Industry Sectors

scitis.io applies data science in a variety of areas

Metalworking

Mechanical engineering companies in the metalwork industry use the scitis.io framework to map out their production process. This way, product quality can be monitored and improved while also monitoring the condition of the machinery. Statistical process supervision, predictive maintenance and the effects of equipment deterioration are all of paramount importance.

By bringing in data from the whole value creation chain, plant-wide correlations can be identified and processes optimised on all levels.

Energy

The major changes in the energy sector that new technologies and information processing have brought about open up whole new lines of business. We enable you to quickly establish the technical foundation and level of expertise necessary for succeeding in these new territories.

Our monitoring enables the safe and efficient operation of industrial plants and prevention of overload damage. With scitis.io Predict, the primary energy consumption can be forecast as a function of the weather and any given weekday.

Process Engineering

Another industry to deploy scitis.io - especially for complex processes with a variety of dependencies, since empirical models based on real data offer such a flexible and cheap alternative to the laborious construction of elaborate physical simulations.

In addition, scitis.io allows for a quick, easy and cost-efficient documentation of process data. Each of our CloudPlugs records production data from its controller and uploads it to the cloud. We provide you with the tools and necessary know-how for outputting the desired information in regular reports.

We enable the plant operator not only to survey the current state of their plant, but also to predict equipment deterioration and likely upcoming failures based on the plant’s own history.

Packaging

At high quantities and low unit prices, scitis.io can prove an invaluable asset in ensuring a smooth process from start to end. By detecting anomalies, failures can be identified early and even completely averted in the best of cases.

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