ABSTRACT
Statistical process control (SPC) is a methodology that uses graphical and statistical tools to analyze, control, and reduce variation. SPC involves collecting sample data and analyzing this data to detect variation. SPC uses run charts, control charts, histograms, distributions, and confidence intervals for real-time analysis. Using the sample data, teams can establish baselines and dynamically improve process capabilities. Understanding the nature of the variation enables the data to be used for management decisions. SPC enables organizations to effectively take corrective action before waste is produced. Therefore, teams can move from opinion-based to data-driven decisionmaking. SPC has three primary objectives:
1. Detecting special cause variation in the process 2. Measuring the natural tolerance of the process (common cause variation) 3. Ensuring the process is in control and capable of meeting specifica-
tions consistently
SPC provides a means for quantifying and reducing variation, which, in turn, improves product and process design. As the team understands the process behavior, this improves the team’s understanding of products and processes. SPC enables real-time monitoring of processes and statistically valid decisions. In addition, by understanding the source of variation, common or special cause, teams can determine when and when not to take action and the appropriate action when needed.
