Design of cumulative count of conforming charts for high yield processes based on average number of items inspected

Abstract

Purpose

This paper aims to propose a new approach to setting the control limits to promote the control performance of the cumulative count of conforming chart (CCC-r chart), in terms of the average number of items inspected (ANI).

Design/methodology/approach

In contemporary high-yield manufacturing processes, the CCC-r chart is often an alternative of p charts or np charts for monitoring the fraction nonconforming (p). When a CCC-r chart is used, the traditional approach based on the equal-tail probabilities to setting control limits demonstrates a poor performance in terms of ANI as p deviates upward from its nominal value p ₀. To improve the performance of CCC-r charts, this research uses a search method based on some analytical results to find the control limits such that the in-control ANI (ANI ₀) is near-maximal and near-unbiased.

Findings

Analytical validation confirms that the proposed approach outperforms the traditional one in terms of the maximum and the unbiasedness of ANI ₀. When p ₀ is not given, simulation results show that the minimum-variance unbiased estimator tends to perform better than the maximum likelihood estimator.

Originality/value

This study numerically shows that the use of the proposed approach achieves the goal of the near-maximal and near-unbiased ANI ₀, and hence improves the performance of CCC-r charts. In addition, because the proposed approach is computational intensive, this study also develops a Visual Basic project to help practitioners obtain the control limits using the proposed approach.

Citation

Chen, J.-T. (2013), "Design of cumulative count of conforming charts for high yield processes based on average number of items inspected", International Journal of Quality & Reliability Management, Vol. 30 No. 9, pp. 942-957. https://doi.org/10.1108/IJQRM-01-2011-0014