intelliDivide Cutting
Predictive accuracy of the machine learning-based model. The deviation of the forecast from the actual time required in seconds per cutting plan is shown.
Prediction accuracy of the simulation model used so far. The deviation of the forecast from the actual time required in seconds per cutting plan is shown.
Until now, the prediction of production time in intelliDivide Cutting was based on a simulation model that could not sufficiently take into account the actual conditions on site. As a result, there could be discrepancies between the forecast and the actual time required.
To improve predictions, we have now implemented a new machine learning-based model that uses anonymized feedback data from saws connected to tapio. This data is continuously fed into the model in order to base the prediction of recalculated cutting plans on actual, real values.
Saws that have already cut over 500 cutting plans and reported back via tapio benefit from even more accurate results by using individual feedback data to predict production time.
These improvements enable better planning of production as well as more accurate calculation of quotations.
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HOMAG Group AG