With the increase of flexibility and production rates, the complexity of manufacturing systems reached a point where the operator in charge of the production activity control of the system is not able to forecast efficiently the impact of his decisions on the global performances. As a matter of fact, more and more Decision Support Systems (DSS) are developed, as much in literature or industrial applications. DSS have one common point: the initialization of their forecasting functionality is based on data coming from the manufacturing system. Furthermore, this feature is fundamental, as it has a direct impact on the accuracy of the forecasts. Considering the variety of input and output data, a data processing is necessary to adapt those coming from the manufacturing system. The aim of this paper is to present several design approaches enabling the integrator of a new manufacturing system to speed up the implementation, with the idea of automate and systematize the maximum design phases thanks the model driven engineering.