Innovative Cloud Computing Strategies for Automotive Supply Chain Data Security and Business Intelligence

Abstract

The purpose of this project is to determine how the security, resilience, and efficiency of the automotive supply chain may be greatly improved by integrating cloud computing, IoT, blockchain, and advanced cryptographic techniques. In order to enhance decision-making and enable predictive maintenance, ideal inventory control, and faster logistics procedures, the architecture makes use of AI-powered analytics and real-time monitoring via IoT devices. Blockchain technology lessens the possibility of manipulation and fraud by guaranteeing the transparency and integrity of supply chain transactions. By guaranteeing that only authorised personnel can access sensitive information, the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) paradigm reduces the danger of unauthorised access. Furthermore, excellent data security is provided by the architecture's non-linear hashing techniques and dynamic key creation, safeguarding vital supply chain data. Performance assessments show that the suggested non-linear hash model is more sensitive and efficient than traditional algorithms, which makes it especially suitable for the intricate and dynamic automotive supply chain environment. The findings demonstrate that the suggested design improves the overall operational efficiency and adaptability of the supply chain in addition to satisfying the industry's needs for scalable and secure data management solutions. The study does, however, also draw attention to the difficulties in integrating these cutting-edge technology, highlighting the necessity of continual managerial involvement and overcoming change-resistant behaviour for successful implementation.