In manufacturing industries, the lifetime of a product is usually characterized by a random variable, say X, and the product is considered to be satisfactory if X is larger than a given lower lifetime limit (L). The probability of a product being satisfactory is called the conforming rate. In practice, instead of using the conforming rate of products, indices such as the lifetime performance index (CL) are utilized to assess whether products’ quality meets the required level. Under a parametric model for lifetimes of products, it is observed that there is a strictly increasing relationship between the conforming rate and the lifetime performance index of products. Hence, the statistical inferences about the conforming rate and CL are equivalent. This article deals with the Bayesian inferences about CL on the basis of progressively censored data when the product’s lifetime follows the Weibull distribution. The Bayesian analysis is based on a conjugate prior for the scale parameter and a discrete prior for the shape parameter of the distribution under consideration. Two illustrative examples are studied to assess the sensitivity of results to the prior parameters and the underlying distribution of the observed data. Finally, a simulation study is conducted to carry out the performance of the obtained results.

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