Quality Big Data Analysis and Management Based on Product Satisfaction Index

The core idea of quality work is to create enduring quality by providing users with reliable, easy-to-use, and surprising products. In the Internet+ era, user demand for quality has changed with the times. Customers are no longer satisfied with the requirements of basic quality functions, and more in pursuit of ease of use, pleasure and even identity. Even if the product has "satisfied with the specifications" in quality, users are still dissatisfied and express their dissatisfaction through the Internet. If we can use the Internet, big data, artificial intelligence and other technologies to collect and analyze user expectations or dissatisfaction with product quality and find improvement directions, then quality management will see great progress. Based on this, we can create a new type of quality big data analysis and management system, which we can call the Product Satisfaction Index (PSI) management system. Through the analysis and transformation of PSI, the customer's textual expectations are turned into numerical quantifiable indicators. Through the management of PSI indicators, quality big data mining and analysis are performed to improve the product to meet customer expectations. In advancing the product satisfaction index (PSI) indicator management system, we should fully implement the definition of PSI indicators, strategic position, data analysis methods, closed-loop management models, review and continuous improvement, and data platforms. Visualize customer needs, analyze the matching between product characteristics and customer needs, and promote continuous improvement of PSI indicators through closed-loop management. The new PSI index management method opens up new ideas and methods for quality performance management, and helps build high-quality development and create customer satisfaction.