Predicting Delivery Delays in Logistics Using Machine Learning
Author : Dr. Krishnaveni Sakkarapani and Sudharsana S
Abstract :
Using machine learning to predict logistics delays entails creating models that examine past data in order to predict possible delays in delivery procedures. The goal of this project is to improve supply chain management's operational efficiency by employing machine learning techniques to predict logistics delays. The machine learning model created for this project analyzes a large dataset with historical shipment data using sophisticated techniques like Random Forest and Decision Tree Classifier. To improve prediction accuracy, important factors affecting delays—such as supplier details, order volumes, and delivery methods—are carefully investigated. Stakeholders may readily obtain real-time predictions and explanations by deploying the model through an intuitive graphical user interface (GUI).
Keywords :
Machine learning, Predicting, Random forest, Decision tree classifier, User friendly GUI, Logistics delay.
