An innovative machine learning model for supply chain management

Abstract

Supply chain management (SCM) integrates all links and business processes involved in the supply chain through the information management system. Applying artificial intelligence algorithms to the SCM system can realize the visualization, automation, and intelligent management of all links in the supply chain. This can effectively help enterprises reduce operating costs and improve their ability to respond to market demands, thereby increasing overall operational efficiency. An effective member selection method is an important basis for smooth dynamic supply chain operation. To address the problem of high numbers of decision attributes and low numbers of data samples for decision analysis, this paper proposes a dynamic supply chain member selection algorithm based on conditional generative adversarial networks (CGANs). To ensure that classification performance will not be reduced, the member classification method on the chain can successfully reduce the data dimension and complexity in the classification process. Furthermore, machine learning is used for analyzing and predicting purchase and inventory links in the supply chain. For the vehicle scheduling module, the path is reasonably planned to improve the operation efficiency. The integrated implementation of the SCM system is finalized using the SSH framework.