A methodology of design of adaptive information models for production management under uncertainty, based on a cognitive approach and taking into account the human factor, is developed. This methodology is based on the idea of designing a variety of adaptive information models based on a single basic model. In this case, a variety of adaptation procedures that are publicly available can be used. As an example, information models based on self-learning procedures are considered. The results of the operation of these models are adaptive parameters (norms) and production estimates. When designing such models, it can be used many of the formal self-learning procedures described in the scientific and technical literature. As an example, the self-learning information model for production management based on a stochastic approximation designed. Sufficient conditions for its correctness are found, which ensure the unfolding of the production potential taking into account the human factor. The application of the self-learning information model is illustrated by the example of wagon-repair production of large-scale corporation Russian Railways.