Reputation-Aware Supplier Assessment for Blockchain-Enabled Supply Chain in Industry 4.0
Xiaolong Xu, Ji Gu, Hanzhi Yan, Wentao Liu, Lianyong Qi, Xiaokang Zhou
Abstract
In Industry 4.0, the development of intelligent supply chains is the key to improving enterprise efficiency and customer satisfaction. At present, the strategy with blockchain to optimize the supply chain ensures the security of data sharing and interaction between different suppliers. However, the quality of service (QoS) loss in time efficiency and supplier cooperation is still a severe problem in these strategies. Therefore, it is essential to conduct reputable supplier selection to reduce QoS loss under the premise of blockchain security. Meanwhile, choosing a reputable supplier as the primary peer in the blockchain consensus process can also effectively reduce transaction latency. To solve this problem, a reputation-aware supplier assessment system, named SAS, is proposed. Specifically, Canopy and K-medoids are utilized to classify suppliers preliminarily. Then backpropagation neural network is deployed to evaluate supplier reputation. The results show that SAS is effective in supplier reputation evaluation through extensive experiments.