Advancing Logistics and Supply Chain Efficiency Through Artificial Intelligence and Machine Learning

Abstract: The logistics and supply chain ecosystem encompasses a network of interconnected entities that must work collaboratively to enhance operational efficiency and reduce overall costs. This study explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in modern supply chain and logistics management. It examines the integration of advanced ML techniques across various supply chain functions such as demand and supply forecasting, pricing strategy formulation, and text analytics. By leveraging these technologies, organizations can streamline processes, mitigate risks, reduce operational costs, and enhance profitability. The research further emphasizes the practical implications of adopting AI and ML, presenting real-world applications that demonstrate their potential in driving data-driven decision-making and achieving competitive advantage in the logistics domain.

Artificial Intelligence, Machine Learning, Supply Chain Management, Logistics, Forecasting, Cost Optimization, Revenue Enhancement, Data Analytic

1. Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big Data Analytics in Operations Management. Production and Operations Management, 27(10), 1868–1889. https://doi.org/10.1111/poms.12838 [Google Scholar] [Crossref]

2. Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2019). Global Supply Chain and Operations Management (3rd ed.). Springer. https://doi.org/10.1007/978-3-319-94313-8 [Google Scholar] [Crossref]

3. Min, H. (2010). Artificial Intelligence in Supply Chain Management: Theory and Applications. International Journal of Logistics: Research and Applications, 13(1), 13–39. https://doi.org/10.1080/13675560902736537 [Google Scholar] [Crossref]

4. Wamba, S. F., 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 [Google Scholar] [Crossref]

5. Christopher, M. (2016). Logistics and Supply Chain Management (5th ed.). Pearson Education. [Google Scholar] [Crossref]

6. Kache, F., & Seuring, S. (2017). Challenges and Opportunities of Digital Information at the Intersection of Big Data Analytics and Supply Chain Management. International Journal of Operations & Production Management, 37(1), 10–36. https://doi.org/10.1108/IJOPM-02-2015-0078 [Google Scholar] [Crossref]

7. Ambe, I. M., & Badenhorst-Weiss, J. A. (2011). Managing and Controlling Public Sector Supply Chains. Supply Chain Management: An International Journal, 16(1), 12–25. https://doi.org/10.1108/13598541111113286 [Google Scholar] [Crossref]

• Advanced Digital Communication Strategies: A Comprehensive Analysis
• Developing an Explainable AI System for Digital Forensics: Enhancing Trust and Transparency in Flagging Events for Legal Evidence
• The Global Impact of Government Censorship on Women’s Access to Information: A Literature Review
• "Reimagining Higher Education Workspaces: A Review on The Transformative Role of Digital Technology Adoption"
• Improved CSP Efficiency: Innovations and Challenges in Thermal Energy Storage Systems