Yes, the model "thinks" the solution found is the best, but it is not. The model is getting confused because of some complex mathematical results that it gets along the way, and never gets to the optimal solution hence "non optimal solution".

Sometimes it goes even worse: not only it does not converge to the best solution (previous paragraph) but also diverges, i.e the error increases (value grows) instead of decrease. This is less common and maybe just means there are planning errors.

This is just a broad idea.