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In this paper we develop approximate Bayes estimators of the scale parameter of the logistic distribution, based on a new life test plan called a progressive first-failure censored plan introduced by [22]. We consider the maximum likelihood and Bayesian inference of the unknown parameter of the model, as well as the reliability and hazard rate functions. Lindley’s approximation [12] and Markov Chain Monte Carlo (MCMC) methods such as importance sampling procedure are applied. The Bayes estimators have been obtained relative to both symmetric (squared error) and asymmetric (linex and general entropy) loss functions. Finally, to assess the performance of the proposed estimators, some numerical results using Monte Carlo simulation study were reported.

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