Product Quality Tracing in Manufacturing Supply Chain Based on Big Data Technology

Abstract

Background: Big data technology has been widely used in manufacturing supply chain management. However, traditional big data technology has some limitations, and it cannot achieve the continuous improvement of whole-process product quality tracing. Objective : The purpose of this study is to overcome the limitations by patents analysis and provide new big data technology and technical modes to make the continuous improvements of whole-process product quality tracing for achieving effective product lifecycle management based on big data technology. Methods: The research method, patent analysis, and comparative analysis are employed in this study to analyze product quality tracing in the manufacturing supply chain based on big data technology. Moreover, the procedure and steps of the new big data technology - Product Digital Twin (PDT), and its technical modes are designed by process design methods. Its key technologies are also analyzed and compared with traditional big data technology by the comparative study. Results: The research achieves the continuous improvements of whole-process product quality tracing based on new big data technology - PDT by patent analysis. The formation process and behavior of manufactured products in the realistic environment are simulated, monitored, diagnosed, predicted, and controlled. In this way, the high-efficient coordination in various stages of the product lifecycle is propelled fundamentally and the continuous improvements of the whole-process product quality tracing based on big data technology is analyzed. Conclusion: Three new technical modes based on big data technology are predicted for future researches and patents, namely, the immersive development mode integrating big data and the virtual reality technology, the knowledge-based multivariant coordinated development mode, and the lifecycle extended development model based on multi-domain interoperability.