This study aims to optimize the automotive manufacturing process for multi supplier collaboration, and proposes a systematic solution based on the integration of blockchain and cloud platform technology to address the core challenges faced by the automotive supply chain in the context of Industry 4.0, such as multi-level supplier cross regional distribution, rapid increase in component types (such as the number of electric vehicle parts growing more than 100 times), and severe information silos. Research and innovate the construction of BaaS architecture to integrate cloud resources, quantify enterprise information contribution using entropy method, and design smart contracts based on Gale Shapley algorithm to achieve supply-demand bilateral matching; We constructed a decentralized application prototype using the Truffle framework and Ganache testing network, and verified the actual effectiveness of three smart contracts: information sharing incentives, supply and demand matching, and privacy protection. The results indicate that this solution effectively addresses the three key challenges of insufficient implementation of cross level information sharing functions in blockchain, weak incentive mechanisms for information sharing, and lack of quantitative models for supply and demand matching. It provides a reusable technical path for optimizing the automotive manufacturing process through multi supplier collaboration, and promotes the digital upgrading of the supply chain and sustainable development of the industry.

Xiao Han. Research on Automotive Manufacturing Process Optimization Methods for Multi-Supplier Collaboration. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 78-86. https://doi.org/10.38007/IJBDIT.2026.070110.

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