In the R&D sector, the implementation of appropriate control tests is essential for monitoring processes and fostering the development of new products. The ability to predict the outcome of a test makes it possible to reduce time-to-market by enabling timely intervention when necessary and minimising waste of resources. To achieve this goal, it is crucial to have a system capable of analysing data in real time, thus providing early predictions of results and allowing for the implementation of targeted support strategies.
At Moxoff, we have developed a solution based on predictive models, which uses data collected during the preliminary phases of a test to accurately estimate the final outcomes. Thanks to Machine Learning techniques, the system analyses the emerging patterns during the execution of the tests and, in addition to predicting their progress, identifies the causes of any failures.
