Predicting Delays for Truck Delivery Logistics: An Application of AI and ML

Supply chain management has a lot of logistical challenges, which can significantly affect the efficacy and efficiency of logistics operations as a whole. Machine learning techniques are increasingly popular because they can evaluate vast data volumes and produce precise predictions. Logistics management must include the forecast of delays in the supply chain. It entails anticipating delays in logistics operations, which can assist logistics managers in effectively planning and allocating resources. This paper summarizes the logistics delay prediction in truck delivery systems and identifies some opportunities and problems. The article also suggests a framework for predicting logistics delays based on machine learning methods and assesses the framework's effectiveness using actual data. Thus, the research uses machine learning and AI approaches to anticipate logistics delays. The study gathered information on previous logistical operations and determined the major causes of delays. Then, models for predicting logistical delays were used using machine learning techniques, such as Artificial Neural Networks (ANN), decision trees, random forests, and support vector machines. Our findings demonstrate that these methods have an accuracy rate of up to 97.6
^%
in predicting logistics delays. Also, we discovered that the Adaboost classifier model performed better than the other models in accuracy and precision. Our study offers insightful information on the application of machine learning methods for anticipating logistics delays, which can assist supply chain managers and logistics firms in better organizing and manage their operations to cut down on delays and increase overall effectiveness.