Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

中文翻译：

---

基于工业异构数据的产品质量管理KPI可视化系统

目的

质量管理体系通常用于满足离散制造行业对产品质量日益严格的要求。然而，传统的经验驱动的质量管理方法无法处理来自多个来源的异构数据，从而导致信息孤岛。本研究旨在提出一个质量管理关键绩效指标可视化 (QM-KPIVIS) 系统，以实现集成质量控制并最终确保产品质量。

设计/方法/途径

基于多个异构数据，提出了一种集成方法来明确量化物联网数据与产品质量之间的关系。具体来说，本研究基于多异构质量信息树识别质量问题的跟踪路径。此外，采用层次分析法计算质量控制过程中质量影响因素的关键绩效指标。

发现

拟议的QM-KPIVIS系统由数据可视化、质量问题处理、质量优化和用户权限管理模块组成，它们以良好协调的方式运行。还进行了实证研究以验证所提出系统的有效性。

原创性/价值

据作者所知，本研究是首次尝试使用工业物联网和多源异构数据进行集成产品质量管理。与传统的经验驱动的质量管理方法相比，所提出的方法更加用户友好和直观，并已初步应用于制造业。