In this paper, we develop and test an advanced model, based on discrete-event simulation, whose purpose is to forecast the demand of spare parts during the whole lifetime of a complex product, such as, for instance, an industrial machine. To run the model, the relevant data of the product manufactured by a targeted company should be collected. With those data, the model provides an estimate of the optimal level of spare parts inventory the company should keep available. The data provided by the model are subsequently applied to a case example, referring to a hypothesised company, manufacturing industrial plants. The application is carried out considering two scenarios, i.e., a ‘traditional’ and an ‘advanced’ approach for demand forecasting, this latter reflecting the circumstance where the company makes use of the proposed forecasting method. The comparison of the outcomes obtained in the two scenarios highlights the efficiency and resolution capacity of the model developed.
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