The article discusses a method for evaluating the stability of a production process using Bayesian statistics and information theory. The researchers show how to determine if a process is becoming stable over time by comparing the initial knowledge about the process with new observations. They use Kullback‐Leibler divergence to measure the difference between the initial and updated knowledge. The study provides numerical examples to illustrate how this method can be applied to assess the stability of a process mean, helping in making decisions for mass production.