Intermittent sampling for statistical process control with the number of defectives

Abstract

An intermittent sampling model for statistical process control (SPC) is introduced to monitor the quality of output from a production process where the number of defective units in a sample is measured in selected time periods. A Limited Memory Influence Diagram (LIMID) model is implemented to determine the time periods in which to collect samples and the decision strategy to minimize total costs of quality control. In periods where samples are collected, the observed defectives determine whether the process is stopped to investigate and repair an assignable cause of variation. Based on sample results, the process can alternatively be allowed to run without interruption until the next predetermined sampling interval, or the results may suggest collecting a sample again in the next period. The model only requires the user to know the result of the current sample to make a decision, in contrast to Bayesian methods that require calculations based on all prior samples and a history of actions. Despite the limited and intermittent nature of the information, the model provides lower quality costs than existing methods for a wide range of production time horizons.