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Decision analysis and optimization are examples of inverse management problems which can be effectively handled by means of Artificial Intelligence (AT) and simulation techniques. However, the application of these techniques in specific industrial contexts finds major constraints in the lack of formalized methodologies and systematic procedures to retrieve and handle the data required for their implementation. The paper proposes Knowledge Discovery (KD) techniques as means of data pre-processing for the development of Al-based decision support systems (DSSs). This use of Knowledge Discovery techniques is illustrated in relation to the design of a hybrid DSS - combining AI and simulation techniques - for the management of maintenance services in the public transport sector.