Algorithm optimization of large-scale supply chain design based on FPGA and neural network - Li, Ting (Microprocessors and microsystems, 2021)
The Supply Chain Management System permits an organization to work quickly and adequately all through a large scale. It will begin with every idea's fundamental comprehension, including Production, Inventory, Location, and Transportation. Consolidating all the cycles will underline the part of SCM (Supply chain Management) in business economics. In the existing method IoT and Convolutional Neural Network for Supply Chain Management (SCM). The drawback of the previous method is un-sensitive in the supply chain in extensive scale management. The proposed method is based on FPGA (Field Programmable Gate Arrays) and Neural Network for Supply chain Management. The outcome and looking at the finished flexibly chain the executive's plan, and the administrator can operate without much of a stretch limit the mix-up and fix it in a brief period. Each organization has its own personal SCM (Supply Chain Management) plan, and the progression of the network domain will choose the system's viability. The proposed Neural Network-based Numeric Framework Algorithm long-chain centers on the SCM (Supply chain Management) framework's all-out methodology and wants to have a superior SCM (Supply Chain Management). Base on the meeting date, the current Neural Network understands the positive and negative perspectives. At last, it can address the inquiries of how to improve the network framework. The organization has a more powerful SCM (Supply chain Management. Furthermore, the significant organization improves to have the option to rival the unfamiliar creations.