This study takes the product shipment and repair data of a technology company in Shenzhen as an example, uses Power BI to build a Data Warehouse and visually analyze the operation of the enterprise. Therefore, the management and relevant departments of the company can have a more intuitive understanding of the sales and quality of the products, and make timely decisions. At the same time, this also helps quickly identify trends, patterns, and anomalies in the business process, and improve production quality. We also use ARIMA model to predict product shipment volume and repair rate. This will provide a scientific basis for the company’s production planning, inventory management, and after-sales service. It also can prompt companies to take corresponding preventive actions, reduce quality costs and customer complaint rates. The ARIMA program coded by Python is integrated into Power BI to provide a practical and interactive charts. Hybrid methods of machine learning and deep learning with ARIMA are also discussed in this paper in order to improve non-liner problem of the prediction.