Employment of Legato.
In this project Legato was used for data analysis and to develop the prediction of the behaviour of the manufacturing line. In the first phase we work with historical data (elaboration of the theoretical model), in the second phase real data is exploited and predictions made at the pace required by the manufacturing line.
Initially Legato worked in depth with a set of about 60,000 records corresponding to some 70 different variables (weights, pressures, speeds, levels, heights …) collected over a week. With this historical data, Legato determined which were the variables that, in the manufacturing phase object of study, were more determinant in the quality of the final product. For this it was necessary to apply a set of Big Data technologies that arrange, classify, clean and qualify the data. Subsequently, the transfer function that describes, over time, the behaviour of the process was calculated.
Once we had obtained this valuable new information, we already knew how things had been done, what the result had been and what factors and to what extent they had intervened in it. We could then begin to determine the criteria for improvement.
Thus, Legato developed a scorecard that offers all this information in real time. Then, after an intense phase of testing and selecting the most efficient combination of algorithms, applying Machine Learning technology, it introduced in this scorecard the descriptive graphics of its prediction, indicating to what extent a certain variable should be adjusted, identified as critical, to obtain a result very close to 100% reliability with respect to what had been previously defined.