Optimal average sample number of the SPRT chart for monitoring fraction nonconforming

•This article studies the effect of the Average Sample Number (ASN) on the performance of the SPRT chart.•A design algorithm is proposed to explore the optimal ASN of the SPRT chart for monitoring the fraction nonconforming p.•The optimal SPRT chart significantly outperforms the optimal np chart, optimal CUSUM chart and basic SPRT chart.

The Sequential Probability Ratio Test (SPRT) control chart is a powerful tool for monitoring manufacturing processes. It is highly suitable for the applications where testing is destructive or very expensive, such as the automobile airbags test. This article studies the effect of the Average Sample Number (ASN) (i.e., the average sample size) on the chart’s performance. A design algorithm is proposed to develop the optimal SPRT chart for monitoring the fraction nonconforming p of Bernoulli processes. By optimizing the ASN and other charting parameters, the average detection speed of the SPRT chart is almost doubled. It is also found that the optimal SPRT chart significantly outperforms the optimal np and binomial CUSUM charts, in terms of Average Number of Defectives (AND), under different combinations of the design specifications. It is observed that the SPRT chart using a relatively smaller ASN and a shorter sampling interval (h) has a higher overall detection effectiveness.