Construction of Data Analysis and Forecasting Model for Supply Chain Channels Based on the Internet of Things

In order to solve the problem of data analysis and demand prediction in supply chain channel management, this paper presents a solution based on Internet of Things (IoT) and machine learning. Through the collection, processing, and analysis of the sales data from the e-commerce platform, the key factors that affect the sales are identified, including price, rating, and promotion efforts. On this basis, a Long Short-Term Memory (LSTM) Neural Network (LSTM) is constructed, and a system including data management, data analysis, demand prediction and visualization is designed and realized. The system uses a front end and a back end separation architecture, using Kafka, Spark Streaming to collect and process data in real time, and MySQL and MongoDB to store and manage data. Based on the development process of software engineering, the design, implementation and testing of the system are carried out. Finally, the validity of the proposed solution is verified by testing and evaluating real data, and it is very important to improve supply chain management.