Using Visual Data Mining to Enhance the Simple Tools in Statistical Process Control: A Case Study

摘要

Statistical process control (SPC) is a collection of problem-solving tools used to achieve process stability and improve process capability through variation reduction. Because of its sound statistical basis and intuitive use of visual displays, SPC has been extensively used in manufacturing and health care and service industries. Deploying SPC involves both a technical aspect and a proper environment for continuous improvement activities based on management support and worker empowerment. Many of the commonly used SPC tools, including histograms, fishbone diagrams, scatter plots, and defect concentration diagrams, were proposed prior to the advent of microcomputers as efficient methods to record and visualize data for single (or few) variable(s) processes. As the volume, variety, and velocity of data continues to evolve, there are opportunities to supplement and improve these methods for understanding and visualizing process variation. In this paper, we propose enhancements to some of the basic quality tools that can be easily applied with a desktop computer. We demonstrate how these updated tools can be used to better characterize, understand, and/or diagnose variation in a case study involving a US manufacturer of structural tubular metal products. Finally, we create the quality visualization toolkit to allow practitioners to implement some of these visualization tools without the need for training, extensive statistical background, and/or specialized statistical software. Copyright © 2014 John Wiley & Sons, Ltd.