Six Sigma quality evaluation of life test data based on Weibull distribution

Abstract

PurposeWhile Six Sigma metrics have been studied by researchers in detail for normal distribution-based data, in this paper, we have attempted to study the Six Sigma metrics for two-parameter Weibull distribution that is useful in many life test data analyses.Design/methodology/approachIn the theory of Six Sigma, most of the processes are assumed normal and Six Sigma metrics are determined for such a process of interest. In reliability studies non-normal distributions are more appropriate for life tests. In this paper, a theoretical procedure is developed for determining Six Sigma metrics when the underlying process follows two-parameter Weibull distribution. Numerical evaluations are also considered to study the proposed method.FindingsIn this paper, by matching the probabilities under different normal process-based sigma quality levels (SQLs), we first determined the Six Sigma specification limits (Lower and Upper Six Sigma Limits- LSSL and USSL) for the two-parameter Weibull distribution by setting different values for the shape parameter and the scaling parameter. Then, the lower SQL (LSQL) and upper SQL (USQL) values are obtained for the Weibull distribution with centered and shifted cases. We presented numerical results for Six Sigma metrics of Weibull distribution with different parameter settings. We also simulated a set of 1,000 values from this Weibull distribution for both centered and shifted cases to evaluate the Six Sigma performance metrics. It is found that the SQLs under two-parameter Weibull distribution are slightly lesser than those when the process is assumed normal.Originality/valueThe theoretical approach proposed for determining Six Sigma metrics for Weibull distribution is new to the Six Sigma Quality practitioners who commonly deal with normal process or normal approximation to non-normal processes. The procedure developed here is, in fact, used to first determine LSSL and USSL followed by which LSQL and USQL are obtained. This in turn has helped to compute the Six Sigma metrics such as defects per million opportunities (DPMOs) and the parts that are extremely good per million opportunities (EGPMOs) under two-parameter Weibull distribution for lower-the-better (LTB) and higher-the-better (HTB) quality characteristics. We believe that this approach is quite new to the practitioners, and it is not only useful to the practitioners but will also serve to motivate the researchers to do more work in this field of research.

Reliability degradation prediction of photovoltaic modules based on dependability methods

“…The Weibull law used to model the distribution of the PV module lifetime, whose survival function is expressed by the following equation (Niveditha and Joghee, 2021):with: ( η ) the scale parameter and ( β ) the form parameter.…”

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“…Regarding product quality, quite a few quality control engineers and experts in statistics have been dedicated to their studies on process capability indicators in terms of evaluation, analysis, and improvement [3,31,[34][35]. In addition, Six Sigma is also a method commonly adopted in the industry to improve process quality [15,[21][22]25]. Subsequently, many scholars discussed the relation between the abovementioned process capability indicators and the Six-Sigma quality level widely used in the industry, and then they proposed the Six-Sigma quality index, hoping to apply the process capability index to the model of Six-Sigma quality management [6,29].…”