Data science in the industry

The sensors built into the machines turn production processes into real data suppliers. Data that represent a huge potential for industrial companies that needs to be tapped. We have been supporting industrial companies for over 10 years. Our data science solutions make production smarter and help you to achieve higher product quality, more efficient processes or increasing availability of your production facilities.

A selection of questions we have answered in our data science projects in industry:

How can the availability of production machines be increased?

The answer is called predictive maintenance. Recognition of patterns in the sensor data of the machines that indicate problems before they actually occur. This allows proactive maintenance measures to be taken before unplanned downtime occurs.

How can quality fluctuations in production be detected early on?

Carrying out cluster analyses to categorize the different quality levels of the goods produced. Patterns in the sequence of these clusters in production provide early information about occurring quality deviations.

How can quality monitoring be automated?

Use of an AI for image recognition in order to identify defective welding seams at an early stage and intervene in the production process. This allows information to be included that cannot be evaluated manually in terms of complexity and quantity.

How can costs be saved during the production process?

By the early detection of production errors. Based on the sensor data of individual process steps, the quality of the final output can be predicted and parts can be shot out early if the prognosis is negative - this helps to save valuable production resources.

How can customer service be improved?

Development of a digital machine file. This makes it possible to check the condition of the machines easily and intuitively and thus provides valuable information for the coordination of maintenance and servicing.

How can production be controlled more in line with demand?

Forecasting future sales volumes by linking a wide range of data, such as historical sales volumes, existing price campaigns or seasonal effects.

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