A Systematic Procurement Supply Chain Optimization Technique Based on Industrial Internet of Things and Application

Abstract

Smart manufacturing has become mainstream in the development of manufacturing industry, where Industrial Internet of Things plays a critical role. In this article, a systematic intelligent technique for procurement supply chain (PSC) optimization is proposed. In this technique, an integrated approach based on variational mode decomposition and long short-term memory network is used to predict the market price. Considering the factors, such as production plan and market fluctuation, a multiperiod dynamic purchasing model is built. A stacked autoencoder under bootstrap aggregation is then trained to evaluate suppliers automatically end-to-end based on various data. Finally, a multiobjective order allocation model is established considering the procurement costs and supplier scores, and solved by particle swarm optimization. The extensive experiments are performed using a realistic industrial application in a zinc smelter company. The experimental results demonstrate that the proposed technique greatly reduces labor costs, improves the efficiency of PSC, and reduces the procurement costs of the company.