<![CDATA[Traditional reactive approaches to supply chain logistics are inadequate because global supply chains are consistently confronted with demand volatility, geopolitical risks and operation inefficiencies. This paper examines how predictive analytics, a fundamental field in data science, can be applied to streamline logistics to make operations not only more cost-efficient but more efficient. The study utilizes machine learning algorithms, time-series forecasting, optimization models, and simulation toolsets to implement a mixed methodology based on literature synthesis, case analysis, and model evaluation on the most important logistics functions. The secondary sources such as industry reports, peer-reviewed articles, and validated case studies were used as a source of data. The results show that predictive analytics produce quantifiable benefits in various areas. Machine learning adoption in demand forecasting and inventory optimization in companies like Amazon and Walmart cut stockouts to less than 5% and lower the number of overstocks by 2050 to up to 25% inventory holding costs. Optimization in transportation: DHL announced that through dynamic route optimization based on AI models, fuel expenses were cut by 15% and delivery times in cities were shortened by 12 percent. Predictive modeling ensured a greater efficiency of the warehouse and resulted in a 15-percent decrease in the variability of order processing and labor allocation optimization. By identifying supplier delays, quality risks and geopolitical threats proactively, risk management applications posted a 45.3 percent reduction in supply chain disruption. Further, the predictive variance analysis delivered 10 percent procurement cost savings to a firm like Nestle, demonstrating the advantages of supplier performance. This study concludes that predictive analytics promotes an active, robust and cost effective supply chain. Predictive analytics is a groundbreaking direction toward the creation of agile logistics systems oriented to Industry 4.0 requirements despite the difficulties in data integration, technical complexity, and upfront costs.]]>
