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The present study puts forward a regression analytic model based on the random forest algorithm, developed to predict, at an early stage, the global academic performance of the undergraduates of a polytechnic higher education institution. The study targets the universe of an institution composed of 5 schools rather than following the usual procedure of delimiting the prediction to one single specific degree course. Hence, we intend to provide the institution with one single tool capable of including the heterogeneity of the universe of students as well as educational dynamics. A different approach to feature selection is proposed, which enables to completely exclude categories of predictive variables, making the model useful for scenarios in which not all categories of data considered are collected. The introduced model can be used at a central level by the decision-makers who are entitled to design actions to mitigate academic failure. © 2019, Springer Nature Switzerland AG.

Abstract
Promotional tools such as cross-market discounts have been increasingly used as a means to increase customer satisfaction and sales. This paper aims to assess whether the implementation of a cross-market discount campaign by a retailing company encouraged customers to increase their purchases level. It contributes to the literature by using neural networks to detect novelties in a real context involving cross-market discounts. Besides the computation of point predictions, the methodology proposed involves the estimation of neural networks prediction intervals. Sales predictions are compared with the observed values in order to detect significant changes in customers' spending. The use of neural networks is validated through the comparison with the forecasting estimates of support vector regression, regression trees, and linear regression. The results reveal that the promotional campaign under analysis did not significantly impact the sales of the rewarded customers.

Abstract
Build to Order or Make to Order is a common approach for highly configured products such as special vehicles (vehicles that are adapted and altered to suit a specific purpose). Examples of such vehicles are special ambulances as well as vehicles adapted for the support and transport of passengers with less mobility. In this type of business, operations are scheduled in response to a confirmed order received from a final customer. Thus, the variability and the uncertainty characterizing what is project based, generate a complexity that requires specifically tailored managerial approaches to handle all the involved processes - from design and engineering to production and delivery. Hence, in this accentuated complexity, it is extremely important to guarantee that both the material and information flows are efficient and effective. The present study, framed in a program of operational improvement in a manufacturer of special vehicles, aims to address some concrete improvement opportunities related to the significant number of raw materials stockouts and to the high number of changes made by the client after production has started. In fact, during the manufacturing and assembly process, there are constant changes that delay and difficult planning and consequently decreases the overall efficiency and effectiveness. Strategies to address all these matters are to be identified and applied. © IEOM Society International.