  Reconfiguration demand is increasing due to frequent requirement changes for
manufacturing systems. Recent approaches aim at investigating feasible
configuration alternatives from which they select the optimal one. This relies
on processes whose behavior is not reliant on e.g. the production sequence.
However, when machine learning is used, components' behavior depends on the
process' specifics, requiring additional concepts to successfully conduct
reconfiguration management. Therefore, we propose the enhancement of the
comprehensive reconfiguration management with transfer learning. This provides
the ability to assess the machine learning dependent behavior of the different
CPPS configurations with reduced effort and further assists the recommissioning
of the chosen one. A real cyber-physical production system from the discrete
manufacturing domain is utilized to demonstrate the aforementioned proposal.
