6G Automotive Supply Chain Network for Supply Chain Performance Evaluation Model

Intelligent network administration and oversight are key components of the 6G future of networks, even though the cloudification of networking with a micro-services-oriented architecture is an established component of 5G. Therefore, a significant role for deep learning (DL), machine learning (ML), and artificial intelligence (AI) can be found in the envisaged 6G model. Upcoming end-to-end automated network operation necessitates the early identification of threats, using resourceful prevention techniques, and the assurance that 6G systems will be self-sufficient. The present piece investigates how AI can be used in 6G data communication and supply chain role 6G networks. In this work, the 6G-based Automotive Supply Chain network is used to evaluate the supply chain using the Deep Learning method. The proposed method integrates an automotive supply chain and deep learning method to improve operational efficiency, improve decision-making and minimise the risks present in the data. Initially, the dataset is collected with the help of a 6G network; next, the dataset is pre-processed. Finally, the dataset is trained by using Deep Q networks. The Guangzhou Automobile Toyota Company dataset is used for evaluation in this work. The proposed work evaluates the enterprise's and suppliers' demands based on the product category, and then it also detects the errors found during the transactions between the enterprise and suppliers. This technique makes it possible for businesses and suppliers to communicate clearly and work collaboratively to pursue additional promotion. Managers in enterprises can use theoretical data to support their research while making judgments.