The short answer is yes,
The longer answer is bayesian methods are quite powerful in estimating the parameters needed to create a reliable and accurate DES simulation, such as processing distributions, lead times, and other variability.
From there you can plug those into any other DES. Its funny you ask this because when before I knew bayes I was using Arena and Simio and they would fit a chosen distribution to your data using MLE. Funny timing you asking because I was just using Simpy and Bayesian methods at Google yesterday to do exactly this.
Alternatively if you don’t care about discrete events but cumulative lead times you can build a supply chain and manufacturing network in PyMC directly, condition all the edges and nodes in your network, and use posterior predictive sampling to get the cumulative lead times.