Research on Quality Management and Optimization under Digital Transformation of China's Manufacturing Industry Based on Big Data Analytics
- DOI
- 10.2991/978-94-6463-262-0_26How to use a DOI?
- Keywords
- data analysis; decision tree; Support vector machine
- Abstract
This paper studies how to optimize quality management using big data analysis in the process of digital transformation in the manufacturing industry of Dongguan City, China. By collecting and pre-processing equipment operation status data, product quality inspection data and work order management data, we apply data analysis methods of support vector machine (SVM) and decision tree to explore the value of these data. The empirical analysis shows that SVM can effectively predict the operating status of equipment to prevent equipment failure; decision trees reveal the important impact of production date and product model on product quality. This study provides strong theoretical and practical support for the digital transformation of manufacturing industries.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Fu Luo AU - Yingying Zhu AU - Xueyang Li AU - Liu Li AU - Kexin Zhang AU - Yanchun Sheng PY - 2023 DA - 2023/10/09 TI - Research on Quality Management and Optimization under Digital Transformation of China's Manufacturing Industry Based on Big Data Analytics BT - Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023) PB - Atlantis Press SP - 223 EP - 229 SN - 2589-4943 UR - https://doi.org/10.2991/978-94-6463-262-0_26 DO - 10.2991/978-94-6463-262-0_26 ID - Luo2023 ER -