| dc.description.abstract | Compared with traditional internal combustion engine vehicles, electric vehicles have higher energy efficiency, thus reducing the damage caused by transportation to the environment, and more and more attention has been paid to electric vehicles with more energy-saving and environmental protection benefits. Various governments around the world have begun to promote them. Electric vehicles are expected to gradually replace traditional means of transportation.
The traditional supply chain is a network composed of suppliers, distributors, warehouses, logistics, and distributors. Looking at the entire supply chain, it is difficult to obtain information about relevant manufacturers in the upstream and downstream of the supply chain. There are problems of information asymmetry and opacity, such as information falsification, low information transmission efficiency, high communication costs, and even the risk of information being tampered with. Therefore, this research expects to use blockchain and IOT technology to create an electric vehicle information system and use the decentralized, transparent, and immutable characteristics of blockchain to solve the problems of information inequality and efficiency between supply chains.
This study will design an information system for electric vehicles. The system will record the inventory and transaction records of raw materials used by electric vehicles so that enterprises can predict the supply and demand of raw materials and components and formulate production plans. And apply the Integrated Information Architecture (Architecture of Integrated Information System, ARIS) software to construct a complete BPMN model to analyze and clarify all the operation processes, combined with blockchain and IOT technology, so that the new supply chain model has higher transparency and quick response ability, and finally establish a unified modeling language (Unified Modeling Language, UML) according to the new process, so that the process can be visualized and people can quickly understand the entire model structure and process, thereby helping enterprises improve efficiency. | en_US |