Artificiale Intelligence for Industry 4.0

Smart Company 4.0

Modern manufacturing companies have different types of data, such as: number of pieces produced, downtime, number of engine revolutions, operating pressure and temperature, vibration, etc.

Modern Artificial Intelligence techniques are able to process large amounts of data allowing predictive analyzes to be carried out, for example to anticipate a potential problem on production machines, predict needs, predict product defects, etc.

Our Smart Company 4.0 solution includes a series of modules to support Industry 4.0: Forecast, Predictive Maintenance, Forecast and Classification of waste.


Forecasting is the key element of planning and implementing strategic choices in good time.

Forecasting results in an objective analysis whose positive implications in an industrial process can be:

  • Predict unplanned peaks in energy consumption
  • Implement strategic and eco-sustainable actions
  • Individuare opportunità di miglioramento della produttività

Our solutions aim to identify intrinsic patterns in the trend of the variables to be predicted.

In order to identify highly complex trends, we use advanced solutions based on Machine Learning and Deep Learning.

We show an example of the dashboard of the module of our Smart Company 4.0 application dedicated to the prediction of plant performance and forecasts on energy consumption.

Predictive Maintenance

Predictive maintenance allows you to monitor the status of a machine in real time to predict in advance if and when a failure will occur.

It involves the joint use of IIoT and Artificial Intelligence solutions.

The result is a constant and precise monitoring of the conditions of an asset with a prompt notification in case an anomalous state is foreseen.

Downtime reduction

Maintenance plan inherent to the characteristics of the company

Increased life-cycle of machinery

Targeted corrective interventions

The concept of predictive maintenance recalls that of anomaly detection, it is the process of identifying events that deviate from the behavior defined as “normal”.

These anomalous events can indicate critical incidents, such as a breakdown.

The chosen solution addresses the problem of predictive maintenance as a case of anomaly detection using a Deep Learning model called Autoencoder.

We show an example of the module dashboard of our Smart Company 4.0 application dedicated to predictive maintenance.

Prediction and classification of waste

The resolution of the case under analysis is addressed in two consecutive steps:

  1. Product quality prediction to prevent interruption of the production chain.
  2. Identification of the typology of defect and the causes related to it.

Only by knowing the causes of waste, in fact, it is possible to act directly to improve the production process and contain costs.

The production of a discard is seen as an event that deviates from the behavior defined as “normal”.

The identification of the type of waste is possible thanks to the use of the Decision Tree Classifier, a supervised Machine Learning algorithm.

The algorithm was chosen for its explanatory nature, thanks to its properties it is possible to indirectly associate the causes of the defect with each type of waste.

We show an example of a dashboard of the module of our Smart Company 4.0 application dedicated to the prediction and classification of scraps.

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