An industrial heterogeneous data based quality management KPI visualization system for product quality control
Article publication date: 9 November 2022
Issue publication date: 6 December 2022
Quality management systems are commonly applied to meet the increasingly stringent requirements for product quality in discrete manufacturing industries. However, traditional experience-driven quality management methods are incapable of handling heterogeneous data from multiple sources, leading to information islands. This study aims to present a quality management key performance indicator visualization (QM-KPIVIS) system to enable integrated quality control and ultimately ensure product quality.
Based on multiple heterogeneous data, an integrated approach is proposed to quantify explicitly the relationship between Internet of Things data and product quality. Specifically, this study identifies the tracing path of quality problems based on multiple heterogeneous quality information tree. In addition, a hierarchical analysis approach is adopted to calculate the key performance indicators of quality influencing factors in the quality control process.
Proposed QM-KPIVIS system consists of data visualization, quality problem processing, quality optimization and user rights management modules, which perform in a well-coordinated manner. An empirical study was also conducted to validate the effectiveness of proposed system.
To the best of the authors’ knowledge, this study is the first attempt to use industrial Internet of Things and multisource heterogeneous data for integrated product quality management. Proposed approach is more user-friendly and intuitive compared to traditional empirically driven quality management methods and has been initially applied in the manufacturing industry.
Zhao, R., Luo, L., Li, P. and Wang, J. (2022), "An industrial heterogeneous data based quality management KPI visualization system for product quality control", Assembly Automation, Vol. 42 No. 6, pp. 796-808. https://doi.org/10.1108/AA-05-2022-0139
Emerald Publishing Limited
Copyright © 2022, Emerald Publishing Limited