Enhancing Logistics Efficiency with AI-Driven Predictive Analytics

EasyChair Preprint 14412

Abstract

In an era marked by rapid technological advancements, logistics and supply chain management are increasingly leveraging artificial intelligence (AI) and predictive analytics to enhance operational efficiency. This paper explores the transformative impact of AI-driven predictive analytics on logistics efficiency, emphasizing how advanced algorithms and data-driven insights are revolutionizing traditional logistics practices. By harnessing vast amounts of historical and real-time data, AI models can forecast demand, optimize inventory management, and streamline transportation routes, thereby reducing operational costs and improving service levels. The integration of AI-driven predictive analytics enables logistics providers to anticipate disruptions, adapt to market changes proactively, and make informed decisions that enhance overall efficiency. Through a review of case studies and current applications, this paper illustrates the practical benefits and challenges associated with implementing these technologies.

Keyphrases: 1. **Predictive Analytics**, 10. **Algorithm Accuracy**, 11. **Transportation Costs**, 12. **Real-Time Data**, 13. **Advanced Analytics**, 14. **IoT in Logistics**, 15. **Blockchain in Supply Chain**, 16. **Operational Efficiency**, 17. **Global Supply Chains**, 18. **Predictive Modeling**, 19. **Fleet Management**, 2. **AI in Logistics**, 20. **Logistics Optimization**, 3. **Machine Learning**, 4. **Demand Forecasting**, 5. **Route Optimization**, 6. **Inventory Management**, 7. **Data Integration**, 8. **Logistics Efficiency**, 9. **Supply Chain Management**

@booklet{EasyChair:14412,
  author    = {Wayzman Kolawole},
  title     = {Enhancing Logistics Efficiency with AI-Driven Predictive Analytics},
  howpublished = {EasyChair Preprint 14412},
  year      = {EasyChair, 2024}}