Total quality management and quality engineering
S. Kumar Gauri; S. Pal
Abstract
Process capability indices are widely used to assess whether the outputs of an in-control process meet the specifications. The commonly used indices are , , and . In most applications, the quality characteristics are assumed to follow normal distribution. But, in practice, many quality characteristics, ...
Read More
Process capability indices are widely used to assess whether the outputs of an in-control process meet the specifications. The commonly used indices are , , and . In most applications, the quality characteristics are assumed to follow normal distribution. But, in practice, many quality characteristics, e.g. count data, proportion defective etc. follow Poisson or binomial distributions, and these characteristics usually have one-sided specification limit. In these cases, computations of or using the standard formula is inappropriate. In order to alleviate the problem, some generalized indices (e.g. index, index, index and index) are proposed in literature. The variant of these indices for one-sided specification are and , and ¸ and , and respectively. All these indices can be computed in any process regardless of whether the quality characteristics are discrete or continuous. However, the same value for different generalized indices and or signifies different capabilities for a process and this poses difficulties in interpreting the estimates of the generalized indices. In this study, the relative goodness of the generalized indices is quantifying capability of a process is assessed. It is found that only or gives proper assessment about the capability of a process. All other generalized indices give a false impression about the capability of a process and thus usages of those indices should be avoided. The results of analysis of multiple case study data taken from Poisson and binomial processes validate the above findings.
R. Hosseiny; V. Amirzadeh; M. A. Yaghoobi
Volume 3, Issue 1 , May 2014, , Pages 26-38
Abstract
Although a control chart can signal an out-of-control state in a process, but it does not always indicate when the process change has begun. Identifying the real time of the change in the process, called the change point, is very important for eliminating the source(s) of the change and assists process ...
Read More
Although a control chart can signal an out-of-control state in a process, but it does not always indicate when the process change has begun. Identifying the real time of the change in the process, called the change point, is very important for eliminating the source(s) of the change and assists process engineers in identifying the responsible special cause and ul t imately in improving the proces s. In this paper, we first introduce an estimator for a change point with linear trend in the Poisson process, based on the likelihood function using a slope parameter. Then we apply Monte Carlo simulation to evaluate the accuracy and the precision performance of the proposed change point estimator. Finally we compare, the proposed estimator with the MLE of the Poisson process change point derived under linear trend disturbance on the basis of cumulative sum (CUSUM) and Shewhart C control charts. The results show that the proposed procedure outperforms the MLE designed for drift time with regard to variance and is more effective in detecting drift time when the magnitude of change is relatively large.
