real-time data analysis, pattern recognition, and automated decision-making. These technologies enhance demand forecasting accuracy, optimize inventory levels, and improve supply chain agility. Additionally, AI-driven predictive models help mitigate disruptions caused by economic fluctuations, geopolitical uncertainties, and environmental challenges. This paper offers a unique perspective by synthesizing the latest advancements in AI and ML applications for predictive analytics in global supply chains. It bridges the gap between academic research and industry practices, providing a valuable resource for researchers, practitioners, and policymakers aiming to enhance supply chain resilience through technological innovation.

References

  1. Agrawal, S., Kumar, S., & Rahman, Z. (2021). The role of artificial intelligence in supply chain management: A review and future research agenda. Journal of Supply Chain Management Research, 14(2), 87–102.
  2. Babiceanu, R. F., & Seker, R. (2020). Big data and machine learning in supply chain resilience: A systematic review. International Journal of Logistics Research and Applications, 23(4), 215–232.
  3. Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2020). Industry 4.0 technologies assessment: A sustainability perspective. International Journal of Production Economics, 229, 107776.
  4. Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1884.
  5. Choi, T. M., Wallace, S. W., & Wang, Y. (2021). Big data analytics in operations management. Production and Operations Management, 30(3), 662-674.
  6. Christopher, M., & Holweg, M. (2018). Supply chain 4.0: Managing supply chains in the era of digitalization. Logistics and Transportation Review, 34(3), 98–115.
  7. Christopher, M., Holweg, M., & Monczka, R. (2021). Supply chain management: A logistics perspective. Journal of Business Logistics, 42(1), 22-41.
  8. Dubey, R., Gunasekaran, A., & Childe, S. J. (2022). Resilient and sustainable supply chains. International Journal of Production Economics, 238, 108195.
  9. Fahimnia, B., Sarkis, J., & Davarzani, H. (2021). Green supply chain management. Transportation Research Part E: Logistics and Transportation Review, 149, 102271.
  10. Govindan, K., & Bouzon, M. (2021). Circular supply chains. Journal of Cleaner Production, 281, 125277.
  11. Gunning, D., Stefik, M., & Choi, J. (2022). Explainable AI: Trends and challenges. AI Magazine, 43(2), 1-15.
  12. Ivanov, D., & Dolgui, A. (2020). OR models for supply chain disruptions. European Journal of Operational Research, 286(2), 501-527.
  13. Kusi-Sarpong, S., Sarkis, J., & Wang, X. (2021). Sustainable logistics and AI. Computers & Industrial Engineering, 153, 107099.
  14. Mishra, D., Choudhary, A., & Sinha, A. (2024). AI and blockchain in SCM. IEEE Transactions on Engineering Management, 71(1), 45-63.
  15. Waller, M. A., & Fawcett, S. E. (2020). Data science, predictive analytics, and big data. Journal of Business Logistics, 41(2), 89-108.