Creating value through

Advanced Analytics.

We analyze complex data to identify effective action strategies and help you improve your products and services.

No matter whether the algorithms are simple or complex

– we will help you find the right approach

for any IoT or IIoT application.

Boost the availability and productivity of your devices and plant and protect yourself against unnecessary spending on operations, maintenance and repairs. Leverage advanced analytics to predict outages and wear & tear, plan maintenance accurately and automate processes.

Our IoT framework provides tried and tested tools that go beyond the hype around AI and machine learning. Our data science experts start by analyzing the volume and nature of your data. On this basis, they develop an intelligent concept for your predictive maintenance application based on experience rules, statistical methods or smart algorithms.

In collaboration with Device Insight, we developed an Industry 4.0 application called SmartConnect.frictionwelding, which enhances our friction welding machine with intelligence. We have been able to reduce maintenance costs and downtimes by up to 50 percent through condition monitoring and predictive maintenance. This cloud-based solution won 1st place at the 2017 Industry 4.0 Innovation Awards.

– Till Maier, Portfolio Manager, KUKA –

Our advanced

analytics tools

Cloud Analytics

  • Our data scientists use the Python Runtime Environment when exploring data. This makes it possible to analyze and operationalize machine learning (ML) models, e.g. neural networks
  • Statistical processes and ML algorithms including time series analyses, linear regressions, expectation maximization algorithms or classification algorithms are applied to all databases
  • Aspects relevant to forecasting and predicting are evaluated in the IoT solution directly

Edge Analytics

  • Edge processing allows data to be filtered and preprocessed near the machine
  • Thanks to local pattern recognition and event processing, you obtain fast, accurate results from the analysis without first having to transfer all of the raw data to the cloud
  • Machine-specific rulesets, e.g. alerting for anomalies, can be set directly at the location where data is processed

Big Data

  • Advanced analytics captures, records and automatically analyses machine, process and production data
  • Motion and/or time series data are stored long-term
  • Depending on the area of application, storage concepts such as hot and cold storage in Blobstore, NoSQL or Data Warehouse can be used

Artificial Intelligence of Things

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