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, ...
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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.
Total quality management and quality engineering
S. Pal; S. Gauri
Abstract
In the real world, the overall quality of a product is often represented partly by the measured values of some quantitative variables and partly by the observed values of some ordinal variables. The settings for the manufacturing processes of such products are required to be optimized considering the ...
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In the real world, the overall quality of a product is often represented partly by the measured values of some quantitative variables and partly by the observed values of some ordinal variables. The settings for the manufacturing processes of such products are required to be optimized considering the quantitative as well as the ordinal response variables. But the simultaneous optimization of the quantitative and the ordinal response variables are rarely attempted by the researchers. In this paper, a new approach for simultaneous optimization of quantitative and ordinal responses are presented, which are developed by integrating multiple regression techniques, ordinal logistic regression technique, and Taguchi’s Signal-to-Noise Ratio (SNR) concept. The effectiveness of the proposed method is evaluated by analyzing two experimental data sets taken from the literature. The comparison of results reveals that the proposed method leads to the best optimal solution with respect to the total SNR as well as the Mean Square Error (MSE) of individual responses