Building A Multi-Platform Smart Warehouse Management System That Supports Inventory Optimization With Arima And K-Means

Tóm tắt

Effective inventory management plays a crucial role in optimizing the supply chain. This study presents the development of a multi-platform (web and mobile application) smart warehouse management system that utilizes a combination of the Autoregressive Integrated Moving Average (ARIMA) model and the K-means clustering algorithm to support inventory optimization. The system employs ARIMA to forecast product demand, and subsequently clusters products based on their demand characteristics using K-means. The forecasting and clustering results are integrated to provide recommendations on optimal quantities of goods for inbound/outbound operations. Users can access and interact with the system through either the web interface or the mobile application, helping to minimize inventory costs, enhance operational efficiency, and increase management flexibility. The research focuses on the multi-platform system development and demonstrates the applicability of the ARIMA and K-means methods in the warehouse management problem.