Enhancing Automotive Supply Chain Efficiency through AI-Driven Cross-Docking

Today’s rapidly developing automotive financing supply chains have increased demand for agility, accuracy and scalability. Traditional cross-docking methods often suffer from delays, misunderstanding of shipments and inefficient docking use due to lack of predictive knowledge and manual intervention. This article introduces a real-time AI-controlled cross-docking system. It was developed to automate demand forecasting, fleet allocation and dynamic package sorting using affordable IoT infrastructure. The proposed system combines linear regression of a machine learning model for vehicle allocation with node.js- based backend and RFID-based package identification and servo- operated ETA prediction using ESP32 hardware processing. React Dashboard offers live visibility into sorting actions, vehicle status, and forecast trends. Extensive simulation and prototype testing show 94.8 percent accuracy in fleet allocation, with average ETA prediction errors of 4.7 minutes and 97.2 per- cent hardware sorting accuracy. This interdisciplinary approach breaks AI, embedded systems and logistics and provides a cheap blueprint for intelligent and scalable supply chain solutions.