A novel method of material demand forecasting for power supply chains in industrial applications

• Main Authors: Yu Xiao, Zhu Jun, Huang Lei, Ashutosh Sharma, Amit Sharma
• Format: Article
• Language: English
• Published: Wiley 2021-09-01
• Series: IET Collaborative Intelligent Manufacturing
• Online Access: https://doi.org/10.1049/cim2.12007

Abstract Based on research on big data, data mining and other relevant technical theories, a power material demand analysis system is designed and implemented based on big data technology. The main aim of the study is to forecast material demand and provide data support for decision‐makers. The system includes a data centre subsystem and an application subsystem. At the same time, two kinds of collaborative transmission process models of supply chain information are established, and simulation analysis is carried out on the two models by the Monte Carlo method to verify the effect of collaborative transmission of information flow in supply chains within big data environments. The major contribution of the work is the design of a supply chain model with the help of big data. The impacts of the Internet of Things with empirical studies and limited models are the focuses of the study. It can be seen from the simulation results that there will be a minimum R to minimise the cost C under the two supply chain information‐transfer process models. The manufacturing cost of the big data platform is about 50% lower than that of the traditional supply chain, and increased delay costs accordingly lead to increased costs for manufacturers in both supply chains.