Routesense AI: Design and Evaluation of an Intelligent Framework for Supply Chain Delay Prediction

Efficient logistics management is a critical requirement in modern supply chain systems, where timely delivery, cost optimization, and operational reliability are essential. The increasing complexity of transportation networks, coupled with dynamic environmental factors such as traffic congestion, weather variations, and fluctuating demand, poses significant challenges to traditional logistics systems. Conventional approaches rely on static routing and manual planning, which lack the capability to adapt to real-time conditions and predict disruptions proactively. This paper presents ROUTESENSE AI, an intelligent logistics platform that integrates predictive analytics and route optimization to enhance delivery efficiency. The proposed system leverages historical and real-time data to forecast potential delivery delays using machine learning techniques. A Random Forest-based predictive model is employed to analyze key features such as distance, traffic conditions, and delivery timelines, enabling accurate delay prediction. Additionally, the system incorporates a dynamic route optimization mechanism that evaluates multiple parameters, including travel time, fuel consumption, and route congestion, to generate optimal delivery paths. The platform is implemented using Python, Django, and Scikit-learn, with a modular architecture that supports scalability and real-time monitoring. Experimental results demonstrate improved prediction accuracy and significant enhancement in routing efficiency compared to traditional systems. ROUTESENSE AI enables proactive decision-making, reduces operational costs, and improves customer satisfaction, thereby offering a scalable and intelligent solution for modern logistics management.