Road map to the statistical process control

Abstract

Since 1920s, when Walter Shewhart first introduced the foundations for control charts, that several developments regarding new implementations of the statistical process control (SPC) have been presented in order to suit different situations that can be found in several processes. It is noted, among others, the Short Run SPC, data non-normality, the presence of auto-correlation in process data, detection of small and moderate shifts in process parameters and the simultaneous control of various quality characteristics. This great diversity of situations is crucial for academic researchers and quality managers in making decisions regarding the choice of the best technique to implement the statistical processes control. For answering this diversity of situations in production systems, this paper presents a road map that allows the decision maker choosing the best technique for implementation. Various techniques are shown, such as the traditional Shewhart control charts, cumulative sums (CUSUM) charts, exponential weighted moving average (EWMA) charts, dimensionless Z/W and Q charts, residuals/forecast errors charts to processes with a significant autocorrelation and multivariate control charts.