LAUSR

Enterprise architecture (EA) is a discipline that provides management with appropriate indicators and controls to steer and model the enterprise during change. However, the management of such change is a challenging task for enterprise architects due to the complex dependencies amongst EA models when evolving from initial (As-is) to posterior (To-be) states. We present an approach supporting design decision during EA evolution, by assisting enterprise architects in computing best alternatives to a posterior state. In doing so, we model EA artefacts dependencies and identify their evolution during change. This model is, then, processed using a control schema to inform EA design decisions. Further, we rationalise on design decision by computing EA models alternatives, using Markov theory. Finally, we evaluate this decision-making approach using a motivating example by simulating a stochastic solution in order to argue about the usefulness and applicability of our proposal.