A data mining approach for improving manufacturing processes quality control

There is a growing gap between the generation of data and our standing of it. As the volume of data increases, inexorably, the proportion of it that people understand decreases. The enemy of quality is variation. Variation exists in everything. Product quality must be the focus for any process. Through the use of appropriate data mining tools and the concept of statistical reasoning, managers and employees have developed better understandings of their processes. Although they haven´t yet figured out how to eliminate variation, data mining has helped in reducing it and in understanding how to operate effectively when it exists. Statistical process control (SPC) can play an essential role in understanding of variation. SPC is an extension of hypothesis-testing and estimation concepts. The utility of statistical process control (SPC) methods has received growing interest in quality assurance to help and improve different processes. The objective of this paper is to provide an overview of X as one of SPC charts, statistical data analysis, and how to benefit from data mining concepts and techniques to get a further insight into SPC design and performance to look for patterns in data and improve manufacturing processes quality control.